- Original Volume 1: Cancer Biology & Genomics for Disease Diagnosis.
On com since 8/11/2015
http://www.amazon.com/dp/B013RVYR2K
-
New Genre Volume One: Cancer Biology and Genomics for Disease Diagnosis
This volume include graphical results of Medical Text Analysis with Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP) algorithms AND the Domain Knowledge Expert (DKE) interpretation of the results in Text format
This volume has the following three parts:
PART A:
PART A.1: The eTOCs in Spanish in Audio format AND
PART A.2: The eTOCs in Bi-lingual format: Spanish and English in Text format
PART B: The graphical results of Medical Text Analysis with Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP) algorithms AND the Domain Knowledge Expert (DKE) interpretation of the results in Text format
PART C: The Editorials of the original e-Book in English in Audio format
PART A:
PART A.1:
The eTOCs in Spanish in Audio format
Serie C: libros electrónicos acerca del cáncer y la oncología
Consultor de contenidos de la serie C: Larry H. Bernstein, MD, FCAP
PRIMER VOLUMEN
Biología y genómica del cáncer
para el
diagnóstico de la enfermedad
Traducción a español
En Amazon.com desde el 11/08/2015
2015
http://www.amazon.com/dp/B013RVYR2K
Stephen J. Williams, PhD, Senior Editor
Leaders in Pharmaceutical Business Intelligence
https://pharmaceuticalintelligence.com/
Redactora jefe de la serie de libros electrónicos BioMed
Leaders in Pharmaceutical Business Intelligence, Boston
avivalev-ari@alum.berkeley.edu
Otros libros sobre el cáncer del equipo de LPBI
Serie C: libros electrónicos acerca del cáncer y la oncología
Consultor de contenidos de la serie C: Larry H. Bernstein, MD, FCAP
SEGUNDO VOLUMEN
Tratamientos contra el cáncer:
Metabólicos, genómicos, intervencionistas; inmunoterapia y nanotecnología para la administración de tratamientos
Traducción a español
En Amazon.com desde el 18/05/2017
http://www.amazon.com/dp/B071VQ6YYK
2017
Autores, redactores y editores
y
Autores y redactores invitados
Tilda Barliya, PhD, tildabarliya@gmail.com
Demet Sag, PhD, demet.sag@gmail.com
Dror Nir, PhD, dror.nir@radbee.com
Ziv Raviv, PhD zraviv06@gmail.com
Danut Dragoi, PhD, Danut.daa@gmail.com
Evelina Cohn, PhD, ecohn2011@yahoo.com
Aviva Lev-Ari, PhD, RN, avivalev-ari@alum.berkeley.edu
Leaders in Pharmaceutical Business Intelligence
PRIMER VOLUMEN
Biología y genómica del cancer para el diagnóstico de la enfermedad
Lista de colaboradores del primer volume
Prólogo, introducción al volumen, Epílogo
Indice de contenidos electrónico (IDCe) – EN ESPAÑOL AUDIO
Los enlaces indicados llevan al contenido original en inglés
| MD | Licenciado/a en medicina y cirugía (Estados Unidos) |
| PhD | Doctorado/a |
| RN | Enfermero/a titulado/a (National Board of Nursing Registration) |
| FCAP | Miembro distinguido (Fellow) del Colegio de Anatomopatólogos de los Estados Unidos |
| Ph.D | Doctorado/a |
| CRA | CRA |
| GCP | GCP |
Parte I
Perspectiva histórica de la demografía, la etiología y los avances en la investigación del cáncer
Capítulo 1: La incidencia del cáncer en las poblaciones del mundo
1.1 ¿Qué es el cáncer?
https://pharmaceuticalintelligence.com/2012/05/07/102/
Prabodh Kandala, PhD
1.2 Metástasis del cáncer
https://pharmaceuticalintelligence.com/2013/07/06/cancer-metastasis/
Tilda Barliya, PhD
1.3 Perspectiva en 2013 de la «guerra contra el cáncer» del 23 de diciembre de 1971
Aviva Lev-Ari, PhD, RN
1.4 La carga global del tratamiento del cáncer y la salud femenina: acceso al mercado y problemas de costes
Aviva Lev-Ari, PhD, RN
1.5 La importancia de los programas de prevención del cáncer: nuevas perspectivas para combatir el cáncer
Ziv Raviv, PhD
1.6 James Watson, code scubridor del ADN junto con Crick en abril de 1953, examina los «grupos de poder de investigación sobre el cáncer»
Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
1.7 Nuevo ecosistema de investigación sobre el cáncer: equipos científicos interinstitucionales
Aviva Lev-Ari, PhD, RN
1.8 Innovaciones sobre el cáncer recopiladas de Internet
https://pharmaceuticalintelligence.com/2012/11/02/cancer-innovations-from-across-the-web/
Larry H Bernstein, MD, FCAP
1.9 Exploración del papel de la vitamina C en el tratamiento del cáncer
https://pharmaceuticalintelligence.com/2013/01/15/exploring-the-role-of-vitamin-c-in-cancer-therapy/
Ritu Saxena PhD
1.10 Relación entre la dieta y el cáncer
https://pharmaceuticalintelligence.com/2013/06/04/relation-of-diet-and-cancer/
Sudipta Saha, PhD
1.11 Asociación entre el cáncer de piel distinto del melanoma y los posteriores cánceres primarios en la población blanca
Aviva Lev-Ari, PhD, RN
1.12 Los hombres con cáncer de próstata tienen más probabilidades de fallecer por otras causas
Prabodh Kandala, PhD
1.13 La lucha de Steve Jobs y Ralph Steinman contra el cáncer de páncreas: así perdimos
Ritu Saxena, PhD
Capítulo 2: Los rápidos avances científicos cambian nuestra visión de cómo se produce el cáncer
2.1 No todas las células cancerosas son iguales: algunos tipos de células controlan el crecimiento continuo del tumor y otros allanan el camino a la metástasis
Prabodh Kandala, PhD
2.2 Un momento. Las mutaciones en el cáncer pueden ser buenas
https://pharmaceuticalintelligence.com/2013/02/04/hold-on-mutations-in-cancer-do-good/
Prabodh Kandala, PhD
2.3 El efecto Warburg ¿causa o efecto del cáncer? ¿Una visión del siglo XXI?
Larry H Bernstein, MD, FCAP
2.4 El ratopín rasurado no padece cáncer
https://pharmaceuticalintelligence.com/2013/06/20/naked-mole-rats-cancer-free/
Larry H Bernstein, MD, FCAP
2.5 El pez cebra, susceptible de padecer cáncer
https://pharmaceuticalintelligence.com/2013/04/02/zebrafish-susceptible-to-cancer/
Larry H Bernstein, MD, FCAP
2.6 Desmitificando a los tiburones, el cáncer y las aletas de tiburón
https://pharmaceuticalintelligence.com/2013/06/22/demythologizing-sharks-cancer-and-shark-fins/
Larry H Bernstein, MD, FCAP
2.7 El funcionamiento interno de las células tumorales predice la progresión del cáncer
Prabodh Kandala, PhD
2.8 En el punto de mira: identidad de las células madre del cáncer
https://pharmaceuticalintelligence.com/2013/03/22/in-focus-identity-of-cancer-stem-cells/
Ritu Saxena, PhD
2.9 En el punto de mira: células tumorales circulantes
https://pharmaceuticalintelligence.com/2013/06/24/in-focus-circulating-tumor-cells/
Ritu Saxena, PhD
2.10 Reescribiendo las matemáticas del crecimiento tumoral; los equipos utilizan modelos matemáticos para diferenciar las mutaciones oncoiniciadoras de las secundarias
Stephen J. Williams, PhD
2.11 Papel de los cilios primarios en el cáncer de ovario
https://pharmaceuticalintelligence.com/2013/01/15/role-of-primary-cilia-in-ovarian-cancer-2/
Aashir Awan, PhD
Capítulo 3: Surgen una base genética y una complejidad genética del cáncer
3.1 La unión de los oligonucleótidos en el ADN y las estructuras reticulares tridimensionales
Larry H Bernstein, MD, FCAP
3.2 Cómo promueven el cáncer los elementos móviles del ADN «basura». Parte 1: tumorigénesis mediada por transposones.
Stephen J. Williams, PhD
3.3 ADN: la basura de uno es el tesoro de otro pero no hay nada que desechar, después de todo
Demet Sag, PhD
3.4 Cuestiones sobre la heterogeneidad tumoral
3.4.1 Aspectos de la medicina personalizada en el cáncer: heterogeneidad intratumoral y evolución ramificada revelada por la secuenciación multirregional
Stephen J. Williams, PhD
3.4.2 Aspectos de la medicina personalizada: debates sobre la heterogeneidad intratumoral en el foro Oncology Pharma de LinkedIn
Stephen J. Williams, PhD
3.5 arrayMap: minería de rasgos genómicos de entidades oncológicas en datos de anomalías en el número de copias (CNA)
Aviva Lev-Ari, PhD, RN
3.6 Cáncer de hígado asociado al VHB y al VHC: conocimientos importantes a partir del genoma
Ritu Saxena, PhD
3.7 Cáncer de glándulas salivales y carcinoma adenoide quístico. Patrones de mutación: secuenciación del exoma y del genoma en el Memorial Sloan-Kettering Cancer Center
Aviva Lev-Ari, PhD, RN
3.8 Cáncer gástrico: reconstrucción pangenómica y firmas mutacionales
Aviva Lev-Ari, PhD, RN
3.9 La ausencia de un gen puede provocar más de una cuarta parte de los cánceres de mama
Aviva Lev-Ari, PhD, RN
3.10 Gen crítico en la reabsorción de calcio: variantes de los genes KCNJ y SLC12A1. Consumo de calcio y protección contra el cáncer
Aviva Lev-Ari, PhD, RN
Capítulo 4: Cómo afectan los factores epigenéticos y metabólicos al crecimiento tumoral
4.1 Epigenética
4.1.1 La magia de la caja de Pandora: epigenética y troncalidad con los ARN largos no codificantes (ARNlnc)
Demet Sag, PhD, CRA, GCP
4.1.2 Subtipos de cáncer de estómago basados en la metilación, identificados por un equipo de Singapur
Aviva Lev-Ari, PhD, RN
4.1.3 El infravalorado epigenoma
https://pharmaceuticalintelligence.com/2013/04/17/the-underappreciated-epigenome/
Demet Sag, Ph.D., CRA, GCP
4.1.4 Tratamiento de diferenciación: la epigenética aborda los tumores sólidos
Stephen J. Williams, PhD
4.1.5 «El SILENCIO de los corderos», presentación del poder del ARN no codificado
Demet Sag, Ph.D., CRA, GCP
4.1.6 Metiltransferasas de ADN. Implicaciones para la regulación epigenética y la orientación del tratamiento contra el cáncer: James Shen, PhD
Aviva Lev-Ari, PhD, RN
4.2 Metabolismo
4.2.1 Las mitocondrias y el cáncer: una descripción general de los mecanismos
https://pharmaceuticalintelligence.com/2012/09/01/mitochondria-and-cancer-an-overview/
Ritu Saxena, PhD
4.2.2 Mecanismo bioenergético: la asociación inversa entre el cáncer y el Alzheimer
Aviva Lev-Ari, PhD, RN
4.2.3 El papel crucial del óxido nítrico en el cáncer
https://pharmaceuticalintelligence.com/2012/10/16/crucial-role-of-nitric-oxide-in-cancer/
Ritu Saxena, PhD
4.2.4 El óxido nítrico mitiga la sensibilidad de las células de melanoma al cisplatino
Stephen J. Williams, PhD
4.2.5 Aumento del riesgo de obesidad y cáncer pero disminución del riesgo de diabetes de tipo 2: el papel del supresor tumoral, fosfatasa y homólogo de la tensina (PTEN)
Aviva Lev-Ari, PhD, RN
4.2.6 Perfil lipídico, grasas saturadas, espectroscopia Raman y citología del cáncer
Larry H Bernstein, MD, FCAP
4.3 Otros factores que afectan al crecimiento tumoral
4.3.1 Apretando las células de cáncer de ovario para predecir el potencial metastásico: la rigidez celular como posible biomarcador
Prabodh Kandala, PhD
4.3.2 Cáncer de próstata: el «mecanismo patológico» impulsado por andrógenos en las formas de aparición temprana de la enfermedad
Aviva Lev-Ari, PhD, RN
Capítulo 5: Los avances en la investigación de los cánceres de mama y gastrointestinales refuerzan la esperanza de curación
5.1 Cáncer de mama
5.1.1 El movimiento celular proporciona indicios sobre la agresividad del cáncer de mama
Prabodh Kandala, PhD
5.1.2 Identificación de los cánceres de mama agresivos mediante la interpretación de los patrones matemáticos en el genoma del cáncer
Prabodh Kandala, PhD
5.1.3 Mecanismo implicado en el crecimiento de las células del cáncer de mama: su función en la detección y el tratamiento precoces
Aviva Lev-Ari, PhD, RN
5.1.4 El BRCA1, supresor tumoral del cáncer de mama y de ovario: funciones en la transcripción, ubiquitinación y reparación del ADN
Sudipta Saha, PhD
5.1.5 Cáncer de mama y mutaciones mitocondriales
https://pharmaceuticalintelligence.com/2013/03/04/breast-cancer-and-mitochondrial-mutations/
Larry H Bernstein, MD, FCAP
5.1.6 Científicos del MIT identifican un gen que controla la agresividad de las células del cáncer de mama
Aviva Lev-Ari PhD RN
5.1.7 La patología molecular de la progresión del cáncer de mama
Tilda Barliya, PhD
5.1.8 En el punto de mira: cáncer de mama triple negativo
https://pharmaceuticalintelligence.com/2013/01/29/in-focus-triple-negative-breast-cancer/
Ritu Saxena, PhD
5.1.9 Sistema automatizado de ecografía mamaria («ABUS») para la exploración completa de la mama: se empieza a estructurar una solución para una necesidad urgente
Dror Nir, PhD
5.1.10 Estado actual de la técnica de diagnóstico oncológico por imagen de la mama.
https://pharmaceuticalintelligence.com/2013/01/21/state-of-the-art-in-oncologic-imaging-of-breast/
Dror Nir, PhD
5.2 Cáncer gastrointestinal
5.2.1 Cáncer de colon
https://pharmaceuticalintelligence.com/2013/04/30/colon-cancer/
Tilda Barliya, PhD
5.2.2 La mutación PIK3CA del cáncer colorrectal puede servir como biomarcador molecular predictivo para el tratamiento adyuvante con aspirina
Aviva Lev-Ari, PhD, RN
5.2.3 Estado actual de la técnica de diagnóstico oncológico por imagen de cánceres colorrectales.
Dror Nir, PhD
5.2.4 Cáncer de páncreas: genética, genómica e inmunoterapia
https://pharmaceuticalintelligence.com/2013/04/11/update-on-pancreatic-cancer/
Tilda Barliya, PhD
5.2.5 Genomas del cáncer de páncreas: genes de la vía de orientación de los axones: aberraciones reveladas
Aviva Lev-Ari, PhD, RN
Parte II
La llegada de la medicina traslacional, las «ómicas» y la medicina personalizada marcan el inicio de nuevos paradigmas en el tratamiento del cáncer y avances en el desarrollo de fármacos
Capítulo 6: Estrategias de tratamiento
6.1 Medicamentos comercializados y nuevos
Cáncer de mama
6.1.1 Tratamiento del cáncer de mama metastásico positivo para HER2
https://pharmaceuticalintelligence.com/2013/03/03/treatment-for-metastatic-her2-breast-cancer/
Larry H Bernstein MD, FCAP
6.1.2 El consumo diario de aspirina está relacionado con una menor mortalidad por cáncer
https://pharmaceuticalintelligence.com/2012/08/11/1796/
Aviva Lev-Ari, PhD, RN
6.1.3 Desarrollado un nuevo medicamento contra el cáncer
https://pharmaceuticalintelligence.com/2012/05/30/new-anti-cancer-drug-developed/
Prabodh Kandala, Ph.D.
6.1.4 Sutent, el fármaco de Pfizer contra el cáncer de riñón, provocó eficazmente la REMISIÓN de la leucemia linfoblástica aguda (LLA) del adulto
Aviva Lev-Ari, PhD, RN
6.1.5 «Morir o no morir». Tiempo y orden de los fármacos combinados para las células del cáncer de mama triple negativo: análisis a nivel de sistemas
Anamika Sarkar, PhD. and Ritu Saxena, PhD
Melanoma
6.1.6 Timosina alfa1 y melanoma
https://pharmaceuticalintelligence.com/2013/02/15/thymosin-alpha1-in-melanoma/
Tilda Barliya, PhD
Leucemia
6.1.7 Leucemia linfoblástica aguda y trasplante de médula ósea
Tilda Barliya PhD
6.2 Agentes naturales
Cáncer de próstata
6.2.1 Los científicos utilizan agentes naturales para el tratamiento de las metástasis óseas del cáncer de próstata
Ritu Saxena, PhD
Cáncer de mama
6.2.2 Un derivado de la marihuana parece prometedor en la lucha contra el cáncer de mama
Prabodh Kandala, PhD
Cáncer de ovario
6.2.3 Atenuando el crecimiento del cáncer de ovario
https://pharmaceuticalintelligence.com/2012/05/11/259/
Prabodh Kandala, PhD
6.3 Posibles agentes terapéuticos
Cáncer gástrico
6.3.1 La integrina β surge como actor importante en la disfunción mitocondrial asociada al cáncer gástrico
Ritu Saxena, PhD
6.3.2 Artritis y cáncer: una nueva técnica de cribado permite encontrar compuestos esquivos para bloquear una enzima reguladora del sistema inmunitario
Prabodh Kandala, PhD
Cáncer de páncreas
6.3.3 Usp9x: una diana terapéutica prometedora para el cáncer de páncreas
Ritu Saxena, PhD
Cáncer de mama
6.3.4 Cáncer de mama, resistencia a los medicamentos y dianas biofarmacéuticas
Larry H Bernstein, MD, FCAP
Cáncer de próstata
6.3.5 Células del cáncer de próstata: los inhibidores de la histona··desacetilasa inducen la transición de epitelial a mesenquimal
Stephen J. Williams, PhD
Glioblastoma
6.3.6 El ácido gamma-linolénico (GLA) como herramienta terapéutica en el tratamiento del glioblastoma
Raphael Nir, PhD, MSM, MSc
6.3.7 Inhibición de Akt para el tratamiento del cáncer. ¿En qué punto nos encontramos actualmente?
Ziv Raviv, PhD
Capítulo 7: Medicina personalizada y terapia dirigida
7.1 En general
7.1.1 Aprovechamiento de la medicina personalizada para el tratamiento del cáncer, perspectivas de prevención y curación: opiniones de los líderes en la investigación del cáncer
Aviva Lev-Ari, PhD, RN
7.1.2 La curación del cáncer basada en la medicina personalizada podría no estar muy lejos
Ritu Saxena, PhD
7.1.3 La medicina personalizada se prepara para combatir el cáncer
https://pharmaceuticalintelligence.com/2013/01/07/personalized-medicine-gearing-up-to-tackle-cancer/
Ritu Saxena, PhD
7.1.4 Detección del cáncer en el Centro de Prevención del Cáncer del Centro Médico Sourasky de Tel-Aviv
Ziv Raviv, PhD
7.1.5 Inspiración de los logros de la Dra. Maureen Cronin en la aplicación de la secuenciación genómica al diagnóstico del cáncer
Aviva Lev-Ari, PhD, RN
7.1.6 Medicina personalizada: biología celular del cáncer y cirugía mínimamente invasiva (CMI)
Aviva Lev-Ari, PhD, RN
7.2 Medicina personalizada y genómica
7.2.1 Genómica del cáncer: a la vanguardia gracias al Programa de Genómica del Cáncer de la UC Santa Cruz
Aviva Lev-Ari, PhD, RN
7.2.2 El análisis panexómico de las mutaciones somáticas del melanoma maligno contribuye al desarrollo de un tratamiento antineoplásico personalizado para esta enfermedad
Ziv Raviv, PhD
7.2.3 Análisis basado en el genotipo para el tratamiento del cáncer mediante el modelado de datos a gran escala: Nayoung Kim, PhD(c)
Aviva Lev-Ari, PhD, RN
7.2.4 Tratamiento de precisión genómico del cáncer: el genoma tumoral digitalizado (WGSA) comparado con la línea germinal nativa. Se necesitan muestras ultracongeladas y muestras incluidas en parafina y fijadas con formol
Aviva Lev-Ari, PhD, RN
7.2.5 LÍDERES en la secuenciación genómica de mutaciones genéticas para la selección de fármacos en el tratamiento personalizado del cáncer: Parte 2
Aviva Lev-Ari, PhD, RN
7.2.6 Cuestiones éticas en la medicina personalizada: análisis de BRCA1/2 en menores y comunicación del riesgo de cáncer de mama
Stephen J. Williams, PhD
7.3 Medicina personalizada y terapia dirigida
7.3.1 El desarrollo de las tratamientos basados en ARNip para el cáncer
Ziv Raviv, PhD
7.3.2 Interferencia del ARNm en la expresión del cáncer
https://pharmaceuticalintelligence.com/2012/10/26/mrna-interference-with-cancer-expression/
Larry H Bernstein, MD, FCAP
7.3.3 CD47: tratamiento antineoplásico dirigido
https://pharmaceuticalintelligence.com/2013/05/07/cd47-target-therapy-for-cancer/
Tilda Barliya, PhD
7.3.4 Hexocinasa unida a mitocondrias como diana para el tratamiento del cáncer
Ziv Raviv, PhD
7.3.5 Para la medicina personalizada utilizando medicamentos contra el cáncer, GSK necesita el modelo de biología de sistemas de Alacris para determinar en una simulación informática el efecto del inhibidor en su «ensayo clínico virtual»
Aviva Lev-Ari, PhD, RN
7.3.6 Opción de tratamiento personalizado del cáncer de páncreas
https://pharmaceuticalintelligence.com/2012/10/16/personalized-pancreatic-cancer-treatment-option
Aviva Lev-Ari, PhD, RN
7.3.7 Nuevo plan para analizar de forma rutinaria a los pacientes con el fin de detectar genes cancerígenos heredados
Stephen J. Williams, PhD
7.3.8 Uso como diana de protooncogenes que no pueden usarse como diana terapéutica
https://pharmaceuticalintelligence.com/2013/11/01/targeting-untargetable-proto-oncogenes/
Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
7.3.9 El futuro de la medicina traslacional con diagnósticos y tratamientos inteligentes: la farmacogenómica
Demet Sag, PhD
7.4 Medicina personalizada en cánceres específicos
7.4.1 Medicina personalizada y cáncer de colon
https://pharmaceuticalintelligence.com/2013/05/25/personalized-medicine-and-colon-cancer/
Tilda Barliya, PhD
7.4.2 Caracterización genómica exhaustiva de los carcinomas epidermoides pulmonares
Aviva Lev-Ari, PhD, RN
7.4.3 Nanocomplejos de ARNip dirigidos, con penetración tumoral, para acreditar el oncogén del cáncer de ovario ID4
Sudipta Saha, PhD
7.4.4 Cáncer y hueso: las vibraciones de baja frecuencia ayudan a mitigar la pérdida de hueso
Ritu Saxena, PhD
7.4.5 Según un estudio, las nuevas directrices de cribado del cáncer de próstata no convencen a todos
Prabodh Kandala, PhD
Parte III
La medicina traslacional, la genómica y las nuevas tecnologías convergen para mejorar la detección precoz
Diagnóstico, detección y biomarcadores
Capítulo 8: Diagnóstico
Diagnóstico: Cáncer de próstata
8.1 En el mercado del diagnóstico molecular del cáncer de próstata, los líderes son: SRI International, Genomic Health con la Cleveland Clinic, Myriad Genetics con la UCSF, GenomeDx y BioTheranostics
Aviva Lev-Ari PhD RN
8.2 El reto fundamental de hoy en día en el cribado del cáncer de próstata
Dror Nir, PhD
Diagnóstico y guía del cáncer de próstata
8.3 Los cánceres de próstata se desplomaron tras la guía de la USPSTF. ¿Volverá a suceder?
Aviva Lev-Ari, PhD, RN
Diagnóstico, guía y aspectos comerciales del cáncer de próstata
8.4 Según un estudio, las nuevas directrices de cribado del cáncer de próstata no convencen a todos
Prabodh Kandala, PhD
Diagnóstico: cáncer de pulmón
8.5 Diagnóstico del cáncer de pulmón en el aliento exhalado mediante nanopartículas de oro
Tilda Barliya PhD
Capítulo 9: Detección
Detección: cáncer de próstata
9.1 Detección precoz del cáncer de próstata: guía de la Asociación Americana de Urología (AUA)
https://pharmaceuticalintelligence.com/2013/05/21/early-detection-of-prostate-cancer-aua-guideline/
Dror Nir, PhD
Detección: cáncer de mama y ovario
9.2 Análisis de múltiples mutaciones genéticas mediante SMP para pacientes: amplios antecedentes familiares de cáncer de mama y ovario, diagnosticados a edades tempranas y con resultado negativo en la prueba de BRCA
Aviva Lev-Ari, PhD, RN
Detección: cáncer de próstata agresivo
9.3 Análisis de sangre para identificar el cáncer de próstata agresivo: un descubrimiento de SRI International, Menlo Park, CA, EUA
Aviva Lev-Ari, PhD, RN
Marcadores de diagnóstico y cribado como método de diagnóstico
9.4 Combinación de tecnología de nanotubos y anticuerpos creados por ingeniería genética para detectar biomarcadores del cáncer de próstata
Stephen J. Williams, PhD
Detección: cáncer de ovario
9.5 Los signos de alerta pueden conducir a una mejor detección temprana del cáncer de ovario
Prabodh Kandala, PhD
9.6 Conociendo el tamaño y la ubicación del tumor, ¿podríamos orientar el tratamiento hacia la RDI aplicando una intervención guiada por imagen?
Dror Nir, PhD
Capítulo 10: Biomarcadores
10.1 Mesotelina: un biomarcador de detección temprana del cáncer (por Jack Andraka)
Tilda Barliya, PhD
Biomarcadores: todos los tipos de cáncer, genómica e histología
10.2 Estaniocalcina: un biomarcador del cáncer
https://pharmaceuticalintelligence.com/2012/12/25/stanniocalcin-a-cancer-biomarker/
Aashir Awan, PhD
10.3 Perfiles genómicos del cáncer de mama para predecir la supervivencia: combinación del análisis histopatológico y de la expresión génica
Aviva Lev-Ari, PhD, RN
Biomarcadores: Cáncer de páncreas
10.4 Desarrollo de herramientas de biomarcadores para el diagnóstico precoz del cáncer de páncreas: Instituto Van Andel y Universidad de Emory
Aviva Lev-Ari, PhD, RN
10.5 Identificado un biomarcador temprano del cáncer de páncreas
https://pharmaceuticalintelligence.com/2012/05/17/early-biomarker-for-pancreatic-cancer-identified/
Prabodh Kandala, PhD
Biomarcadores: cáncer de cabeza y cuello
10.6 Estudios sobre el cáncer de cabeza y cuello sugieren que hay marcadores alternativos, más útiles, para el pronóstico que las pruebas de ADN del VPH
Aviva Lev-Ari, PhD, RN
10.7 Abre el servicio de exomas para enfermedades raras y cáncer avanzado en OncoSpire, de la Clínica Mayo
Aviva Lev-Ari, PhD, RN
Marcadores de diagnóstico y cribado como método de diagnóstico
10.8 En busca de claridad sobre el cribado del cáncer de próstata, el seguimiento posquirúrgico y la predicción de la remisión a largo plazo
Larry H Bernstein, MD, FCAP, Dror Nir, PhD and Aviva Lev-Ari, PhD, RN
Capítulo 11: Diagnóstico por imagen del cáncer
11.1 Introducción por Dror Nir, PhD
11.2 Ecografía
11.2.1 2013, EL AÑO DE LA ECOGRAFÍA
https://pharmaceuticalintelligence.com/2013/04/10/2013-year-of-the-ultrasound/
Dror Nir, PhD
11.2.2 Diagnóstico por imagen: ¿ver o imaginar? (Parte 1)
https://pharmaceuticalintelligence.com/2012/09/10/imaging-seeing-or-imagining-part-1/
Dror Nir, PhD
11.2.3 Detección precoz del cáncer de próstata: guía de la Asociación Americana de Urología (AUA)
https://pharmaceuticalintelligence.com/2013/05/21/early-detection-of-prostate-cancer-aua-guideline/
Dror Nir, PhD
11.2.4 El reto fundamental de hoy en día en el cribado del cáncer de próstata
Dror Nir, PhD
11.2.5 Estado actual de la técnica de diagnóstico oncológico por imagen del cáncer de próstata
https://pharmaceuticalintelligence.com/2013/01/28/state-of-the-art-in-oncologic-imaging-of-prostate/
Dror Nir, PhD
11.2.6 De la AUA 2013: Biopsias con plantilla asistidas por «HistoScanning» para pacientes con biopsias guiadas por ecografía transrectal negativas previas
Dror Nir, PhD
11.2.7 En el camino para mejorar la biopsia de próstata
https://pharmaceuticalintelligence.com/2013/02/15/on-the-road-to-improve-prostate-biopsy/
Dror Nir, PhD
11.2.8 La ecografía como instrumento para medir la elasticidad de los tejidos: «elastografía de ondas de cizall» comparada con «imágenes de deformación»
Dror Nir, PhD
11.2.9 ¿Qué podría transformar a un perdedor en un ganador?
https://pharmaceuticalintelligence.com/2012/11/19/what-could-transform-an-underdog-into-a-winner/
Dror Nir, PhD
11.2.10 Cribado del cáncer de ovario mediante ecografía
https://pharmaceuticalintelligence.com/2013/04/28/ultrasound-based-screening-for-ovarian-cancer/
Dror Nir, PhD
11.2.11 Tratamiento del cáncer guiado por imagen: una disciplina que necesita guías
Dror Nir, PhD
11.3 RM y TEP/RM
11.3.1 Introducción de las imágenes inteligentes en la práctica diaria de los radiólogos
Dror Nir, PhD
11.3.2 Diagnóstico por imagen: ¿ver o imaginar? (Parte 2)
[La parte 1 está incluida en la sección de ecografía anterior]
https://pharmaceuticalintelligence.com/2012/09/29/imaging-seeing-or-imagining-part-2/
Dror Nir, PhD
11.3.3 Biopsias guiadas por imagen: ¿se puede elegir una estrategia preferida?
Dror Nir, PhD
11.3.4 Nuevos resultados clínicos apoyan el guiado por imagen en la biopsia de próstata dirigida
Dror Nir, PhD
11.3.5 Imágenes de cuerpo entero como herramienta de cribado del cáncer: ¿son la respuesta a una necesidad clínica no cubierta?
