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Archive for the ‘Cancer and Current Therapeutics’ Category


WordCloud Visualization of LPBI’s Top Sixteen Articles on CANCER in eight categories and by Views at All Time and their Research Categories in the Ontology of PharmaceuticalIntelligence.com

Curator: Stephen J. Williams, PhD and WordCloud Producers: Daniel Menzin, Noam Steiner-Tomer, Zach Day, Ofer Markman, PhD and Aviva Lev-Ari, PhD, RN

Introduction (From Cancer Volume 1): 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 cancer.  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 we 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.

Purpose:  To Curate a listing of articles in CANCER representative of  the Agora of the LPBI Journal for the purpose of generating WordClouds for eventual Natural Language Processing.

Methods:

For a full description of methodology please contact the LPBI Group at avivalev-ari@alum.berkeley.edu , LinkedIn, or through Twitter @pharma_BI.

Methods in Brief:

 A listing of all Cancer articles which had been viewed at least 131 times was generated.  They could either be authored, curated, written, or reported articles.  The initial list was generated by Daniel, Chief Technology Officer.  This listing was generated as an Excel worksheet.  (A Total of 1555 articles had views of at least 133 total all time views of which 352 were explicitly on CancerEach article was read and verified for cancer-related content).

Each Cancer article was then categorized  according to the STYLE in which it was written as follows

    • Authored; requires original thought, ideas, and multiple references; has a methodology
    • Curated; multiple disparate sources connected by a theme generated by the curator
    • Written: as a writer; only one or two references but having some input into content
    • Reported: an article which only reports on a topic or event; usually a new report or press announcement

After categorizing the STYLE,  the AUTHORED, CURATED, AND WRITTEN articles (263 articles) were further sub-categorized based on the following subject material categories:

    • Therapeutic 
    • Diagnosis 
    • Imaging 
    • Mechanisms of tumorigenesis 
    • Genomics 
    • Resistance and Adverse Events 
    • Patient Care and Personalized Care 
    • Cancer Models and Research 

Each article author or curated was also recorded in the Excel spreadsheet.  A mind map of each of the major authors and curators on the topic of Cancer was generated by curating common themes in the articles as well as opinion pieces written by each of the main editors of the Cancer Volumes (I and II).  The mind-map guided the further selection of 16 articles which were representative of the above sub-categories and reflective of the editor(s) theory of cancer etiology and vision of paradigm changes within the field.  WordClouds were generated from these listing of 16 representative articles of the Agora of Cancer offerings within the LPBI database.

INTERNS PLEASE PUT SOME METHODOLOGY ON HOW YOU GENERATED THE WORDCLOUD

Results:

Article Selection and Categorization

Of the 352 CANCER articles, there were 69 AUTHORED, 178 CURATED, 16 WRITTEN, and 89 REPORTED articles. Sub-categorization of the Authored, Curated, and Written articles yielded the following 

    • Therapeutic (69 articles)
    • Diagnosis (36 articles)
    • Imaging (16 articles)
    • Mechanisms of tumorigenesis (40 articles)
    • Genomics (69 articles)
    • Resistance and Adverse Events (12 articles)
    • Patient Care and Personalized Care (12 articles)
    • Cancer Models and Research (12 articles)

This resulted in 263 article which were either authored, curated or written.  These 263 articles were then used for further sub-selection based on  the Mind Map generated (as described below). 

Generation of a Mind Map of Editors of Cancer Volume 1 and 2

In the Vision of Dr. Larry H. Bernstein:

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 the Vision of Dr. Stephen J. Williams

This ebook 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.

In the Vision of Dr. Dror Nir

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 characterisation 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.

 

Using these VISIONS of CANCER a mind map was generated for each of these authors/editors. Mind maps consisted of a thematic sentence to describe their individual VISION of CANCER and a second sentence describing what each author/editor saw as greatest PARADIGM SHIFT in their respective sub-disciplines of cancer (basic and clinical).  The MIND MAP is shown below:

 

Category Article name Intern Name
Therapeutic (69 articles)

 

 

Targeting the Wnt Pathway [7.11]

 

https://pharmaceuticalintelligence.com/2015/04/10/targeting-the-wnt-pathway-7-11/

Noam
Therapeutic (69 articles)

 

 

Warburg Effect and Mitochondrial Regulation- 2.1.3 Daniel
Therapeutic (69 articles)

 

 

Cancer Mutations Across the Landscape Daniel
Therapeutic (69 articles)

 

 

   
Diagnosis (36 articles)

 

 

Targeting Cancer Neoantigens and Metabolic Change in T-cells

 

https://pharmaceuticalintelligence.com/2016/05/19/targeting-cancer-neoantigens-and-metabolic-change-in-t-cells/

 

 

 

Noam

 

 

Diagnosis (36 articles)

 

 

 

 

In Search of Clarity on Prostate Cancer Screening, Post-Surgical Followup, and Prediction of Long Term Remission

 

Daniel
Diagnosis (36 articles)

 

 

 

 

 

 

 
Imaging (16 articles)

 

 

State of the art in oncologic imaging of Prostate

 

https://pharmaceuticalintelligence.com/2013/01/28/state-of-the-art-in-oncologic-imaging-of-prostate/

 

Noam
Imaging (16 articles)

 

 

   
Mechanisms of tumorigenesis (40 articles)

 

 

Neuroblastoma: A review

 

https://pharmaceuticalintelligence.com/2013/06/01/neuroblastoma-a-review/

 

Noam
Mechanisms of tumorigenesis (40 articles)

 

 

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View? Daniel
Mechanisms of tumorigenesis (40 articles)

 

 

How Mobile Elements in “Junk DNA Promote Cancer – Part 1: Transposon-mediated Tumorigenesis” Daniel
Mechanisms of tumorigenesis (40 articles)

 

 

   
Genomics (69 articles)

 

 

Akt inhibition for cancer treatment, where do we stand today? Daniel
Genomics (69 articles)

 

 

Thymosin alpha1 and melanoma Daniel
Genomics (69 articles)

 

 

AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo Daniel
Genomics (69 articles)

 

 

Steroids, Inflammation, and CAR-T Therapy Daniel
Genomics (69 articles)

 

 

   
Resistance and Adverse Events (12 articles)

 

 

Predicting Tumor Response, Progression, and Time to Recurrence Daniel
Resistance and Adverse Events (12 articles)

 

 

   
     
Patient Care and Personalized Care (12 articles)

 

 

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com Aviva
Patient Care and Personalized Care (12 articles)

 

 

   
     
Cancer Models and Research (12 articles)

 

 

Humanized Mice May Revolutionize Cancer Drug Discovery Aviva
Cancer Models and Research (12 articles)

 

 

   
     
     
     
     
     
     
     
     
     
     
     

Article Title (Live Link) All Time Views Categories of Research
#1

Targeting the Wnt Pathway [7.11]
3,233 Academic PublishingBiochemical pathwaysBiological NetworksCancer and Current TherapeuticsCANCER BIOLOGY & Innovations in Cancer TherapyCell BiologyCurationDisease BiologyGastroenterologyGene RegulationGenetics & Innovations in TreatmentLiver & Digestive Diseases ResearchMetabolism

#1

Targeting the Wnt Pathway [7.11]

Article #1: Word Cloud by NT

 

   

Article Title (Live Link) All Time Views Categories of Research
#2

Warburg Effect and Mitochondrial Regulation- 2.1.3
~361
Academic Publishing
Amino acidsAnaerobic GlycolysisBiochemical pathwaysBiological NetworksCancer and Current TherapeuticsCANCER BIOLOGY & Innovations in Cancer TherapyCell BiologyChemical Biology and its relations to Metabolic DiseaseClinical DiagnosticsCurationCytoskeletonDevelopmental biologyDisease BiologyEnzyme InductionEnzymes and isoenzymesFatty acidsGene RegulationGenomic ExpressionHexokinaseInosine nucleotidesLipid metabolismLipidsLiver & Digestive Diseases ResearchLoss of function geneMetabolismMetabolomicsMethodsmtDNAOxidative phosphorylationPhosphorylationProteinsProteomicsPyridine nucleotidesPyruvate KinaseSignalingSignaling & Cell CircuitsSmall Molecules in Development of Therapeutic DrugsWarburg effect

#2

Warburg Effect and Mitochondrial Regulation- 2.1.3

Article #2: Word Cloud by DM

   

Article Title (Live Link) All Time Views Categories of Research
#3

Cancer Mutations Across the Landscape
~173 Biological Networks, Gene Regulation and EvolutionCANCER BIOLOGY & Innovations in Cancer TherapyCell Biology, Signaling & Cell CircuitsComputational Biology/Systems and BioinformaticsDisease Biology, Small Molecules in Development of Therapeutic DrugsGenome BiologyGenomic Testing: Methodology for DiagnosisMedical and Population GeneticsMolecular Genetics & PharmaceuticalPersonalized and Precision Medicine & Genomic Research

#3

Cancer Mutations Across the Landscape

Article #3: Word Cloud by DM

   

Article Title (Live Link) All Time Views Categories of Research
#4

Targeting Cancer Neoantigens and Metabolic Change in T-cells
~263
Apoptosis
AutophagyCANCER BIOLOGY & Innovations in Cancer TherapyClinical & TranslationalCurationImmunologyImmunotherapyInflammasome

#4

Targeting Cancer Neoantigens and Metabolic Change in T-cells

Article #4: Word Cloud by NT

   