Dror Nir, PhD
11.3.6 Estado actual de la técnica de diagnóstico oncológico por imagen del linfoma
https://pharmaceuticalintelligence.com/2013/02/03/state-of-the-art-in-oncologic-imaging-of-lymphoma/
Dror Nir, PhD
11.3.7 Un rincón en el sistema ECO de las imágenes médicas
https://pharmaceuticalintelligence.com/2012/12/09/a-corner-in-the-medical-imagings-eco-system/
Dror Nir, PhD
11.4 TAC, mamografía y TEP/TAC
11.4.1 Causas y características de imagen de los falsos positivos y los falsos negativos en la 18F-TEP/TAC en el diagnóstico oncológico por imagen
Dror Nir, PhD
11.4.2 Tratamiento mínimamente invasivo guiado por imagen para el carcinoma hepatocelular inoperable
Dror Nir, PhD
11.4.3 Mejora de las pruebas de imagen basadas en mamografía para una mejor planificación del tratamiento
Dror Nir, PhD
11.4.4 Cerrando los puntos ciegos de la mamografía
https://pharmaceuticalintelligence.com/2012/11/04/closing-the-mammography-gap/
Dror Nir, PhD
11.4.5 Estado de la técnica de diagnóstico oncológico por imagen de los pulmones
https://pharmaceuticalintelligence.com/2013/01/23/state-of-the-art-in-oncologic-imaging-of-lungs/
Dror Nir, PhD
11.4.6 Informe sobre el cáncer de ovario y la cirugía guiada por fluorescencia
Tilda Barliya, PhD
11.5 Tomografía de coherencia óptica (TCO)
11.5.1 Tomografía de coherencia óptica: tecnología emergente en el tratamiento de los pacientes con cáncer
Dror Nir, PhD
11.5.2 Un nuevo dispositivo de formación de imágenes promete mejorar el control de calidad de las tumorectomías por cáncer de mama, teniendo en cuenta su coste
Dror Nir, PhD
11.5.3 Biopsia virtual: ¿es posible?
https://pharmaceuticalintelligence.com/2013/03/03/virtual-biopsy-is-it-possible/
Dror Nir, PhD
11.5.4 Nuevos avances en la medición de las propiedades mecánicas de los tejidos
Dror Nir, PhD
Resumen por Dror Nir, PhD
Capítulo 12. La nanotecnología aporta nuevos avances en el tratamiento, la detección y el diagnóstico por imagen del cáncer
Introducción
12.1 Nanotecnología del ADN
https://pharmaceuticalintelligence.com/2013/05/15/dna-nanotechnology/
Tilda Barliya, PhD
12.2 Nanotecnología, medicina personalizada y secuenciación del ADN
Tilda Barliya, PhD
12.3 Tratamiento nanotecnológico del cáncer de mama
https://pharmaceuticalintelligence.com/2012/12/09/naotech-therapy-for-breast-cancer/
Tilda Barliya, PhD
12.4 Cáncer de próstata y nanotecnología
https://pharmaceuticalintelligence.com/2013/02/07/prostate-cancer-and-nanotecnology/
Tilda Barliya, PhD
12.5 Nanotecnología: detección y tratamiento del cáncer metastásico en los ganglios linfáticos
Tilda Barliya, PhD
12.6 La nanotecnología aborda el cáncer cerebral
https://pharmaceuticalintelligence.com/2012/11/23/nanotechnology-tackles-brain-cancer/
Tilda Barliya, PhD
12.7 Cáncer de pulmón (CPNM), administración de fármacos y nanotecnología
Tilda Barliya, PhD
Epílogo del volumen por Larry H. Bernstein, MD, FACP
Los miembros del equipo oncológico de Leaders of Pharmaceutical Business Intelligence expresan su opinión acerca de las fronteras de la investigación sobre el cáncer en su PROPIO ámbito de experiencia
Temas actuales de investigación avanzada en el tratamiento de pacientes con cáncer basado en la resonancia magnética
Autor: Dror Nir, PhD
PRIMER VOLUMEN
Biología y genómica del cancer para el
diagnóstico de la enfermedad
2015
Cancer Biology and Genomics for Disease Diagnosis
En Amazon.com desde el 11/08/2015
http://www.amazon.com/dp/B013RVYR2K
Stephen J. Williams, PhD, Senior Editor
Leaders in Pharmaceutical Business Intelligence
https://pharmaceuticalintelligence.com/
Redactora jefe de la serie de libros electrónicos BioMed
Leaders in Pharmaceutical Business Intelligence, Boston
avivalev-ari@alum.berkeley.edu
PART A:
PART A.2:
The eTOCs in Bi-lingual format:
Spanish and English in Text format
Serie C: libros electrónicos acerca del cáncer y la oncología
Consultor de contenidos de la serie C: Larry H. Bernstein, MD, FCAP
PRIMER VOLUMEN
Biología y genómica del cáncer
para el
diagnóstico de la enfermedad
Traducción a español
En Amazon.com desde el 11/08/2015
2015
http://www.amazon.com/dp/B013RVYR2K
Stephen J. Williams, PhD, Senior Editor
Leaders in Pharmaceutical Business Intelligence
https://pharmaceuticalintelligence.com/
Redactora jefe de la serie de libros electrónicos BioMed
Leaders in Pharmaceutical Business Intelligence, Boston
avivalev-ari@alum.berkeley.edu
Series C: e-Books on Cancer & Oncology
Series C Content Consultant: Larry H. Bernstein, MD, FCAP
VOLUME ONE
Cancer Biology and Genomics
for
Disease Diagnosis
On Amazon.com since 8/11/2015
2015
http://www.amazon.com/dp/B013RVYR2K
Stephen J. Williams, PhD, Senior Editor
Leaders in Pharmaceutical Business Intelligence
https://pharmaceuticalintelligence.com/
Editor-in-Chief BioMed e-Series of e-Books
Leaders in Pharmaceutical Business Intelligence, Boston
avivalev-ari@alum.berkeley.edu
Otros libros sobre el cáncer del equipo de LPBI
Serie C: libros electrónicos acerca del cáncer y la oncología
Consultor de contenidos de la serie C: Larry H. Bernstein, MD, FCAP
SEGUNDO VOLUMEN
Tratamientos contra el cáncer:
Metabólicos, genómicos, intervencionistas; inmunoterapia y nanotecnología para la administración de tratamientos
Traducción a español
En Amazon.com desde el 18/05/2017
http://www.amazon.com/dp/B071VQ6YYK
2017
Autores, redactores y editores
y
Autores y redactores invitados
Tilda Barliya, PhD, tildabarliya@gmail.com
Demet Sag, PhD, demet.sag@gmail.com
Dror Nir, PhD, dror.nir@radbee.com
Ziv Raviv, PhD zraviv06@gmail.com
Danut Dragoi, PhD, Danut.daa@gmail.com
Evelina Cohn, PhD, ecohn2011@yahoo.com
Aviva Lev-Ari, PhD, RN, avivalev-ari@alum.berkeley.edu
Leaders in Pharmaceutical Business Intelligence
Other Books on Cancer by LPBI’s Team
Series C: e-Books on Cancer & Oncology
Series C Content Consultant: Larry H. Bernstein, MD, FCAP
VOLUME TWO
Cancer Therapies:
Metabolic, Genomics, Interventional, Immunotherapy and Nanotechnology in Therapy Delivery
On Amazon.com since 5/18/2017
http://www.amazon.com/dp/B071VQ6YYK
2017
Authors, Curators and Editors
and
Guest Authors and Curators
Tilda Barliya, PhD, tildabarliya@gmail.com
Demet Sag, PhD, demet.sag@gmail.com
Dror Nir, PhD, dror.nir@radbee.com
Ziv Raviv, PhD zraviv06@gmail.com
Danut Dragoi, PhD, Danut.daa@gmail.com
Evelina Cohn, PhD, ecohn2011@yahoo.com
Aviva Lev-Ari, PhD, RN, avivalev-ari@alum.berkeley.edu
Leaders in Pharmaceutical Business Intelligence
PRIMER VOLUMEN
Biología y genómica del cancer para el diagnóstico de la enfermedad
VOLUME ONE
Cancer Biology and Genomics for Disease Diagnosis
Lista de colaboradores del primer volumen
List of Contributors to Volume One
(Nota: los artículos originales y seleccionados aparecen en negrita). (Otros artículos representan reseñas de publicaciones interesantes)
(Note: original authored and curated articles are in bold-faced type). Other articles represent scientific reports of interesting literature)
2.11, 10.2
1.2, 5.1.7, 5.2.1, 5.2.4, 6.1.6, 6.1.7, 7.3.3, 7.4.1, 8.5, 10.1, 11.4.6, 12.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7
Prólogo, introducción al volumen, 1.6, 1.8, 2.3, 2.4, 2.5, 2.6, 3.1, 4.2.6, 5.1.5, 6.1.1, 6.3.4, 7.3.2, 7.3.8, 10.8, Epílogo
Prologue, Volume Introduction, 1.6, 1.8, 2.3, 2.4, 2.5, 2.6, 3.1, 4.2.6, 5.1.5, 6.1.1, 6.3.4, 7.3.2, 7.3.8, 10.8, Epilogue
1.1, 1.12, 2.1, 2.2, 2.7, 4.3.1, 5.1.1, 5.1.2, 6.1.3, 6.2.2, 6.2.3, 6.3.2, 8.4, 10.5
1.3, 1.4, 1.7, 1.11, 3.5, 3.7, 3.8, 3.9, 3.10, 4.1.2, 4.1.6, 4.2.2, 4.2.5, 4.3.2, 5.1.3, 5.1.6, 5.2.2, 5.2.5, 6.1.2, 6.1.4, 7.1.1, 7.1.5, 7.1.6, 7.2.1, 7.2.3, 7.2.4, 7.2.5, 7.3.5, 7.3.6, 7.4.2, 8.1, 8., 9.2, 9.3, 10.3, 10.4, 10.6, 10.7
5.1.9, 5.1.10, 5.2.3, 8.2, 9.1, 9.6, 11.1.1, 11.1.2, 11.2.1, 11.2.2, 11.2.3, 11.2.4, 11.2.5, 11.2.6, 11.2.7, 11.2.8, 11.2.9, 11.2.10, 11.2.11, 11.3.1, 11.3.2, 11.3.3, 11.3.4, 11.3.5, 11.3.6, 11.3.7, 11.4.1, 11.4.2, 11.4.3, 11.4.4, 11.4.5, 11.5.1, 11.5.2, 11.5.3, 11.5.4
6.3.6
1.5, 6.3.7, 7.1.4, 7.2.2, 7.3.1, 7.3.4
3.3, 4.1.1, 4.1.3, 4.1.5, 7.3.9
1.10, 5.1.4, 7.4.3
6.1.5
1.9, 1.13, 2.8, 2.9, 3.6, 4.7, 4.2.3, 5.1.8, 6.1.5, 6.2.1, 6.3.1, 6.3.3, 7.1.2, 7.1.3, 7.4.4
2.10, 3.2, 3.4.1, 3.4.2, 4.1.4, 4.2.4, 6.3.5, 7.2.6, 7.3.7, 9.4
Indice de contenidos electrónico (IDCe)
EN ESPAÑOL TEXTO
electronic Table of Contents
Los enlaces indicados llevan al contenido original en inglés
| MD | Licenciado/a en medicina y cirugía (Estados Unidos) |
| PhD | Doctorado/a |
| RN | Enfermero/a titulado/a (National Board of Nursing Registration) |
| FCAP | Miembro distinguido (Fellow) del Colegio de Anatomopatólogos de los Estados Unidos |
| Ph.D | Doctorado/a |
| CRA | CRA |
| GCP | GCP |
Parte I
Perspectiva histórica de la demografía, la etiología y los avances en la investigación del cáncer
Part I
Historical Perspective of Cancer Demographics, Etiology, and Progress in Research
Capítulo 1: La incidencia del cáncer en las poblaciones del mundo
Chapter 1: The Occurrence of Cancer in World Populations
1.1 ¿Qué es el cáncer?
1.1 Understanding Cancer
https://pharmaceuticalintelligence.com/2012/05/07/102/
Prabodh Kandala, PhD
1.2 Metástasis del cáncer
1.2 Cancer Metastasis
https://pharmaceuticalintelligence.com/2013/07/06/cancer-metastasis/
Tilda Barliya, PhD
1.3 Perspectiva en 2013 de la «guerra contra el cáncer» del 23 de diciembre de 1971
1.3 2013 Perspective on “War on Cancer” on December 23, 1971
Aviva Lev-Ari, PhD, RN
1.4 La carga global del tratamiento del cáncer y la salud femenina: acceso al mercado y problemas de costes
1.4 Global Burden of Cancer Treatment & Women Health: Market Access & Cost Concerns
Aviva Lev-Ari, PhD, RN
1.5 La importancia de los programas de prevención del cáncer: nuevas perspectivas para combatir el cáncer
1.5 The Importance of Cancer Prevention Programs: New Perspectives for Fighting Cancer
Ziv Raviv, PhD
1.6 James Watson, codescubridor del ADN junto con Crick en abril de 1953, examina los «grupos de poder de investigación sobre el cáncer»
1.6 The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953
Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
1.7 Nuevo ecosistema de investigación sobre el cáncer: equipos científicos interinstitucionales
1.7 New Ecosystem of Cancer Research: Cross Institutional Team Science
Aviva Lev-Ari, PhD, RN
1.8 Innovaciones sobre el cáncer recopiladas de Internet
1.8 Cancer Innovations from across the Web
https://pharmaceuticalintelligence.com/2012/11/02/cancer-innovations-from-across-the-web/
Larry H Bernstein, MD, FCAP
1.9 Exploración del papel de la vitamina C en el tratamiento del cáncer
1.9 Exploring the role of vitamin C in Cancer therapy
https://pharmaceuticalintelligence.com/2013/01/15/exploring-the-role-of-vitamin-c-in-cancer-therapy/
Ritu Saxena PhD
1.10 Relación entre la dieta y el cáncer
1.10 Relation of Diet and Cancer
https://pharmaceuticalintelligence.com/2013/06/04/relation-of-diet-and-cancer/
Sudipta Saha, PhD
1.11 Asociación entre el cáncer de piel distinto del melanoma y los posteriores cánceres primarios en la población blanca
1.11 Association between Non-melanoma Skin Cancer and subsequent Primary Cancers in White Population
Aviva Lev-Ari, PhD, RN
1.12 Los hombres con cáncer de próstata tienen más probabilidades de fallecer por otras causas
1.12 Men With Prostate Cancer More Likely to Die from Other Causes
Prabodh Kandala, PhD
1.13 La lucha de Steve Jobs y Ralph Steinman contra el cáncer de páncreas: así perdimos
1.13 Battle of Steve Jobs and Ralph Steinman with Pancreatic Cancer: How we Lost
Ritu Saxena, PhD
Capítulo 2: Los rápidos avances científicos cambian nuestra visión de cómo se produce el cáncer
Chapter 2: Rapid Scientific Advances Changes Our View on How Cancer Forms
2.1 No todas las células cancerosas son iguales: algunos tipos de células controlan el crecimiento continuo del tumor y otros allanan el camino a la metástasis
2.1 All Cancer Cells Are Not Created Equal: Some Cell Types Control Continued Tumor Growth, Others Prepare the Way for Metastasis
Prabodh Kandala, PhD
2.2 Un momento. Las mutaciones en el cáncer pueden ser buenas
2.2 Hold on. Mutations in Cancer do Good
https://pharmaceuticalintelligence.com/2013/02/04/hold-on-mutations-in-cancer-do-good/
Prabodh Kandala, PhD
2.3 El efecto Warburg ¿causa o efecto del cáncer? ¿Una visión del siglo XXI?
2.3 Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?
Larry H Bernstein, MD, FCAP
2.4 El ratopín rasurado no padece cáncer
2.4 Naked Mole Rats Cancer-Free
https://pharmaceuticalintelligence.com/2013/06/20/naked-mole-rats-cancer-free/
Larry H Bernstein, MD, FCAP
2.5 El pez cebra, susceptible de padecer cáncer
2.5 Zebrafish—Susceptible to Cancer
https://pharmaceuticalintelligence.com/2013/04/02/zebrafish-susceptible-to-cancer/
Larry H Bernstein, MD, FCAP
2.6 Desmitificando a los tiburones, el cáncer y las aletas de tiburón
2.6 Demythologizing Sharks, Cancer, and Shark Fins
https://pharmaceuticalintelligence.com/2013/06/22/demythologizing-sharks-cancer-and-shark-fins/
Larry H Bernstein, MD, FCAP
2.7 El funcionamiento interno de las células tumorales predice la progresión del cáncer
2.7 Tumor Cells’ Inner Workings Predict Cancer Progression
Prabodh Kandala, PhD
2.8 En el punto de mira: identidad de las células madre del cáncer
2.8 In Focus: Identity of Cancer Stem Cells
https://pharmaceuticalintelligence.com/2013/03/22/in-focus-identity-of-cancer-stem-cells/
Ritu Saxena, PhD
2.9 En el punto de mira: células tumorales circulantes
2.9 In Focus: Circulating Tumor Cells
https://pharmaceuticalintelligence.com/2013/06/24/in-focus-circulating-tumor-cells/
Ritu Saxena, PhD
2.10 Reescribiendo las matemáticas del crecimiento tumoral; los equipos utilizan modelos matemáticos para diferenciar las mutaciones oncoiniciadoras de las secundarias
2.10 Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers
Stephen J. Williams, PhD
2.11 Papel de los cilios primarios en el cáncer de ovario
2.11 Role of Primary Cilia in Ovarian Cancer
https://pharmaceuticalintelligence.com/2013/01/15/role-of-primary-cilia-in-ovarian-cancer-2/
Aashir Awan, PhD
Capítulo 3: Surgen una base genética y una complejidad genética del cáncer
Chapter 3: A Genetic Basis and Genetic Complexity of Cancer Emerges
3.1 La unión de los oligonucleótidos en el ADN y las estructuras reticulares tridimensionales
3.1 The Binding of Oligonucleotides in DNA and 3-D Lattice Structures
Larry H Bernstein, MD, FCAP
3.2 Cómo promueven el cáncer los elementos móviles del ADN «basura». Parte 1: tumorigénesis mediada por transposones.
3.2 How Mobile Elements in “Junk” DNA Promote Cancer. Part 1: Transposon-mediated Tumorigenesis.
Stephen J. Williams, PhD
3.3 ADN: la basura de uno es el tesoro de otro pero no hay nada que desechar, después de todo
3.3 DNA: One Man’s Trash is another Man’s Treasure, but there is no JUNK after all
Demet Sag, PhD
3.4 Cuestiones sobre la heterogeneidad tumoral
3.4 Issues of Tumor Heterogeneity
3.4.1 Aspectos de la medicina personalizada en el cáncer: heterogeneidad intratumoral y evolución ramificada revelada por la secuenciación multirregional
3.4.1 Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Stephen J. Williams, PhD
3.4.2 Aspectos de la medicina personalizada: debates sobre la heterogeneidad intratumoral en el foro Oncology Pharma de LinkedIn
3.4.2 Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn
Stephen J. Williams, PhD
3.5 arrayMap: minería de rasgos genómicos de entidades oncológicas en datos de anomalías en el número de copias (CNA)
3.5 arrayMap: Genomic Feature Mining of Cancer Entities of Copy Number Abnormalities (CNAs) Data
Aviva Lev-Ari, PhD, RN
3.6 Cáncer de hígado asociado al VHB y al VHC: conocimientos importantes a partir del genoma
3.6 HBV and HCV-associated Liver Cancer: Important Insights from the Genome
Ritu Saxena, PhD
3.7 Cáncer de glándulas salivales y carcinoma adenoide quístico. Patrones de mutación: secuenciación del exoma y del genoma en el Memorial Sloan-Kettering Cancer Center
3.7 Salivary Gland Cancer – Adenoid Cystic Carcinoma: Mutation Patterns: Exome- and Genome-Sequencing @ Memorial Sloan-Kettering Cancer Center
Aviva Lev-Ari, PhD, RN
3.8 Cáncer gástrico: reconstrucción pangenómica y firmas mutacionales
3.8 Gastric Cancer: Whole-genome Reconstruction and Mutational Signatures
Aviva Lev-Ari, PhD, RN
3.9 La ausencia de un gen puede provocar más de una cuarta parte de los cánceres de mama
3.9 Missing Gene may Drive more than a quarter of Breast Cancers
Aviva Lev-Ari, PhD, RN
3.10 Gen crítico en la reabsorción de calcio: variantes de los genes KCNJ y SLC12A1. Consumo de calcio y protección contra el cáncer
3.10 Critical Gene in Calcium Reabsorption: Variants in the KCNJ and SLC12A1 genes – Calcium Intake and Cancer Protection
Aviva Lev-Ari, PhD, RN
Capítulo 4: Cómo afectan los factores epigenéticos y metabólicos al crecimiento tumoral
Chapter 4: How Epigenetic and Metabolic Factors Affect Tumor Growth
4.1 Epigenética
4.1 Epigenetics
4.1.1 La magia de la caja de Pandora: epigenética y troncalidad con los ARN largos no codificantes (ARNlnc)
4.1.1 The Magic of the Pandora’s Box : Epigenetics and Stemness with Long non-coding RNAs (lincRNA)
Demet Sag, PhD, CRA, GCP
4.1.2 Subtipos de cáncer de estómago basados en la metilación, identificados por un equipo de Singapur
4.1.2 Stomach Cancer Subtypes Methylation-based identified by Singapore-Led Team
Aviva Lev-Ari, PhD, RN
4.1.3 El infravalorado epigenoma
4.1.3 The Underappreciated EpiGenome
https://pharmaceuticalintelligence.com/2013/04/17/the-underappreciated-epigenome/
Demet Sag, Ph.D., CRA, GCP
4.1.4 Tratamiento de diferenciación: la epigenética aborda los tumores sólidos
4.1.4 Differentiation Therapy – Epigenetics Tackles Solid Tumors
Stephen J. Williams, PhD
4.1.5 «El SILENCIO de los corderos», presentación del poder del ARN no codificado
4.1.5 “The SILENCE of the Lambs” Introducing The Power of Uncoded RNA
Demet Sag, Ph.D., CRA, GCP
4.1.6 Metiltransferasas de ADN. Implicaciones para la regulación epigenética y la orientación del tratamiento contra el cáncer: James Shen, PhD
4.1.6 DNA Methyltransferases – Implications to Epigenetic Regulation and Cancer Therapy Targeting: James Shen, PhD
Aviva Lev-Ari, PhD, RN
4.2 Metabolismo
4.2 Metabolism
4.2.1 Las mitocondrias y el cáncer: una descripción general de los mecanismos
4.2.1 Mitochondria and Cancer: An overview of mechanisms
https://pharmaceuticalintelligence.com/2012/09/01/mitochondria-and-cancer-an-overview/
Ritu Saxena, PhD
4.2.2 Mecanismo bioenergético: la asociación inversa entre el cáncer y el Alzheimer
4.2.2 Bioenergetic Mechanism: The Inverse Association of Cancer and Alzheimer’s
Aviva Lev-Ari, PhD, RN
4.2.3 El papel crucial del óxido nítrico en el cáncer
4.2.3 Crucial role of Nitric Oxide in Cancer
https://pharmaceuticalintelligence.com/2012/10/16/crucial-role-of-nitric-oxide-in-cancer/
Ritu Saxena, PhD
4.2.4 El óxido nítrico mitiga la sensibilidad de las células de melanoma al cisplatino
4.2.4 Nitric Oxide Mitigates Sensitivity of Melanoma Cells to Cisplatin
Stephen J. Williams, PhD
4.2.5 Aumento del riesgo de obesidad y cáncer pero disminución del riesgo de diabetes de tipo 2: el papel del supresor tumoral, fosfatasa y homólogo de la tensina (PTEN)
4.2.5 Increased risks of obesity and cancer, Decreased risk of type 2 diabetes: The role of Tumor-suppressor phosphatase and tensin homologue (PTEN)
Aviva Lev-Ari, PhD, RN
4.2.6 Perfil lipídico, grasas saturadas, espectroscopia Raman y citología del cáncer
4.2.6 Lipid Profile, Saturated Fats, Raman Spectrosopy, Cancer Cytology
Larry H Bernstein, MD, FCAP
4.3 Otros factores que afectan al crecimiento tumoral
4.3 Other Factors Affecting Tumor Growth
4.3.1 Apretando las células de cáncer de ovario para predecir el potencial metastásico: la rigidez celular como posible biomarcador
4.3.1 Squeezing Ovarian Cancer Cells to Predict Metastatic Potential: Cell Stiffness as Possible Biomarker
Prabodh Kandala, PhD
4.3.2 Cáncer de próstata: el «mecanismo patológico» impulsado por andrógenos en las formas de aparición temprana de la enfermedad
4.3.2 Prostate Cancer: Androgen-driven “Pathomechanism” in Early-onset Forms of the Disease
Aviva Lev-Ari, PhD, RN
Capítulo 5: Los avances en la investigación de los cánceres de mama y gastrointestinales refuerzan la esperanza de curación
Chapter 5: Advances in Breast and Gastrointestinal Cancer Research Supports Hope for Cure
5.1 Cáncer de mama
5.1 Breast Cancer
5.1.1 El movimiento celular proporciona indicios sobre la agresividad del cáncer de mama
5.1.1 Cell Movement Provides Clues to Aggressive Breast Cancer
Prabodh Kandala, PhD
5.1.2 Identificación de los cánceres de mama agresivos mediante la interpretación de los patrones matemáticos en el genoma del cáncer
5.1.2 Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome
Prabodh Kandala, PhD
5.1.3 Mecanismo implicado en el crecimiento de las células del cáncer de mama: su función en la detección y el tratamiento precoces
5.1.3 Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment
Aviva Lev-Ari, PhD, RN
5.1.4 El BRCA1, supresor tumoral del cáncer de mama y de ovario: funciones en la transcripción, ubiquitinación y reparación del ADN
5.1.4 BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair
Sudipta Saha, PhD
5.1.5 Cáncer de mama y mutaciones mitocondriales
5.1.5 Breast Cancer and Mitochondrial Mutations
https://pharmaceuticalintelligence.com/2013/03/04/breast-cancer-and-mitochondrial-mutations/
Larry H Bernstein, MD, FCAP
5.1.6 Científicos del MIT identifican un gen que controla la agresividad de las células del cáncer de mama
5.1.6 MIT Scientists Identified Gene that Controls Aggressiveness in Breast Cancer Cells
Aviva Lev-Ari PhD RN
5.1.7 La patología molecular de la progresión del cáncer de mama
5.1.7 “The Molecular pathology of Breast Cancer Progression”
Tilda Barliya, PhD
5.1.8 En el punto de mira: cáncer de mama triple negativo
5.1.8 In focus: Triple Negative Breast Cancer
https://pharmaceuticalintelligence.com/2013/01/29/in-focus-triple-negative-breast-cancer/
Ritu Saxena, PhD
5.1.9 Sistema automatizado de ecografía mamaria («ABUS») para la exploración completa de la mama: se empieza a estructurar una solución para una necesidad urgente
5.1.9 Automated Breast Ultrasound System (‘ABUS’) for full breast scanning: The beginning of structuring a solution for an acute need!
Dror Nir, PhD
5.1.10 Estado actual de la técnica de diagnóstico oncológico por imagen de la mama.
5.1.10 State of the art in oncologic imaging of breast.
https://pharmaceuticalintelligence.com/2013/01/21/state-of-the-art-in-oncologic-imaging-of-breast/
Dror Nir, PhD
5.2 Cáncer gastrointestinal
5.2 Gastrointestinal Cancer
5.2.1 Cáncer de colon
5.2.1 Colon Cancer
https://pharmaceuticalintelligence.com/2013/04/30/colon-cancer/
Tilda Barliya, PhD
5.2.2 La mutación PIK3CA del cáncer colorrectal puede servir como biomarcador molecular predictivo para el tratamiento adyuvante con aspirina
5.2.2 PIK3CA mutation in Colorectal Cancer may serve as a Predictive Molecular Biomarker for adjuvant Aspirin therapy
Aviva Lev-Ari, PhD, RN
5.2.3 Estado actual de la técnica de diagnóstico oncológico por imagen de cánceres colorrectales.
5.2.3 State of the art in oncologic imaging of colorectal cancers.
Dror Nir, PhD
5.2.4 Cáncer de páncreas: genética, genómica e inmunoterapia
5.2.4 Pancreatic Cancer: Genetics, Genomics and Immunotherapy
https://pharmaceuticalintelligence.com/2013/04/11/update-on-pancreatic-cancer/
Tilda Barliya, PhD
5.2.5 Genomas del cáncer de páncreas: genes de la vía de orientación de los axones: aberraciones reveladas
5.2.5 Pancreatic cancer genomes: Axon guidance pathway genes – aberrations revealed
Aviva Lev-Ari, PhD, RN
Parte II
La llegada de la medicina traslacional, las «ómicas» y la medicina personalizada marcan el inicio de nuevos paradigmas en el tratamiento del cáncer y avances en el desarrollo de fármacos
Part II
Advent of Translational Medicine, “omics”, and Personalized Medicine Ushers in New Paradigms in Cancer Treatment and Advances in Drug Development
Capítulo 6: Estrategias de tratamiento
Chapter 6: Treatment Strategies
6.1 Medicamentos comercializados y nuevos
6.1 Marketed and Novel Drugs
Cáncer de mama
Breast Cancer
6.1.1 Tratamiento del cáncer de mama metastásico positivo para HER2
6.1.1 Treatment for Metastatic HER2 Breast Cancer
https://pharmaceuticalintelligence.com/2013/03/03/treatment-for-metastatic-her2-breast-cancer/
Larry H Bernstein MD, FCAP
6.1.2 El consumo diario de aspirina está relacionado con una menor mortalidad por cáncer
6.1.2 Aspirin a Day Tied to Lower Cancer Mortality
https://pharmaceuticalintelligence.com/2012/08/11/1796/
Aviva Lev-Ari, PhD, RN
6.1.3 Desarrollado un nuevo medicamento contra el cáncer
6.1.3 New Anti-Cancer Drug Developed
https://pharmaceuticalintelligence.com/2012/05/30/new-anti-cancer-drug-developed/
Prabodh Kandala, Ph.D.