Article Title (Live Link) All Time Views Categories of Research
#5

In Search of Clarity on Prostate Cancer Screening, Post-Surgical Followup, and Prediction of Long Term Remission
~134 Biomarkers & Medical DiagnosticsCANCER BIOLOGY & Innovations in Cancer TherapyMedical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra SoundPersonalized and Precision Medicine & Genomic ResearchPharmaceutical Industry Competitive Intelligence

#5

In Search of Clarity on Prostate Cancer Screening, Post-Surgical Followup, and Prediction of Long Term Remission

Article #5: Word Cloud by DM

   

Article Title (Live Link) All Time Views Categories of Research
#6

State of the art in oncologic imaging of Prostate
~204
Bio Instrumentation in Experimental Life Sciences Research
Biomarkers & Medical DiagnosticsCANCER BIOLOGY & Innovations in Cancer TherapyEcosystems & Industrial Concentration in the Medical Device SectorHealth Economics and Outcomes ResearchImaging-based Cancer Patient ManagementMedical Devices R&D and InventionsMedical Devices R&D InvestmentMedical Imaging Technology, Image Processing/Computing, MRI, CT, Nuclear Medicine, Ultra SoundPersonalized and Precision Medicine & Genomic Research

#6

State of the art in oncologic imaging of Prostate

Article #6: Word Cloud by NT

   

Article Title (Live Link) All Time Views Categories of Research
#7

Neuroblastoma: A review
689 Biomarkers & Medical DiagnosticsBioSimilarsCANCER BIOLOGY & Innovations in Cancer TherapyCancer Prevention: Research & ProgramsCell Biology, Signaling & Cell CircuitsDisease Biology, Small Molecules in Development of Therapeutic DrugsGenome BiologyPersonalized and Precision Medicine & Genomic ResearchPopulation Health Management, Genetics & Pharmaceutical

#7

Neuroblastoma: A Review

Article #7: Word Cloud by NT

   

Article Title (Live Link) All Time Views Categories of Research
#8

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?
17,152 Biological NetworksCANCER BIOLOGY & Innovations in Cancer TherapyCell BiologyDisease BiologyGenome BiologyImaging-based Cancer Patient ManagementInternational Global Work in PharmaceuticalLiver & Digestive Diseases ResearchMetabolomicsMolecular Genetics & PharmaceuticalNutritionPharmaceutical Industry Competitive IntelligencePharmaceutical R&D InvestmentPopulation Health ManagementProteomicsStem Cells for Regenerative MedicineTechnology Transfer: Biotech and Pharmaceutical

#8

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

Article #8: Word Cloud by DM

   

Article Title (Live Link) All Time Views Categories of Research
#9

How Mobile Elements in “Junk DNA Promote Cancer – Part 1: Transposon-mediated Tumorigenesis”
928
Biological Networks, Gene Regulation and Evolution
CANCER BIOLOGY & Innovations in Cancer TherapyComputational Biology/Systems and BioinformaticsDisease Biology, Small Molecules in Development of Therapeutic DrugsGenome BiologyHealth Economics and Outcomes ResearchMolecular Genetics & PharmaceuticalPersonalized and Precision Medicine & Genomic ResearchUncategorized

#9

How Mobile Elements in “Junk DNA Promote Cancer – Part 1: Transposon-mediated Tumorigenesis”

Article #9: Word Cloud by DM

   

Article Title (Live Link) All Time Views Categories of Research
#10

Akt inhibition for cancer treatment, where do we stand today?
4,873 CANCER BIOLOGY & Innovations in Cancer TherapyCell Biology, Signaling & Cell Circuits

#10

Akt inhibition for cancer treatment, where do we stand today?

Article #10: Word Cloud by DM

   

Article Title (Live Link) All Time Views Categories of Research
#11

Thymosin alpha1 and melanoma
~573 Bio Instrumentation in Experimental Life Sciences ResearchBiomarkers & Medical DiagnosticsBioSimilarsCANCER BIOLOGY & Innovations in Cancer TherapyDisease Biology, Small Molecules in Development of Therapeutic DrugsDrug Delivery Platform TechnologyHealth Economics and Outcomes ResearchHuman Immune System in Health and in DiseasePopulation Health Management, Genetics & PharmaceuticalRegulated Clinical Trials: Design, Methods, Components and IRB related issues

#11

Thymosin alpha1 and melanoma

Article #11: Word Cloud by DM

   

Article Title (Live Link) All Time Views Categories of Research
#12

AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo
3,473
Biological Networks, Gene Regulation and Evolution
BioSimilarsCANCER BIOLOGY & Innovations in Cancer TherapyCell Biology, Signaling & Cell CircuitsChemical Biology and its relations to Metabolic DiseaseChemical GeneticsDisease Biology, Small Molecules in Development of Therapeutic DrugsGenome BiologyHealth Economics and Outcomes ResearchMetabolomicsMolecular Genetics & PharmaceuticalNutrigenomicsPersonalized and Precision Medicine & Genomic Research

#12

AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

Article #12: Word Cloud by DM

   

Article Title (Live Link) All Time Views Categories of Research
#13

Steroids, Inflammation, and CAR-T Therapy]
828 Cancer and Current TherapeuticsCANCER BIOLOGY & Innovations in Cancer TherapyFDAFDA Regulatory AffairsImmuno-Oncology & GenomicsInnovation in Immunology DiagnosticsLymphomaPersonal Health Applications: Tech Innovations serves HealhCarePersonalized and Precision Medicine & Genomic Research

#13

Steroids, Inflammation, and CAR-T Therapy

Article #13: Word Cloud by DM

  

Article Title (Live Link) All Time Views Categories of Research
#14

Predicting Tumor Response, Progression, and Time to Recurrence
504
Bio Instrumentation in Experimental Life Sciences Research
Biological NetworksBiomarkers & Medical DiagnosticsCANCER BIOLOGY & Innovations in Cancer TherapyCell BiologyChemical GeneticsComputational Biology/Systems and BioinformaticsDisease BiologyGene Regulation and EvolutionGenome BiologyGenomic Testing: Methodology for DiagnosisImaging-based Cancer Patient ManagementSignaling & Cell CircuitsSmall Molecules in Development of Therapeutic DrugsTechnology Transfer: Biotech and Pharmaceutical

#14

Predicting Tumor Response, Progression, and Time to Recurrence

Article #14: Word Cloud by DM

  

Article Title (Live Link) All Time Views Categories of Research
#15

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com

~216
CANCER BIOLOGY & Innovations in Cancer TherapyInterviews with Scientific Leaders

#15

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com

Article #15: Word Cloud by ZD

   

Article Title (Live Link) All Time Views Categories of Research
#16

Humanized Mice May Revolutionize Cancer Drug Discovery
~341
BioTechnology – Venture Creation
BioTechnology – Venture Creation, Venture CapitalCancer and Current TherapeuticsCANCER BIOLOGY & Innovations in Cancer TherapyMonoclonal ImmunotherapyPatentsPharmaceutical Drug DiscoveryPharmacodynamics and Pharmacokinetics

#16

Humanized Mice May Revolutionize Cancer Drug Discovery

Article #16: Word Cloud by ZD

CATEGORIZATION OF THE UNIVERSE OF CANCER OFFERINGS IN THE AGORA OF LPBI

Purpose: To Curate a listing of articles in CANCER for the purpose of generating WordClouds for eventual Natural Language Processing

Initial Request: Aviva requested 12 articles in CANCER to be used to generate a WordCloud for AI machine learning

Problem: Only 12 article only represents less than 1% of all CANCER OFFERINGS by LPBI and would severely limit the ability to generate a meaningful WordCloud. Dr Williams then used a methodology to curate a meaningful list which could be repeated on extended offerings and subjects.

Solution: Dr. Williams generated a listing of all Cancer articles which had been viewed at least 131 times. They could either be authored, curated, written, or reported articles. The initial list was generated by Daniel, Chief Technology Officer. This listing was generated as an Excel worksheet. (A Total of 1555 articles had views of at least 133 total all time views of which 352 were explicitly on Cancer. Each article was read for content).

  • Williams then categorized each article according to the STYLE in which it was written as follows
    • Authored; requires original thought, ideas, and multiple references; has a methodology
    • Curated; multiple disparate sources connected by a theme generated by the curator
    • Written: as a writer; only one or two references but having some input into content
    • Reported: an article which only reports on a topic or event; usually a new report or press announcement

Of the 352 CANCER articles, there were 69 AUTHORED, 178 CURATED, 16 WRITTEN, and 89 REPORTED articles

  • Williams, after categorizing the STYLE, then categorized the AUTHORED, CURATED, AND WRITTEN articles (263 articles) based on the following subject material categories:
    • Therapeutic (69 articles)
    • Diagnosis (36 articles)
    • Imaging (16 articles)
    • Mechanisms of tumorigenesis (40 articles)
    • Genomics (69 articles)
    • Resistance and Adverse Events (12 articles)
    • Patient Care and Personalized Care (12 articles)
    • Cancer Models and Research (12 articles)

The following tables represent the articles in each sub-category

Therapeutic (69 articles)

Akt inhibition for cancer treatment, where do we stand today?

Crucial role of Nitric Oxide in Cancer

Targeting Mitochondrial-bound Hexokinase for Cancer Therapy

The Development of siRNA-Based Therapies for Cancer

Nanotech Therapy for Breast Cancer

Thymosin alpha1 and melanoma

What can we expect of tumor therapeutic response?

β Integrin emerges as an important player in mitochondrial dysfunction associated Gastric Cancer

Personalized Medicine and Colon Cancer

Pancreatic Cancer: a discovery in Toulouse that would slow its progression

Quantitative Systems Pharmacology – Use in Oncology Clinical Development: Anna Georgieva Kondic, PhD

Predicting Tumor Response, Progression, and Time to Recurrence

Usp9x: Promising therapeutic target for pancreatic cancer

Targeting Epithelial To Mesenchymal Transition (EMT) As A Therapy Strategy For Pancreatic Cancer

VEGF activation and signaling, lysine methylation, and activation of receptor tyrosine kinase

Brain Cancer Vaccine in Development and other considerations

Paclitaxel vs Abraxane (albumin-bound paclitaxel)

Mesothelin: An early detection biomarker for cancer (By Jack Andraka)

Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Hemeostasis of Immune Responses for Good and Bad

AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo

Targeting the Wnt Pathway [7.11]

Monoclonal Antibody Therapy and Market

Non-small Cell Lung Cancer drugs – where does the Future lie?

CD47: Target Therapy for Cancer

Peroxisome proliferator-activated receptor (PPAR-gamma) Receptors Activation: PPARγ transrepression for Angiogenesis in Cardiovascular Disease and PPARγ transactivation for Treatment of Diabetes

Cardio-oncology and Onco-Cardiology Programs: Treatments for Cancer Patients with a History of Cardiovascular Disease

Mitochondrial fission and fusion: potential therapeutic targets?

Heroes in Medical Research: Barnett Rosenberg and the Discovery of Cisplatin

Imatinib (Gleevec) May Help Treat Aggressive Lymphoma: Chronic Lymphocytic Leukemia (CLL)

Lung Cancer (NSCLC), drug administration and nanotechnology

Soft Tissue Sarcoma: an Overview

Steroids, Inflammation, and CAR-T Therapy

14th ANNUAL BIOTECH IN EUROPE FORUM For Global Partnering & Investment 9/30 – 10/1/2014 • Congress Center Basel – SACHS Associates, London

Moderna Therapeutics Deal with Merck: Are Personalized Vaccines here?

Good and Bad News Reported for Ovarian Cancer Therapy

Topoisomerase 1 inhibitors and cancer therapy

J.P. Morgan 34th Annual Healthcare Conference & Biotech Showcase™ January 11 – 15, 2016 in San Francisco

Bisphosphonates and Bone Metabolism

Cancer Immunotherapy

Signaling of Immune Response in Colon Cancer

Oncolytic Virus Immuno-Therapy: New Approach for a New Class of Immunotherapy Drugs

Angiogenesis Inhibitors [9.5]

Novel Approaches to Cancer Therapy [11.1]

Findings on Bacillus Calmette–Guérin (BCG) for Superficial Bladder Cancer

Are CXCR4 Antagonists Making a Comeback in Cancer Chemotherapy?

The 2nd ANNUAL Sachs Cancer Bio Partnering & Investment Forum Promoting Public & Private Sector Collaboration & Investment in Drug Development, 19th March 2014 • New York Academy of Sciences • USA

Bispecific and Trispecific Engagers: NK-T Cells and Cancer Therapy

Immune-Oncology Molecules In Development & Articles on Topic in @pharmaceuticalintelligence.com

Monoclonal Antibody Therapy: What is in the name or clear description?

Nanoparticle Delivery to Cancer Drug Targets

Autophagy-Modulating Proteins and Small Molecules Candidate Targets for Cancer Therapy: Commentary of Bioinformatics Approaches

Immunotherapy in Cancer: A Series of Twelve Articles in the Frontier of Oncology by Larry H Bernstein, MD, FCAP

2014 MassBio Annual Meeting 4/3 – 4/4 2014, Royal Sonesta Hotel, Cambridge, MA

AACR2016 – Cancer immunotherapy

Report on Cancer Immunotherapy Market & Clinical Pipeline Insight

CD-4 Therapy for Solid Tumors

In focus: Melanoma therapeutics

Allogeneic Stem Cell Transplantation [9.3]

Cyclic Dinucleotides and Histone deacetylase inhibitors

Cancer Innovations from across the Web

“””Thymosin alpha1 and melanoma””,168

NGS Market: Trends and Development for Genotype-Phenotype Associations Research”””

Myelodysplastic syndrome and acute myeloid leukemia following adjuvant chemotherapy

Cancer Cell Therapy: Global Start up Acquisitions in Oncolytic Viruses (OV) Therapeutics – a Pipeline of 70 OVs in Clinical Development and another 95 in Preclinical Programs

Cancer Vaccines: Targeting Cancer Genes for Immunotherapy – A Conference by Keystone Symposia on Molecular and Cellular Biology

IDO for Commitment of a Life Time: The Origins and Mechanisms of IDO, indolamine 2, 3-dioxygenase

The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology

Advances in Cancer Immunotherapy

COMBAT study: Combination of BL-8040 and KEYTRUDA® (pembrolizumab) for Pancreatic Cancer: Collaboration Agreement Merck, BioLineRx and MD Anderson Cancer Center

DIAGNOSIS (36 articles)

Nanotechnology: Detecting and Treating metastatic cancer in the lymph node

Acute Lymphoblastic Leukemia (ALL) and Nanotechnology

Role of Progesterone in Breast Cancer Progression

Today’s fundamental challenge in Prostate cancer screening

Sensors and Signaling in Oxidative Stress

City of Hope, Duarte, California – Combining Science with Soul to Create Miracles at a Comprehensive Cancer Center designated by the National Cancer Institute – An Interview with the Provost and Chief Scientific Officer of City of Hope, Steven T. Rosen, M.D.

In Focus: Identity of Cancer Stem Cells

Thermodynamic Modeling for Cancer Cells

Glypican-1 identifies cancer exosomes

Ultrasound-based Screening for Ovarian Cancer

Ovarian Cancer and fluorescence-guided surgery: A report

Diagnosing Lung Cancer in Exhaled Breath using Gold Nanoparticles

Pancreatic Cancer at the Crossroads of Metabolism

Virtual Biopsy – is it possible?

Prostate Cancer and Nanotecnology

Metabolomics based biomarker discoveries

Personalized Medicine: Cancer Cell Biology and Minimally Invasive Surgery (MIS)

Pancreatic Cancer Targeted Treatment?

Breast Cancer: Genomic profiling to predict Survival: Combination of Histopathology and Gene Expression Analysis

Targeting Cancer Neoantigens and Metabolic Change in T-cells

Cancer Immunotherapy Conference & Biomarkers for Cancer Immunotherapy Symposium, March 6-11, 2016 | Moscone North Convention Center | San Francisco, CA

New insights in cancer, cancer immunogenesis and circulating cancer cells

Circulating Biomarkers World Congress, March 23-24, 2015, Boston: Exosomes, Microvesicles, Circulating DNA, Circulating RNA, Circulating Tumor Cells, Sample Preparation

Prostate Cancer: Diagnosis and Novel Treatment – Articles of Note @PharmaceuticalIntelligence.com

Cancer Biomarkers

What about PDL-1 in oncotherapy diagnostics for NSCLC?

Novel biomarkers for targeting cancer immunotherapy

Hematological Cancer Classification

Cancer Biomarkers [11.3.2.3]

In Search of Clarity on Prostate Cancer Screening, Post-Surgical Followup, and Prediction of Long Term Remission

Biomarkers identified for recurrence in HBV-related HCC patients post surgery

Recent comprehensive review on the role of ultrasound in breast cancer management

Automated Breast Ultrasound System (‘ABUS’) for full breast scanning: The beginning of structuring a solution for an acute need!

“””The Molecular pathology of Breast Cancer Progression””,296

Medical MEMS BioMEMS and Sensor Applications”””

Battle of Steve Jobs and Ralph Steinman with Pancreatic cancer: How we lost

Metabolic drivers in aggressive brain tumors

IMAGING (16 articles)

Nanotechnology and MRI imaging

The unfortunate ending of the Tower of Babel construction project and its effect on modern imaging-based cancer patients’ management

Improving Mammography-based imaging for better treatment planning

State of the art in oncologic imaging of Colorectal cancers.

State of the art in oncologic imaging of Prostate.

Imaging Technology in Cancer Surgery

State of the art in oncologic imaging of lungs.

Causes and imaging features of false positives and false negatives on 18F-PET/CT in oncologic imaging

Clinical Trials on Schwannoma & Benign Intracranial Tumors Radiosurgery Treatment

Whole-body imaging as cancer screening tool; answering an unmet clinical need?

Improving Mammography-based imaging for better treatment planning

Imaging: seeing or imagining? (Part 1)

Knowing the tumor’s size and location, could we target treatment to THE ROI by applying imaging-guided intervention?

Imaging: seeing or imagining? (Part 2)

Tumor Imaging and Targeting: Predicting Tumor Response to Treatment: Where we stand?

State of the art in oncologic imaging of breast.

Mechanisms of tumorigenesis (40 articles)

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

In focus: Circulating Tumor Cells

Summary of Transcription, Translation ond Transcription Factors

Mitochondria: More than just the “powerhouse of the cell”

How Mobile Elements in “Junk DNA Promote Cancer – Part 1: Transposon-mediated Tumorigenesis”

Demythologizing sharks, cancer, and shark fins

A Synthesis of the Beauty and Complexity of How We View Cancer

Neuroblastoma: A review

Refined Warburg Hypothesis -2.1.2

Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

Epistemology of the Origin of Cancer: a New Paradigm

Role of Primary Cilia in Ovarian Cancer

Prologue to Cancer – e-book Volume One – Where are we in this journey?