6.1.4 Sutent, el fármaco de Pfizer contra el cáncer de riñón, provocó eficazmente la REMISIÓN de la leucemia linfoblástica aguda (LLA) del adulto
6.1.4 Pfizer’s Kidney Cancer Drug Sutent Effectively caused REMISSION to Adult Acute Lymphoblastic Leukemia (ALL)
Aviva Lev-Ari ,PhD, RN
6.1.5 «Morir o no morir». Tiempo y orden de los fármacos combinados para las células del cáncer de mama triple negativo: análisis a nivel de sistemas
6.1.5 “To Die or Not To Die” – Time and Order of Combination drugs for Triple Negative Breast Cancer cells: A Systems Level Analysis
Anamika Sarkar, PhD. and Ritu Saxena, PhD
Melanoma
Melanoma
6.1.6 Timosina alfa1 y melanoma
6.1.6 “Thymosin alpha1 and melanoma”
https://pharmaceuticalintelligence.com/2013/02/15/thymosin-alpha1-in-melanoma/
Tilda Barliya, PhD
Leucemia
Leukemia
6.1.7 Leucemia linfoblástica aguda y trasplante de médula ósea
6.1.7 Acute Lymphoblastic Leukemia and Bone Marrow Transplantation
Tilda Barliya PhD
6.2 Agentes naturales
6.2 Natural agents
Cáncer de próstata
Prostate Cancer
6.2.1 Los científicos utilizan agentes naturales para el tratamiento de las metástasis óseas del cáncer de próstata
6.2.1 Scientists use natural agents for prostate cancer bone metastasis treatment
Ritu Saxena, PhD
Cáncer de mama
Breast Cancer
6.2.2 Un derivado de la marihuana parece prometedor en la lucha contra el cáncer de mama
6.2.2 Marijuana Compound Shows Promise In Fighting Breast Cancer
Prabodh Kandala, PhD
Cáncer de ovario
Ovarian Cancer
6.2.3 Atenuando el crecimiento del cáncer de ovario
6.2.3 Dimming ovarian cancer growth
https://pharmaceuticalintelligence.com/2012/05/11/259/
Prabodh Kandala, PhD
6.3 Posibles agentes terapéuticos
6.3 Potential Therapeutic Agents
Cáncer gástrico
Gastric Cancer
6.3.1 La integrina β surge como actor importante en la disfunción mitocondrial asociada al cáncer gástrico
6.3.1 β Integrin emerges as an important player in mitochondrial dysfunction associated Gastric Cancer
Ritu Saxena, PhD
6.3.2 Artritis y cáncer: una nueva técnica de cribado permite encontrar compuestos esquivos para bloquear una enzima reguladora del sistema inmunitario
6.3.2 Arthritis, Cancer: New Screening Technique Yields Elusive Compounds to Block Immune-Regulating Enzyme
Prabodh Kandala, PhD
Cáncer de páncreas
Pancreatic Cancer
6.3.3 Usp9x: una diana terapéutica prometedora para el cáncer de páncreas
6.3.3 Usp9x: Promising therapeutic target for pancreatic cancer
Ritu Saxena, PhD
Cáncer de mama
Breast Cancer
6.3.4 Cáncer de mama, resistencia a los medicamentos y dianas biofarmacéuticas
6.3.4 Breast Cancer, drug resistance, and biopharmaceutical targets
Larry H Bernstein, MD, FCAP
Cáncer de próstata
Prostate Cancer
6.3.5 Células del cáncer de próstata: los inhibidores de la histona··desacetilasa inducen la transición de epitelial a mesenquimal
6.3.5 Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition
Stephen J. Williams, PhD
Glioblastoma
Glioblastoma
6.3.6 El ácido gamma-linolénico (GLA) como herramienta terapéutica en el tratamiento del glioblastoma
6.3.6 Gamma Linolenic Acid (GLA) as a Therapeutic tool in the Management of Glioblastoma
Raphael Nir, PhD, MSM, MSc
6.3.7 Inhibición de Akt para el tratamiento del cáncer. ¿En qué punto nos encontramos actualmente?
6.3.7 Akt inhibition for cancer treatment, where do we stand today?
Ziv Raviv, PhD
Capítulo 7: Medicina personalizada y terapia dirigida
Chapter 7: Personalized Medicine and Targeted Therapy
7.1 En general
7.1 General
7.1.1 Aprovechamiento de la medicina personalizada para el tratamiento del cáncer, perspectivas de prevención y curación: opiniones de los líderes en la investigación del cáncer
7.1.1 Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders
Aviva Lev-Ari, PhD, RN
7.1.2 La curación del cáncer basada en la medicina personalizada podría no estar muy lejos
7.1.2 Personalized medicine-based cure for cancer might not be far away
Ritu Saxena, PhD
7.1.3 La medicina personalizada se prepara para combatir el cáncer
7.1.3 Personalized medicine gearing up to tackle cancer
https://pharmaceuticalintelligence.com/2013/01/07/personalized-medicine-gearing-up-to-tackle-cancer/
Ritu Saxena, PhD
7.1.4 Detección del cáncer en el Centro de Prevención del Cáncer del Centro Médico Sourasky de Tel-Aviv
7.1.4 Cancer Screening at Sourasky Medical Center Cancer Prevention Center in Tel-Aviv
Ziv Raviv, PhD
7.1.5 Inspiración de los logros de la Dra. Maureen Cronin en la aplicación de la secuenciación genómica al diagnóstico del cáncer
7.1.5 Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics
Aviva Lev-Ari, PhD, RN
7.1.6 Medicina personalizada: biología celular del cáncer y cirugía mínimamente invasiva (CMI)
7.1.6 Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)
Aviva Lev-Ari, PhD, RN
7.2 Medicina personalizada y genómica
7.2 Personalized Medicine and Genomics
7.2.1 Genómica del cáncer: a la vanguardia gracias al Programa de Genómica del Cáncer de la UC Santa Cruz
7.2.1 Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz
Aviva Lev-Ari, PhD, RN
7.2.2 El análisis panexómico de las mutaciones somáticas del melanoma maligno contribuye al desarrollo de un tratamiento antineoplásico personalizado para esta enfermedad
7.2.2 Whole exome somatic mutations analysis of malignant melanoma contributes to the development of personalized cancer therapy for this disease
Ziv Raviv, PhD
7.2.3 Análisis basado en el genotipo para el tratamiento del cáncer mediante el modelado de datos a gran escala: Nayoung Kim, PhD(c)
7.2.3 Genotype-based Analysis for Cancer Therapy using Large-scale Data Modeling: Nayoung Kim, PhD(c)
Aviva Lev-Ari, PhD, RN
7.2.4 Tratamiento de precisión genómico del cáncer: el genoma tumoral digitalizado (WGSA) comparado con la línea germinal nativa. Se necesitan muestras ultracongeladas y muestras incluidas en parafina y fijadas con formol
7.2.4 Cancer Genomic Precision Therapy: Digitized Tumor’s Genome (WGSA) Compared with Genome-native Germ Line: Flash-frozen specimen and Formalin-fixed paraffin-embedded Specimen Needed
Aviva Lev-Ari, PhD, RN
7.2.5 LÍDERES en la secuenciación genómica de mutaciones genéticas para la selección de fármacos en el tratamiento personalizado del cáncer: Parte 2
7.2.5 LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2
Aviva Lev-Ari, PhD, RN
7.2.6 Cuestiones éticas en la medicina personalizada: análisis de BRCA1/2 en menores y comunicación del riesgo de cáncer de mama
7.2.6 Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk
Stephen J. Williams, PhD
7.3 Medicina personalizada y terapia dirigida
7.3 Personalized Medicine and Targeted Therapy
7.3.1 El desarrollo de las tratamientos basados en ARNip para el cáncer
7.3.1 The Development of siRNA-Based Therapies for Cancer
Ziv Raviv, PhD
7.3.2 Interferencia del ARNm en la expresión del cáncer
7.3.2 mRNA interference with cancer expression
https://pharmaceuticalintelligence.com/2012/10/26/mrna-interference-with-cancer-expression/
Larry H Bernstein, MD, FCAP
7.3.3 CD47: tratamiento antineoplásico dirigido
7.3.3 CD47: Target Therapy for Cancer
https://pharmaceuticalintelligence.com/2013/05/07/cd47-target-therapy-for-cancer/
Tilda Barliya, PhD
7.3.4 Hexocinasa unida a mitocondrias como diana para el tratamiento del cáncer
7.3.4 Targeting Mitochondrial-bound Hexokinase for Cancer Therapy
Ziv Raviv, PhD
7.3.5 Para la medicina personalizada utilizando medicamentos contra el cáncer, GSK necesita el modelo de biología de sistemas de Alacris para determinar en una simulación informática el efecto del inhibidor en su «ensayo clínico virtual»
7.3.5 GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”
Aviva Lev-Ari, PhD, RN
7.3.6 Opción de tratamiento personalizado del cáncer de páncreas
7.3.6 Personalized Pancreatic Cancer Treatment Option
https://pharmaceuticalintelligence.com/2012/10/16/personalized-pancreatic-cancer-treatment-option
Aviva Lev-Ari, PhD, RN
7.3.7 Nuevo plan para analizar de forma rutinaria a los pacientes con el fin de detectar genes cancerígenos heredados
7.3.7 New scheme to routinely test patients for inherited cancer genes
Stephen J. Williams, PhD
7.3.8 Uso como diana de protooncogenes que no pueden usarse como diana terapéutica
7.3.8 Targeting Untargetable Proto-Oncogenes
https://pharmaceuticalintelligence.com/2013/11/01/targeting-untargetable-proto-oncogenes/
Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
7.3.9 El futuro de la medicina traslacional con diagnósticos y tratamientos inteligentes: la farmacogenómica
7.3.9 The Future of Translational Medicine with Smart Diagnostics and Therapies: PharmacoGenomics
Demet Sag, PhD
7.4 Medicina personalizada en cánceres específicos
7.4 Personalized Medicine in Specific Cancers
7.4.1 Medicina personalizada y cáncer de colon
7.4.1 Personalized medicine and Colon cancer
https://pharmaceuticalintelligence.com/2013/05/25/personalized-medicine-and-colon-cancer/
Tilda Barliya, PhD
7.4.2 Caracterización genómica exhaustiva de los carcinomas epidermoides pulmonares
7.4.2 Comprehensive Genomic Characterization of Squamous Cell Lung Cancers
Aviva Lev-Ari, PhD, RN
7.4.3 Nanocomplejos de ARNip dirigidos, con penetración tumoral, para acreditar el oncogén del cáncer de ovario ID4
7.4.3 Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene ID4
Sudipta Saha, PhD
7.4.4 Cáncer y hueso: las vibraciones de baja frecuencia ayudan a mitigar la pérdida de hueso
7.4.4 Cancer and Bone: low magnitude vibrations help mitigate bone loss
Ritu Saxena, PhD
7.4.5 Según un estudio, las nuevas directrices de cribado del cáncer de próstata no convencen a todos
7.4.5 New Prostate Cancer Screening Guidelines Face a Tough Sell, Study Suggests
Prabodh Kandala, PhD
Parte III
La medicina traslacional, la genómica y las nuevas tecnologías convergen para mejorar la detección precoz
Part III
Translational Medicine, Genomics, and New Technologies Converge to Improve Early Detection
Diagnóstico, detección y biomarcadores
Diagnosis, Detection And Biomarkers
Capítulo 8: Diagnóstico
Chapter 8: Diagnosis
Diagnóstico: Cáncer de próstata
Diagnosis: Prostate Cancer
8.1 En el mercado del diagnóstico molecular del cáncer de próstata, los líderes son: SRI International, Genomic Health con la Cleveland Clinic, Myriad Genetics con la UCSF, GenomeDx y BioTheranostics
8.1 Prostate Cancer Molecular Diagnostic Market – the Players are: SRI Int’l, Genomic Health w/Cleveland Clinic, Myriad Genetics w/UCSF, GenomeDx and BioTheranostics
Aviva Lev-Ari PhD RN
8.2 El reto fundamental de hoy en día en el cribado del cáncer de próstata
8.2 Today’s fundamental challenge in Prostate cancer screening
Dror Nir, PhD
Diagnóstico y guía del cáncer de próstata
Diagnosis & Guidance: Prostate Cancer
8.3 Los cánceres de próstata se desplomaron tras la guía de la USPSTF. ¿Volverá a suceder?
8.3 Prostate Cancers Plunged After USPSTF Guidance, Will It Happen Again?
Aviva Lev-Ari, PhD, RN
Diagnóstico, guía y aspectos comerciales del cáncer de próstata
Diagnosis, Guidance and Market Aspects: Prostate Cancer
8.4 Según un estudio, las nuevas directrices de cribado del cáncer de próstata no convencen a todos
8.4 New Prostate Cancer Screening Guidelines Face a Tough Sell, Study Suggests
Prabodh Kandala, PhD
Diagnóstico: cáncer de pulmón
Diagnossis: Lung Cancer
8.5 Diagnóstico del cáncer de pulmón en el aliento exhalado mediante nanopartículas de oro
8.5 Diagnosing lung cancer in exhaled breath using gold nanoparticles
Tilda Barliya PhD
Capítulo 9: Detección
Chapter 9: Detection
Detección: cáncer de próstata
Detection: Prostate Cancer
9.1 Detección precoz del cáncer de próstata: guía de la Asociación Americana de Urología (AUA)
9.1 Early Detection of Prostate Cancer: American Urological Association (AUA) Guideline
https://pharmaceuticalintelligence.com/2013/05/21/early-detection-of-prostate-cancer-aua-guideline/
Dror Nir, PhD
Detección: cáncer de mama y ovario
Detection: Breast & Ovarian Cancer
9.2 Análisis de múltiples mutaciones genéticas mediante SMP para pacientes: amplios antecedentes familiares de cáncer de mama y ovario, diagnosticados a edades tempranas y con resultado negativo en la prueba de BRCA
9.2 Testing for Multiple Genetic Mutations via NGS for Patients: Very Strong Family History of Breast & Ovarian Cancer, Diagnosed at Young Ages, & Negative on BRCA Test
Aviva Lev-Ari, PhD, RN
Detección: cáncer de próstata agresivo
Detection: Aggressive Prostate Cancer
9.3 Análisis de sangre para identificar el cáncer de próstata agresivo: un descubrimiento de SRI International, Menlo Park, CA, EUA
9.3 A Blood Test to Identify Aggressive Prostate Cancer: a Discovery @ SRI International, Menlo Park, CA
Aviva Lev-Ari, PhD, RN
Marcadores de diagnóstico y cribado como método de diagnóstico
Diagnostic Markers & Screening as Diagnosis Method
9.4 Combinación de tecnología de nanotubos y anticuerpos creados por ingeniería genética para detectar biomarcadores del cáncer de próstata
9.4 Combining Nanotube Technology and Genetically Engineered Antibodies to Detect Prostate Cancer Biomarkers
Stephen J. Williams, PhD
Detección: cáncer de ovario
Detection: Ovarian Cancer
9.5 Los signos de alerta pueden conducir a una mejor detección temprana del cáncer de ovario
9.5 Warning signs may lead to better early detection of ovarian cancer
Prabodh Kandala, PhD
9.6 Conociendo el tamaño y la ubicación del tumor, ¿podríamos orientar el tratamiento hacia la RDI aplicando una intervención guiada por imagen?
9.6 Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?
Dror Nir, PhD
Capítulo 10: Biomarcadores
Chapter 10: Biomarkers
10.1 Mesotelina: un biomarcador de detección temprana del cáncer (por Jack Andraka)
10.1 Mesothelin: An early detection biomarker for cancer (By Jack Andraka)
Tilda Barliya, PhD
Biomarcadores: todos los tipos de cáncer, genómica e histología
Biomarkers: All Types of Cancer, Genomics and Histology
10.2 Estaniocalcina: un biomarcador del cáncer
10.2 Stanniocalcin: A Cancer Biomarker
https://pharmaceuticalintelligence.com/2012/12/25/stanniocalcin-a-cancer-biomarker/
Aashir Awan, PhD
10.3 Perfiles genómicos del cáncer de mama para predecir la supervivencia: combinación del análisis histopatológico y de la expresión génica
10.3 Breast Cancer: Genomic Profiling to Predict Survival: Combination of Histopathology and Gene Expression Analysis
Aviva Lev-Ari, PhD, RN
Biomarcadores: Cáncer de páncreas
Biomarkers: Pancreatic Cancer
10.4 Desarrollo de herramientas de biomarcadores para el diagnóstico precoz del cáncer de páncreas: Instituto Van Andel y Universidad de Emory
10.4 Biomarker tool development for Early Diagnosis of Pancreatic Cancer: Van Andel Institute and Emory University
Aviva Lev-Ari, PhD, RN
10.5 Identificado un biomarcador temprano del cáncer de páncreas
10.5 Early Biomarker for Pancreatic Cancer Identified
https://pharmaceuticalintelligence.com/2012/05/17/early-biomarker-for-pancreatic-cancer-identified/
Prabodh Kandala, PhD
Biomarcadores: cáncer de cabeza y cuello
Biomarkers: Head and Neck Cancer
10.6 Estudios sobre el cáncer de cabeza y cuello sugieren que hay marcadores alternativos, más útiles, para el pronóstico que las pruebas de ADN del VPH
10.6 Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing
Aviva Lev-Ari, PhD, RN
10.7 Abre el servicio de exomas para enfermedades raras y cáncer avanzado en OncoSpire, de la Clínica Mayo
10.7 Opens Exome Service for Rare Diseases & Advanced Cancer @ Mayo Clinic’s OncoSpire
Aviva Lev-Ari, PhD, RN
Marcadores de diagnóstico y cribado como método de diagnóstico
Diagnostic Markers and Screening as Diagnosis Methods
10.8 En busca de claridad sobre el cribado del cáncer de próstata, el seguimiento posquirúrgico y la predicción de la remisión a largo plazo
10.8 In Search of Clarity on Prostate Cancer Screening, Post-Surgical Followup, and Prediction of Long Term Remission
Larry H Bernstein, MD, FCAP, Dror Nir, PhD and Aviva Lev-Ari, PhD, RN
Capítulo 11: Diagnóstico por imagen del cáncer
Chapter 11: Imaging In Cancer
11.1 Introducción por Dror Nir, PhD
11.1 Introduction by Dror Nir, PhD
11.2 Ecografía
11.2 Ultrasound
11.2.1 2013, EL AÑO DE LA ECOGRAFÍA
11.2.1 2013 – YEAR OF THE ULTRASOUND
https://pharmaceuticalintelligence.com/2013/04/10/2013-year-of-the-ultrasound/
Dror Nir, PhD
11.2.2 Diagnóstico por imagen: ¿ver o imaginar? (Parte 1)
11.2.2 Imaging: seeing or imagining? (Part 1)
https://pharmaceuticalintelligence.com/2012/09/10/imaging-seeing-or-imagining-part-1/
Dror Nir, PhD
11.2.3 Detección precoz del cáncer de próstata: guía de la Asociación Americana de Urología (AUA)
11.2.3 Early Detection of Prostate Cancer: American Urological Association (AUA) Guideline
https://pharmaceuticalintelligence.com/2013/05/21/early-detection-of-prostate-cancer-aua-guideline/
Dror Nir, PhD
11.2.4 El reto fundamental de hoy en día en el cribado del cáncer de próstata
11.2.4 Today’s fundamental challenge in Prostate cancer screening
Dror Nir, PhD
11.2.5 Estado actual de la técnica de diagnóstico oncológico por imagen del cáncer de próstata
11.2.5 State of the art in oncologic imaging of Prostate
https://pharmaceuticalintelligence.com/2013/01/28/state-of-the-art-in-oncologic-imaging-of-prostate/
Dror Nir, PhD
11.2.6 De la AUA 2013: Biopsias con plantilla asistidas por «HistoScanning» para pacientes con biopsias guiadas por ecografía transrectal negativas previas
11.2.6 From AUA 2013: “HistoScanning”- aided template biopsies for patients with previous negative TRUS biopsies
Dror Nir, PhD
11.2.7 En el camino para mejorar la biopsia de próstata
11.2.7 On the road to improve prostate biopsy
https://pharmaceuticalintelligence.com/2013/02/15/on-the-road-to-improve-prostate-biopsy/
Dror Nir, PhD
11.2.8 La ecografía como instrumento para medir la elasticidad de los tejidos: «elastografía de ondas de cizall» comparada con «imágenes de deformación»
11.2.8 Ultrasound imaging as an instrument for measuring tissue elasticity: “Shear-wave Elastography” VS. “Strain-Imaging”
Dror Nir, PhD
11.2.9 ¿Qué podría transformar a un perdedor en un ganador?
11.2.9 What could transform an underdog into a winner?
https://pharmaceuticalintelligence.com/2012/11/19/what-could-transform-an-underdog-into-a-winner/
Dror Nir, PhD
11.2.10 Cribado del cáncer de ovario mediante ecografía
11.2.10 Ultrasound-based Screening for Ovarian Cancer
https://pharmaceuticalintelligence.com/2013/04/28/ultrasound-based-screening-for-ovarian-cancer/
Dror Nir, PhD
11.2.11 Tratamiento del cáncer guiado por imagen: una disciplina que necesita guías
11.2.11 Imaging Guided Cancer-Therapy – a Discipline in Need of Guidance
Dror Nir, PhD
11.3 RM y TEP/RM
11.3 MRI & PET/MRI
11.3.1 Introducción de las imágenes inteligentes en la práctica diaria de los radiólogos
11.3.1 Introducing smart-imaging into radiologists’ daily practice
Dror Nir, PhD
11.3.2 Diagnóstico por imagen: ¿ver o imaginar? (Parte 2)
11.3.2 Imaging: seeing or imagining? (Part 2)
[La parte 1 está incluida en la sección de ecografía anterior]
[Part 1 is included in the ultrasound section above]
https://pharmaceuticalintelligence.com/2012/09/29/imaging-seeing-or-imagining-part-2/
Dror Nir, PhD
11.3.3 Biopsias guiadas por imagen: ¿se puede elegir una estrategia preferida?
11.3.3 Imaging-guided biopsies: Is there a preferred strategy to choose?
Dror Nir, PhD
11.3.4 Nuevos resultados clínicos apoyan el guiado por imagen en la biopsia de próstata dirigida
11.3.4 New clinical results support Imaging-guidance for targeted prostate biopsy
Dror Nir, PhD
11.3.5 Imágenes de cuerpo entero como herramienta de cribado del cáncer: ¿son la respuesta a una necesidad clínica no cubierta?
11.3.5 Whole-body imaging as cancer screening tool; answering an unmet clinical need?
Dror Nir, PhD
11.3.6 Estado actual de la técnica de diagnóstico oncológico por imagen del linfoma
11.3.6 State of the art in oncologic imaging of Lymphoma
https://pharmaceuticalintelligence.com/2013/02/03/state-of-the-art-in-oncologic-imaging-of-lymphoma/
Dror Nir, PhD
11.3.7 Un rincón en el sistema ECO de las imágenes médicas
11.3.7 A corner in the medical imaging’s ECO system
https://pharmaceuticalintelligence.com/2012/12/09/a-corner-in-the-medical-imagings-eco-system/
Dror Nir, PhD
11.4 TAC, mamografía y TEP/TAC
11.4 CT, Mammography & PET/CT
11.4.1 Causas y características de imagen de los falsos positivos y los falsos negativos en la 18F-TEP/TAC en el diagnóstico oncológico por imagen
11.4.1 Causes and imaging features of false positives and false negatives on 18F-PET/CT in oncologic imaging
Dror Nir, PhD
11.4.2 Tratamiento mínimamente invasivo guiado por imagen para el carcinoma hepatocelular inoperable
11.4.2 Minimally invasive image-guided therapy for inoperable hepatocellular carcinoma
Dror Nir, PhD
11.4.3 Mejora de las pruebas de imagen basadas en mamografía para una mejor planificación del tratamiento
11.4.3 Improving Mammography-based imaging for better treatment planning
Dror Nir, PhD
11.4.4 Cerrando los puntos ciegos de la mamografía
11.4.4 Closing the Mammography gap
https://pharmaceuticalintelligence.com/2012/11/04/closing-the-mammography-gap/
Dror Nir, PhD
11.4.5 Estado de la técnica de diagnóstico oncológico por imagen de los pulmones
11.4.5 State of the art in oncologic imaging of lungs
https://pharmaceuticalintelligence.com/2013/01/23/state-of-the-art-in-oncologic-imaging-of-lungs/
Dror Nir, PhD
11.4.6 Informe sobre el cáncer de ovario y la cirugía guiada por fluorescencia
11.4.6 Ovarian Cancer and fluorescence-guided surgery: A report
Tilda Barliya, PhD
11.5 Tomografía de coherencia óptica (TCO)
11.5 Optical Coherent Tomography (OCT)
11.5.1 Tomografía de coherencia óptica: tecnología emergente en el tratamiento de los pacientes con cáncer
11.5.1 Optical Coherent Tomography – emerging technology in cancer patient management
Dror Nir, PhD
11.5.2 Un nuevo dispositivo de formación de imágenes promete mejorar el control de calidad de las tumorectomías por cáncer de mama, teniendo en cuenta su coste
11.5.2 New Imaging device bears a promise for better quality control of breast-cancer lumpectomies – considering the cost impact
Dror Nir, PhD
11.5.3 Biopsia virtual: ¿es posible?
11.5.3 Virtual Biopsy – is it possible?
https://pharmaceuticalintelligence.com/2013/03/03/virtual-biopsy-is-it-possible/
Dror Nir, PhD
11.5.4 Nuevos avances en la medición de las propiedades mecánicas de los tejidos
11.5.4 New development in measuring mechanical properties of tissue
Dror Nir, PhD
Resumen por Dror Nir, PhD
Summary by Dror Nir, PhD
Capítulo 12. La nanotecnología aporta nuevos avances en el tratamiento, la detección y el diagnóstico por imagen del cáncer
Chapter 12. Nanotechnology Imparts New Advances in Cancer Treatment, Detection, and Imaging
Introducción
Introduction
12.1 Nanotecnología del ADN
12.1 DNA Nanotechnology
https://pharmaceuticalintelligence.com/2013/05/15/dna-nanotechnology/
Tilda Barliya, PhD
12.2 Nanotecnología, medicina personalizada y secuenciación del ADN
12.2 Nanotechnology, personalized medicine and DNA sequencing
Tilda Barliya, PhD
12.3 Tratamiento nanotecnológico del cáncer de mama
12.3 Nanotech Therapy for Breast Cancer
https://pharmaceuticalintelligence.com/2012/12/09/naotech-therapy-for-breast-cancer/
Tilda Barliya, PhD
12.4 Cáncer de próstata y nanotecnología
12.4 Prostate Cancer and Nanotecnology
https://pharmaceuticalintelligence.com/2013/02/07/prostate-cancer-and-nanotecnology/
Tilda Barliya, PhD
12.5 Nanotecnología: detección y tratamiento del cáncer metastásico en los ganglios linfáticos
12.5 Nanotechnology: Detecting and Treating metastatic cancer in the lymph node
Tilda Barliya, PhD
12.6 La nanotecnología aborda el cáncer cerebral
12.6 Nanotechnology Tackles Brain Cancer
https://pharmaceuticalintelligence.com/2012/11/23/nanotechnology-tackles-brain-cancer/
Tilda Barliya, PhD
12.7 Cáncer de pulmón (CPNM), administración de fármacos y nanotecnología
12.7 Lung Cancer (NSCLC), drug administration and nanotechnology
Tilda Barliya, PhD
Epílogo del volumen por Larry H. Bernstein, MD, FACP
Volume Epilogue by Larry H. Bernstein, MD, FACP
Los miembros del equipo oncológico de Leaders of Pharmaceutical Business Intelligence expresan su opinión acerca de las fronteras de la investigación sobre el cáncer en su PROPIO ámbito de experiencia
Cancer Team Members @ Leaders of Pharmaceutical Business Intelligence Express Their Views on the Frontier of Cancer Research in Their OWN Domain of Expertise
Temas actuales de investigación avanzada en el tratamiento de pacientes con cáncer basado en la resonancia magnética
Current Advanced Research Topics in MRI-based Management of Cancer Patients
Autor: Dror Nir, PhD
Author: Dror Nir, PhD
PRIMER VOLUMEN
Biología y genómica del cáncer
para el
diagnóstico de la enfermedad
2015
Cancer Biology and Genomics for Disease Diagnosis
En Amazon.com desde el 11/08/2015
http://www.amazon.com/dp/B013RVYR2K
Stephen J. Williams, PhD, Senior Editor
Leaders in Pharmaceutical Business Intelligence
https://pharmaceuticalintelligence.com/
Redactora jefe de la serie de libros electrónicos BioMed
Leaders in Pharmaceutical Business Intelligence, Boston
avivalev-ari@alum.berkeley.edu
PART B:
The graphical results of Medical Text Analysis with Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP) algorithms
Madison Davis
AND
the Domain Knowledge Expert (DKE) interpretation of the results in Text format
Stephen J. Williams, PhD, Senior Editor
NEW GENRE e-Series:
Series C: Cancer Volume 1 – PART B
PART B:
Medical Text Analysis with Wolfram Language for Biological Sciences Symbolic Computation Language for Natural Language Processing (NLP)
NLP Software
WOLFRAM FUNCTION REPOSITORY
Instant-use add-on functions for the Wolfram Language
- HypergraphPlot – Plot a hypergraph defined by a list of hyperedges
https://resources.wolframcloud.com/FunctionRepository/resources/HypergraphPlot/
and
- TreePlot – Plot a tree diagram specified by edge rules
https://reference.wolfram.com/language/ref/TreePlot.html
-
NLP Results
-
Interpretation of NLP Results
The article titles listed, below contain the URL for accessing the Text CONTENTS included in the Medical Text Analysis with NLP algorithms
Preface on the Methodology of Interpretation of NLP Results, and 24 plot interpretation
by
Cancer & Genomics Domain Knowledge Expert
STEPHEN J. WILLIAMS, PhD
NLP Code writing [PART B Appendix]
and
Visualization plot production for Chapters 1 to 12 [N=24]
by
MADISON DAVIS
Text Preparation for NLP, Chapters 1 to 6
by
DANIELLE SMOLYAR, BSc
Text Preparation for NLP, Chapters 7 to 12
by
PREMALATA PATI, PhD
On the Methodology of Interpretation of NLP Results
Preface to PART B
NEW GENRE e-SERIES – Series C, Cancer, Volume 1
Volume 1: Cancer Biology & Genomics for Disease Diagnosis. On Amazon.com since 8/11/2015
http://www.amazon.com/dp/B013RVYR2K
By
STEPHEN J. WILLIAMS, PhD
Background and Methodology of Interpretation of NLP Results for Articles included in Cancer, Volume 1
Author, Curator and Original Book Co-Editor: Stephen J. Williams, Ph.D.