“””The Molecular pathology of Breast Cancer Progression””, 325

Ultrasound-based Screening for Ovarian Cancer”””

The “Cancer establishments examined by James Watson 4/1953”

Lipids link to breast cancer

“””Thymosin alpha1 and melanoma””, 169

Amplifying Information Using S-Clustering and Relationship to Kullback-Liebler Distance: An Application to Myocardial Infarction”””

Wnt/β-catenin Signaling [7.10]

Cancer Signaling Pathways and Tumor Progression: Images of Biological Processes in the Voice of a Pathologist Cancer Expert

Mitochondrial Damage and Repair under Oxidative Stress

Nitric Oxide has a Ubiquitous Role in the Regulation of Glycolysis – with a Concomitant Influence on Mitochondrial Function

Autophagy

Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

Pancreatic Cancer and Crossing Roads of Metabolism

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis

Warburg Effect Revisited – 2

Prostate Cancer Cells: Histone Deacetylase Inhibitors Induce Epithelial-to-Mesenchymal Transition

Naked Mole Rats Cancer-Free

In focus: Triple Negative Breast Cancer

Heat Shock Proteins (HSP) and Molecular Chaperones

Nonhematologic Cancer Stem Cells [11.2.3]

Mitochondria and Cancer: An overview of mechanisms

Growth Factors, Suppressors and Receptors in Tumorigenesis [7.1]

Upregulate Tumor Suppressor Pathways [7.5]

Nrf2 Role in Blocking DNA Damage

Prostate Cancer: Androgen-driven “Pathomechanism in Early-onset Forms of the Disease”

Cancer Metastasis

Halstedian model of cancer progression

Otto Warburg, A Giant of Modern Cellular Biology

Tang Prize for 2014: Immunity and Cancer

Genomics (69 articles)

Pancreatic Cancer: Genetics, Genomics and Immunotherapy

Summary of Signaling and Signaling Pathways

In focus: Melanoma Genetics

Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

Multiple Lung Cancer Genomic Projects Suggest New Targets, Research Directions for Non-Small Cell Lung Cancer

Stanniocalcin: A Cancer Biomarker.

The Underappreciated EpiGenome

Li -Fraumeni Syndrome and Pancreatic Cancer

“To Die or Not To Die” – Time and Order of Combination drugs for Triple Negative Breast Cancer cells: A Systems Level Analysis

Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell

Metabolomics and prostate cancer

“The Molecular pathology of Breast Cancer Progression”, 172 Bioinformatic Tools for Cancer Mutational Analysis: COSMIC and Beyond”

Personalized Medicine in NSCLC

Notes On Tumor Heterogeneity: Targets and Mechanisms, from the 2015 AACR Meeting in Philadelphia PA

AstraZeneca’s WEE1 protein inhibitor AZD1775 Shows Success Against Tumors with a SETD2 mutation

A Primer on DNA and DNA Replication

Integrins, Cadherins, Signaling and the Cytoskeleton

The Molecular pathology of Breast Cancer Progression

Signaling transduction tutorial

Hematologic Malignancies [2.4.3]

Sunitinib brings Adult Acute Lymphoblastic Leukemia (ALL) to Remission – RNA Sequencing – FLT3 Receptor Blockade

Hypoxia Inducible Factor 1 (HIF-1)[7.9]

Observations on Human Papilloma Virus and Cancer

The role and importance of transcription factors

Differentiation Therapy – Epigenetics Tackles Solid Tumors

CRISPR-Cas9 Foundational Technology originated at UC, Berkeley & UCSF, Broad Institute is developing Biotech Applications — Intellectual Property emerging as Legal Potential Dispute

Bioinformatic Tools for Cancer Mutational Analysis: COSMIC and Beyond

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

The Human Proteome Map Completed

CRISPR/Cas9 Finds Its Way As an Important Tool For Drug Discovery & Development

Mitochondrial Isocitrate Dehydrogenase and Variants

Pathway Specific Targeting in Anticancer Therapies [7.7]

Protein-binding, Protein-Protein interactions & Therapeutic Implications [7.3]

Gene Editing with CRISPR gets Crisper

Delineating a Role for CRISPR-Cas9 in Pharmaceutical Targeting

RNA and the Transcription the Genetic Code

Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility

Finding the Genetic Links in Common Disease: Caveats of Whole Genome Sequencing Studies

LEADERS in Genome Sequencing of Genetic Mutations for Therapeutic Drug Selection in Cancer Personalized Treatment: Part 2

Introduction to Metabolomics

CRACKING THE CODE OF HUMAN LIFE: Recent Advances in Genomic Analysis and Disease – Part IIC

Sirtuins [7.8]

Highlights from 8th Annual Personalized Medicine Conference, November 28-29, 2012, Harvard Medical School, Boston, MA

2019 Trends in Precision Medicine: A Perspective from Foundation Medicine

Loss of Gene Islands May Promote a Cancer Genome’s Evolution: A new Hypothesis on Oncogenesis

Warburg Effect and Mitochondrial Regulation- 2.1.3

Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

The Magic of the Pandora’s Box : Epigenetics and Stemness with Long non-coding RNAs (lincRNA)

HBV and HCV-associated Liver Cancer: Important Insights from the Genome

PostTranslational Modification of Proteins

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

eProceeding 2019 Koch Institute Symposium – 18th Annual Cancer Research Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PMET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA

Genomics and Epigenetics: Genetic Errors and Methodologies – Cancer and Other Diseases

Genomics and Metabolomics Advances in Cancer

Deciphering the Epigenome

Tumor Ammonia Recycling: How Cancer Cells Use Glutamate Dehydrogenase to Recycle Tumor Microenvironment Waste Products for Biosynthesis

Cancer Mutations Across the Landscape

BRCA1 a tumour suppressor in breast and ovarian cancer – functions in transcription, ubiquitination and DNA repair

2016 World Medical Innovation Forum: CANCER, April 25-27, 2016, Partners HealthCare, Boston, at the Westin Hotel, Boston

Winning Over Cancer Progression: New Oncology Drugs to Suppress Passengers Mutations vs. Driver Mutations

Gene Amplification and Activation of the Hedgehog Pathway

Bioinformatics Tool Review: Genome Variant Analysis Tools

Gastric Cancer: Whole-genome reconstruction and mutational signatures

7th Annual Novel Strategies for Kinase Inhibitors Exploring New Therapeutic Areas September 24-25, 2013 | Boston, MA

Targeting Untargetable Proto-Oncogenes

Salivary Gland Cancer – Adenoid Cystic Carcinoma: Mutation Patterns: Exome- and Genome-Sequencing @ Memorial Sloan-Kettering Cancer Center

Heroes in Medical Research: Dr. Robert Ting, Ph.D. and Retrovirus in AIDS and Cancer

DNA: One man’s trash is another man’s treasure, but there is no JUNK after all

PIK3CA mutation in Colorectal Cancer may serve as a Predictive Molecular Biomarker for adjuvant Aspirin therapy

Resistance and Adverse Events (12 articles)

Tumor Associated Macrophages: The Double-Edged Sword Resolved?

Predicting Tumor Response, Progression, and Time to Recurrence

Development of Chemoresistance to Targeted Therapies: Alterations of Cell Signaling & the Kinome

Can IntraTumoral Heterogeneity Be Thought of as a Mechanism of Resistance?

Rapid regression of HER2 breast cancer

Liver Toxicity halts Clinical Trial of IAP Antagonist for Advanced Solid Tumors

Myc and Cancer Resistance

Mechanisms of Drug Resistance

New Generation of Platinated Compounds to Circumvent Resistance

Breast Cancer, drug resistance, and biopharmaceutical targets

Issues Need to be Resolved With ImmunoModulatory Therapies: NK cells, mAbs, and adoptive T cells

Curation of Recently Halted Oncology Trials Due to Serious Adverse Events – 2015

Patient and Personalized Care (12 articles)

The Experience of a Patient with Thyroid Cancer

Management of Follicular Lymphoma

Acoustic Neuroma, Neurinoma or Vestibular Schwannoma: Treatment Options

Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.

The Relation between Coagulation and Cancer affects Supportive Treatments

NIH Considers Guidelines for CAR-T therapy: Report from Recombinant DNA Advisory Committee

Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk

Cancer and Nutrition

Harnessing Personalized Medicine for Cancer Management, Prospects of Prevention and Cure: Opinions of Cancer Scientific Leaders @ http://pharmaceuticalintelligence.com

Environment and Cancer [11.3.4]

Hormonal Therapy, Complementary and Alternative Therapies – 9.4

Relation of Diet and Cancer

Research and Cancer Models (12 articles)

The SCID Pig: How Pigs are becoming a Great Alternate Model for Cancer Research

The SCID Pig II: Researchers Develop Another SCID Pig, And Another Great Model For Cancer Research

The Discovery and Properties of Avemar – Fermented Wheat Germ Extract: Carcinogenesis Suppressor

Zebrafish—Susceptible to Cancer

Humanized Mice May Revolutionize Cancer Drug Discovery

Heroes in Medical Research: Developing Models for Cancer Research

Recent Breakthroughs in Cancer Research at the Technion-Israel Institute of Technology- 2015

Guidelines for the welfare and use of animals in cancer research

Colon cancer and organoids

Organoid Development

New Ecosystem of Cancer Research: Cross Institutional Team Science

Koch Institute for Integrative Cancer Research @MIT – Summer Symposium 2014: RNA Biology, Cancer and Therapeutic Implications, June 13, 2014 8:30AM – 4:30PM, Kresge Auditorium @MIT

In the following now we will pick articles based on an even distribution between the subcategories

 

 

CancerandOncologyseriesCcover

 

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

2015

http://www.amazon.com/dp/B013RVYR2K

Stephen J. Williams, PhD, Senior Editor

sjwilliamspa@comcast.net

Tilda Barliya, PhD, Editor

tildabarliya@gmail.com

Ritu Saxena, PhD, Editor

ritu.uab@gmail.com

Leaders in Pharmaceutical Business Intelligence 

 

130204154011

 

A scanning electron micrograph of a squamous cell carcinoma, a type of skin cancer. The cell has been frozen and split open to reveal its nucleus.

Credit: Anne Weston, LRI, CRUK. Wellcome Images

 

 

Aviva Lev-Ari, PhD, RN

Editor-in-Chief BioMed e-Series of e-Books

Leaders in Pharmaceutical Business Intelligence, Boston

avivalev-ari@alum.berkeley.edu

 

 

Open Access Online Journal

http://www.pharmaceuticalIntelligence.com

is a scientific, medical and business, multi-expert authoring environment for information syndication in several domains of Life Sciences, Medicine, Pharmaceutical and Healthcare Industries, BioMedicine, Medical Technologies & Devices. Scientific critical interpretations and original articles are written by PhDs, MDs, MD/PhDs, PharmDs, Technical MBAs as Experts, Authors, Writers (EAWs) on an Equity Sharing basis.

This e-Book is a comprehensive review of recent Original Research on Cancer & Genomics including related opportunities for Targeted Therapy written by Experts, Authors, Writers. The results of Original Research are gaining value added for the e-Reader by the Methodology of Curation. The e-Book’s articles have been published on the Open Access Online Scientific Journal, since April 2012.  All new articles on this subject, will continue to be incorporated, as published with periodical updates. © Leaders in Pharmaceutical Business Intelligence, all rights reserved.

 

List of Contributors to Volume One

(Note: original authored and curated  articles are in bold-faced type). Other articles represent reports of interesting literature)

 
 
Aashir Awan PhD
2.11, 10.2
 
Tilda Barliya PhD
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, 11.4.612.1, 12.2, 12.3, 12.4, 12.5, 12.6, 12.7
 
Larry H Bernstein MD, FCAP
Prologue, Volume Introduction, 1.6, 1.8, 2.3, 2.4, 2.5, 2.63.1, 4.2.6, 5.1.5, 6.1.1, 6.3.4, 7.3.2, 7.3.8, 10.8, Epilogue
 
Prabodh Kumar Kandala PhD
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
 
Aviva Lev-Ari PhD RN
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, 6.1.47.1.1, 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
 
Dror Nir PhD
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
 
Ziv Raviv PhD
1.5, 6.3.7, 7.1.4, 7.2.2, 7.3.1, 7.3.4
 
Demet Sag, PhD, CRA, GCP
3.34.1.1, 4.1.3, 4.1.5, 7.3.9
 
Sudipta Saha PhD
1.10, 5.1.4, 7.4.3
 
 
Ritu Saxena PhD
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
 
Stephen J. Williams PhD
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

 

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 cancer.  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 we 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 ebook 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).

  1. Maintaining Proliferative Signals
  2. Avoiding Immune Destruction
  3. Evading Growth Suppressors
  4. Resisting Cell Death
  5. Becoming Immortal
  6. Angiogenesis
  7. Deregulating Cellular Energy
  8. 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.

Part I

Historical Perspective of Cancer Demographics, Etiology, and Progress in Research

 

Chapter 1:  The Occurrence of Cancer in World Populations

 

1.1   Understanding Cancer

 Prabodh Kandala, PhD

1.2  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

1.7      New Ecosystem of Cancer Research: Cross Institutional Team Science

Aviva Lev-Ari, PhD, RN

1.8       Cancer Innovations from across the Web

Larry H Bernstein, MD, FCAP 

1.9         Exploring the role of vitamin C in Cancer therapy

Ritu Saxena PhD

1.10        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

 

Chapter 2.  Rapid Scientific Advances Changes Our View on How Cancer Forms

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

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

Larry H Bernstein, MD, FCAP

2.5           Zebrafish—Susceptible to Cancer

Larry H Bernstein, MD, FCAP

2.6         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

Ritu Saxena, PhD

2.9      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

Aashir Awan, PhD

 

Chapter 3:  A Genetic Basis and Genetic Complexity of Cancer Emerges

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

 

Chapter 4: How Epigenetic and Metabolic Factors Affect Tumor Growth

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

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

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

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

 

Chapter 5: Advances in Breast and Gastrointestinal Cancer Research Supports Hope for Cure

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

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

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.

Dror Nir, PhD

 

5.2 Gastrointestinal Cancer

5.2.1         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

Tilda Barliya, PhD

5.2.5     Pancreatic cancer genomes: Axon guidance pathway genes – aberrations revealed

Aviva Lev-Ari, PhD, RN

 

Part II

Advent of Translational Medicine, “omics”, and Personalized Medicine Ushers in New Paradigms in Cancer Treatment and

Advances in Drug Development

 

Chapter 6:  Treatment Strategies

6.1 Marketed and Novel Drugs

Breast Cancer                                   

6.1.1     Treatment for Metastatic HER2 Breast Cancer

Larry H Bernstein MD, FCAP

6.1.2          Aspirin a Day Tied to Lower Cancer Mortality

Aviva Lev-Ari, PhD, RN

6.1.3       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”

 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

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

 

Chapter 7:  Personalized Medicine and Targeted Therapy

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

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

Larry H Bernstein, MD, FCAP

7.3.3       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

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

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

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

 

Part III

Translational Medicine, Genomics, and New Technologies Converge to Improve Early Detection

Diagnosis, Detection And Biomarkers

 

 

Chapter 8:  Diagnosis

Diagnosis: Prostate Cancer

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         Today’s fundamental challenge in Prostate cancer screening

Dror Nir, PhD

Diagnosis & Guidance: Prostate Cancer

8.3      Prostate Cancers Plunged After USPSTF Guidance, Will It Happen Again?

Aviva Lev-Ari, PhD, RN

Diagnosis, Guidance and Market Aspects: 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

Chapter 9:  Detection

 

Detection: Prostate Cancer

9.1     Early Detection of Prostate Cancer: American Urological Association (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

 

Chapter 10:  Biomarkers

Biomarkers: Pancreatic Cancer

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

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

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 Followup, and Prediction of Long Term Remission

Larry H Bernstein, MD, FCAP

 

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 characterisation 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

 

11.2  Ultrasound

11.2.1        2013 – YEAR OF THE ULTRASOUND

Dror Nir, PhD

11.2.2      Imaging: seeing or imagining? (Part 1)

Dror Nir, PhD

11.2.3        Early Detection of Prostate Cancer: American Urological Association (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

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

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?

Dror Nir, PhD

11.2.10        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]

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

Dror Nir, PhD

11.3.7      A corner in the medical imaging’s 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

Dror Nir, PhD

11.4.5       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  Optical Coherent Tomography (OCT)

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?

Dror Nir, PhD

11.5.4      New development in measuring mechanical properties of tissue

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                                 

 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.

12.1     DNA Nanotechnology

Tilda Barliya, PhD

12.2     Nanotechnology, personalized medicine and DNA sequencing

Tilda Barliya, PhD       

12.3     Nanotech Therapy for Breast Cancer

Tilda Barliya, PhD

12.4     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

Tilda Barliya, PhD

12.7     Lung Cancer (NSCLC), drug administration and nanotechnology

Tilda Barliya, PhD

Volume Epilogue by Larry H. Bernstein, MD, FACP

Epilogue: Envisioning New Insights in Cancer Translational Biology

Larry H. Berstein, MD, FACP

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Systems Biology analysis of Transcription Networks, Artificial Intelligence, and High-End Computing Coming to Fruition in Personalized Oncology

Curator: Stephen J. Williams, Ph.D.

In the June 2020 issue of the journal Science, writer Roxanne Khamsi has an interesting article “Computing Cancer’s Weak Spots; An algorithm to unmask tumors’ molecular linchpins is tested in patients”[1], describing some early successes in the incorporation of cancer genome sequencing in conjunction with artificial intelligence algorithms toward a personalized clinical treatment decision for various tumor types.  In 2016, oncologists Amy Tiersten collaborated with systems biologist Andrea Califano and cell biologist Jose Silva at Mount Sinai Hospital to develop a systems biology approach to determine that the drug ruxolitinib, a STAT3 inhibitor, would be effective for one of her patient’s aggressively recurring, Herceptin-resistant breast tumor.  Dr. Califano, instead of defining networks of driver mutations, focused on identifying a few transcription factors that act as ‘linchpins’ or master controllers of transcriptional networks withing tumor cells, and in doing so hoping to, in essence, ‘bottleneck’ the transcriptional machinery of potential oncogenic products. As Dr. Castilano states

“targeting those master regulators and you will stop cancer in its tracks, no matter what mutation initially caused it.”

It is important to note that this approach also relies on the ability to sequence tumors  by RNA-seq to determine the underlying mutations which alter which master regulators are pertinent in any one tumor.  And given the wide tumor heterogeneity in tumor samples, this sequencing effort may have to involve multiple biopsies (as discussed in earlier posts on tumor heterogeneity in renal cancer).