Free, open access scientific publications, i.e., PubMed and other sources in public domain are available for Text Analysis using many computational text-mining algorithms including machine learning applications for biomedical text. Ontologies have been developed to promote the standardization and harmonization of terminology using data representations, which are inherently critical for semantic based natural language processing algorithms adding interpretative capabilities for the content syntax. However, most of these standardized ontologies represent genetic and bio-molecular entities, and not scientific and biomedical lexicons. Therefore, it had been suggested that manual curation of the biomedical text was the most suitable method to prepare texts for machine learning algorithms. However, due to the rapid expansion of the life science scientific literature, manual curation efforts were found to be too laborious and time-consuming to be of use for natural language processing of all the biomedical text.
Machine learning based methods have used supervised learning techniques to train classifiers on “gold standard” annotated libraries in order for algorithms to better determine associations between text and ontological concepts. A more accurate method would allow a network of experts to manually curate and develop libraries of scientific-textual ontologies related to corpuses of literature. Much work has been done in improving deep learning methods for text mining in order to extract concepts and formulate the concepts into standardized ontologies. Some of these efforts have shown improved degrees of accuracy [1-3] for entity recognition of ontology concepts from texts. The use of deep learning can enhance older machine learning algorithms by making stronger relationships between word dependence, context, and word sequences. However, many of these efforts, as below, only make use of Gene Ontology as an input into their systems, thus limiting the analysis to only text rich in description of genes or certain proteins, and may miss the contextual content of the biological concepts.
Manda et al. had shown that semantic based automated machine learning algorithms may be useful with ontology-based annotation systems to retrieve key concepts from texts. However, these deep learning and machine learning algorithms only produce the best results if ontologies are meticulously encoded [4]. From this study it is deduced that a manual method of curation may work better with semantic based ML algorithms. These semantic ontologies have been created to assist in the annotation in order to enrich publications to offer a better access to both their content and the metadata describing the entire document. For example several semantic ontologies, specific in scientific literature have been developed including: EXPO, SWAN, GO, and Dublin Core Terms (reviewed in [5]).
However, in the example of the Dublin Core classification, the ontologies are divided into structure (describing a paper’s sections), rhetorical elements (such as Introduction, Methods, Results etc.), or bibliographic ontologies. This inherently limits the ability to do contextual analysis on the contents and merely allows for the structural categorization of text. Therefore, a machine learning algorithm may deduce that a line of text belongs to the Methods section of a paper associated with a certain reference but may not be able to distinguish between use of certain methodology in context of the subject matter. In addition, this procedure of classification may not be useful for Web 2.0 biomedical content as that information is not structured in this manner, yet it contains rich information. Therefore, development of a domain ontology residing with other structural ontologies may result in deeper, contextual analysis of a text. For example, with the EXPO ontology, a text analysis algorithm may associate “experimental design strategy” with “procedure” and “scientific activity” or “fact” with “variable” and “hypothesis forming” however these associations are rather context agnostic and at best descriptive. Much insight may gleaned whether a particular paper is of a certain structural format, for instance, a review versus experimental paper. However there is no way of determining if it is a paper on a more complex concept such as “resistance” of “immunologic drugs” and of ”DNA repair”.
An attempt to model this conceptual analysis was seen with ScholOnto (an ontology-based digital library server for research documents and discourse), which was one of the first works to provide certain conceptional and relational analysis on scientific discourse. Such ontologies described Claim of a paper or evaluations, or goals. Claims assert new relationships with other claims, or between concepts [6]. However these ontologies are rather descriptive and provide more of a model of the scientific process, being made up of “discourse elements” like as in the SWAN (a semantically organized community-curated distributed knowledge base for neuroscience literature) where the discourse elements are research statements like a claim or hypothesis, research questions, and structured comments [7]. Thus, it has been suggested multiple layers of ontologies should be used for deep learning of scientific text.
Hypergraphs, Tree Diagrams, and Interpretations of NLP results in format of Plots on Biomedical Text
Hyper-graphs have been used to analyze biomedical data and text. These types of graphs can show links between data and concepts or ontologies not typically evident upon just reading a certain journal article or review, especially with large data sets. For example, Feng et al., used hypergraphs to represent complex biological systems and highlight central important responses in common to a variety of pathogenic viruses. The use of hypergraphs was a superior method to display the “betweeness centrality” and to identify important genes involved in the pathologic response as hypergraphs can more faithfully identify, and potentially predict important relationships inferred from complex data [8].
In an article “Weaving the fabric of science: Dynamic network models of science’s unfolding structure”, Shi et. al, use hypergraphs, network models, and weighted random walk models to analyze millions of abstracts in PubMed to notice patterns in how science is conducted. They show that network distances between biomedical things (people, methods, diseases etc.) is relatively small and display how science moves from last year answered questions to the next years investigations, showing a modal disposition to how science is conducted [9].
The hypergraph, comprised of hyperedges which connect nodes, shows both common neighborhoods but a predictive local random walk where one can easily move from one node to another, by picking a random hyperedge, noticing connected nodes, then moving to a random node. In this random walk scenario one can visualize predictions which can be made by seemingly connecting what would be random nodes or concepts. Distances between nodes can be calculated and statistical inferences can be made as to the “closeness” of the concept, text to the other node. The connections can be at outer edges or connect through main concepts of, for instance the chapter in question, showing how terms are related to concepts.
In addition, inspection of hypergraphs and tree diagrams can indicate certain gaps of knowledge in the literature.
The ability of determining these “gaps of knowledge” can be evident upon a manual curation methodology in which multiple ontologies are layered upon a set of scientific works and subsequent visualization is made of literature search data. Shi et. al, had performed an example of this in a curation entitled
Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer
This curation consists of a series of papers published in tandem around 2015 on mutational analysis of lung cancer genomes (see reference herein [9]). These papers described mutations in multiple potential driver genes in lung cancer from studies performed in various cohorts of patients with associated clinical criteria such as smoking status, sex and age. Genes with recurring mutations were highlighted and pathway analyses were performed and described using the KEGG and GO ontology to determine pathways and cellular processes altered between the three independent studies. These results from analysis using these ontologies were then layered onto the PubMed database in order to re-search these pathway and cellular process ontology terms in the context of lung cancer research and known genetic information.
This analysis was then visualized and certain knowledge gaps were apparent, especially showing at that time, around 2014, little research had been published on immunology and lung cancer. This was a few years before many of the immunomodulatory chemotherapies had been developed or even the first clinical trial of use of these immune-chemotherapy in lung cancer patients had been initiated.
In contrast, much had been known about the association with many of these mutant genes and the cell cycle. Interestingly, in the following years, as the immune-oncology agents and checkpoint inhibitors were being developed, this knowledge gap was quickly filled in, as more and more publications realized the importance of the immunologic component in the development and therapy of lung cancer [10-12].
Natural Language Processing and Visualization Results in Plot format for Articles included in Cancer Volume 1 in the Context of an Editor’s Perspective
Volume 1: Cancer Biology & Genomics for Disease Diagnosis. On Amazon.com since 8/11/2015
http://www.amazon.com/dp/B013RVYR2K
In order to fashion a meaningful interpretation of a hypergraph or tree diagram on a corpus of knowledge such as a medical e-Book, we first should understand the editor’s vision behind the way the e-Book was constructed. This gives imperative background information which is useful in providing a knowledgeable interpretation of the NLP results on chapters within this e-Book on Cancer.
- Background on the construction and design of this e-Book is warranted.
In discussion with the Senior Editor Larry H. Bernstein M.D. FCAP and other editors, Tilda Barliya, Ph.D., Ritu Saxena, Ph.D. and Aviva Lev-Ari , Ph.D. R.N., the Senior Editor felt that, instead of producing a cancer book which only contains topical chapters (such as therapies used in each different cancer type), it was decided that, the volume one of this two e-Book Series:
Series C: e-Books on Cancer & Oncology
which contains: Volume 1 and Volume 2, should explain the paradigm shift from Cancer as disparate diseases of individual tissue patho-physiologies to the new view of Cancer as a disease involving distinct bio-targets which can be targeted in a precision manner.
This paradigm shift is evident in the way in which new onco-therapies are investigated and approved, where a drug is approved for a certain bio-target like a mutant BRAF versus a certain tumor type, for instance, a pancreatic adenocarcinoma. Therefore, the Senior Editor felt the first few chapters should be dedicated to the reader’s understanding the basic biology behind the oncogenic process, with later chapters dealing with individual and more advanced topics.
In the voice of the Senior Editor Larry H. Bernstein M.D. FCAP
The evolution of progress we have achieved in cancer research, diagnosis, and therapeutics has originated from an emergence of scientific disciplines and the focus on cancer has been recent.
and
A multidisciplinary approach has led us to a unique multidisciplinary or systems view of cancer, with different fields of study offering their unique expertise, contributions, and viewpoints on the etiology of cancer. Diverse fields in immunology, biology, biochemistry, toxicology, molecular biology, virology, mathematics, social activism and policy, and engineering have made such important contributions to our understanding of cancer, that without cooperation among these diverse fields our knowledge of cancer would never had evolved as it has.
And in the book’s Epilogue (Larry H. Bernstein M.D. FCAP)
This volume and its content have been conceived and organized to capture the organized events that emerge in embryological development, leading to the major organ systems that we recognize anatomically and physiologically as an integrated being. We capture the dynamic interactions between the systems under stress that are elicited by cytokine-driven hormonal responses, long thought to be circulatory and multi-system, that affect the major compartments of fat and lean body mass, and are as much the drivers of metabolic pathway changes that emerge as epigenetics, without disregarding primary genetic diseases.
The greatest difficulty in organizing such a work is in whether it is to be merely a compilation of cancer expression organized by organ systems, or whether it is to capture developing concepts of underlying stem cell expressed changes that were once referred to as “dedifferentiation”. In proceeding through the stages of neoplastic transformation, there occur adaptive local changes in cellular utilization of anabolic and catabolic pathways, and a retention or partial retention of functional specificities.
This effectively results in the same cancer types not all fitting into the same “shoe”. There is a sequential loss of identity associated with cell migration, cell-cell interactions with underlying stroma, and metastasis, but cells may still retain identifying “signatures” in microRNA combinatorial patterns. The story is still incomplete, with gaps in our knowledge that challenge the imagination.
And in the voice of another editor of this e-Book: Stephen J. Williams, Ph.D.
“This e-Book highlights some of the recent trends and discoveries in cancer research and cancer treatment, with particular attention how new technological and informatics advancements have ushered in paradigm shifts in how we think about, diagnosing, and treating cancer. The book is organized with the 8 hallmarks of cancer in mind, concepts which are governing principles of cancer from Drs. Hanahan and Weinberg (Hallmarks of Cancer) [13, 14].
- Maintaining Proliferative Signals
- Avoiding Immune Destruction
- Evading Growth Suppressors
- Resisting Cell Death
- Becoming Immortal
- Angiogenesis
- Deregulating Cellular Energy
- Activating Invasion and Metastasis
Therefore, the reader is asked to understand how each of these underlying principles are being translated to current breakthrough discoveries, in association with the basic biological knowledge we have amassed through diligent research and how these principals and latest research can be used by the next generation of cancer scientist and oncologist to provide the future breakthroughs. As the past basic research had provided a new platform for the era of genomics in oncology, it is up to this next generation of scientists and oncologists to provide the basic research for the next platform which will create the future breakthroughs to combat this still deadly disease.”
This flow of the book, meant to be read sequentially and in its entirety, from basic biology to more applied topics, is evident in the striking difference in hyper-graph patterns of Chapters 1-6 versus 8-12 (with Chapter 7 an intermediate pattern). It is also striking that, if one just randomly walks through this book or the hypergraphs, this structure of the e-Book would not be as apparent as it does while reading it sequentially from the important Preface and Introduction through the Chapters in order.
References
- Habibi M, Weber L, Neves M, Wiegandt DL, Leser U: Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics 2017, 33(14):i37-i48.
- Lyu C, Chen B, Ren Y, Ji D: Long short-term memory RNN for biomedical named entity recognition. BMC bioinformatics 2017, 18(1):462.
- Wang X, Zhang Y, Ren X, Zitnik M, Shang J, Langlotz C, Han J: Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics 2019, 35(10):1745-1752.
- Manda P, SayedAhmed S, Maohanty SD: Automated ontology-based annotation of scientific literature using deep learning. Proceedings of Semantic Big Data 2020:6.
- Ruiz-Iniesta A, Corcho Ó: A review of ontologies for describing scholarly and scientific documents. In: SePublica: 2014; 2014.
- Buckingham Shum S, Motta E, Domingue J: ScholOnto: an ontology-based digital library server for research documents and discourse. International Journal on Digital Libraries 2000, 3(3):237-248.
- Ciccarese P, Wu E, Wong G, Ocana M, Kinoshita J, Ruttenberg A, Clark T: The SWAN biomedical discourse ontology. Journal of biomedical informatics 2008, 41(5):739-751.
- Feng S, Heath E, Jefferson B, Joslyn C, Kvinge H, Mitchell HD, Praggastis B, Eisfeld AJ, Sims AC, Thackray LB et al: Hypergraph models of biological networks to identify genes critical to pathogenic viral response. BMC bioinformatics 2021, 22(1):287.
- Shi F, Foster JG, Evans JA: Weaving the fabric of science: Dynamic network models of science’s unfolding structure. Social Networks 2015, 43:73-85.
- Clinical Lung Cancer Research Project Consortium: A genomics-based classification of human lung tumors. Science translational medicine 2013, 5(209):209ra153.
- Khagi Y, Kurzrock R, Patel SP: Next generation predictive biomarkers for immune checkpoint inhibition. Cancer metastasis reviews 2017, 36(1):179-190.
- Tan WL, Jain A, Takano A, Newell EW, Iyer NG, Lim WT, Tan EH, Zhai W, Hillmer AM, Tam WL et al: Novel therapeutic targets on the horizon for lung cancer. The Lancet Oncology 2016, 17(8):e347-e362.
- Hanahan D, Weinberg RA: Hallmarks of cancer: the next generation. Cell 2011, 144(5):646-674.
- Hanahan D, Weinberg RA: The hallmarks of cancer. Cell 2000, 100(1):57-70.
Parte I
Perspectiva histórica de la demografía, la etiología y los avances en la investigación del cáncer
Part I
Historical Perspective of Cancer Demographics, Etiology, and Progress in Research
Capítulo 1: La incidencia del cáncer en las poblaciones del mundo
Chapter 1: The Occurrence of Cancer in World Population
Hypergraph Plot #1 and Tree Diagram Plot #1 for Chapter 1
based on 13 articles & on 10 keywords
cancer, group, innovations, area, therapy, cancer, business, information, section, advancements
HYPERGRAPH PLOT INTERPRETATION
by Dr. Stephen J. Williams
A hypergraph visualizes the relationships between terms, concepts, and their groupings. In text analysis this can refer to the visual representation of relationship and grouping of terms, concepts, within ontologies. Interpretations of hypergraphs within the context that their keywords are extracted from can lead to deeper understanding of the article and could allow for inference on the gaps of knowledge where connections are more disparate. We shall use this fundamental method in the subsequent interpretations of the included hypergraph for Chapter 1. The interpretations of the tree diagrams will focus on hierarchal structure and potential outcomes of relationships of concepts.
The included hypergraph is a visualization of a chapter which focuses on the global burden of cancer, population-based epidemiology of various cancers, preventative strategies, and innovative team science in cancer research.
A discussion of the basic pathophysiology of cancer is included. This chapter crosses multiple ontologies within the cancer genre including metabolomics, cancer prevention, signaling and biological networks, and outcomes of health research. Each of the arms of this hypergraph can be associated with pharma, scientific networks, cancer-focused areas, informatics, therapy, and innovation. The therapeutic area of cancer displays a tight-knit grouping where terminology is focused with the role of pharma central with informatics or knowledge base efforts as well as public efforts including special interest groups (area, section, group) on the periphery. This suggests, in the context of this chapter, that pharmaceutical and biopharmaceutical efforts appear to have taken a lead in cancer discovery with outside groups such as academics, bioinformaticians supplying the support structure. However, equally important is the gap in innovation, as this hypergraph suggests that innovation (abstract, abstraction) appears solely tied to or driven by the business sector.
In summary, an interpretation of this hypergraph would be as follows:
Innovations in cancer therapy are tied with and driven by the business sector, with information databases and areas related to the public sector, academia, and scientific networks supplying the support networks.
Tree Diagram Plot #1 for Chapter 1
based on 13 articles & on 10 keywords
cancer, group, innovations, area, therapy, cancer, business, information, section, advancements
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
A Tree Diagram represents the hierarchal organization of information as well as potential outcomes of a decision flow. Chapter 1 of Cancer Volume 1 deals with cancer as it relates to world populations and looks at the problem of cancer from an epidemiological perspective, with a brief introduction into the biology of cancer. As we completed the Text Analysis through the Wolfram NLP platform a semantic analysis had emerged. Certain synonyms are abstracted even if the algorithm is given the same conceptual areas of the chapter. This was actually fortuitous as this feature of the platform added contextual enhancements to the visualizations. The most striking plot is seen at the far left where the grouping [cancer, malignant, neoplasm, malignant tumor, metastatic tumor] show a unique pattern suggesting these terms are used interchangeably. This may suggest an inherent fault in the oncology literature and among scientists as sometimes these words are used more loosely, even given that there are strict tumor classification guidelines by the WHO and FIGO with regard to stage, grade, and metastatic potential of a tumor type.
This is essentially a call for the scientific curator and journal publishers to develop and require more stringent semantic rules governing both use of ontologies and language within a particular biomedical text. It is also very interesting to see “business” closeness to “cancer” than other categories such as “information”. As cancer therapeutic development has flourished, it is a potential hazard, at least to the cancer control efforts at the population scale, for one industry to be so prominent, although the cancer informatics area has produced great strides in the “cognition”, “knowledge” and “measures of information”, especially in the field of bioinformatics. This area is largely due to a volunteer army of academics who have toiled to produce curated knowledge bases, to the benefit of all scientists, included those in the private sector. In addition, when cancer epidemiologists refer to “group” or “area”, we refer to select population groups or geographic areas. In essence, this tree diagram of a chapter on Occurrence of Cancer in World Populations reveals that our scientific lexicon needs to be more aggressively communicated and our emphasis on team science may be more siloed in its execution.
1.1 Understanding Cancer
https://pharmaceuticalintelligence.com/2012/05/07/102/
Prabodh Kandala, PhD
1.2 Cancer Metastasis
https://pharmaceuticalintelligence.com/2013/07/06/cancer-metastasis/
Tilda Barliya, PhD
1.3 2013 Perspective on “War on Cancer” on December 23, 1971
Aviva Lev-Ari, PhD, RN
1.4 Global Burden of Cancer Treatment & Women Health: Market Access & Cost Concerns
Aviva Lev-Ari, PhD, RN
1.5 The Importance of Cancer Prevention Programs: New Perspectives for Fighting Cancer
Ziv Raviv, PhD
1.6 The “Cancer establishments” examined by James Watson, co-discoverer of DNA w/Crick, 4/1953
Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
1.7 New Ecosystem of Cancer Research: Cross Institutional Team Science
Aviva Lev-Ari, PhD, RN
1.8 Cancer Innovations from across the Web
https://pharmaceuticalintelligence.com/2012/11/02/cancer-innovations-from-across-the-web/
Larry H Bernstein, MD, FCAP
1.9 Exploring the role of vitamin C in Cancer therapy
https://pharmaceuticalintelligence.com/2013/01/15/exploring-the-role-of-vitamin-c-in-cancer-therapy/
Ritu Saxena PhD
1.10 Relation of Diet and Cancer
https://pharmaceuticalintelligence.com/2013/06/04/relation-of-diet-and-cancer/
Sudipta Saha, PhD
1.11 Association between Non-melanoma Skin Cancer and subsequent Primary Cancers in White Population
Aviva Lev-Ari, PhD, RN
1.12 Men With Prostate Cancer More Likely to Die from Other Causes
Prabodh Kandala, PhD
1.13 Battle of Steve Jobs and Ralph Steinman with Pancreatic Cancer: How we Lost
Ritu Saxena, PhD
Capítulo 2: Los rápidos avances científicos cambian nuestra visión de cómo se produce el cáncer
Chapter 2: Rapid Scientific Advances Changes Our View on How Cancer Forms
Hypergraph Plot #2 and Tree Diagram Plot #2 for Chapter 2
based on 10 articles & on 13 keywords
cancer, metastasis, deleterious, cell, ecological, passenger, control, process, mutations, tumor, cancer, growth, mcfarland
HYPERGRAPH PLOT INTERPRETATION
by Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 2 of Cancer Volume 1, represented by the abstraction of 13 key concepts. This chapter focuses on how our views on the etiology of cancer has evolved over the last sixty years, given the vast increase in scientific knowledge, advancements in high-throughput techniques of genomics, proteomics, and metabolomics, and the attention to translational research, spurred on by the advent of massive biobanks and bioinformatic efforts. This chapter deals with the changes in the cellular views we have learned while Chapter 3 focuses more on the genetic changes we had learned about and eventually enhanced our understanding of the early and late stages of this disease, including predisposing factors. Along with the advanced knowledge came advances in measurement and modeling systems. These concepts are reflected nicely in the hypergraph, such as the links between pathologic processes and growth control, and the use of comparative physiology in zebrafish and other animal models which gave clues to how a cell gains the malignant phenotype. Other insights are gleamed from the connections and the distance of each concept apparently unlinked from each other. In one instance processes such as metabolomics warrant a closer investigation in light of cancer and passenger mutations may be more related to cancer control than in circulating tumor cells and malignant late-stage cancer as once was thought. This may warrant increased investigation into the roles of cancer stem cell biology and the early angiogenic switch through deleterious mutations, not in driver genes, but in metabolic genes and those targets which are involved in regulated the microenvironment of the cancer stem cell.
In summary, an interpretation of this hypergraph would be as follows:
Metabolic and pathologic processes involved in early cancer may be linked to driver mutations affecting cell growth but later passenger mutations affected tumor microenvironment.
Tree Diagram Plot #2 for Chapter 2
based on 10 articles & on 13 keywords
cancer, metastasis, deleterious, cell, ecological, passenger, control, process, mutations, tumor, cancer, growth, mcfarland
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
The tree diagram for Chapter 2 is more complex than the previous chapter 1 analysis and rightly so. Chapter 2 of Cancer Volume 1 deals with how our view on cancer, how cancer develops, and how best to diagnose and treat cancer has changed, especially in light of all the research discoveries being made at light speed in the past 20 years. The chapter starts with a discussion of one of the earliest views, namely the last century findings of the metabolic perturbations in cancer, yet through advancements in genetics, proteomics, and development of high techniques and models to enhance scientific investigation into the biology and pathophysiology of cancer, our view has expanded to include genomics and epigenetics as critical as metabolic perturbations once were, to the development of a tumor and malignancy. These changes in our “view on cancer” has also shifted the treatment paradigm to a more personalized and targeted therapy modalities. The aforementioned interpretation of this chapter in Cancer Volume 1 is from the perspective of a cancer biologist and oncologist and therefore we turn to the semantics in the tree diagram to determine if bias exists in the mind of the scientific writer and chapter editor(s). Given the vast connections of the tree diagram, it is apparent that this view held by the editors and writer of this chapter is not biased but is rooted in hard evidence and the intersection of ontologies linked to the various articles within this chapter. For example, the seemingly inherent resistance of sharks, mole rats, and zebrafish for malignant transformation in ontologies related to Cancer Biology is crossed with ontologies related to Glycobiology, Genetics, Biological Networks, and Metabolism. Such a recognition by an expert author to realize the various ontologies that a scientific finding may be classified in will inherently lead to such a rich and diverse tree diagram as seen here. In addition, these connections, along with the authority of an expert editor and scientific curator, can, in an unbiased manner, integrate these seemingly disparate views on a complex topic such as cancer. As per the tree diagram, the linking of tumor, growth, cancer, control with flora, botany shows how the field has looked to other animal models in a comparative biology discipline to understand the pathology of human cancer (for example looking in sharks and zebrafish for clues on genetics, metabolism and cancer. In addition, recognizing cellular processes governing activities related to growth and cancer control has prompted investigations into cellular signaling, stem cells and biomarkers, all of which are ontologies heavily used within this chapter. It is interesting that no link between “deleterious” and “passenger (mutation) is made although this would be an interesting subject for further research.
2.1 All Cancer Cells Are Not Created Equal: Some Cell Types Control Continued Tumor Growth, Others Prepare the Way for Metastasis
Prabodh Kandala, PhD
2.2 Hold on. Mutations in Cancer do Good
https://pharmaceuticalintelligence.com/2013/02/04/hold-on-mutations-in-cancer-do-good/
Prabodh Kandala, PhD
2.3 Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?
Larry H Bernstein, MD, FCAP
2.4 Naked Mole Rats Cancer-Free
https://pharmaceuticalintelligence.com/2013/06/20/naked-mole-rats-cancer-free/
Larry H Bernstein, MD, FCAP
2.5 Zebrafish—Susceptible to Cancer
https://pharmaceuticalintelligence.com/2013/04/02/zebrafish-susceptible-to-cancer/
Larry H Bernstein, MD, FCAP
2.6 Demythologizing Sharks, Cancer, and Shark Fins
https://pharmaceuticalintelligence.com/2013/06/22/demythologizing-sharks-cancer-and-shark-fins/
Larry H Bernstein, MD, FCAP
2.7 Tumor Cells’ Inner Workings Predict Cancer Progression
Prabodh Kandala, PhD
2.8 In Focus: Identity of Cancer Stem Cells
https://pharmaceuticalintelligence.com/2013/03/22/in-focus-identity-of-cancer-stem-cells/
Ritu Saxena, PhD
2.9 In Focus: Circulating Tumor Cells
https://pharmaceuticalintelligence.com/2013/06/24/in-focus-circulating-tumor-cells/
Ritu Saxena, PhD
2.10 Rewriting the Mathematics of Tumor Growth; Teams Use Math Models to Sort Drivers from Passengers
Stephen J. Williams, PhD
2.11 Role of Primary Cilia in Ovarian Cancer
https://pharmaceuticalintelligence.com/2013/01/15/role-of-primary-cilia-in-ovarian-cancer-2/
Aashir Awan, PhD
Capítulo 3: Surgen una base genética y una complejidad genética del cáncer
Chapter 3: A Genetic Basis and Genetic Complexity of Cancer Emerges
Hypergraph Plot #3 and Tree Diagram Plot #3 for Chapter 3
based on 12 articles & on 17 keywords
binding, oligonucleotides, lattice, dna, structures, renewal, advanced, colorectal, adenoma, risk, variant, alleles, critical, genes, calcium, reabsorption, aacr
HYPERGRAPH PLOT INTERPRETATION
by Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 3 of Cancer Volume 1, represented by the extraction of 17 key concepts from the text of this chapter. This chapter focuses on the genetic complexity of cancer and how this genetic complexity affects tumor growth and metastases. Special emphasis is given to the complex structure of DNA in the chromatin as well as the dynamic flux of mobile elements and noncoding intronic sequences important in controlling gene and protoncogene expression. Some examples of this complexity is the heterogeneity of tumors, the ability of viruses to cause liver cancer, and the importance of variants in etiology and predisposition of cancers of the breast and gastric system. An interesting insight, potentially not visible unless given the visualization analysis, is the association between genomics, heritable mutations and with growth control of adenomas, especially for the colorectal system.
Also inferred is that genomic effects may be more prominent in early tumorigenesis than in late stage although this would differ from the previous analysis of chapter 2. An explanation could be potential epigenetic changes are more important in late stage, as gleamed from this hypergraph. In addition, cellular processes may be governed by genomic, intronic (junk) DNA than previously thought. Cancer control may be associated with deleterious passenger mutations and associated with variant alleles and reflected in cancer heterogeneity. However, it seems there is no connecting edge with the pathogenesis of cancer and those passenger mutations as may depend more on the driver mutations. Risk of cancer and genetic variants are tied together however, as evident from the gap in knowledge of the variants role in biological processes, more research is needed in this area. The clinical importance of alterations in various metabolic processes to cancer risk is also poorly understood and may complicate the prediction of cancer risk based solely on omics data. It would be expected that such a broader topic would have a more interconnected hypergraph (and less like spoke and hub like later special topic chapters). This may reflect in how authors, scientists, and clinicians still have poorly integrated past knowledge of functional processes with new focus on mutational data.