As stated in the article, Califano co-founded a company called Darwin-Health in 2015 to guide doctors by identifying the key transcription factors in a patient’s tumor and suggesting personalized therapeutics to those identified molecular targets (OncoTarget™).  He had collaborated with the Jackson Laboratory and most recently Columbia University to conduct a $15 million 3000 patient clinical trial.  This was a bit of a stretch from his initial training as a physicist and, in 1986, IBM hired him for some artificial intelligence projects.  He then landed in 2003 at Columbia and has been working on identifying these transcriptional nodes that govern cancer survival and tumorigenicity.  Dr. Califano had figured that the number of genetic mutations which potentially could be drivers were too vast:

A 2018 study which analyzed more than 9000 tumor samples reported over 1.5 million mutations[2]

and impossible to develop therapeutics against.  He reasoned that you would just have to identify the common connections between these pathways or transcriptional nodes and termed them master regulators.

A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples

Chen H, Li C, Peng X, et al. Cell. 2018;173(2):386-399.e12.

Abstract

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on “chromatin-state” to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers.

 

A diagram of how concentrating on these transcriptional linchpins or nodes may be more therapeutically advantageous as only one pharmacologic agent is needed versus multiple agents to inhibit the various upstream pathways:

 

 

From: Khamsi R: Computing cancer’s weak spots. Science 2020, 368(6496):1174-1177.

 

VIPER Algorithm (Virtual Inference of Protein activity by Enriched Regulon Analysis)

The algorithm that Califano and DarwinHealth developed is a systems biology approach using a tumor’s RNASeq data to determine controlling nodes of transcription.  They have recently used the VIPER algorithm to look at RNA-Seq data from more than 10,000 tumor samples from TCGA and identified 407 transcription factor genes that acted as these linchpins across all tumor types.  Only 20 to 25 of  them were implicated in just one tumor type so these potential nodes are common in many forms of cancer.

Other institutions like the Cold Spring Harbor Laboratories have been using VIPER in their patient tumor analysis.  Linchpins for other tumor types have been found.  For instance, VIPER identified transcription factors IKZF1 and IKF3 as linchpins in multiple myeloma.  But currently approved therapeutics are hard to come by for targets with are transcription factors, as most pharma has concentrated on inhibiting an easier target like kinases and their associated activity.  In general, developing transcription factor inhibitors in more difficult an undertaking for multiple reasons.

Network-based inference of protein activity helps functionalize the genetic landscape of cancer. Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, Califano A:. Nature genetics 2016, 48(8):838-847 [3]

Abstract

Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, VIPER (Virtual Inference of Protein-activity by Enriched Regulon analysis), for the accurate assessment of protein activity from gene expression data. We use VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all TCGA samples. In addition to accurately inferring aberrant protein activity induced by established mutations, we also identify a significant fraction of tumors with aberrant activity of druggable oncoproteins—despite a lack of mutations, and vice-versa. In vitro assays confirmed that VIPER-inferred protein activity outperforms mutational analysis in predicting sensitivity to targeted inhibitors.

 

 

 

 

Figure 1 

Schematic overview of the VIPER algorithm From: Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, Califano A: Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nature genetics 2016, 48(8):838-847.

(a) Molecular layers profiled by different technologies. Transcriptomics measures steady-state mRNA levels; Proteomics quantifies protein levels, including some defined post-translational isoforms; VIPER infers protein activity based on the protein’s regulon, reflecting the abundance of the active protein isoform, including post-translational modifications, proper subcellular localization and interaction with co-factors. (b) Representation of VIPER workflow. A regulatory model is generated from ARACNe-inferred context-specific interactome and Mode of Regulation computed from the correlation between regulator and target genes. Single-sample gene expression signatures are computed from genome-wide expression data, and transformed into regulatory protein activity profiles by the aREA algorithm. (c) Three possible scenarios for the aREA analysis, including increased, decreased or no change in protein activity. The gene expression signature and its absolute value (|GES|) are indicated by color scale bars, induced and repressed target genes according to the regulatory model are indicated by blue and red vertical lines. (d) Pleiotropy Correction is performed by evaluating whether the enrichment of a given regulon (R4) is driven by genes co-regulated by a second regulator (R4∩R1). (e) Benchmark results for VIPER analysis based on multiple-samples gene expression signatures (msVIPER) and single-sample gene expression signatures (VIPER). Boxplots show the accuracy (relative rank for the silenced protein), and the specificity (fraction of proteins inferred as differentially active at p < 0.05) for the 6 benchmark experiments (see Table 2). Different colors indicate different implementations of the aREA algorithm, including 2-tail (2T) and 3-tail (3T), Interaction Confidence (IC) and Pleiotropy Correction (PC).

 Other articles from Andrea Califano on VIPER algorithm in cancer include:

Resistance to neoadjuvant chemotherapy in triple-negative breast cancer mediated by a reversible drug-tolerant state.

Echeverria GV, Ge Z, Seth S, Zhang X, Jeter-Jones S, Zhou X, Cai S, Tu Y, McCoy A, Peoples M, Sun Y, Qiu H, Chang Q, Bristow C, Carugo A, Shao J, Ma X, Harris A, Mundi P, Lau R, Ramamoorthy V, Wu Y, Alvarez MJ, Califano A, Moulder SL, Symmans WF, Marszalek JR, Heffernan TP, Chang JT, Piwnica-Worms H.Sci Transl Med. 2019 Apr 17;11(488):eaav0936. doi: 10.1126/scitranslmed.aav0936.PMID: 30996079

An Integrated Systems Biology Approach Identifies TRIM25 as a Key Determinant of Breast Cancer Metastasis.

Walsh LA, Alvarez MJ, Sabio EY, Reyngold M, Makarov V, Mukherjee S, Lee KW, Desrichard A, Turcan Ş, Dalin MG, Rajasekhar VK, Chen S, Vahdat LT, Califano A, Chan TA.Cell Rep. 2017 Aug 15;20(7):1623-1640. doi: 10.1016/j.celrep.2017.07.052.PMID: 28813674

Inhibition of the autocrine IL-6-JAK2-STAT3-calprotectin axis as targeted therapy for HR-/HER2+ breast cancers.

Rodriguez-Barrueco R, Yu J, Saucedo-Cuevas LP, Olivan M, Llobet-Navas D, Putcha P, Castro V, Murga-Penas EM, Collazo-Lorduy A, Castillo-Martin M, Alvarez M, Cordon-Cardo C, Kalinsky K, Maurer M, Califano A, Silva JM.Genes Dev. 2015 Aug 1;29(15):1631-48. doi: 10.1101/gad.262642.115. Epub 2015 Jul 30.PMID: 26227964

Master regulators used as breast cancer metastasis classifier.

Lim WK, Lyashenko E, Califano A.Pac Symp Biocomput. 2009:504-15.PMID: 19209726 Free

 

Additional References

 

  1. Khamsi R: Computing cancer’s weak spots. Science 2020, 368(6496):1174-1177.
  2. Chen H, Li C, Peng X, Zhou Z, Weinstein JN, Liang H: A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples. Cell 2018, 173(2):386-399 e312.
  3. Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, Califano A: Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nature genetics 2016, 48(8):838-847.

 

Other articles of Note on this Open Access Online Journal Include:

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

 

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Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 3:00 PM-5:30 PM Educational Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

Register for FREE at https://www.aacr.org/

uesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Bioinformatics and Systems Biology

The Clinical Proteomic Tumor Analysis Consortium: Resources and Data Dissemination

This session will provide information regarding methodologic and computational aspects of proteogenomic analysis of tumor samples, particularly in the context of clinical trials. Availability of comprehensive proteomic and matching genomic data for tumor samples characterized by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) program will be described, including data access procedures and informatic tools under development. Recent advances on mass spectrometry-based targeted assays for inclusion in clinical trials will also be discussed.

Amanda G Paulovich, Shankha Satpathy, Meenakshi Anurag, Bing Zhang, Steven A Carr

Methods and tools for comprehensive proteogenomic characterization of bulk tumor to needle core biopsies

Shankha Satpathy
  • TCGA has 11,000 cancers with >20,000 somatic alterations but only 128 proteins as proteomics was still young field
  • CPTAC is NCI proteomic effort
  • Chemical labeling approach now method of choice for quantitative proteomics
  • Looked at ovarian and breast cancers: to measure PTM like phosphorylated the sample preparation is critical

 

Data access and informatics tools for proteogenomics analysis

Bing Zhang
  • Raw and processed data (raw MS data) with linked clinical data can be extracted in CPTAC
  • Python scripts are available for bioinformatic programming

 

Pathways to clinical translation of mass spectrometry-based assays

Meenakshi Anurag

·         Using kinase inhibitor pulldown (KIP) assay to identify unique kinome profiles

·         Found single strand break repair defects in endometrial luminal cases, especially with immune checkpoint prognostic tumors

·         Paper: JNCI 2019 analyzed 20,000 genes correlated with ET resistant in luminal B cases (selected for a list of 30 genes)

·         Validated in METABRIC dataset

·         KIP assay uses magnetic beads to pull out kinases to determine druggable kinases

·         Looked in xenografts and was able to pull out differential kinomes

·         Matched with PDX data so good clinical correlation

·         Were able to detect ESR1 fusion correlated with ER+ tumors

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Survivorship

Artificial Intelligence and Machine Learning from Research to the Cancer Clinic

The adoption of omic technologies in the cancer clinic is giving rise to an increasing number of large-scale high-dimensional datasets recording multiple aspects of the disease. This creates the need for frameworks for translatable discovery and learning from such data. Like artificial intelligence (AI) and machine learning (ML) for the cancer lab, methods for the clinic need to (i) compare and integrate different data types; (ii) scale with data sizes; (iii) prove interpretable in terms of the known biology and batch effects underlying the data; and (iv) predict previously unknown experimentally verifiable mechanisms. Methods for the clinic, beyond the lab, also need to (v) produce accurate actionable recommendations; (vi) prove relevant to patient populations based upon small cohorts; and (vii) be validated in clinical trials. In this educational session we will present recent studies that demonstrate AI and ML translated to the cancer clinic, from prognosis and diagnosis to therapy.
NOTE: Dr. Fish’s talk is not eligible for CME credit to permit the free flow of information of the commercial interest employee participating.