In summary, an interpretation of this hypergraph could be as follows:
Cancer is a complex genetic and epigenetic disease affecting multiple cellular processes. Tumor heterogeneity creates a complex pattern with multiple variants affecting outcomes, especially in solid tumors.
Tree Diagram Plot #3 for Chapter 3
based on 12 articles & on 17 keywords
binding, oligonucleotides, lattice, dna, structures, renewal, advanced, colorectal, adenoma, risk, variant, alleles, critical, genes, calcium, reabsorption, aacr
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
Chapter 3 discusses the genetic basis of cancer and, as such, contains articles of a more technical nature. Discussions of structure of DNA, mobile and transposable elements in DNA, noncoding DNA, tumor heterogeneity, and various genetic drivers of specific cancers are discussed. The tree diagram projects the basis of these articles as genetic variants is closely associated with risk of cancer and structural elements of the DNA lattice, even considered a framework of DNA and thought once to be static, is associated with an active process of opening and relaxing, leading to an important concept when considering the importance of DNA as an active changing structure to the cancer development process.
The term adenoma is correctly classified by the algorithm as a benign tumor but very interesting its close proximity to cancer risk and variants, terms not canonically associated with adenomas except in the case of colorectal cancer, also discussed, where there are definitely a progression from adenomas to carcinomas with underlying genetic variant risk factors.
Calcium is correctly identified as a metal and effects of its biodistribution is discussed in this chapter. Also, interesting to note is that “variant” is associated with “type” and “quantity”, two areas of active investigation as massive genomic studies are underway to determine and quantify risk variants in multiple populations, and the effects of variants to the risk of environmentally induced cancer development. The field of genomic epidemiology is a rapidly expanding field yet hampered by the inability to relate risk variants to causality. Perhaps a deeper analysis of the literature with enhanced ontological terms may provide some clues to help in these investigations. Issues of tumor heterogeneity, one article in the chapter, has ontologies related to stem cells {“renewal”}, type of variants, and probability of variants to risk of developing advanced and resistant tumors.
3.1 The Binding of Oligonucleotides in DNA and 3-D Lattice Structures
Larry H Bernstein, MD, FCAP
3.2 How Mobile Elements in “Junk” DNA Promote Cancer. Part 1: Transposon-mediated Tumorigenesis.
Stephen J. Williams, PhD
3.3 DNA: One Man’s Trash is another Man’s Treasure, but there is no JUNK after all
Demet Sag, PhD
3.4 Issues of Tumor Heterogeneity
3.4.1 Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Stephen J. Williams, PhD
3.4.2 Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn
Stephen J. Williams, PhD
3.5 arrayMap: Genomic Feature Mining of Cancer Entities of Copy Number Abnormalities (CNAs) Data
Aviva Lev-Ari, PhD, RN
3.6 HBV and HCV-associated Liver Cancer: Important Insights from the Genome
Ritu Saxena, PhD
3.7 Salivary Gland Cancer – Adenoid Cystic Carcinoma: Mutation Patterns: Exome- and Genome-Sequencing @ Memorial Sloan-Kettering Cancer Center
Aviva Lev-Ari, PhD, RN
3.8 Gastric Cancer: Whole-genome Reconstruction and Mutational Signatures
Aviva Lev-Ari, PhD, RN
3.9 Missing Gene may Drive more than a quarter of Breast Cancers
Aviva Lev-Ari, PhD, RN
3.10 Critical Gene in Calcium Reabsorption: Variants in the KCNJ and SLC12A1 genes – Calcium Intake and Cancer Protection
Aviva Lev-Ari, PhD, RN
Capítulo 4: Cómo afectan los factores epigenéticos y metabólicos al crecimiento tumoral
Chapter 4: How Epigenetic and Metabolic Factors Affect Tumor Growth
Hypergraph Plot #4 and Tree Diagram Plot #4 for Chapter 4
based on 14 articles & on 8 keywords
epigenetics, stemness, long, coding, potential, cell, stiffness, biomarker
HYPERGRAPH PLOT INTERPRETATION
Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 4 of Cancer Volume 1, represented by the extraction of 8 key concepts from the text of this chapter. This chapter focuses on the epigenetic and metabolic changes seen from the initial transformation of a cell to the malignant phenotype through development of a high-grade metastatic tumor. Important links between metabolism, Alzheimer’s, and diseases of obesity are made with epigenetic links like loss of PTEN expression and effect of altered lipid profiles and nitric oxide metabolism on the etiology of cancer. Hormonal imbalances driving endocrine-related cancers like prostate cancer and breast and ovarian are discussed in light of these cellular, epigenetic, and metabolic perturbations. This is exemplified in the hypergraph as there are strong edges between epigenetics, metabolomics, and cancer stem cell biology. A review of the recent literature shows that there have been over 140 papers on the link between these in the last four years (2016-2020).
The hypergraph also shows that cell morphology, metabolomics and epigenetics are tightly correlated with cancer cell stemness. Also inferred from this hypergraph: the link between cellular movement, stiffness and long non-coding RNA needs to be investigated. A gap gleamed from this hypergraph would be the recognition for the need of more investigation into the link between stemness, cell stiffness in the context of epigenetics. This is clear given, since 2012, there has been only 24 papers published articles on these combined subjects.
In summary, an interpretation of this hypergraph could be as follows:
There are very strong correlations and links in the literature concerning tumor growth, control, cancer stem cells and epigenetics, however much research is still needed.
Deep Learning analysis of curated literature may offer insights into knowledge gaps which need to be filled.
Tree Diagram Plot #4 for Chapter 4
based on 14 articles & on 8 keywords
epigenetics, stemness, long, coding, potential, cell, stiffness, biomarker
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
Chapter 4 contains very pertinent articles related to “How Epigenetic and Metabolic Factors Affect Tumor Growth”. This is a critical chapter as it not only integrates the older metabolic view but a newer epigenetic view in light of a genetic view of cancer development. In addition, this chapter discusses the many therapeutic advances using bio-targets related to epigenetics, such as histone deacetylases and corresponding inhibitors, as well as the effect epigenetics has on the ability of tumor suppressors and oncogenes to function. The role of obesity and risk factors related to metabolism including nitric oxide are discussed. The importance of long non-coding RNA to the malignant transformation process as well as a therapeutic target are also discussed.
However, it is unfortunately apparent, from inspection of the tree diagram, that our scientific community’s lexicon may not be as easily recognized by natural language processing algorithms. This analysis points to a critical need for the scientific community to work closer with ontology creators and curators of scientific and biomedical literature to link the potential outcomes closer with biological processes. In this regard, the scientific community has shown to be rather siloed. Perhaps this had been eluded to by the confusion as to “coding” to mean computer code and not DNA or RNA code. It is understandable confusion however there is much needed work to be done in this area of cross-terminology and cross-ontological terms.
In addition, a better communication between the scientific, lay, and technical communities is warranted to produce more enriched and context-related lexicons and ontological terms. However, the importance of cell stiffness and other biophysical properties of cancer cells has not been widely investigated, and its relation to cancer cell stemness and the malignant process should be a focus of additional investigation in the context of epigenetic and metabolic processes, and as a potential new class of “biophysical” bio-targeting strategies. As such, “biomarker” was an important concept which was extracted from our text yet either the lexicon or ontologies of most analytic algorithms can’t reconcile with this term. Therefore, this shows a mandatory call for ontologies and lexicons to be updated for use within the biomedical genre.
4.1 Epigenetics
4.1.1 The Magic of the Pandora’s Box: Epigenetics and Stemness with Long non-coding RNAs (lincRNA)
Demet Sag, PhD, CRA, GCP
4.1.2 Stomach Cancer Subtypes Methylation-based identified by Singapore-Led Team
Aviva Lev-Ari, PhD, RN
4.1.3 The Underappreciated EpiGenome
https://pharmaceuticalintelligence.com/2013/04/17/the-underappreciated-epigenome/
Demet Sag, Ph.D., CRA, GCP
4.1.4 Differentiation Therapy – Epigenetics Tackles Solid Tumors
Stephen J. Williams, PhD
4.1.5 “The SILENCE of the Lambs” Introducing The Power of Uncoded RNA
Demet Sag, Ph.D., CRA, GCP
4.1.6 DNA Methyltransferases – Implications to Epigenetic Regulation and Cancer Therapy Targeting: James Shen, PhD
Aviva Lev-Ari, PhD, RN
4.2 Metabolism
4.2.1 Mitochondria and Cancer: An overview of mechanisms
https://pharmaceuticalintelligence.com/2012/09/01/mitochondria-and-cancer-an-overview/
Ritu Saxena, PhD
4.2.2 Bioenergetic Mechanism: The Inverse Association of Cancer and Alzheimer’s
Aviva Lev-Ari, PhD, RN
4.2.3 Crucial role of Nitric Oxide in Cancer
https://pharmaceuticalintelligence.com/2012/10/16/crucial-role-of-nitric-oxide-in-cancer/
Ritu Saxena, PhD
4.2.4 Nitric Oxide Mitigates Sensitivity of Melanoma Cells to Cisplatin
Stephen J. Williams, PhD
4.2.5 Increased risks of obesity and cancer, Decreased risk of type 2 diabetes: The role of Tumor-suppressor phosphatase and tensin homologue (PTEN)
Aviva Lev-Ari, PhD, RN
4.2.6 Lipid Profile, Saturated Fats, Raman Spectrosopy, Cancer Cytology
Larry H Bernstein, MD, FCAP
4.3 Other Factors Affecting Tumor Growth
4.3.1 Squeezing Ovarian Cancer Cells to Predict Metastatic Potential: Cell Stiffness as Possible Biomarker
Prabodh Kandala, PhD
4.3.2 Prostate Cancer: Androgen-driven “Pathomechanism” in Early-onset Forms of the Disease
Aviva Lev-Ari, PhD, RN
Capítulo 5: Los avances en la investigación de los cánceres de mama y gastrointestinales refuerzan la esperanza de curación
Chapter 5: Advances in Breast and Gastrointestinal Cancer Research Supports Hope for Cure
Hypergraph Plot #5 and Tree Diagram Plot #5 for Chapter 5
based on 14 articles & on 7 keywords
cell, movement, aggressive, breast, cancer, clinical, preclinical
HYPERGRAPH PLOT INTERPRETATION
Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 5 of Cancer Volume 1, represented by the extraction of 7 key concepts from the text of this chapter. This chapter focuses on advances in our knowledge of breast and gastrointestinal (GI) tumors.
Article subjects were the link between cellular motility processes and aggressiveness of neoplastic breast cancer cells and the effects that mitochondrial mutations have on the ability of breast cancer to evade multiple therapies. The discussion of BRCA1/2 on breast and ovarian cancer risk susceptibility and molecular pathology of multiple forms of breast cancer, including the highly aggressive triple negative breast cancer is given in light of some recent advances on the imaging of these cancers.
Certain potential bio-targets of GI cancer, like PI3K, kinase iso-forms and axon pathway genes, are presented in this chapter. The hypergraph reflects this as aggressive, breast, cancer, cell, movement are tightly linked edges however “changes in breast cancer cell movement” are connected on different edges, possibly meaning that more research needs to be produced in this area. Disparate gaps are seen with the imaging of metastatic tumors of the breast which have no shared edges, although cancer therapy is connected with imaging modalities at the cellular level.
In summary, an interpretation of this hypergraph could be as follows:
There is not much known on the general biology of many solid tumors although most is known about breast cancer. We may have good imaging modalities for breast but still lacking diagnostic tools for triple negative breast cancer.
Tree Diagram Plot #5 for Chapter 5
based on 14 articles & on 7 keywords
cell, movement, aggressive, breast, cancer, clinical, preclinical
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
Chapter 5 in Cancer Volume 1, entitled “Advances in Breast Cancer and GI (Gastrointestinal) Cancer” gives an in-depth analysis, in light of the previous chapter’s discussion on the pathophysiology and etiology of cancer, of the clinical and preclinical issues faced with breast and colon cancer specialists. Articles address the genetics of breast cancer and breast cancer risk genes such as BRCA1/2, PI3CK, and mitochondrial mutations associated with aggressive breast cancer. Attention is given to the challenges in detecting triple-negative breast cancer, a highly aggressive form of breast cancer which has little therapeutic options given its HER2 negative, estrogen receptor negative, and progesterone receptor negative status. This form of cancer is very resistant to traditional therapies such as the CMF (cisplatin, methotrexate, fluorouracil) regiment and impossible to demarcate the cancer borders, which complicates surgical removal. In addition, the only current detection and prognostic biomarker of this aggressive cancer is by detection of circulating tumor cells, requiring specialized equipment and substantial amounts of liquid biopsy material. Various genomic, imaging detection of colon cancer and colon cancer preventive strategies are also discussed.
Here, we can gain much insight from the tree diagram, using the key concepts abstracted from this chapter. Again, as shown by the tree diagram, this chapter focuses on cancer and its most malignant and aggressive forms, in light of some preclinical findings of genetic biomarkers of risk and potential biotargets obtained using whole exome sequencing. In addition, clinical findings are closely associated in this chapter as aspirin has been found to act as chemo-preventive agent yet can enhance cisplatin chemotherapy in GI associated cancers, and potentially breast cancer as well. An interesting bidirectional relationship is made between cell movement, malignancy, and changes is epigenome and metabolomics in this diagram, and as discussed in two articles related to these topics. A suggestion is also made that the preclinical and clinical findings we see in breast cancer could be translated to other solid tumors, yet this has to be determined. However, many genomic studies, as well as PD1 inhibitors as the first therapeutic to be approved based on detection of a biomarker may show this will be the new normal for cancer therapy and etiology of other solid cancers.
5.1 Breast Cancer
5.1.1 Cell Movement Provides Clues to Aggressive Breast Cancer
Prabodh Kandala, PhD
5.1.2 Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome
Prabodh Kandala, PhD
5.1.3 Mechanism involved in Breast Cancer Cell Growth: Function in Early Detection & Treatment
Aviva Lev-Ari, PhD, RN
5.1.4 BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair
Sudipta Saha, PhD
5.1.5 Breast Cancer and Mitochondrial Mutations
https://pharmaceuticalintelligence.com/2013/03/04/breast-cancer-and-mitochondrial-mutations/
Larry H Bernstein, MD, FCAP
5.1.6 MIT Scientists Identified Gene that Controls Aggressiveness in Breast Cancer Cells
Aviva Lev-Ari PhD RN
5.1.7 “The Molecular pathology of Breast Cancer Progression”
Tilda Barliya, PhD
5.1.8 In focus: Triple Negative Breast Cancer
https://pharmaceuticalintelligence.com/2013/01/29/in-focus-triple-negative-breast-cancer/
Ritu Saxena, PhD
5.1.9 Automated Breast Ultrasound System (‘ABUS’) for full breast scanning: The beginning of structuring a solution for an acute need!
Dror Nir, PhD
5.1.10 State of the art in oncologic imaging of breast.
https://pharmaceuticalintelligence.com/2013/01/21/state-of-the-art-in-oncologic-imaging-of-breast/
Dror Nir, PhD
5.2 Gastrointestinal Cancer
5.2.1 Colon Cancer
https://pharmaceuticalintelligence.com/2013/04/30/colon-cancer/
Tilda Barliya, PhD
5.2.2 PIK3CA mutation in Colorectal Cancer may serve as a Predictive Molecular Biomarker for adjuvant Aspirin therapy
Aviva Lev-Ari, PhD, RN
5.2.3 State of the art in oncologic imaging of colorectal cancers.
Dror Nir, PhD
5.2.4 Pancreatic Cancer: Genetics, Genomics and Immunotherapy
https://pharmaceuticalintelligence.com/2013/04/11/update-on-pancreatic-cancer/
Tilda Barliya, PhD
5.2.5 Pancreatic cancer genomes: Axon guidance pathway genes – aberrations revealed
Aviva Lev-Ari, PhD, RN
Parte II
La llegada de la medicina traslacional, las «ómicas» y la medicina personalizada marcan el inicio de nuevos paradigmas en el tratamiento del cáncer y avances en el desarrollo de fármacos
Part II
Advent of Translational Medicine, “omics,” and Personalized Medicine Ushers in New Paradigms in Cancer Treatment and Advances in Drug Development
Capítulo 6: Estrategias de tratamiento
Chapter 6: Treatment Strategies
Hypergraph Plot #6 and Tree Diagram Plot #6 for Chapter 6
based on 17 articles & on 18 keywords
targeted, antibody, trastuzmad, chemically, linked, cytoxin, breast, cancer, cells, value, status, mutations, pik, pten, marker, efficiency, akt, inhibitors
HYPERGRAPH PLOT INTERPRETATION
Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 6 of Cancer Volume 1, represented by the extraction of 18 key concepts from the text of this chapter. This chapter examines the treatment strategies for cancer in light of the new paradigm of personalized medicine, precision bio-targets, and the impact of translational research and medicine of cancer outcomes and prognosis. The hypergraph shows a very interesting link within concepts for [breast, chemical, cytotoxic, antibody, and efficient]. There is a strong literature to support this finding with 297 papers on this concept of using trastuzamab (one of the topics and keywords extracted) linked with a cytotoxic chemical. Even more interesting is that 125 papers since 2012 on preclinical studies of antibody drug conjugates in breast cancer have been published with 57 reports on antibody drug conjugates and breast cancer in clinical trials since 2012.
However also inferred from this hypergraph is so much needed research in areas. Disparate concepts include toxins used with validated therapeutic biomarkers on metastatic breast cancer ( only 10 papers published since before 2010 yet 29 since so a growing interest). The use of natural product toxins such as bee pollen has only begun to be investigated using validated therapeutic biomarkers. In addition, there are some concepts that share only an edge included artifacts and biomarkers. This needs to be addressed as there have not been many (only 8 papers) on the reproducibility and cross-validation of therapeutic biomarkers in advanced breast cancer.
In summary, an interpretation of this hypergraph could be as follows:
There may be therapeutic value in investigating the use of antibody drug conjugates in advanced metastatic breast cancer. However, there is a large gap in studies to validate and reproduce biomarker studies in the clinical and preclinical settings.
Tree Diagram Plot #6 for Chapter 6
based on 17 articles & on 18 keywords
targeted, antibody, trastuzmad, chemically, linked, cytoxin, breast, cancer, cells, value, status, mutations, pik, pten, marker, efficiency, akt, inhibitors
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
The tree diagram for Chapter 6 is has been enriched above the 18 key concepts extracted from this chapter, and now contains 26 important concepts that the natural language processing algorithm believes should be included in an interpretation or at least as additional hierarchal structures. To understand this let us discuss first the details of this chapter, entitled “Treatment Strategies: Personalized and Translational Medicine”. This chapter now represents the first detailed discussion of the impact of the new era of personalized medicine on the treatment of cancer and how translational research is assisting scientists in discovering new biotargets for various cancers. For example, articles on the use of Herceptin as a personalized therapy for breast cancer or the kidney cancer drug Sutent (sunitinib, a first of kind multi-tyrosine kinase inhibitor) discuss how genetic biomarkers are used to determine suitability of a patient for these drugs. Our ability to analyze the mutational spectrum of circulating tumor cells from triple negative breast cancer patients has allowed us to personalize combination therapies for this disease, including newly developed EGFR inhibitors. Other articles focus of translational research, a new paradigm in drug discovery, and new biotargets such as integrins.
As mentioned before, I personally find hypergraphs, with their rich connections, useful for gaining insights into potential gaps of knowledge as well as making additional connections between seemingly disparate literature. The tree diagram helps determine the structure of the corpus of knowledge, enumerating potential outcomes of a body of texts, as well as relationships withing key conceptual ontologies. Although “chemically”, “linked”, “antibody {correctly categorized as protein}, and “cytotoxic” are at far ends these are closer concepts (as I mention with the interpretation of the hypergraph). It is interesting to see efficiency close to marker as biomarkers have clearly led to more efficient selection of chemotherapy. It is important thought to have high validity, sensitivity, and specificity, which the algorithm suggests as “artifact” however additions to lexicons and ontologies with more technical terms may resolve this.
In addition, value proposition and worth of personalized therapy to the cancer patient is a validated discussion. Much of the hierarchal structure of this tree suggests that detailed and topical chapters such as these offer great descriptive value to NLP algorithms and can therefore be used as such. In essence, the more detailed the more ordered and suitable the hierarchy for NLP analysis.
6.1 Marketed and Novel Drugs
Breast Cancer
6.1.1 Treatment for Metastatic HER2 Breast Cancer
https://pharmaceuticalintelligence.com/2013/03/03/treatment-for-metastatic-her2-breast-cancer/
Larry H Bernstein MD, FCAP
6.1.2 Aspirin a Day Tied to Lower Cancer Mortality
https://pharmaceuticalintelligence.com/2012/08/11/1796/
Aviva Lev-Ari, PhD, RN
6.1.3 New Anti-Cancer Drug Developed
https://pharmaceuticalintelligence.com/2012/05/30/new-anti-cancer-drug-developed/
Prabodh Kandala, Ph.D.
6.1.4 Pfizer’s Kidney Cancer Drug Sutent Effectively caused REMISSION to Adult Acute Lymphoblastic Leukemia (ALL)
Aviva Lev-Ari, PhD, RN
6.1.5 “To Die or Not To Die” – Time and Order of Combination drugs for Triple Negative Breast Cancer cells: A Systems Level Analysis
Anamika Sarkar, PhD. and Ritu Saxena, PhD
Melanoma
6.1.6 “Thymosin alpha1 and melanoma”
https://pharmaceuticalintelligence.com/2013/02/15/thymosin-alpha1-in-melanoma/
Tilda Barliya, PhD
Leukemia
6.1.7 Acute Lymphoblastic Leukemia and Bone Marrow Transplantation
Tilda Barliya PhD
6.2 Natural agents
Prostate Cancer
6.2.1 Scientists use natural agents for prostate cancer bone metastasis treatment
Ritu Saxena, PhD
Breast Cancer
6.2.2 Marijuana Compound Shows Promise In Fighting Breast Cancer
Prabodh Kandala, PhD
Ovarian Cancer
6.2.3 Dimming ovarian cancer growth
https://pharmaceuticalintelligence.com/2012/05/11/259/
Prabodh Kandala, PhD
6.3 Potential Therapeutic Agents
Gastric Cancer
6.3.1 β Integrin emerges as an important player in mitochondrial dysfunction associated Gastric Cancer
Ritu Saxena, PhD
6.3.2 Arthritis, Cancer: New Screening Technique Yields Elusive Compounds to Block Immune-Regulating Enzyme
Prabodh Kandala, PhD
Pancreatic Cancer
6.3.3 Usp9x: Promising therapeutic target for pancreatic cancer
Ritu Saxena, PhD
Breast Cancer
6.3.4 Breast Cancer, drug resistance, and biopharmaceutical targets
Larry H Bernstein, MD, FCAP
Prostate Cancer
6.3.5 Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition
Stephen J. Williams, PhD
Glioblastoma
6.3.6 Gamma Linolenic Acid (GLA) as a Therapeutic tool in the Management of Glioblastoma
Raphael Nir, PhD, MSM, MSc
6.3.7 Akt inhibition for cancer treatment, where do we stand today?
Ziv Raviv, PhD
Capítulo 7: Medicina personalizada y terapia dirigida
Chapter 7: Personalized Medicine and Targeted Therapy
Hypergraph Plot #7 and Tree Diagram Plot #7 for Chapter 7
based on 26 articles & on 17 keywords
cancer, medicine, treatment, breast, sequencing, oncology, cells, patients, personalized, mutations, pharmacology, molecular, clinical, drug, genetic, genes, therapy
HYPERGRAPH PLOT INTERPRETATION
By Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 7 of Cancer Volume 1, represented by the extraction of 17 expert-validated key concepts from the text of the 26 highly-curated articles in this chapter. This chapter is the transition point in this Cancer e-Book from looking at the basic pathologic concepts that govern the etiology of cancer to a more applied and clinical view of how those paradigm shifts discussed earlier are manifestoed into personalized medicine. The chapter discusses the general principles of personalized medicine, the role of genomics, the utility of biomarker selection of appropriate patient cohorts, the importance of development of curated biobank systems, and articles on targeted therapies and precision medicines used in various cancer types.
The chapter involves more than 30 different ontological terms and articles are highly curated. As such, the hypergraph reflects this diversity of thought, with multiple nodes connected among multiple edges. Diverse concepts such as medical specialties, pharmacology, therapy and personalized medical care are connected with various cancers as well as molecular oncology and clinical genetics. This can be seen in articles on cancer genomics, large scale exome sequencing, cancer genetic screening programs, and applying genomics to cancer diagnosis and precision medicine. Targeted therapies like mRNA interference, siRNA therapeutics, system biology and in-silico approaches to drug development, pharmacogenomics give strong correlation with these diverse connections seen in this hypergraph. In addition, much information is given on targeted therapies in colon, breast, and prostate cancer: all found within this hypergraph and linked by multiple edges. Ethical concerns are discussed in the realm of medical care and aid with edges to personalized molecular oncology and clinical genetics. However, learned professions do not project through specialization in the clinical area and this may be an opportunity for enhanced medical training for professionals who are adept in the new era of personalized medicine.
In summary, an interpretation of this hypergraph could be as follows:
Personalized molecular oncology is a medical specialty of pharmacology, a learned profession, where medical care and therapy for various tumor types depends on clinical genetics to evaluate the specific medical agents suitable for patients.
Tree Diagram Plot #7 for Chapter 7
based on 26 articles & on 17 keywords
cancer, medicine, treatment, breast, sequencing, oncology, cells, patients, personalized, mutations, pharmacology, molecular, clinical, drug, genetic, genes, therapy
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
The tree diagram for Chapter 7, on Personalized Medicine and Targeted Therapy, has extraction of 17 expert-validated key concepts from the text of the 26 highly-curated articles in this chapter. The NLP algorithm correctly identifies “cancer” as being a malignant or neoplastic disease or malady, with important concepts such as drug correctly identified as an agent or medication.
The term oncology maps with a medical specialty as well as the term pharmacology. “Medicine” is categorized by the NLP algorithm as a profession and treatment is associated with aid, attention, and care. It is disappointing the NLP platform does not have a lexicon for words like personalized, which suggests that this NLP algorithms would be assisted by the addition of further ontologies and dictionaries on this subject matter. In addition, it appears, from this tree diagram, that much work is needed in developing ontologies relating various categories of medical care and therapeutics with areas of personalization.
Much of the hierarchal structure of this tree suggests that detailed and topical chapters such as these offer great descriptive value to NLP algorithms and can therefore be used as such, except when talking about terms related to personalized medicine. The ability of the NLP algorithm to cross-reference oncology and pharmacology as medical profession shows ability to recognize this subject area however the inability to recognize personalized with genetics hampers a good interpretation of the tree diagram in the context of the chapter subject.
7.1 General
7.1.1 Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders
Aviva Lev-Ari, PhD, RN
7.1.2 Personalized medicine-based cure for cancer might not be far away
Ritu Saxena, PhD
7.1.3 Personalized medicine gearing up to tackle cancer
https://pharmaceuticalintelligence.com/2013/01/07/personalized-medicine-gearing-up-to-tackle-cancer/
Ritu Saxena, PhD
7.1.4 Cancer Screening at Sourasky Medical Center Cancer Prevention Center in Tel-Aviv
Ziv Raviv, PhD
7.1.5 Inspiration From Dr. Maureen Cronin’s Achievements in Applying Genomic Sequencing to Cancer Diagnostics
Aviva Lev-Ari, PhD, RN
7.1.6 Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)
Aviva Lev-Ari, PhD, RN
7.2 Personalized Medicine and Genomics
7.2.1 Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz
Aviva Lev-Ari, PhD, RN
7.2.2 Whole exome somatic mutations analysis of malignant melanoma contributes to the development of personalized cancer therapy for this disease
Ziv Raviv, PhD
7.2.3 Genotype-based Analysis for Cancer Therapy using Large-scale Data Modeling: Nayoung Kim, PhD(c)
Aviva Lev-Ari, PhD, RN
7.2.4 Cancer Genomic Precision Therapy: Digitized Tumor’s Genome (WGSA) Compared with Genome-native Germ Line: Flash-frozen specimen and Formalin-fixed paraffin-embedded Specimen Needed
Aviva Lev-Ari, PhD, RN
7.2.5 LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2
Aviva Lev-Ari, PhD, RN
7.2.6 Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk
Stephen J. Williams, PhD
7.3 Personalized Medicine and Targeted Therapy
7.3.1 The Development of siRNA-Based Therapies for Cancer
Ziv Raviv, PhD
7.3.2 mRNA interference with cancer expression
https://pharmaceuticalintelligence.com/2012/10/26/mrna-interference-with-cancer-expression/
Larry H Bernstein, MD, FCAP
7.3.3 CD47: Target Therapy for Cancer
https://pharmaceuticalintelligence.com/2013/05/07/cd47-target-therapy-for-cancer/
Tilda Barliya, PhD
7.3.4 Targeting Mitochondrial-bound Hexokinase for Cancer Therapy
Ziv Raviv, PhD
7.3.5 GSK for Personalized Medicine using Cancer Drugs needs Alacris systems biology model to determine the in silico effect of the inhibitor in its “virtual clinical trial”
Aviva Lev-Ari, PhD, RN
7.3.6 Personalized Pancreatic Cancer Treatment Option
https://pharmaceuticalintelligence.com/2012/10/16/personalized-pancreatic-cancer-treatment-option
Aviva Lev-Ari, PhD, RN
7.3.7 New scheme to routinely test patients for inherited cancer genes
Stephen J. Williams, PhD
7.3.8 Targeting Untargetable Proto-Oncogenes
https://pharmaceuticalintelligence.com/2013/11/01/targeting-untargetable-proto-oncogenes/
Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
7.3.9 The Future of Translational Medicine with Smart Diagnostics and Therapies: PharmacoGenomics
Demet Sag, PhD
7.4 Personalized Medicine in Specific Cancers
7.4.1 Personalized medicine and Colon cancer
https://pharmaceuticalintelligence.com/2013/05/25/personalized-medicine-and-colon-cancer/
Tilda Barliya, PhD
7.4.2 Comprehensive Genomic Characterization of Squamous Cell Lung Cancers
Aviva Lev-Ari, PhD, RN
7.4.3 Targeted Tumor-Penetrating siRNA Nanocomplexes for Credentialing the Ovarian Cancer Oncogene ID4
Sudipta Saha, PhD
7.4.4 Cancer and Bone: low magnitude vibrations help mitigate bone loss
Ritu Saxena, PhD
7.4.5 New Prostate Cancer Screening Guidelines Face a Tough Sell, Study Suggests
Prabodh Kandala, PhD
Parte III
La medicina traslacional, la genómica y las nuevas tecnologías convergen para mejorar la detección precoz
Part III
Translational Medicine, Genomics, and New Technologies Converge to Improve Early Detection
Diagnóstico, detección y biomarcadores
Diagnosis, Detection And Biomarkers
Capítulo 8: Diagnóstico
Chapter 8: Diagnosis
Hypergraph Plot #8 and Tree Diagram Plot #8 for Chapter 8
based on 5 articles & on 18 keywords
cancer, prostate, men, test, screening, patients, risl, psa, stage, health, breath, incidence, genomic, early, healthy, cases, disease, tumors
HYPERGRAPH PLOT INTERPRETATION
Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 8 of Cancer Volume 1, represented by the extraction of 18 expert-validated key concepts from the text of the 5 articles in this chapter. This chapter consists of articles on the diagnosis and early detection of Prostate, lung cancer, market players in the development of diagnostic tests, and guidance established for early detection by regulatory agencies and medical associations. An additional article discusses the use of gold nanoparticles in the detection of organic volatiles from exhaled breath from lung cancer patients and the utility of principle component analysis of these chemicals to develop a more sensitive early detection test for lung cancer. Issues related to prostate screening efforts including confusion on guidelines is also discussed. This hypergraph displayed looks drastically different than previous chapters 1-7.