Ron C. Anafi, Rick L. Stevens, Orly Alter, Guy Fish

Overview of AI approaches in cancer research and patient care

Rick L. Stevens
  • Deep learning is less likely to saturate as data increases
  • Deep learning attempts to learn multiple layers of information
  • The ultimate goal is prediction but this will be the greatest challenge for ML
  • ML models can integrate data validation and cross database validation
  • What limits the performance of cross validation is the internal noise of data (reproducibility)
  • Learning curves: not the more data but more reproducible data is important
  • Neural networks can outperform classical methods
  • Important to measure validation accuracy in training set. Class weighting can assist in development of data set for training set especially for unbalanced data sets

Discovering genome-scale predictors of survival and response to treatment with multi-tensor decompositions

Orly Alter
  • Finding patterns using SVD component analysis. Gene and SVD patterns match 1:1
  • Comparative spectral decompositions can be used for global datasets
  • Validation of CNV data using this strategy
  • Found Ras, Shh and Notch pathways with altered CNV in glioblastoma which correlated with prognosis
  • These predictors was significantly better than independent prognostic indicator like age of diagnosis

 

Identifying targets for cancer chronotherapy with unsupervised machine learning

Ron C. Anafi
  • Many clinicians have noticed that some patients do better when chemo is given at certain times of the day and felt there may be a circadian rhythm or chronotherapeutic effect with respect to side effects or with outcomes
  • ML used to determine if there is indeed this chronotherapy effect or can we use unstructured data to determine molecular rhythms?
  • Found a circadian transcription in human lung
  • Most dataset in cancer from one clinical trial so there might need to be more trials conducted to take into consideration circadian rhythms

Stratifying patients by live-cell biomarkers with random-forest decision trees

Stratifying patients by live-cell biomarkers with random-forest decision trees

Guy Fish CEO Cellanyx Diagnostics

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session
Tumor Biology, Molecular and Cellular Biology/Genetics, Bioinformatics and Systems Biology, Prevention Research

The Wound Healing that Never Heals: The Tumor Microenvironment (TME) in Cancer Progression

This educational session focuses on the chronic wound healing, fibrosis, and cancer “triad.” It emphasizes the similarities and differences seen in these conditions and attempts to clarify why sustained fibrosis commonly supports tumorigenesis. Importance will be placed on cancer-associated fibroblasts (CAFs), vascularity, extracellular matrix (ECM), and chronic conditions like aging. Dr. Dvorak will provide an historical insight into the triad field focusing on the importance of vascular permeability. Dr. Stewart will explain how chronic inflammatory conditions, such as the aging tumor microenvironment (TME), drive cancer progression. The session will close with a review by Dr. Cukierman of the roles that CAFs and self-produced ECMs play in enabling the signaling reciprocity observed between fibrosis and cancer in solid epithelial cancers, such as pancreatic ductal adenocarcinoma.

Harold F Dvorak, Sheila A Stewart, Edna Cukierman

 

The importance of vascular permeability in tumor stroma generation and wound healing

Harold F Dvorak

Aging in the driver’s seat: Tumor progression and beyond

Sheila A Stewart

Why won’t CAFs stay normal?

Edna Cukierman

 

Tuesday, June 23

3:00 PM – 5:00 PM EDT

 

 

 

 

 

 

 

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM
Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

Read Full Post »

Live Notes, Real Time Conference Coverage AACR 2020 #AACR20: Tuesday June 23, 2020 Noon-2:45 Educational Sessions


Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 Noon-2:45 Educational Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

Register for FREE at https://www.aacr.org/

 

Presidential Address

Elaine R Mardis, William N Hait

DETAILS

Welcome and introduction

William N Hait

 

Improving diagnostic yield in pediatric cancer precision medicine

Elaine R Mardis
  • Advent of genomics have revolutionized how we diagnose and treat lung cancer
  • We are currently needing to understand the driver mutations and variants where we can personalize therapy
  • PD-L1 and other checkpoint therapy have not really been used in pediatric cancers even though CAR-T have been successful
  • The incidence rates and mortality rates of pediatric cancers are rising
  • Large scale study of over 700 pediatric cancers show cancers driven by epigenetic drivers or fusion proteins. Need for transcriptomics.  Also study demonstrated that we have underestimated germ line mutations and hereditary factors.
  • They put together a database to nominate patients on their IGM Cancer protocol. Involves genetic counseling and obtaining germ line samples to determine hereditary factors.  RNA and protein are evaluated as well as exome sequencing. RNASeq and Archer Dx test to identify driver fusions
  • PECAN curated database from St. Jude used to determine driver mutations. They use multiple databases and overlap within these databases and knowledge base to determine or weed out false positives
  • They have used these studies to understand the immune infiltrate into recurrent cancers (CytoCure)
  • They found 40 germline cancer predisposition genes, 47 driver somatic fusion proteins, 81 potential actionable targets, 106 CNV, 196 meaningful somatic driver mutations

 

 

Tuesday, June 23

12:00 PM – 12:30 PM EDT

Awards and Lectures

NCI Director’s Address

Norman E Sharpless, Elaine R Mardis

DETAILS

Introduction: Elaine Mardis

 

NCI Director Address: Norman E Sharpless
  • They are functioning well at NCI with respect to grant reviews, research, and general functions in spite of the COVID pandemic and the massive demonstrations on also focusing on the disparities which occur in cancer research field and cancer care
  • There are ongoing efforts at NCI to make a positive difference in racial injustice, diversity in the cancer workforce, and for patients as well
  • Need a diverse workforce across the cancer research and care spectrum
  • Data show that areas where the clinicians are successful in putting African Americans on clinical trials are areas (geographic and site specific) where health disparities are narrowing
  • Grants through NCI new SeroNet for COVID-19 serologic testing funded by two RFAs through NIAD (RFA-CA-30-038 and RFA-CA-20-039) and will close on July 22, 2020

 

Tuesday, June 23

12:45 PM – 1:46 PM EDT

Virtual Educational Session

Immunology, Tumor Biology, Experimental and Molecular Therapeutics, Molecular and Cellular Biology/Genetics

Tumor Immunology and Immunotherapy for Nonimmunologists: Innovation and Discovery in Immune-Oncology

This educational session will update cancer researchers and clinicians about the latest developments in the detailed understanding of the types and roles of immune cells in tumors. It will summarize current knowledge about the types of T cells, natural killer cells, B cells, and myeloid cells in tumors and discuss current knowledge about the roles these cells play in the antitumor immune response. The session will feature some of the most promising up-and-coming cancer immunologists who will inform about their latest strategies to harness the immune system to promote more effective therapies.

Judith A Varner, Yuliya Pylayeva-Gupta

 

Introduction

Judith A Varner
New techniques reveal critical roles of myeloid cells in tumor development and progression
  • Different type of cells are becoming targets for immune checkpoint like myeloid cells
  • In T cell excluded or desert tumors T cells are held at periphery so myeloid cells can infiltrate though so macrophages might be effective in these immune t cell naïve tumors, macrophages are most abundant types of immune cells in tumors
  • CXCLs are potential targets
  • PI3K delta inhibitors,
  • Reduce the infiltrate of myeloid tumor suppressor cells like macrophages
  • When should we give myeloid or T cell therapy is the issue
Judith A Varner
Novel strategies to harness T-cell biology for cancer therapy
Positive and negative roles of B cells in cancer
Yuliya Pylayeva-Gupta
New approaches in cancer immunotherapy: Programming bacteria to induce systemic antitumor immunity

 

 

Tuesday, June 23

12:45 PM – 1:46 PM EDT

Virtual Educational Session

Cancer Chemistry

Chemistry to the Clinic: Part 2: Irreversible Inhibitors as Potential Anticancer Agents

There are numerous examples of highly successful covalent drugs such as aspirin and penicillin that have been in use for a long period of time. Despite historical success, there was a period of reluctance among many to purse covalent drugs based on concerns about toxicity. With advances in understanding features of a well-designed covalent drug, new techniques to discover and characterize covalent inhibitors, and clinical success of new covalent cancer drugs in recent years, there is renewed interest in covalent compounds. This session will provide a broad look at covalent probe compounds and drug development, including a historical perspective, examination of warheads and electrophilic amino acids, the role of chemoproteomics, and case studies.