This visual difference is of utmost importance and a graphical example of the difference between the early chapters and this and subsequent chapters. The chapter 8 hypergraph (as well as 9-12) displays more a a wheel type or hub and spoke design whereas the previous chapters have a more complex flower petal with multiple intersecting edges. As Dr. Bernstein (Senior Editor) and I had discussed when formulating the order of articles in the book, Dr. Bernstein felt, as I did, the early chapters should give basic biological concepts regarding the changes occurring during neoplastic transformation and cancer.
In the Preface much emphasis was given on the 8 Hallmark of Cancer by Hanahan and Weinberg; concepts which are carried into the later chapters. Chapters 8-12 now represent more focused and topical subject areas, contrary to the first 7, which integrated multiple biological concepts, and ergo, the hypergraph pattern. Chapter 8 demarcates the book from basic concepts to more applied topical subjects, and ergo the hub and spoke pattern.
This tight hub and spoke pattern is suggesting that all subject matter and terminology are related to core concepts derived from the said chapter. Concepts like risk screening for prostate incidence projects and is related to cancer, disease through the hub of diagnostics however prostate risk from screening is separate edges from health outcomes and early disease as discussed in the articles in this chapter on the confusion of guidelines from Prostate Specific Antigen (PSA) results; in essence PSA is a good biomarker for recurrence but very hard to determine absolute cutoff points for early detection.
In summary, an interpretation of this hypergraph could be as follows:
Testing for early detection of neoplastic diseases is imperative for improving health outcomes, with more research needed to develop related technologies
Tree Diagram Plot #8 for Chapter 8
based on 5 articles & on 18 keywords
cancer, prostate, men, test, screening, patients, risl, psa, stage, health, breath, incidence, genomic, early, healthy, cases, disease, tumors
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
The tree diagram for Chapter 8 is has been enriched above the extraction of 18 expert-validated key concepts from the text of the 5 articles in this chapter, and now contains 32 related words that the natural language processing algorithm believes should be included in an interpretation or at least as additional hierarchal structures.
The NLP algorithm correctly identifies “cancer” as being a malignant or neoplastic disease or malady, with important concepts such as stage of disease and health outcome associated with these terms. The tree diagram also indicates the NLP algorithm is good at extracting that the chapter focus is also related to “screening” which is related to “risk” and therefore searches using this terminology would inherently draw the reader to this chapter on Diagnostic biomarkers.
Words such as “men” and “prostate” are also correctly associated with each other. Much of the hierarchal structure of this tree suggests that detailed and topical chapters such as these offer great descriptive value to NLP algorithms and can therefore be used as such. In essence, the more detailed the more ordered and suitable the hierarchy for NLP analysis.
Prostate Cancer
8.4 New Prostate Cancer Screening Guidelines Face a Tough Sell, Study Suggests
Prabodh Kandala, PhD
Diagnossis: Lung Cancer
8.5 Diagnosing lung cancer in exhaled breath using gold nanoparticles
Tilda Barliya PhD
Capítulo 9: Detección
Chapter 9: Detection
Hypergraph Plot #9 and Tree Diagram Plot #9 for Chapter 9
based on 6 articles & on 19 keywords
cancer, prostate, detection, ovarium, biomarkers, screening, women, breast, imaging, patients, treatment, tumor, biomarker, test, serum, cells, psa, clinical, symptoms
HYPERGRAPH PLOT INTERPRETATION
by Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 9 of Cancer Volume 1 “Detection”, represented by the extraction of 19 expert-validated key concepts from the text of the 6 articles in this chapter. This chapter consists of articles on early detection of Prostate, lung cancer, market players in the development of diagnostic tests, and guidance established for early detection by regulatory agencies and medical associations. An additional article discusses the use of image guided tomography and positive results with intervention. Issues related to prostate screening efforts including confusion on guidelines is also discussed. The chapter 9 hypergraph displays a hub and spoke design. This tight hub and spoke pattern suggest that all subject matter and terminology are related to core concepts derived from the said chapter.
It is interesting to note the tight grouping and common edges with prostate, ovarian, screening, imaging and imaging technologies, cancer screening tests, and clinical results. Serum, test, and prostate also display common edges, possibly due to the use of PSA for prostate and CA125 in ovarian, although one would expect, based on literature a common edge with ovarian and ultrasound or imaging and serum and test. Although CA125 is a common serum test for ovarian cancer, guidelines have been established that ultrasound must be combined for good diagnostic and prognostic value. An interesting gap of knowledge would probably be discovery of serum tests for breast cancer, or other solid cancers, which is sorely lacking. In essence more discovery and research is needed in this area, eluded in this hypergraph.
In summary, an interpretation of this hypergraph could be as follows:
Testing for early detection of endocrine neoplastic diseases like prostate and ovarian have had success with serum tests combined with imaging modalities yet more discovery is needed to aid diagnostic capability.
Tree Diagram Plot #9 for Chapter 9
based on 6 articles & on 19 keywords
cancer, prostate, detection, ovarium, biomarkers, screening, women, breast, imaging, patients, treatment, tumor, biomarker, test, serum, cells, psa, clinical, symptoms
TREE DIAGRAM PLOT INTERPRETATION
by Dr Stephen J. Williams
The tree diagram for Chapter 8 has been enriched above the extraction of 19 expert-validated key concepts from the text of the 5 articles in this chapter, and now contains 27 related or synonymous words that the natural language processing algorithm believes should be included in an interpretation or at least as additional hierarchal structures. Interestingly this time the NLP algorithm incorrectly identifies “cancer” as being associated with the horoscope symbol of arthropod however correctly identifies serum with a bodily fluid as well as “humor” related to the humour in the body as one of the main bodily components.
The tree diagram also indicates the NLP algorithm is good at extracting that the chapter focus is also related to “screening” which is related to “risk” and therefore searches using this terminology would inherently draw the reader to this chapter on Diagnostic biomarkers. Words such as “men” and “prostate” are also correctly associated with each other. Much of the hierarchal structure of this tree suggests that detailed and topical chapters such as these offer great descriptive value to NLP algorithms but interestingly some terms are lacking and is of low order of two. In essence, the algorithm could be assisted by addition of better keywords, more articles, or highly curated articles with additional ontologies.
Detection: Prostate Cancer
9.1 Early Detection of Prostate Cancer: American Urological Association (AUA) Guideline
https://pharmaceuticalintelligence.com/2013/05/21/early-detection-of-prostate-cancer-aua-guideline/
Dror Nir, PhD
Detection: Breast & Ovarian Cancer
9.2 Testing for Multiple Genetic Mutations via NGS for Patients: Very Strong Family History of Breast & Ovarian Cancer, Diagnosed at Young Ages, & Negative on BRCA Test
Aviva Lev-Ari, PhD, RN
Detection: Aggressive Prostate Cancer
9.3 A Blood Test to Identify Aggressive Prostate Cancer: a Discovery @ SRI International, Menlo Park, CA
Aviva Lev-Ari, PhD, RN
Diagnostic Markers & Screening as Diagnosis Method
9.4 Combining Nanotube Technology and Genetically Engineered Antibodies to Detect Prostate Cancer Biomarkers
Stephen J. Williams, PhD
Detection: Ovarian Cancer
9.5 Warning signs may lead to better early detection of ovarian cancer
Prabodh Kandala, PhD
9.6 Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?
Dror Nir, PhD
Capítulo 10: Biomarcadores
Chapter 10: Biomarkers
Hypergraph Plot #10 and Tree Diagram Plot #10 for Chapter 10
based on 8 articles & on 24 keywords
cancer, prostate, patients, clinical, risk, test, biomarker, proteins, psa, pancreatic, reoccurrence, men, sequencing, expression, tumor, mesothelin, hpv, disease, early, detection, research, prostatectomy, screening, dna
HYPERGRAPH PLOT INTERPRETATION
by Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 10 of Cancer Volume 1, represented by the extraction of 24 key concepts from the text of this chapter. This chapter examines the role of biomarker development and testing on personalized medicine in oncology. Discussions include genomic profiles as biomarker panels, biomarkers useful in early detection of prostate, pancreatic, and breast cancer, head and neck cancer, as well as some of the new guidelines concerning the use of biomarkers as prognostic, diagnostic, and predictive tools in oncology. Issues related to the use of biomarkers with regard to clinical decisions of personalized medicine strategies include sensitivity, specificity, and choice of biomarker test in formulating guidance and usefulness precision medicine strategy, whether they be prognostic, predictive, diagnostic, risk assessment, safety, or companion diagnostic biomarkers.
The hypergraph once again shows a tight hub and spoke pattern suggesting that all subject matter and terminology are related to core concepts derived from this chapter. This also shows how early detection of disease, especially pancreatic and prostate cancer are linked to screening as well as expression analysis to determine risk as well as some biomarkers like Prostate Specific Antigen (PSA) is useful to determine recurrence after treatment. This is also explained in the chapter under multiple articles. Gaps on knowledge and areas of scientific investigation is highlighted, including the need to conduct more rigorous validation studies to assess potential biomarkers to assess early detection and cancer risk, especially with prostate and pancreatic cancer. This interpretation is validated by the multiple articles pointing out the confusion among Early Detection Research Networks and medical associations over guidelines for use of PSA as an early detection and prognostic biomarker as well as the confusion regarding HPV as a biomarker for head and neck cancer.
In summary, an interpretation of this hypergraph could be as follows:
Recent investigations of potential diagnostic, prognostic, predictive biomarkers for various neoplastic diseases may enhance personalized medicine strategies upon proper validation studies to assist development of new biomarkers for these cancers.
Tree Diagram Plot #10 for Chapter 10
based on 8 articles & on 24 keywords
cancer, prostate, patients, clinical, risk, test, biomarker, proteins, psa, pancreatic, reoccurrence, men, sequencing, expression, tumor, mesothelin, hpv, disease, early, detection, research, prostatectomy, screening, dna
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
The tree diagram for Chapter 10 is has been enriched above the 24 key concepts extracted from this chapter, and now contains 48 important words that the natural language processing algorithm believes should be included in an interpretation or at least as additional hierarchal structures. To understand this let us discuss first the details of this chapter, entitled “Biomarkers”. This chapter represents a detailed synopsis of current state of various types of biomarkers (prognostic, predictive, risk susceptibility, diagnostic) in the context of personalized medicine and various cancers such as pancreatic, head and neck, and prostate cancer. Potential biomarkers are discussed as well as issues surrounding the drafting of clinical guidance for established biomarkers, including PSA. As mentioned before, I personally find hypergraphs, with their rich connections, useful for gaining insights into potential gaps of knowledge as well as making additional connections between seemingly disparate literature. The tree diagram helps determine the structure of the corpus of knowledge, enumerating potential outcomes of a body of texts, as well as relationships within key conceptual ontologies.
The NLP algorithm correctly associates “prostate” “cancer” in “men” and “PSA” as “risk” and “recurrence” biomarker (associated with the title).
In addition, it associated “research” into “expression” and “detection” in “tumor” as “important” for “discovery”. It is important thought to have high validity, sensitivity, and specificity, which the algorithm suggests as “test” however additions to lexicons and ontologies with more technical terms may resolve this. The development of biomarker tests with high validity,
reproducibility, and sensitivity/specificity is a high priority for current biomarker research, as well as research into development of genomic panels as valid biomarkers for diagnostic screening. Much of the hierarchal structure of this tree suggests that detailed and topical chapters such as these offer great descriptive value to NLP algorithms and can therefore be used as such. In essence, the more detailed the more ordered and suitable the hierarchy for NLP analysis.
10.1 Mesothelin: An early detection biomarker for cancer (By Jack Andraka)
Tilda Barliya, PhD
Biomarkers: All Types of Cancer, Genomics and Histology
10.2 Stanniocalcin: A Cancer Biomarker
https://pharmaceuticalintelligence.com/2012/12/25/stanniocalcin-a-cancer-biomarker/
Aashir Awan, PhD
10.3 Breast Cancer: Genomic Profiling to Predict Survival: Combination of Histopathology and Gene Expression Analysis
Aviva Lev-Ari, PhD, RN
Biomarkers: Pancreatic Cancer
10.4 Biomarker tool development for Early Diagnosis of Pancreatic Cancer: Van Andel Institute and Emory University
Aviva Lev-Ari, PhD, RN
10.5 Early Biomarker for Pancreatic Cancer Identified
https://pharmaceuticalintelligence.com/2012/05/17/early-biomarker-for-pancreatic-cancer-identified/
Prabodh Kandala, PhD
Biomarkers: Head and Neck Cancer
10.6 Head and Neck Cancer Studies Suggest Alternative Markers More Prognostically Useful than HPV DNA Testing
Aviva Lev-Ari, PhD, RN
10.7 Opens Exome Service for Rare Diseases & Advanced Cancer @ Mayo Clinic’s OncoSpire
Aviva Lev-Ari, PhD, RN
Diagnostic Markers and Screening as Diagnosis Methods
10.8 In Search of Clarity on Prostate Cancer Screening, Post-Surgical Follow up, and Prediction of Long Term Remission
Larry H Bernstein, MD, FCAP, Dror Nir, PhD and Aviva Lev-Ari, PhD, RN
Capítulo 11: Diagnóstico por imagen del cáncer
Chapter 11: Imaging In Cancer
Hypergraph Plot #11 and Tree Diagram Plot #11 for Chapter 11
based on 29 articles & on 17 keywords
imaging, prostate, patients, ultrasound, biopsy, tissue, ct, fdg, mri, pet, breast, clinical, screening, detection, tumor, elastography, disease
HYPERGRAPH PLOT INTERPRETATION
by Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 11 of Cancer Volume 1, represented by the extraction of 17 key concepts from the text of this chapter. This chapter is focused on advances in tumor imaging and the role biomarkers have revolutionized this subspecialty to improve diagnosis and personalized treatment of cancer. The majority of this chapter represents the expert opinion of Dr. Dror Nir. Innovative imaging technologies like histoscanning ultrasound, PET/MRI, CT mammography, virtual biopsy, and optical coherent tomography are presented in the context of their contributions to ovarian, breast, and prostate cancers. The hypergraph displays the hub and spoke pattern as some of the previous chapters, suggesting first a chapter which is tightly fashioned on a specific topic, but also that concepts such as scientific process, end organ, diagnosis, animal modeling, and endocrine cancers are strongly represented within this chapter as the spokes share common edges with the core concepts. However, on closer inspections certain links between nodes and edges are interestingly apparent. For instance, breast is in common with diagnosis and imaging through the common key words and disease projects through the chapter key words to project on growth and diagnostic assay, suggesting these new discoveries around diagnosis and imaging of breast cancer have well developed biomarker assays to support the imaging modalities, such as the advances seen in fluorochrome guided imaging and fluorescent based image-guided therapy. Commonality of edges of note include animal modeling with imaging and with endocrine tumors although a gap between endocrine tumors and imaging suggests that advances in this area may soon be around the corner.
In addition, it is apparent that we need more research into discovery of biomarkers and biomarker assays to support such imaging advances, and to confer increased clinical guidance.
In summary, an interpretation of this hypergraph could be as follows:
Investing in and combining newer and innovative imaging modalities that will generate imaging-biomarkers characteristic of cancer will significantly drive improvements in cancer management.
Tree Diagram Plot #11 for Chapter 11
based on 29 articles & on 17 keywords
imaging, prostate, patients, ultrasound, biopsy, tissue, ct, fdg, mri, pet, breast, clinical, screening, detection, tumor, elastography, disease
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
The tree diagram for Chapter 11 is has been enriched above the 17 key concepts extracted from this chapter of 29 articles. This is a very technical chapter on imaging of tumors and from first inspection, it appears that many semantic lexicons may not contain the necessary terminology found in most biomedical ontologies, and thus, the need for enrichment using specialized ontologies developed by experts in the field.
For instance, the semantic natural language processing algorithm, believes that the term ”PET” should be interpreted to mean animal while this is a commonly used acronym for Positron Emission Tomography. However, many of the semantically linked terms are usable, such as growth-tumor, detection-find-discovery, disease-malady-sickness.
However, the library correctly understands the prostate to be an endocrine tissue, therefore much can be gleamed from both the hypergraph and tree diagram from either an expert in the field or one who has at least read the introduction to this chapter. There is a strong association between detection and tumor, suggesting the algorithm correctly identifies the topic of this chapter as imaging and screening of tumors. An association is made also with the clinical and preclinical aspects of this expanding subspecialty within oncology. In addition, it is very welcome that a less technical lexicon may lead one to interpret that “pictorial representation” of “endocrine” “growth” is important in “diagnosis” as well as “disease”.
This is surprising given the technical nature of this chapter, however, the addition of sublayers or a third order of ontologies would be warranted and enrich both the natural language processing algorithms as well as the analysis of the outputs.
11.1 Introduction by Dror Nir, PhD
11.2 Ultrasound
11.2.1 2013 – YEAR OF THE ULTRASOUND
https://pharmaceuticalintelligence.com/2013/04/10/2013-year-of-the-ultrasound/
Dror Nir, PhD
11.2.2 Imaging: seeing or imagining? (Part 1)
https://pharmaceuticalintelligence.com/2012/09/10/imaging-seeing-or-imagining-part-1/
Dror Nir, PhD
11.2.3 Early Detection of Prostate Cancer: American Urological Association (AUA) Guideline
https://pharmaceuticalintelligence.com/2013/05/21/early-detection-of-prostate-cancer-aua-guideline/
Dror Nir, PhD
11.2.4 Today’s fundamental challenge in Prostate cancer screening
Dror Nir, PhD
11.2.5 State of the art in oncologic imaging of Prostate
https://pharmaceuticalintelligence.com/2013/01/28/state-of-the-art-in-oncologic-imaging-of-prostate/
Dror Nir, PhD
11.2.6 From AUA 2013: “HistoScanning”- aided template biopsies for patients with previous negative TRUS biopsies
Dror Nir, PhD
11.2.7 On the road to improve prostate biopsy
https://pharmaceuticalintelligence.com/2013/02/15/on-the-road-to-improve-prostate-biopsy/
Dror Nir, PhD
11.2.8 Ultrasound imaging as an instrument for measuring tissue elasticity: “Shear-wave Elastography” VS. “Strain-Imaging”
Dror Nir, PhD
11.2.9 What could transform an underdog into a winner?
https://pharmaceuticalintelligence.com/2012/11/19/what-could-transform-an-underdog-into-a-winner/
Dror Nir, PhD
11.2.10 Ultrasound-based Screening for Ovarian Cancer
https://pharmaceuticalintelligence.com/2013/04/28/ultrasound-based-screening-for-ovarian-cancer/
Dror Nir, PhD
11.2.11 Imaging Guided Cancer-Therapy – a Discipline in Need of Guidance
Dror Nir, PhD
11.3 MRI & PET/MRI
11.3.1 Introducing smart-imaging into radiologists’ daily practice
Dror Nir, PhD
11.3.2 Imaging: seeing or imagining? (Part 2)
[Part 1 is included in the ultrasound section above]
https://pharmaceuticalintelligence.com/2012/09/29/imaging-seeing-or-imagining-part-2/
Dror Nir, PhD
11.3.3 Imaging-guided biopsies: Is there a preferred strategy to choose?
Dror Nir, PhD
11.3.4 New clinical results support Imaging-guidance for targeted prostate biopsy
Dror Nir, PhD
11.3.5 Whole-body imaging as cancer screening tool; answering an unmet clinical need?
Dror Nir, PhD
11.3.6 State of the art in oncologic imaging of Lymphoma
https://pharmaceuticalintelligence.com/2013/02/03/state-of-the-art-in-oncologic-imaging-of-lymphoma/
Dror Nir, PhD
11.3.7 A corner in the medical imaging’s ECO system
https://pharmaceuticalintelligence.com/2012/12/09/a-corner-in-the-medical-imagings-eco-system/
Dror Nir, PhD
11.4 CT, Mammography & PET/CT
11.4.1 Causes and imaging features of false positives and false negatives on 18F-PET/CT in oncologic imaging
Dror Nir, PhD
11.4.2 Minimally invasive image-guided therapy for inoperable hepatocellular carcinoma
Dror Nir, PhD
11.4.3 Improving Mammography-based imaging for better treatment planning
Dror Nir, PhD
11.4.4 Closing the Mammography gap
https://pharmaceuticalintelligence.com/2012/11/04/closing-the-mammography-gap/
Dror Nir, PhD
11.4.5 State of the art in oncologic imaging of lungs
https://pharmaceuticalintelligence.com/2013/01/23/state-of-the-art-in-oncologic-imaging-of-lungs/
Dror Nir, PhD
11.4.6 Ovarian Cancer and fluorescence-guided surgery: A report
Tilda Barliya, PhD
11.5 Tomografía de coherencia óptica (TCO)
11.5.1 Optical Coherent Tomography – emerging technology in cancer patient management
Dror Nir, PhD
11.5.2 New Imaging device bears a promise for better quality control of breast-cancer lumpectomies – considering the cost impact
Dror Nir, PhD
11.5.3 Virtual Biopsy – is it possible?
https://pharmaceuticalintelligence.com/2013/03/03/virtual-biopsy-is-it-possible/
Dror Nir, PhD
11.5.4 New development in measuring mechanical properties of tissue
Dror Nir, PhD
Capítulo 12. La nanotecnología aporta nuevos avances en el tratamiento, la detección y el diagnóstico por imagen del cáncer
Chapter 12. Nanotechnology Imparts New Advances in Cancer Treatment, Detection, and Imaging
Hypergraph Plot #12 and Tree Diagram Plot #12 for Chapter 12
based on 7 articles & on 25 keywords
dna, drug, nanotechnology, tumor, lung, brain, tnf, nanoparticles, systematic, prostate, delivery, treatment, breast, particles, respiratory, dose, genetic, patients, personalized, therapy, nanopore, blood, clinical, particle, alveolar
HYPERGRAPH INTERPRETATION
by Dr. Stephen J. Williams
The included hypergraph is a visualization of chapter 12 of Cancer Volume 1 on Nanotechnology, represented by the extraction of 25 expert-validated key concepts from the text of the 7 articles in this chapter. Nanotechnology is a multidisciplinary field of science that encompasses engineering, chemistry, physics, and biology in the design, synthesis and characterization of materials and devices with nanometer scale organization. This chapter discusses some of the nanotechnology clinical applications developed to achieve maximal therapeutic value and minimize toxicities. Articles include the influence of nanotechnology on DNA sequencing, drug design for personalized medicine, and specific applications of nanotechnology in the diagnosis and treatment of various cancers, including brain, prostate, breast and lung cancer. The hypergraph displays one of the most enriched hub and wheel patterns, most likely the result of the highly curated nature of the articles contained in this topical chapter. With a core of dose delivery of nanoparticle for personalized treatment and therapy of prostate, brain and blood cancers, this hypergraph displays the reality of how expansive this field is growing.
In summary, an interpretation of this hypergraph could be as follows:
Nanotechnology is an emerging field making rapid advances in personalized therapy in oncology.
To this effect, a summary of the hypergraphs presented here is warranted. The earliest chapters, with heavy emphasis on biological concepts (and complex connections within hypergraphs) transitioned to more topical chapters (with a more organized hub and spoke design) on discrete topics within clinical oncology. This in fact was the purpose of the senior editor Dr. Larry Bernstein, who, in the Epilogue, mentions “The greatest difficulty in organizing such a work is in whether it is to be merely a compilation of cancer expression organized by organ systems, or whether it is to capture developing concepts” and “What we have laid out is a map with substructural ordered concepts forming subsets within the structural (mind) map”.
Together, these hypergraphs represent the transition of thought of cancer as a disease of altered biological concepts translated into the real-world applied clinical manifestation of these modern concepts as highlighted by the 8 hallmarks of cancer and such a work is to (quote the Senior Editor) “to be read sequentially and in its entirety” to fully grasp the impact of these paradigm shifts toward the treatment of cancer.
Tree Diagram Plot #12 for Chapter 12
based on 7 articles & on 25 keywords
dna, drug, nanotechnology, tumor, lung, brain, tnf, nanoparticles, systematic, prostate, delivery, treatment, breast, particles, respiratory, dose, genetic, patients, personalized, therapy, nanopore, blood, clinical, particle, alveolar
TREE DIAGRAM PLOT INTERPRETATION
by Dr. Stephen J. Williams
The tree diagram for Chapter 12, “Nanotechnology” represents an extraction of 26 expert-validated keywords from 7 articles in this chapter. NLP algorithms deduced 52 total potential related concepts and words projected onto 14 subtopics as shown in the hypergraph. This was a tremendous enrichment, as the articles are very highly curated and contain multiple ontologies within them. The NLP algorithm associates nanotechnology with drug, treatment, therapy, particles, and dose while enriching the analysis to expand to many synonymous terms such as agent, medical treatment, and categorizing nanotechnology with engineering. Perhaps due to the specific nature of the topic and the highly curated articles, the NLP algorithm makes great use of the extracted keywords and expand its lexicon. Much of the hierarchal structure of this tree suggests that detailed and topical chapters such as these offer great descriptive value to NLP algorithms and can therefore be used as such. In essence, the more detailed the more ordered and suitable the hierarchy for NLP analysis.
To summarize the tree diagrams and hypergraphs after performing natural language processing on this corpus of knowledge (Cancer Volume 1) we may deduce the following:
- Natural language processing of highly curated articles within a corpus of knowledge produces extremely rich connections between concepts and a highly curated nature allows enhanced text analysis and higher order and enrichment of terminology
- Distinct patterns can be seen between chapters of curated articles of a conceptual nature (chapters 1-7) and of a highly discrete topical nature (chapters 8-12)
- For an NLP algorithm to achieve a higher order (greater than three) level of analysis and text mining of biomedical text, multiple medically-related ontologies and lexicons should be added to the NLP platform databank and incorporated into its algorithms.
12.1 DNA Nanotechnology
https://pharmaceuticalintelligence.com/2013/05/15/dna-nanotechnology/
Tilda Barliya, PhD
12.2 Nanotechnology, personalized medicine and DNA sequencing
Tilda Barliya, PhD
12.3 Nanotech Therapy for Breast Cancer
https://pharmaceuticalintelligence.com/2012/12/09/naotech-therapy-for-breast-cancer/
Tilda Barliya, PhD
12.4 Prostate Cancer and Nanotecnology
https://pharmaceuticalintelligence.com/2013/02/07/prostate-cancer-and-nanotecnology/
Tilda Barliya, PhD
12.5 Nanotechnology: Detecting and Treating metastatic cancer in the lymph node
Tilda Barliya, PhD
12.6 Nanotechnology Tackles Brain Cancer
https://pharmaceuticalintelligence.com/2012/11/23/nanotechnology-tackles-brain-cancer/
Tilda Barliya, PhD
12.7 Lung Cancer (NSCLC), drug administration and nanotechnology
Tilda Barliya, PhD
PART B Appendix
NLP Code writing for Chapters 1 to 12
by
MADISON DAVIS
Code used in production of Hyper-graph Plots
edgeList = {};
edgeList = Append[edgeList,Prepend[words1, “cancer”]] ;
For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]];
ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle ->Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]]
THE ABOVE CODE IS USED FOR HYPERGRAPH PLOTS
CODE at the Chapter Level
| Hypergraph Plot #1 for Chapter 1 based on 13 articles & on 10 keywords | wordSheet ={
cancer, group, innovations, area, therapy, cancer, business, information, section, advancements };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #2 for Chapter 2 based on 10 articles & on 13 keywords | wordSheet ={
cancer, metastasis, deleterious, cell, ecological, passenger, control, process, mutations, tumor, cancer, growth, mcfarland };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #3 for Chapter 3 based on 12 articles & on 17 keywords | wordSheet ={
binding, oligonucleotides, lattice, dna, structures, renewal, advanced, colorectal, adenoma, risk, variant, alleles, critical, genes, calcium, reabsorption, aacr };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #4
for Chapter 4 based on 14 articles & on 8 keywords |
wordSheet ={
epigenetics, stemness, long, coding, potential, cell, stiffness, biomarker };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #5
for Chapter 5 based on 14 articles & on 7 keywords |
wordSheet ={
cell, movement, aggressive, breast, cancer, clinical, preclinical };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #6
for Chapter 6 based on 17 articles & on 18 keywords |
wordSheet ={
targeted, antibody, trastuzmad, chemically, linked, cytoxin, breast, cancer, cells, value, status, mutations, pik, pten, marker, efficiency, akt, inhibitors };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #7
for Chapter 7 based on 26 articles & on 17 keywords |
wordSheet ={
cancer, medicine, treatment, breast, sequencing, oncology, cells, patients, personalized, mutations, pharmacology, molecular, clinical, drug, genetic, genes, therapy };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #8
for Chapter 8 based on 5 articles & on 18 keywords |
wordSheet ={
cancer, prostate, men, test, screening, patients, risl, psa, stage, health, breath, incidence, genomic, early, healthy, cases, disease, tumors };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #9
for Chapter 9 based on 6 articles & on 19 keywords |
wordSheet ={
cancer, prostate, detection, ovarium, biomarkers, screening, women, breast, imaging, patients, treatment, tumor, biomarker, test, serum, cells, psa, clinical, symptoms };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #10
for Chapter 10 based on 8 articles & on 24 keywords |
wordSheet ={
cancer, prostate, patients, clinical, risk, test, biomarker, proteins, psa, pancreatic, reoccurrence, men, sequencing, expression, tumor, mesothelin, hpv, disease, early, detection, research, prostatectomy, screening, dna };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #11
for Chapter 11 based on 29 articles & on 17 keywords |
wordSheet ={
imaging, prostate, patients, ultrasound, biopsy, tissue, ct, fdg, mri, pet, breast, clinical, screening, detection, tumor, elastography, disease };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
| Hypergraph Plot #12
for Chapter 12 based on 7 articles & on 25 keywords |
wordSheet ={
dna, drug, nanotechnology, tumor, lung, brain, tnf, nanoparticles, systematic, prostate, delivery, treatment, breast, particles, respiratory, dose, genetic, patients, personalized, therapy, nanopore, blood, clinical, particle, alveolar };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
edgeList = {}; edgeList = Append[edgeList,Prepend[words1, “”]] ; For[i = 1, i <= Length[words1], i++, edgeList = Append[edgeList, Append[broadTerms[[i]], words1[[i]]]]]; ResourceFunction[“HypergraphPlot”][edgeList, VertexSize -> 0.01, VertexLabels -> Automatic, PlotTheme -> “Detailed”, SubsetEdge -> False, SubsetBoundaryScale -> 7, VertexStyle -> Red,”BaseLayout” -> “RadialEmbedding”,”SubsetEdgeStyle” -> ColorData[100, “ColorList”]] |
Code used in production of Tree Diagram Plots
edgeList = {};
For[i = 1 , i <= Length[broadTerms], i++, edgeList = Append[edgeList, broadTerms[[i]]]];
For[i = 1 , i <= Length[broadTerms], i++, For[j = 1, j <= Length[edgeList[[i]]], j++, edgeList[[i]][[j]] = words1[[i]] -> edgeList[[i]][[j]]]];
For[i = 1 , i <= Length[words1], i++, edgeList = Append[edgeList, {“chapter 1” -> words1[[i]]}]];
TreePlot[Flatten[edgeList],VertexLabels ->Placed[Automatic,Center],VertexSize->0.01,LayerSizeFunction->(#&)]
THE ABOVE CODE IS USED FOR TREE DIAGRAM PLOTS
CODE at the Chapter Level
| Tree Diagram Plot #1
for Chapter 1 based on 13 articles & on 10 keywords |
wordSheet ={
cancer, group, innovations, area, therapy, cancer, business, information, section, advancements };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
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| Tree Diagram Plot #2
for Chapter 2 based on 10 articles & on 13 keywords |
wordSheet ={
cancer, metastasis, deleterious, cell, ecological, passenger, control, process, mutations, tumor, cancer, growth, mcfarland };
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| Tree Diagram Plot #3
for Chapter 3 based on 12 articles & on 17 keywords
|
wordSheet ={
binding, oligonucleotides, lattice, dna, structures, renewal, advanced, colorectal, adenoma, risk, variant, alleles, critical, genes, calcium, reabsorption, aacr };
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| Tree Diagram Plot #4
for Chapter 4 based on 14 articles & on 8 keywords |
wordSheet ={
epigenetics, stemness, long, coding, potential, cell, stiffness, biomarker };
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| Tree Diagram Plot #5
for Chapter 5 based on 14 articles & on 7 keywords |
wordSheet ={
cell, movement, aggressive, breast, cancer, clinical, preclinical };
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| Tree Diagram Plot #6
for Chapter 6 based on 17 articles & on 18 keywords |
wordSheet ={
targeted, antibody, trastuzmad, chemically, linked, cytoxin, breast, cancer, cells, value, status, mutations, pik, pten, marker, efficiency, akt, inhibitors };
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| Tree Diagram Plot #7
for Chapter 7 based on 26 articles & on 17 keywords |
wordSheet ={
cancer, medicine, treatment, breast, sequencing, oncology, cells, patients, personalized, mutations, pharmacology, molecular, clinical, drug, genetic, genes, therapy };
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edgeList = {}; For[i = 1 , i <= Length[broadTerms], i++, edgeList = Append[edgeList, broadTerms[[i]]]]; For[i = 1 , i <= Length[broadTerms], i++, For[j = 1, j <= Length[edgeList[[i]]], j++, edgeList[[i]][[j]] = words1[[i]] -> edgeList[[i]][[j]]]]; For[i = 1 , i <= Length[words1], i++, edgeList = Append[edgeList, {“Chapter 7” -> words1[[i]]}]]; TreePlot[Flatten[edgeList], VertexSize -> 0.1, VertexLabels -> Automatic] |
| Tree Diagram Plot #8
for Chapter 8 based on 5 articles & on 18 keywords |
wordSheet ={
cancer, prostate, men, test, screening, patients, risl, psa, stage, health, breath, incidence, genomic, early, healthy, cases, disease, tumors };
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edgeList = {}; For[i = 1 , i <= Length[broadTerms], i++, edgeList = Append[edgeList, broadTerms[[i]]]]; For[i = 1 , i <= Length[broadTerms], i++, For[j = 1, j <= Length[edgeList[[i]]], j++, edgeList[[i]][[j]] = words1[[i]] -> edgeList[[i]][[j]]]]; For[i = 1 , i <= Length[words1], i++, edgeList = Append[edgeList, {“Chapter 8” -> words1[[i]]}]]; TreePlot[Flatten[edgeList], VertexSize -> 0.1, VertexLabels -> Automatic] |
| Tree Diagram Plot #9
for Chapter 9 based on 6 articles & on 19 keywords |
wordSheet ={
cancer, prostate, detection, ovarium, biomarkers, screening, women, breast, imaging, patients, treatment, tumor, biomarker, test, serum, cells, psa, clinical, symptoms };
topN = Length[wordSheet]; words = {}; For[i = 1, i <= topN, i++, words = Append[words, ToString[wordSheet[[i]]]]]; maxN = 3; broadTerms = {}; words1 = {}; words2 = {}; For[i = 1, i <= topN, i++,If[Length[Position[WordData[], Extract[words, i]]] > 0, broadTerms = Append[broadTerms, Flatten[Values[WordData[Extract[words, i], “BroaderTerms”]]]]; words1 = Append[words1, Extract[words, i]], words2 = Append[words2, Extract[words, i]]]]; For[i = 1, i <= Length[broadTerms], i++, If[Length[broadTerms[[i]]] > maxN, broadTerms[[i]] = broadTerms[[i]][[1 ;; maxN]]]];
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| Tree Diagram Plot #10
for Chapter 10 based on 8 articles & on 24 keywords |
wordSheet ={
cancer, prostate, patients, clinical, risk, test, biomarker, proteins, psa, pancreatic, reoccurrence, men, sequencing, expression, tumor, mesothelin, hpv, disease, early, detection, research, prostatectomy, screening, dna };
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| Tree Diagram Plot #11
for Chapter 11 based on 29 articles & on 17 keywords |
wordSheet ={
imaging, prostate, patients, ultrasound, biopsy, tissue, ct, fdg, mri, pet, breast, clinical, screening, detection, tumor, elastography, disease };
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| Tree Diagram Plot #12
for Chapter 12 based on 7 articles & on 25 keywords |
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dna, drug, nanotechnology, tumor, lung, brain, tnf, nanoparticles, systematic, prostate, delivery, treatment, breast, particles, respiratory, dose, genetic, patients, personalized, therapy, nanopore, blood, clinical, particle, alveolar };
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PART C:
The Editorials of the original e-Book in
English in Audio format
Preface
Cancer is the second most cause of medically related deaths in the developed world. However, concerted efforts among most developed nations to eradicate the disease, such as increased government funding for cancer research and a mandated ‘war on cancer’ in the mid 70’s has translated into remarkable improvements in diagnosis, early detection, and cancer survival rates for many individual cancers. For example, survival rate for breast and colon cancer have improved dramatically over the last 40 years. In the UK, overall median survival times have improved from one year in 1972 to 5.8 years for patients diagnosed in 2007. In the US, the overall 5 year survival improved from 50% for all adult cancers and 62% for childhood cancer in 1972 to 68% and childhood cancer rate improved to 82% in 2007. However, for some cancers, including lung, brain, pancreatic and ovarian cancer, there has been little improvement in survival rates since the “war on cancer” has started.
Many of the improvements in survival rates are a direct result of the massive increase in the knowledge of tumor biology obtained through ardent basic research. Breakthrough discoveries regarding oncogenes, cancer cell signaling, survival, and regulated death mechanisms, tumor immunology, genetics and molecular biology, biomarker research, and now nanotechnology and imaging, have directly led to the advances we now experience in early detection, chemotherapy, personalized medicine, as well as new therapeutic modalities such as cancer vaccines and immunotherapies and combination chemotherapies. Molecular and personalized therapies such as trastuzumab and aromatase inhibitors for breast cancer, imatnib for CML and GIST related tumors, bevacizumab for advanced colorectal cancer have been a direct result of molecular discoveries into the nature of cancer.
This e-book highlights some of the recent trends and discoveries in cancer research and cancer treatment, with particular attention how new technological and informatics advancements have ushered in paradigm shifts in how we think about, diagnose, and treat cancer. The book is organized with the 8 hallmarks of cancer in mind, concepts which are governing principles of cancer from Drs. Hanahan and Weinberg (Hallmarks of Cancer).
- Maintaining Proliferative Signals
- Avoiding Immune Destruction
- Evading Growth Suppressors
- Resisting Cell Death
- Becoming Immortal
- Angiogenesis
- Deregulating Cellular Energy
- Activating Invasion and Metastasis
Therefore, the reader is asked to understand how each of these underlying principles are being translated to current breakthrough discoveries, in association with the basic biological knowledge we have amassed through diligent research and how these principals and latest research can be used by the next generation of cancer scientist and oncologist to provide the future breakthroughs. As the past basic research had provided a new platform for the era of genomics in oncology, it is up to this next generation of scientists and oncologists to provide the basic research for the next platform which will create the future breakthroughs to combat this still deadly disease.
Volume Introduction by Larry H. Bernstein, MD, FACP
The evolution of cancer therapy and cancer research: How we got here?
The evolution of progress we have achieved in cancer research, diagnosis, and therapeutics has originated from an emergence of scientific disciplines and the focus on cancer has been recent. We can imagine this from a historical perspective with respect to two observations. The first is that the oldest concepts of medicine lie with the anatomic dissection of animals and the repeated recurrence of war, pestilence, and plague throughout the middle age, and including the renaissance. In the awakening, architecture, arts, music, math, architecture and science that accompanied the invention of printing blossomed, a unique collaboration of individuals working in disparate disciplines occurred, and those who were privileged received an education, which led to exploration, and with it, colonialism. This also led to the need to increasingly, if not without reprisal, questioning long-held church doctrines. It was in Vienna that Rokitansky developed the discipline of pathology, and his student Semelweis identified an association between then unknown infection and childbirth fever.
The extraordinary accomplishments of John Hunter in anatomy and surgery came during the twelve years war, and his student, Edward Jenner, observed the association between cowpox and smallpox resistance. The development of a nursing profession is associated with the work of Florence Nightengale during the Crimean War (at the same time as Leo Tolstoy). These events preceded the work of Pasteur, Metchnikoff, and Koch in developing a germ theory, although Semelweis had committed suicide by infecting himself with syphilis. The first decade of the Nobel Prize was dominated by discoveries in infectious disease and public health (Ronald Ross, Walter Reed) and we know that the Civil War in America saw an epidemic of Yellow Fever, and the Armed Services Medical Museum was endowed with a large repository of osteomyelitis specimens. We also recall that the Russian physician and play writer, Anton Checkov, wrote about the conditions in prison camps.
But the pharmacopeia was about to open with the discoveries of insulin, antibiotics, vitamins, and thyroid hormones, and Karl Landsteiner’s discovery of red cell antigenic groups (but also pioneered in discoveries in meningitis and poliomyelitis, and conceived of the term hapten) with the introduction of transfusion therapy, that would lead to transplantation medicine. The next phase would be the discovery of cancer, which was highlighted by the identification of leukemia by Rudolph Virchow, who cautioned about the limitations of microscopy. This period is highlighted by the classic work – “Microbe Hunters”.
A multidisciplinary approach has led us to a unique multidisciplinary or systems view of cancer, with different fields of study offering their unique expertise, contributions, and viewpoints on the etiology of cancer. Diverse fields in immunology, biology, biochemistry, toxicology, molecular biology, virology, mathematics, social activism and policy, and engineering have made such important contributions to our understanding of cancer, that without cooperation among these diverse fields our knowledge of cancer would never had evolved as it has. In a series of posts “Heroes in Medical Research:” the work of researchers are highlighted as examples of how disparate scientific disciplines converged to produce seminal discoveries which propelled the cancer field, although, at the time, they seemed like serendipitous findings. In the post Heroes in Medical Research: Barnett Rosenberg and the Discovery of Cisplatin (Translating Basic Research to the Clinic) discusses the seminal yet serendipitous discoveries by bacteriologist Dr. Barnett Rosenberg, which eventually led to the development of cisplatin, a staple of many chemotherapeutic regimens. Molecular biologist Dr. Robert Ting, working with soon-to-be Nobel Laureate virologist Dr. James Gallo on AIDS research and the associated Karposi’s sarcoma identified one of the first retroviral oncogenes, revolutionizing previous held misconceptions of the origins of cancer (described in Heroes in Medical Research: Dr. Robert Ting, Ph.D. and Retrovirus in AIDS and Cancer). The 20th century also saw the development of revolutionary tools for cancer research (highlighted in the post Heroes in Medical Research: Developing Models for Cancer Research), which greatly enhanced both our understanding of the neoplastic process, the genetic factors involved in cancer, and gave us the ability to rapidly develop new cancer chemotherapeutics.
Each of these paths of discovery in cancer research have led to the unique strategies of cancer therapeutics and detection for the purpose of reducing the burden of human cancer. However, we must recall that this work has come at great cost, while it is indeed cause for celebration. The current failure rate of clinical trials at over 70 percent, has been a cause for disappointment, and has led to serious reconsideration of how we can proceed with greater success. The result of the evolution of the cancer field is evident in the many parts and chapters of this ebook. Volume 4 contains chapters that are in a predetermined order:
- The concepts of neoplasm, malignancy, carcinogenesis, and metastatic potential, which encompass:
(a) How cancer cells bathed in an oxygen rich environment rely on anaerobic glycolysis for energy, and the secondary consequences of sarcopenia associated with progression
(b) How advances in genetic analysis, molecular and cellular biology, metabolomics had expanded our basic knowledge of the mechanisms which are involved in cellular transformation to the cancerous state.
(c) How molecular techniques continue to advance our understanding how genetics, epigenetics, and alterations in cellular metabolism contribute to cancer and afford new pathways for therapeutic intervention.
- The distinct features of cancers of specific tissue sites of origin
- The diagnosis of cancer by
(a) Clinical presentation
(b) Age of onset and stage of life
(c) Biomarker features
(d) Radiological and ultrasound imaging
- Treatments
- Prognostic differences within and between cancer types
We have introduced the emergence of a disease of great complexity that has been clouded in more questions than answers until the emergence of molecular biology in the mid 20th century, and then had to await further discoveries going into the 21st century. What gave the research impetus was the revelation of
(1) the mechanism of transcription of the DNA into amino acid sequences
(2) the identification of stresses imposed on cellular function
(3) the elucidation of the substructure of the cell – cell membrane, mitochondria, ribosomes, lysosomes – and their functions, respectively
(4) the elucidation of oligonucleotide sequences
(5) the further elucidation of functionally relevant noncoding ncDNA
(6) the technology to synthesis mRNA and siRNA sequences
(7) the repeated discovery of isoforms of critical enzymes and their pleiotropic properties
(8) the regulatory pathways involved in “signaling”
This is a brief outline of the modern progression of advances in our understanding of cancer. Let us go back to the beginning and check out a sequence of Nobel Prizes awarded and related work that have a historical relationship to what we know. The first discovery was the finding by Louis Pasteur that fungi that grew in an oxygen poor environment did not put down filaments. They did not utilize oxygen and they produced used energy by fermentation. This was the basis for Otto Warburg sixty years later to make the comparison to cancer cells that grew in the presence of oxygen, but relied on anaerobic glycolysis. He used a manometer to measure respiration in tissue one cell layer thick to measure CO2 production in an adiabatic system.
The Nobel Prize in Physiology or Medicine 1922
Archibald V. Hill, Otto Meyerhof
“for his discovery relating to the production of heat in the muscle”
Hill started his research work in 1909. It was due to J.N. Langley, Head of the Department of Physiology at that time that Hill took up the study on the nature of muscular contraction. Langley drew his attention to the important (later to become classic) work carried out by Fletcher and Hopkins on the problem of lactic acid in muscle, particularly in relation to the effect of oxygen upon its removal in recovery.
In 1919 he took up again his study of the physiology of muscle, and came into close contact with Meyerhof of Kiel who, approaching the problem from a different angle, has arrived at results closely analogous to his study. They have cooperated continuously ever since, by personal contact and through correspondence. In 1919 Hill’s friend W. Hartree, mathematician and engineer, joined in the myothermic investigations – a cooperation which had rewarding results.
Otto Meyerhof
Under the influence of Otto Warburg, then at Heidelberg, Meyerhof became more and more interested in cell physiology. . In 1923 he was offered a Professorship of Biochemistry in the United States, but Germany was unwilling to lose him and in 1924 he was asked by the Kaiser Wilhelm Gesellschaft to join the group working at Berlin-Dahlem, which included C. Neuberg, F. Haber, M. Polyani, and H. Freundlich.
In 1929 he was asked to take charge of the newly founded Kaiser Wilhelm Institute for Medical Research at Heidelberg. In 1938 conditions became too difficult for him and he decided to leave Germany. From 1938 to 1940 he was Director of Research at the Institut de Biologie physico-chimique at Paris. In 1940, however, when the Nazis invaded France, he had to move to the United States, where the post of Research Professor of Physiological Chemistry had been created for him by the University of Pennsylvania and the Rockefeller Foundation. Meyerhof’s own account states that he was occupied chiefly with oxidation mechanisms in cells and with extending methods of gas analysis through the calorimetric measurement of heat production, and especially the respiratory processes of nitrifying bacteria.
The physico-chemical analogy between oxygen respiration and alcoholic fermentation caused him to study both these processes in the same subject, namely, yeast extract. By this work he discovered a co-enzyme of respiration, which could be found in all the cells and tissues up till then investigated. At the same time he also found a co-enzyme of alcoholic fermentation. He also discovered the capacity of the SH-group to transfer oxygen; after Hopkins had isolated from cells the SH bodies concerned, Meyerhof showed that the unsaturated fatty acids in the cell are oxidized with the help of the sulphydryl group. After studying closer the respiration of muscle, Meyerhof investigated the energy changes in muscle.
The previous speaker has already told you about the considerable progress achieved by the English scientists Fletcher and Hopkins by their recognition of the fact that lactic acid formation in the muscle is closely connected with the contraction process. These investigations were the first to throw light upon the highly paradoxical fact, already established by the physiologist Hermann, that the muscle can perform a considerable part of its external function in the complete absence of oxygen. As, on the other hand, it was indisputable that in the last resort the energy for muscle activity comes from the oxidation of nutriment, the connection between activity and combustion clearly had to be an indirect one. In fact, Fletcher and Hopkins observed that in the absence of oxygen in the muscle, lactic acid appears, slowly in the relaxed state and rapidly in the active state, and that this lactic acid disappears again in the presence of oxygen. Obviously, then, oxygen is involved not while the muscle is active, but only when it is in the relaxed state
The Nobel Prize in Physiology or Medicine 1937
Albert von Szent-Györgyi Nagyrápolt
“for his discoveries in connection with the biological combustion processes, with special reference to vitamin C and the catalysis of fumaric acid”
The Nobel Prize in Physiology or Medicine 1953
Hans Adolf Krebs
“for his discovery of the citric acid cycle”
In the course of the 1920’s and 1930’s great progress was made in the study of the intermediary reactions by which sugar is anaerobically fermented to lactic acid or to ethanol and carbon dioxide. The success was mainly due to the joint efforts of the schools of Meyerhof, Embden, Parnas, von Euler, Warburg and the Coris, who built on the pioneer work of Harden and of Neuberg. This work brought to light the main intermediary steps of anaerobic fermentations. In contrast, very little was known in the earlier 1930’s about the intermediary stages through which sugar is oxidized in living cells. When, in 1930, I left the laboratory of Otto Warburg (under whose guidance I had worked since 1926 and from whom I have learnt more than from any other single teacher), I was confronted with the question of selecting a major field of study and I felt greatly attracted by the problem of the intermediary pathway of oxidations. These reactions represent the main energy source in higher organisms, and in view of the importance of energy production to living organisms (whose activities all depend on a continuous supply of energy) the problem seemed well worthwhile studying.
The Nobel Prize in Physiology or Medicine 1953
Fritz Albert Lipmann
“for his discovery of co-enzyme A and its importance for intermediary metabolism”.
In my development, the recognition of facts and the rationalization of these facts into a unified picture, have interplayed continuously. After my apprenticeship with Otto Meyerhof, a first interest on my own became the phenomenon we call the Pasteur effect, this peculiar depression of the wasteful fermentation in the respiring cell. By looking for a chemical explanation of this economy measure on the cellular level, I was prompted into a study of the mechanism of pyruvic acid oxidation, since it is at the pyruvic stage where respiration branches off from fermentation. For this study I chose as a promising system a relatively simple looking pyruvic acid oxidation enzyme in a certain strain of Lactobacillus delbrueckii1.
The most important event during this whole period, I now feel, was the accidental observation that in the L. delbrueckii system, pyruvic acid oxidation was completely dependent on the presence of inorganic phosphate. This observation was made in the course of attempts to replace oxygen by methylene blue. To measure the methylene blue reduction manometrically, I had to switch to a bicarbonate buffer instead of the otherwise routinely used phosphate. In bicarbonate, pyruvate oxidation was very slow, but the addition of a little phosphate caused a remarkable increase in rate. The phosphate effect was removed by washing with a phosphate free acetate buffer. Then it appeared that the reaction was really fully dependent on phosphate.
A coupling of this pyruvate oxidation with adenylic acid phosphorylation was attempted. Addition of adenylic acid to the pyruvic oxidation system brought out a net disappearance of inorganic phosphate, accounted for as adenosine triphosphate.
Toward the end of the war, while still in the army, I discovered in an American army bookmobile several miscellaneous issues of Genetics, one containing the beautiful paper in which Luria and demonstrated for the first time rigorously, the spontaneous nature of certain bacterial mutants. I think I have never read a scientific article with such enthusiasm; for me, bacterial genetics was established. Several months later, I also “discovered” the paper by Avery, MacLeod, and McCarty6 – another fundamental revelation. In 1946 I attended the memorable symposium at Cold Spring Harbor where Delbrück and Bailey, and Hershey, revealed their discovery of virus recombination at the same time that Lederberg and Tatum announced their discovery of bacterial sexuality7. In 1947 I was invited to the Growth Symposium to present a report1 on enzyme adaptation. It became clear to me that this remarkable phenomenon was almost entirely shrouded in mystery. On the other hand, by its regularity, its specificity, and by the molecular-level interaction it exhibited between a genetic determinant and a chemical determinant, it seemed of such interest and of a significance so profound that there was no longer any question as to whether I should pursue its study.
In order to understand how this problem was considered in 1946, it would be well to remember that at that time the structure of DNA was not known, little was known about the structure of proteins, and nothing was known of their biosynthesis. It was necessary to resolve the following question: Does the inducer effect total synthesis of a new protein molecule from its precursors, or is it rather a matter of the activation, conversion, or “remodeling” of one or more precursors?
Hugo Theorell
For his work on the nature and effects of oxidation enzymes
From 1933 until 1935 Theorell held a Rockefeller Fellowship and worked with Otto Warburg at Berlin-Dahlem, and here he became interested in oxidation enzymes. At Berlin-Dahlem he produced, for the first time, the oxidation enzyme called «the yellow ferment» and he succeeded in splitting it reversibly into a coenzyme part, which was found to be flavin mononucleotide, and a colourless protein part. On return to Sweden, he was appointed Head of the newly established Biochemical Department of the Nobel Medical Institute, which was opened in 1937.
Nobel Prize in Physiology or Medicine 1962
Watson & Crick
for double helix model, a landmark in this journey
The Nobel Prize in Physiology or Medicine 1965
François Jacob, André Lwoff and Jacques Monod
“for their discoveries concerning genetic control of enzyme and virus synthesis”.
In 1958 the remarkable analogy revealed by genetic analysis of lysogeny and that of the induced biosynthesis of ß-galactosidase led François Jacob, with Jacques Monod, to study the mechanisms responsible for the transfer of genetic information as well as the regulatory pathways which, in the bacterial cell, adjust the activity and synthesis of macromolecules. Following this analysis, Jacob and Monod proposed a series of new concepts, those of messenger RNA, regulator genes, operons and allosteric proteins.
Francois Jacob
Having determined the constants of growth in the presence of different carbohydrates, it occurred to
me that it would be interesting to determine the same constants in paired mixtures of carbohydrates. From the first experiment on, I noticed that, whereas the growth was kinetically normal in the presence of certain mixtures (that is, it exhibited a single exponential phase), two complete growth cycles could be observed in other carbohydrate mixtures, these cycles consisting of two exponential phases separated by a-complete cessation of growth.
Lwoff, after considering this strange result for a moment, said to me, “That could have something to do with enzyme adaptation.”
“Enzyme adaptation? Never heard of it!” I said.
Lwoff’s only reply was to give me a copy of the then recent work of Marjorie Stephenson, in which a chapter summarized with great insight the still few studies concerning this phenomenon, which had been discovered by Duclaux at the end of the last century. Studied by Dienert and by Went as early as 1901 and then by Euler and Josephson, it was more or less rediscovered by Karström, who should be credited with giving it a name and attracting attention to its existence.
Lwoff’s intuition was correct. The phenomenon of “diauxy” that I had discovered was indeed closely related to enzyme adaptation, as my experiments, included in the second part of my doctoral dissertation, soon convinced me. It was actually a case of the “glucose effect” discovered by Dienert as early as 1900.
That agents that uncouple oxidative phosphorylation, such as 2,4-dinitrophenol, completely inhibit adaptation to lactose or other carbohydrates suggested that “adaptation” implied an expenditure of chemical potential and therefore probably involved the true synthesis of an enzyme. With Alice Audureau, I sought to discover the still quite obscure relations between this phenomenon and the one Massini, Lewis, and others had discovered: the appearance and selection of “spontaneous” mutants.
We showed that an apparently spontaneous mutation was allowing these originally “lactose-negative” bacteria to become “lactose-positive”. However, we proved that the original strain (Lac-) and the mutant strain (Lac+) did not differ from each other by the presence of a specific enzyme system, but rather by the ability to produce this system in the presence of lactose. This mutation involved the selective control of an enzyme by a gene, and the conditions ecessaryy for its expression seemed directly linked to the chemical activity of the system.
I had an opportunity to visit Morgan’s laboratory at the California Institute of Technology. This was a revelation for me – a revelation of genetics, at that time practically unknown in France; a revelation of what a group of scientists could be like when engaged in creative activity and sharing in a constant exchange of ideas, bold speculations, and strong criticisms. It was a revelation of personalities of great stature, such as George Beadle and others. Upon my return to France, I had again taken up the study of bacterial growth. But my mind remained full of the concepts of genetics and I was confident of its ability to analyze and convinced that one day these ideas would be applied to bacteria.
The Nobel Prize in Physiology or Medicine 1968
Robert W. Holley, Har Gobind Khorana and Marshall W. Nirenberg
“for their interpretation of the genetic code and its function in protein synthesis.”
The Nobel Prize in Physiology or Medicine 1969
Max Delbrück, Alfred D. Hershey and Salvador E. Luria
“for their discoveries concerning the replication mechanism and the genetic structure of viruses.”
The Nobel Prize in Physiology or Medicine 1974
Albert Claude, Christian de Duve and George E. Palade
“for their discoveries concerning the structural and functional organization of the cell”.
In 1946-1947, I had the good fortune of spending 18 months at the Medical Nobel Institute in Stockholm, in the laboratory of Hugo Theorell, who was awarded the Nobel Prize in 1955. I then spent 6 months as a Rockefeller Foundation fellow at Washington University, under Carl and Gerty Cori who jointly received the Nobel Prize while I was there. In St. Louis, I collaborated with Earl Sutherland, Nobel laureate in 1971. Indeed, I have been very fortunate in the choice of my mentors, all sticklers for technical excellence and intellectual rigour, those prerequisites of good scientific work.
I returned to Louvain in March 1947 to take over the teaching of physiological chemistry at the medical faculty, becoming full professor in 1951. Insulin, together with glucagon which I had helped rediscover, was still my main focus of interest, and our first investigations were accordingly directed on certain enzymatic aspects of carbohydrate metabolism in liver, which were expected to throw light on the broader problem of insulin action. But fate had a surprise in store for me, in the form of a chance observation, the so-called “latency” of acid phosphatase. It was essentially irrelevant to the object of our research, but I from then on pursued this accidental finding, drawing most of my collaborators along with me. The studies led to the discovery of the lysosome, and later of the peroxisome.
In 1962, I was appointed a professor at the Rockefeller Institute in New York, now the Rockefeller University, the institution where Albert Claude had made his pioneering studies between 1929 and 1949, and where George Palade had been working since 1946. In New York, I was able to develop a second flourishing group, which follows the same general lines of research as the Belgian group, but with a program of its own.
I created a new institute with a number of colleagues, the International Institute of Cellular and Molecular Pathology, or ICP, located on the new site of the Louvain Medical School in Brussels. The aim of the ICP is to accelerate the translation of basic knowledge in cellular and molecular biology into useful practical applications.
The Nobel Prize in Physiology or Medicine 1975
David Baltimore, Renato Dulbecco and Howard Martin Temin
“for their discoveries concerning the interaction between tumour viruses and the genetic material of the cell”.
The Nobel Prize in Physiology or Medicine 1976
Baruch S. Blumberg and D. Carleton Gajdusek
“for their discoveries concerning new mechanisms for the origin and dissemination of infectious diseases”
The editors of the Nobelprize.org website of the Nobel Foundation have asked me to provide a supplement to the autobiography that I wrote in 1976 and to recount the events that happened after the award. Much of what I will have to say relates to the scientific developments since the last essay. These are described in greater detail in a scientific memoir first published in 2002 (Blumberg, B. S., Hepatitis B. The Hunt for a Killer Virus, Princeton University Press, 2002, 2004).
The Nobel Prize in Physiology or Medicine 1980
Baruj Benacerraf, Jean Dausset and George D. Snell
“for their discoveries concerning genetically determined structures on the cell surface that regulate immunological reactions”.
The Nobel Prize in Physiology or Medicine 1992
Edmond H. Fischer and Edwin G. Krebs
“for their discoveries concerning reversible protein phosphorylation as a biological regulatory mechanism”
The Nobel Prize in Physiology or Medicine 1994
Alfred G. Gilman and Martin Rodbell
“for their discovery of G-proteins and the role of these proteins in signal transduction in cells”
The Nobel Prize in Physiology or Medicine 2011
Bruce A. Beutler and Jules A. Hoffmann
“for their discoveries concerning the activation of innate immunity”
and the other half to
Ralph M. Steinman
“for his discovery of the dendritic cell and its role in adaptive immunity”.
Contemporary Scientists
Renato L. Baserga, M.D.
Kimmel Cancer Center and Keck School of Medicine
Dr. Baserga’s research focuses on the multiple roles of the type 1 insulin-like growth factor receptor (IGF-IR) in the proliferation of mammalian cells. The IGF-IR activated by its ligands is mitogenic, is required for the establishment and the maintenance of the transformed phenotype, and protects tumor cells from apoptosis. It, therefore, serves as an excellent target for therapeutic interventions aimed at inhibiting abnormal growth.
In basic investigations, this group is presently studying the effects that the number of IGF-IRs and specific mutations in the receptor itself have on its ability to protect cells from apoptosis. This investigation is strictly correlated with IGF-IR signaling, and part of this work tries to elucidate the pathways originating from the IGF-IR that are important for its functional effects. Baserga’s group has recently discovered a new signaling pathway used by the IGF-IR to protect cells from apoptosis, a unique pathway that is not used by other growth factor receptors. This pathway depends on the integrity of serines 1280-1283 in the C-terminus of the receptor, which bind 14.3.3 and cause the mitochondrial translocation of Raf-1. Another recent discovery of this group has been the identification of a mechanism by which the IGF-IR can actually induce differentiation in certain types of cells. When cells have IRS-1 (a major substrate of the IGF-IR), the IGF-IR sends a proliferative signal; in the absence of IRS-1, the receptor induces cell differentiation. The extinction of IRS-1 expression is usually achieved by DNA methylation.
Janardan Reddy, MD
Northwestern University
The central effort of our research has been on a detailed analysis at the cellular and molecular levels of the pleiotropic responses in liver induced by structurally diverse classes of chemicals that include fibrate class of hypolipidemic drugs, and phthalate ester plasticizers, which we designated hepatic peroxisome proliferators. Our work has been central to the establishment of several principles, namely that hepatic peroxisome proliferation is associated with increases in fatty acid oxidation systems in liver, and that peroxisome proliferators, as a class, are novel nongenotoxic hepatocarcinogens. We introduced the concept that sustained generation of reactive oxygen species leads to oxidative stress and serves as the basis for peroxisome proliferator-induced liver cancer development. Furthermore, based on the tissue/cell specificity of pleiotropic responses and the coordinated transcriptional regulation of fatty acid oxidation system genes, we postulated that peroxisome proliferators exert their action by a receptor-mediated mechanism.
- This receptor concept laid the foundation for the discovery of a three-member peroxisome proliferator-activated receptor (PPARalpha-, ß-, and gamma) subfamily of nuclear receptors. Of these, PPARalpha is responsible for peroxisome proliferator-induced pleiotropic responses, including hepatocarcinogenesis and energy combustion as it serves as a fatty acid sensor and regulates fatty acid oxidation. Our current work focuses on the molecular mechanisms responsible for PPAR action and generation of fatty acid oxidation deficient mouse knockout models. Transcription of specific genes by nuclear receptors is a complex process involving the participation of multiprotein complexes composed of transcription coactivators.
Jose Delgado de Salles Roselino, Ph.D.
Leloir Institute, Brazil
Warburg effect, in reality “Pasteur-effect” was the first example of metabolic regulation described. A decrease in the carbon flux originated at the sugar molecule towards the end metabolic products, ethanol and carbon dioxide that was observed when yeast cells were transferred from anaerobic environmental condition to an aerobic one. In Pasteur´s works, sugar metabolism was measured mainly by the decrease of sugar concentration in the yeast growth media observed after a measured period of time. The decrease of the sugar concentration in the media occurs at great speed in yeast grown in anaerobiosis condition and its speed was greatly reduced by the transfer of the yeast culture to an aerobic condition. This finding was very important for the wine industry of France in Pasteur time, since most of the undesirable outcomes in the industrial use of yeast were perceived when yeasts cells took very long time to create a rather selective anaerobic condition. This selective culture media was led by the carbon dioxide higher levels produced by fast growing yeast cells and by a great alcohol content in the yeast culture media.
This finding was required to understand Lavoisier’s results indicating that chemical and biological oxidation of sugars produced the same calorimetric results. This observation requires a control mechanism (metabolic regulation) to avoid burning living cells by fast heat released by the sugar biological oxidative processes (metabolism). In addition, Lavoisier´s results were the first indications that both processes happened inside similar thermodynamics limits. In much resumed form, these observations indicate the major reasons that led Warburg to test failure in control mechanisms in cancer cells in comparison with the ones observed in normal cells.
Biology inside classical thermo dynamics poses some challenges to scientists. For instance, all classical thermodynamics must be measured in reversible thermodynamic conditions. In an isolated system, increase in P (pressure) leads to decrease in V (volume) all this in a condition in which infinitesimal changes in one affect in the same way the other, a continuum response. Not even a quantic amount of energy will stand beyond those parameters. In a reversible system, a decrease in V, under same condition, will led to an increase in P. In biochemistry, reversible usually indicates a reaction that easily goes from A to B or B to A.
This observation confirms the important contribution of E Schrodinger in his What´s Life: “This little book arose from a course of public lectures, delivered by a theoretical physicist to an audience of about four hundred which did not substantially dwindle, though warned at the outset that the subject-matter was a difficult one and that the lectures could not be termed popular, even though the physicist’s most dreaded weapon, mathematical deduction, would hardly be utilized. The reason for this was not that the subject was simple enough to be explained without mathematics, but rather that it was much too involved to be fully accessible to mathematics.”
Hans Krebs describes the cyclic nature of the citrate metabolism. Two major research lines search to understand the mechanism of energy transfer that explains how ADP is converted into ATP. One followed the organic chemistry line of reasoning and therefore, searched how the breakdown of carbon-carbon link could have its energy transferred to ATP synthesis. A major leader of this research line was B. Chance who tried to account for two carbon atoms of acetyl released as carbon dioxide in the series of Krebs cycle reactions. The intermediary could store in a phosphorylated amino acid the energy of carbon-carbon bond breakdown. This activated amino acid could transfer its phosphate group to ADP producing ATP. Alternatively, under the possible influence of the excellent results of Hodgkin and Huxley a second line of research appears. The work of Hodgkin & Huxley indicated the storage of electrical potential energy in transmembrane ionic asymmetries and presented the explanation for the change from resting to action potential in excitable cells. This second line of research, under the leadership of P Mitchell postulated a mechanism for the transfer of oxide/reductive power of organic molecules oxidation through electron transfer as the key for energetic transfer mechanism required for ATP synthesis.
Paul Boyer could present how the energy was transduced by a molecular machine that changes in conformation in a series of 3 steps while rotating in one direction in order to produce ATP and in opposite direction in order to produce ADP plus Pi from ATP (reversibility). Nonetheless, a victorious Peter Mitchell obtained the correct result in the conceptual dispute, over the B. Chance point of view, after he used E. Coli mutants to show H gradients in membrane and its use as energy source. However, this should not detract from the important work of Chance.
B. Chance got the simple and rapid polarographic assay method of oxidative phosphorylation and the idea of control of energy metabolism that bring us back to Pasteur. This second result seems to have being neglected in the years of obesity epidemics when we search for a single molecular mechanism required for the understanding of the buildup of chemical reserve in our body. It does not mean that here the role of central nervous system is neglected. In short, in respiring mitochondria the rate of electron transport, and thus the rate of ATP production, is determined primarily by the relative concentrations of ADP, ATP and phosphate in the external media (cytosol) and not by the concentration of respiratory substrate as pyruvate. Therefore, when the yield of ATP is high as is in aerobiosis and the cellular use of ATP is not changed, the oxidation of pyruvate and therefore of glycolysis is quickly (without change in gene expression), throttled down to the resting state. The dependence of respiratory rate on ADP concentration is also seen in intact cells. A muscle at rest and using no ATP has very low respiratory rate.
Chapter 11 Imaging In Cancer
11.1 Introduction by Dror Nir, PhD
The concept of personalized medicine has been around for many years. Recent advances in cancer treatment choice, availability of treatment modalities, including “adaptable” drugs and the fact that patients’ awareness increases, put medical practitioners under pressure to better clinical assessment of this disease prior to treatment decision and quantitative reporting of treatment outcome. In practice, this translates into growing demand for accurate, noninvasive, nonuser-dependent probes for cancer detection and localization. The advent of medical-imaging technologies such as image-fusion, functional-imaging and noninvasive tissue characterization is playing an imperative role in answering this demand thus transforming the concept of personalized medicine in cancer into practice. The leading modality in that respect is medical imaging. To date, the main imaging systems that can provide reasonable level of cancer detection and localization are: CT, mammography, Multi-Sequence MRI, PET/CT and ultrasound. All of these require skilled operators and experienced imaging interpreters in order to deliver what is required at a reasonable level. It is generally agreed by radiologists and oncologists that in order to provide a comprehensive work-flow that complies with the principles of personalized medicine, future cancer patients’ management will heavily rely on computerized image interpretation applications that will extract from images in a standardized manner measurable imaging biomarkers leading to better clinical assessment of cancer patients.
Read more: The Incentive for Imaging based cancer patient’ management and Imaging-biomarkers is Imaging-based tissue characterization
Dror Nir, PhD
Summary by Dror Nir, PhD
Establishing personalized medicine is expected to reduce over-diagnosis and treatment of cancer. This is a major unmet need in health-care systems worldwide. We have reasons to believe that investing in the development of innovative imaging technologies that will generate imaging-biomarkers characteristics of cancer will significantly improve cancer management and will generate good return on investment for all stakeholders.
Chapter 12. Nanotechnology Imparts New Advances in Cancer Treatment, Detection, and Imaging
By Tilda Barliya, PhD
Introduction
Nanotechnology is a multidisciplinary field of science that involves engineering, chemistry, physics and biology in the design, synthesis, characterization, and application of materials and devices whose smallest functional organization in at least one dimension is on the nanometer scale or one billionth of a meter. Applications to medicine and physiology imply materials and devices designed to interact with the body at sub-cellular molecular scales with a high degree of specificity which can potentially be translated into diagnosis, targeted drug designed to achieve maximal therapeutic affects with minimal side effects, imaging and medical devices. In this chapter, we will introduce and discuss some of the nanotechnology’s clinical applications.
Volume Epilogue by Larry H. Bernstein, MD, FACP
Epilogue: Envisioning New Insights in Cancer Translational Biology
Larry H. Berstein, MD, FACP
Envisioning New Insights in Cancer Translational Biology
The foregoing summary leads to a beginning as it is a conclusion. It concludes a body of work in the e-book Series C:
Cancer Biology and Genomics for Disease Diagnosis
Perspectives in Cancer Research and Therapeutic Breakthroughs, 2013,
Volume One
http://www.amazon.com/dp/B013RVYR2K
that has been presented by the cancer team of professional experts in various aspects of cancer research in the emerging fields of targeted pharmacology, nanotechnology, cancer imaging, molecular pathology, transcriptional and regulatory ‘OMICS’, metabolism, medical and allied health related sciences, synthetic biology, pharmaceutical discovery, and translational medicine.
This volume and its content have been conceived and organized to capture the organized events that emerge in embryological development, leading to the major organ systems that we recognize anatomically and physiologically as an integrated being. We capture the dynamic interactions between the systems under stress that are elicited by cytokine-driven hormonal responses, long thought to be circulatory and multisystem, that affect the major compartments of fat and lean body mass, and are as much the drivers of metabolic pathway changes that emerge as epigenetics, without disregarding primary genetic diseases.
The greatest difficulty in organizing such a work is in whether it is to be merely a compilation of cancer expression organized by organ systems, or whether it is to capture developing concepts of underlying stem cell expressed changes that were once referred to as “dedifferentiation.” In proceeding through the stages of neoplastic transformation, there occur adaptive local changes in cellular utilization of anabolic and catabolic pathways, and a retention or partial retention of functional specificities.
This effectively results in the same cancer types not all fitting into the same “shoe”. There is a sequential loss of identity associated with cell migration, cell-cell interactions with underlying stroma, and metastasis, but cells may still retain identifying “signatures” in microRNA combinatorial patterns. The story is still incomplete, with gaps in our knowledge that challenge the imagination.
What we have laid out is a map with substructural ordered concepts forming subsets within the structural maps. There are the traditional energy pathways with terms aerobic and anaerobic glycolysis, gluconeogenesis, triose phosphate branch chains, pentose shunt, and TCA cycle vs the Lynen cycle, the Cori cycle, glycogenolysis, lipid peroxidation, oxidative stress, autosomy and mitosomy, and genetic transcription, cell degradation and repair, muscle contraction, nerve transmission, and their involved anatomic structures (cytoskeleton, cytoplasm, mitochondria, liposomes and phagosomes, contractile apparatus, synapse.
Then there is beneath this macro-domain the order of signaling pathways that regulate these domains and through mechanisms of cellular regulatory control have pleiotropic inhibitory or activation effects, that are driven by extracellular and intracellular energy modulating conditions through three recognized structures: the mitochondrial inner membrane, the intercellular matrix, and the ion-channels.
What remains to be done?
- There is still to be elucidated the differences in patterns within cancer types the distinct phenotypic and genotypic features that mitigate anaplastic behavior. One leg of this problem lies in the density of mitochondria, that varies between organ types, but might vary also within cell type of a common function. Another leg of this problem has also appeared to lie in the cell death mechanism that relates to the proeosomal activity acting on both the ribosome and mitochondrion in a coordinated manner. This is an unsolved mystery of molecular biology.
- Then there is a need to elucidate the major differences between tumors of endocrine, sexual, and structural organs, which are distinguished by primarily a synthetic or primarily a catabolic function, and organs that are neither primarily one or the other. For example, tumors of the thyroid and parathyroids, islet cells of pancreas, adrenal cortex, and pituitary glands have the longest 5-year survivals. They and the sexual organs are in the visceral compartment. The rest of the visceral compartment would be the liver, pancreas, salivary glands, gastrointestinal tract, and lungs (which are embryologically an outpouching of the gastrointestinal tract), kidneys and lower urinary tract. Cancers of these organs have a much less favorable survival (brain, breast and prostate, lymphatic, blood forming organ, skin). The case is intermediate for breast and prostate between the endocrine organs and GI tract, based on natural history, irrespective of the available treatments. Just consider the dilemma over what we do about screening for prostate cancer in men over the age of 60 years age who have a 70 percent incident silent carcinoma of the prostate that could be associated with unrelated cause of death. The very rapid turnover of the gastric and colonic GI epithelium, and of the subepithelial B cell mucosal lymphocytic structures is associated with a greater aggressiveness of the tumor.
- However, we have to reconsider the observation by NO Kaplan than the synthetic and catabolic functions are highlighted by differences in the expressions of the balance of the two major pyridine nucleotides – DPN (NAD) and TPN (NADP) – which also might be related to the density of mitochondria which is associated with both NADP and synthetic activity, and with efficient aerobic function. These are in equilibrium through the “transhydrogenase reaction” co-discovered by Kaplan, in Fritz Lipmann’s laboratory. There does arise a conundrum involving the regulation of mitochondria in these high turnover epithelial tissues that rely on aerobic energy, and generate ATP through TPN linked activity, when they undergo carcinogenesis. The cells replicate and they become utilizers of glycolysis, while at the same time, the cell death pathway is quiescent. The result becomes the introduction of peripheral muscle and liver synthesized protein cannabolization (cancer cachexia) to provide glucose from proteolytic amino acid sources.
- There is also the structural compartment of the lean body mass. This is the heart, skeletal structures (includes smooth muscle of GI tract, uterus, urinary bladder, brain, bone, bone marrow). The contractile component is associated with sarcomas. What is most striking is that the heart, skeletal muscle, and inflammatory cells are highly catabolic, not anabolic. NO Kaplan referred tp them as DPN (NAD) tissues. This compartment requires high oxygen supply, and has a high mechanical function. But again, we return to the original observations of energy requirements at rest being different than at high demand. At work, skeletal muscle generates lactic acid, but the heart can use lactic acid as fuel,.
- The liver is supplied by both the portal vein and the hepatic artery, so it is not prone to local ischemic injury (Zahn infarct). It is exceptional in that it carries out synthesis of all the circulating transport proteins, has a major function in lipid synthesis and in glycogenesis and glycogenolysis, with the added role of drug detoxification through the P450 system. It is not only the largest organ (except for brain), but is highly active both anabolically and catabolically (by ubiquitilation).
- The expected cellular turnover rates for these tissues and their balance of catabolic and anabolic function would have to be taken into account to account for the occurrence and the activities of oncogenesis. This is by no means a static picture, but a dynamic organism constantly in flux imposed by internal and external challenges. It is also important to note the organs have a concentration of mitochondria, associated with energy synthetic and catabolic requirements provided by oxygen supply and the electron transport mechanism for oxidative phosphorylation. For example, tissues that are primarily synthetic do not have intermittent states of resting and high demand, as seen in skeletal muscle, or perhaps myocardium (which is syncytial and uses lactic acid generated from skeletal muscle when there is high demand).
- The existence of lncDNA has been discovered only as a result of the human genome project (HGP). This was previously known only as “dark DNA”. It has become clear that lncDNA has an important role in cellular regulatory activities centered in the chromatin modeling. Moreover, just as proteins exhibit functionality in their folding, related to tertiary structure and highly influenced by location of –S-S- bridges and amino acid residue distances (allosteric effects), there is a less studied effect as the chromatin becomes more compressed within the nucleus,that should have a bearing on cellular expression.
According to Jose Eduardo de Salles Roselino , when the Na/Glucose transport system (for a review Silvermann, M. in Annu. Rev. Biochem.60: 757-794(1991)) was found in kidneys as well as in key absorptive cells of digestive tract, it should be stressed its functional relationship with “internal milieu” and real meaning, homeostasis. It is easy to understand how the major topic was presented as how to prevent diarrheal deaths in infants, while detected in early stages. However, from a biochemical point of view, as presented in Schrödinger´s What is life? (biochemistry offering a molecular view for two legs of biology, physiology and genetics). Why should it be driven to the sole target of understanding genetics? Why the understanding of physiology in molecular terms should be so neglected?
From a biochemical point of view, there is a single protein, which is found to transport the cation most directly related to water maintenance, the internal solvent that bath our cells and the hydrocarbon whose concentration is kept under homeostatic control on that solvent. Completely at variance with what is presented in microorganisms as previously mentioned in Moyed and Umbarger revision (Ann. Rev42: 444(1962)) that does not regulates the environment where they live and appears to influence it only as an incidental result of their metabolism.
In case any attempt is made in order to explain why the best leg that supports scientific reasoning from biology for medical purposes was led to atrophy, several possibilities can be raised. However, none of them could be placed strictly in scientific terms. Factors that bare little relationship with scientific progress in general terms must also be taken into account.
One simple possibility of explanation can be found in one review (G. Scatchard – Solutions of Electrolytes Ann. Rev. Physical Chemistry 14: 161-176 (1963)). A simple reading of it and the sophisticated differences among researchers will discourage one hundred per cent of biologists to keep in touch with this line of research. Biochemists may keep on reading. However, consider that first: Complexity is not amenable to reductionist vision in all cases. Second, as coupling between scalar flows such as chemical reactions and vector flows such as diffusion flows, heat flows, and electrical current can occur only in anisotropic system…let them with their problems of solvents, ions and etc. and let our biochemical reactions on another basket. At the interface, for instance, at membrane level, we will agree that ATP is converted to ADP because it is far from equilibrium and the continuous replenishment of ATP that maintain relatively constant ATP levels inside the cell and this requires some non-stationary flow.
Our major point must be to understand that our biological limits are far clearer present in our limited ability to regulate the information stored in the DNA than in the amount of information we have in the DNA as the master regulator of the cells.
The amazing revelation that Masahiro Chiga (discovery of liver adenylate kinase distinct from that of muscle) taught me (LHB) is – draw 2 circles that intersect, one of which represents what we know, the other – what we don’t know. We don’t teach how much we don’t know! Even today, as much as 40 years ago, there is a lot we need to get on top of this.
The observation is rather similar to the presentations I (Jose Eduardo de Salles Rosalino) was previously allowed to make of the conformational energy as made by R Marcus in his Nobel lecture revised (J. of Electroanalytical Chemistry 438:(1997) p251-259. His description of the energetic coordinates of a landscape of a chemical reaction is only a two-dimensional cut of what in fact is a volcano crater (in three dimensions) (each one varie but the sum of the two is constant. Solvational+vibrational=100% in ordinate) nuclear coordinates in abcissa. In case we could represent it by research methods that allow us to discriminate in one-by-one degree of different pairs of energy, we would most likely have 360 other similar representations of the same phenomenon. The real representation would take into account all those 360 representation together. In case our methodology was not that fine, for instance it discriminates only differences of minimal 10 degrees in 360 possible, will have 36 partial representations of something that to be perfectly represented will require all 36 being taken together. Can you reconcile it with ATGC? Yet, when complete genome sequences were presented they were described as we will know everything about this living being. The most important problems in biology will be viewed by limited vision always and the awareness of this limited is something we should acknowledge and teach it. Therefore, our knowledge is made up of partial representations.
Even though we may have complete genome data for the most intricate biological problems, they are not so amenable to this level of reductionism. However, from general views of signals and symptoms we could get to the most detailed molecular view and in this case the genome provides an anchor. This is somehow, what Houssay was saying to me and to Leloir when he pointed out that only in very rare occasions biological phenomena could be described in three terms: Pacco, the dog and the anesthetic (previous e-mail). The non-coding region, to me will be important guiding places for protein interactions.
Cancer Team Members @ Leaders of Pharmaceutical Business Intelligence Express Their Views on the Frontier of Cancer Research in Their OWN Domain of Expertise
Current Advanced Research Topics in MRI-based Management of Cancer Patients
Author: Dror Nir, PhD
Step forward towards quantitative and reproducible MRI of cancer patients is the combination of structure and morphology based imaging with expressions of typical bio-chemical processes using imaging contrast materials. The following list brings the latest publications on this subject in Radiology magazine.
The Effects of Applying Breast Compression in Dynamic Contrast Material–enhanced MR Imaging
Abstract
Purpose: To evaluate the effects of breast compression on breast cancer masses, contrast material enhancement of glandular tissue, and quality of magnetic resonance (MR) images in the identification and characterization of breast lesions.
Materials and Methods: This was a HIPAA-compliant, institutional review board–approved retrospective study, with waiver of informed consent. Images from 300 MR imaging examinations in 149 women (mean age ± standard deviation, 51.5 years ± 10.9; age range, 22–76 years) were evaluated. The women underwent diagnostic MR imaging (no compression) and MR-guided biopsy (with compression) between June 2008 and February 2013. Breast compression was expressed as a percentage relative to the non-compressed breast. Percentage enhancement difference was calculated between non-compressed- and compressed-breast images obtained in early and delayed contrast-enhanced phases. Breast density, lesion type (mass vs non-mass-like enhancement [NMLE]), lesion size, percentage compression, and kinetic curve type were evaluated. Linear regression, receiver operating characteristic (ROC) curve analysis, and κ test were performed.
Conclusion: Breast compression during biopsy affected breast lesion detection, lesion size, and dynamic contrast-enhanced MR imaging interpretation and performance. Limiting the application of breast compression is recommended, except when clinically necessary.
Localized Prostate Cancer Detection with 18F FACBC PET/CT: Comparison with MR Imaging and Histopathologic Analysis
Abstract
Purpose: To characterize uptake of 1-amino-3-fluorine 18-fluorocyclobutane-1-carboxylic acid (18F FACBC) in patients with localized prostate cancer, benign prostatic hyperplasia (BPH), and normal prostate tissue and to evaluate its potential utility in delineation of intraprostatic cancers in histo-pathologically confirmed localized prostate cancer in comparison with magnetic resonance (MR) imaging.
Materials and Methods: Institutional review board approval and written informed consent were obtained for this HIPAA-compliant prospective study. Twenty-one men underwent dynamic and static abdominopelvic 18F FACBC combined positron emission tomography (PET) and computed tomography (CT) and multiparametric (MP) 3-T endorectal MR imaging before robotic-assisted prostatectomy. PET/CT and MR images were co-registered by using pelvic bones as fiducial markers; this was followed by manual adjustments. Whole-mount histopathologic specimens were sliced with an MR-based patient-specific mold. 18F FACBC PET standardized uptake values (SUVs) were compared with those at MR imaging and histopathologic analysis for lesion- and sector-based (20 sectors per patient) analysis. Positive and negative predictive values for each modality were estimated by using generalized estimating equations with logit link function and working independence correlation structure.
Conclusion: 18F FACBC PET/CT shows higher uptake in intraprostatic tumor foci than in normal prostate tissue; however, 18F FACBC uptake in tumors is similar to that in BPH nodules. Thus, it is not specific for prostate cancer. Nevertheless, combined 18F FACBC PET/CT and T2-weighted MR imaging enable more accurate localization of prostate cancer lesions than either modality alone.
Illuminating Radio-genomic Characteristics of Glioblastoma Multiforme through Integration of MR Imaging, Messenger RNA Expression, and DNA Copy Number Variation
Abstract
Purpose: To perform a multilevel radio-genomics study to elucidate the glioblastoma multiforme (GBM) magnetic resonance (MR) imaging radio-genomic signatures resulting from changes in messenger RNA (mRNA) expression and DNA copy number variation (CNV).
Materials and Methods: Radiogenomic analysis was performed at MR imaging in 23 patients with GBM in this retrospective institutional review board–approved HIPAA-compliant study. Six MR imaging features—contrast enhancement, necrosis, contrast-to-necrosis ratio, infiltrative versus edematous T2 abnormality, mass effect, and subventricular zone (SVZ) involvement—were independently evaluated and correlated with matched genomic profiles (global mRNA expression and DNA copy number profiles) in a significant manner that also accounted for multiple hypothesis testing by using gene set enrichment analysis (GSEA), resampling statistics, and analysis of variance to gain further insight into the radiogenomic signatures in patients with GBM
Conclusion: Construction of an MR imaging, mRNA, and CNV radio-genomic association map has led to identification of MR traits that are associated with some known high-grade glioma biomarkers and association with genomic biomarkers that have been identified for other malignancies but not GBM. Thus, the traits and genes identified on this map highlight new candidate radio-genomic biomarkers for further evaluation in future studies.
PET/MR Imaging: Technical Aspects and Potential Clinical Applications
Abstract
Instruments that combine positron emission tomography (PET) and magnetic resonance (MR) imaging have recently been assembled for use in humans, and may have diagnostic performance superior to that of PET/computed tomography (CT) for particular clinical and research applications. MR imaging has major strengths compared with CT, including superior soft-tissue contrast resolution, multiplanar image acquisition, and functional imaging capability through specialized techniques such as diffusion-tensor imaging, diffusion-weighted (DW) imaging, functional MR imaging, MR elastography, MR spectroscopy, perfusion-weighted imaging, MR imaging with very short echo times, and the availability of some targeted MR imaging contrast agents. Furthermore, the lack of ionizing radiation from MR imaging is highly appealing, particularly when pediatric, young adult, or pregnant patients are to be imaged, and the safety profile of MR imaging contrast agents compares very favorably with iodinated CT contrast agents. MR imaging also can be used to guide PET image reconstruction, partial volume correction, and motion compensation for more accurate disease quantification and can improve anatomic localization of sites of radiotracer uptake, improve diagnostic performance, and provide for comprehensive regional and global structural, functional, and molecular assessment of various clinical disorders. In this review, we discuss the historical development, software-based registration, instrumentation and design, quantification issues, potential clinical applications, potential clinical roles of image segmentation and global disease assessment, and challenges related to PET/MR imaging.
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