Benjamin F Cravatt, Richard A. Ward, Sara J Buhrlage

 

Discovering and optimizing covalent small-molecule ligands by chemical proteomics

Benjamin F Cravatt
  • Multiple approaches are being investigated to find new covalent inhibitors such as: 1) cysteine reactivity mapping, 2) mapping cysteine ligandability, 3) and functional screening in phenotypic assays for electrophilic compounds
  • Using fluorescent activity probes in proteomic screens; have broad useability in the proteome but can be specific
  • They screened quiescent versus stimulated T cells to determine reactive cysteines in a phenotypic screen and analyzed by MS proteomics (cysteine reactivity profiling); can quantitate 15000 to 20,000 reactive cysteines
  • Isocitrate dehydrogenase 1 and adapter protein LCP-1 are two examples of changes in reactive cysteines they have seen using this method
  • They use scout molecules to target ligands or proteins with reactive cysteines
  • For phenotypic screens they first use a cytotoxic assay to screen out toxic compounds which just kill cells without causing T cell activation (like IL10 secretion)
  • INTERESTINGLY coupling these MS reactive cysteine screens with phenotypic screens you can find NONCANONICAL mechanisms of many of these target proteins (many of the compounds found targets which were not predicted or known)

Electrophilic warheads and nucleophilic amino acids: A chemical and computational perspective on covalent modifier

The covalent targeting of cysteine residues in drug discovery and its application to the discovery of Osimertinib

Richard A. Ward
  • Cysteine activation: thiolate form of cysteine is a strong nucleophile
  • Thiolate form preferred in polar environment
  • Activation can be assisted by neighboring residues; pKA will have an effect on deprotonation
  • pKas of cysteine vary in EGFR
  • cysteine that are too reactive give toxicity while not reactive enough are ineffective

 

Accelerating drug discovery with lysine-targeted covalent probes

 

Tuesday, June 23

12:45 PM – 2:15 PM EDT

Virtual Educational Session

Molecular and Cellular Biology/Genetics

Virtual Educational Session

Tumor Biology, Immunology

Metabolism and Tumor Microenvironment

This Educational Session aims to guide discussion on the heterogeneous cells and metabolism in the tumor microenvironment. It is now clear that the diversity of cells in tumors each require distinct metabolic programs to survive and proliferate. Tumors, however, are genetically programmed for high rates of metabolism and can present a metabolically hostile environment in which nutrient competition and hypoxia can limit antitumor immunity.

Jeffrey C Rathmell, Lydia Lynch, Mara H Sherman, Greg M Delgoffe

 

T-cell metabolism and metabolic reprogramming antitumor immunity

Jeffrey C Rathmell

Introduction

Jeffrey C Rathmell

Metabolic functions of cancer-associated fibroblasts

Mara H Sherman

Tumor microenvironment metabolism and its effects on antitumor immunity and immunotherapeutic response

Greg M Delgoffe
  • Multiple metabolites, reactive oxygen species within the tumor microenvironment; is there heterogeneity within the TME metabolome which can predict their ability to be immunosensitive
  • Took melanoma cells and looked at metabolism using Seahorse (glycolysis): and there was vast heterogeneity in melanoma tumor cells; some just do oxphos and no glycolytic metabolism (inverse Warburg)
  • As they profiled whole tumors they could separate out the metabolism of each cell type within the tumor and could look at T cells versus stromal CAFs or tumor cells and characterized cells as indolent or metabolic
  • T cells from hyerglycolytic tumors were fine but from high glycolysis the T cells were more indolent
  • When knock down glucose transporter the cells become more glycolytic
  • If patient had high oxidative metabolism had low PDL1 sensitivity
  • Showed this result in head and neck cancer as well
  • Metformin a complex 1 inhibitor which is not as toxic as most mito oxphos inhibitors the T cells have less hypoxia and can remodel the TME and stimulate the immune response
  • Metformin now in clinical trials
  • T cells though seem metabolically restricted; T cells that infiltrate tumors are low mitochondrial phosph cells
  • T cells from tumors have defective mitochondria or little respiratory capacity
  • They have some preliminary findings that metabolic inhibitors may help with CAR-T therapy

Obesity, lipids and suppression of anti-tumor immunity

Lydia Lynch
  • Hypothesis: obesity causes issues with anti tumor immunity
  • Less NK cells in obese people; also produce less IFN gamma
  • RNASeq on NOD mice; granzymes and perforins at top of list of obese downregulated
  • Upregulated genes that were upregulated involved in lipid metabolism
  • All were PPAR target genes
  • NK cells from obese patients takes up palmitate and this reduces their glycolysis but OXPHOS also reduced; they think increased FFA basically overloads mitochondria
  • PPAR alpha gamma activation mimics obesity

 

 

Tuesday, June 23

12:45 PM – 2:45 PM EDT

Virtual Educational Session

Clinical Research Excluding Trials

The Evolving Role of the Pathologist in Cancer Research

Long recognized for their role in cancer diagnosis and prognostication, pathologists are beginning to leverage a variety of digital imaging technologies and computational tools to improve both clinical practice and cancer research. Remarkably, the emergence of artificial intelligence (AI) and machine learning algorithms for analyzing pathology specimens is poised to not only augment the resolution and accuracy of clinical diagnosis, but also fundamentally transform the role of the pathologist in cancer science and precision oncology. This session will discuss what pathologists are currently able to achieve with these new technologies, present their challenges and barriers, and overview their future possibilities in cancer diagnosis and research. The session will also include discussions of what is practical and doable in the clinic for diagnostic and clinical oncology in comparison to technologies and approaches primarily utilized to accelerate cancer research.

 

Jorge S Reis-Filho, Thomas J Fuchs, David L Rimm, Jayanta Debnath

DETAILS

Tuesday, June 23

12:45 PM – 2:45 PM EDT

 

High-dimensional imaging technologies in cancer research

David L Rimm

  • Using old methods and new methods; so cell counting you use to find the cells then phenotype; with quantification like with Aqua use densitometry of positive signal to determine a threshold to determine presence of a cell for counting
  • Hiplex versus multiplex imaging where you have ten channels to measure by cycling of flour on antibody (can get up to 20plex)
  • Hiplex can be coupled with Mass spectrometry (Imaging Mass spectrometry, based on heavy metal tags on mAbs)
  • However it will still take a trained pathologist to define regions of interest or field of desired view

 

Introduction

Jayanta Debnath

Challenges and barriers of implementing AI tools for cancer diagnostics

Jorge S Reis-Filho

Implementing robust digital pathology workflows into clinical practice and cancer research

Jayanta Debnath

Invited Speaker

Thomas J Fuchs
  • Founder of spinout of Memorial Sloan Kettering
  • Separates AI from computational algothimic
  • Dealing with not just machines but integrating human intelligence
  • Making decision for the patients must involve human decision making as well
  • How do we get experts to do these decisions faster
  • AI in pathology: what is difficult? =è sandbox scenarios where machines are great,; curated datasets; human decision support systems or maps; or try to predict nature
  • 1) learn rules made by humans; human to human scenario 2)constrained nature 3)unconstrained nature like images and or behavior 4) predict nature response to nature response to itself
  • In sandbox scenario the rules are set in stone and machines are great like chess playing
  • In second scenario can train computer to predict what a human would predict
  • So third scenario is like driving cars
  • System on constrained nature or constrained dataset will take a long time for commuter to get to decision
  • Fourth category is long term data collection project
  • He is finding it is still finding it is still is difficult to predict nature so going from clinical finding to prognosis still does not have good predictability with AI alone; need for human involvement
  • End to end partnering (EPL) is a new way where humans can get more involved with the algorithm and assist with the problem of constrained data
  • An example of a workflow for pathology would be as follows from Campanella et al 2019 Nature Medicine: obtain digital images (they digitized a million slides), train a massive data set with highthroughput computing (needed a lot of time and big software developing effort), and then train it using input be the best expert pathologists (nature to human and unconstrained because no data curation done)
  • Led to first clinically grade machine learning system (Camelyon16 was the challenge for detecting metastatic cells in lymph tissue; tested on 12,000 patients from 45 countries)
  • The first big hurdle was moving from manually annotated slides (which was a big bottleneck) to automatically extracted data from path reports).
  • Now problem is in prediction: How can we bridge the gap from predicting humans to predicting nature?
  • With an AI system pathologist drastically improved the ability to detect very small lesions

 

Virtual Educational Session

Epidemiology

Cancer Increases in Younger Populations: Where Are They Coming from?

Incidence rates of several cancers (e.g., colorectal, pancreatic, and breast cancers) are rising in younger populations, which contrasts with either declining or more slowly rising incidence in older populations. Early-onset cancers are also more aggressive and have different tumor characteristics than those in older populations. Evidence on risk factors and contributors to early-onset cancers is emerging. In this Educational Session, the trends and burden, potential causes, risk factors, and tumor characteristics of early-onset cancers will be covered. Presenters will focus on colorectal and breast cancer, which are among the most common causes of cancer deaths in younger people. Potential mechanisms of early-onset cancers and racial/ethnic differences will also be discussed.

Stacey A. Fedewa, Xavier Llor, Pepper Jo Schedin, Yin Cao

Cancers that are and are not increasing in younger populations

Stacey A. Fedewa

 

  • Early onset cancers, pediatric cancers and colon cancers are increasing in younger adults
  • Younger people are more likely to be uninsured and these are there most productive years so it is a horrible life event for a young adult to be diagnosed with cancer. They will have more financial hardship and most (70%) of the young adults with cancer have had financial difficulties.  It is very hard for women as they are on their childbearing years so additional stress
  • Types of early onset cancer varies by age as well as geographic locations. For example in 20s thyroid cancer is more common but in 30s it is breast cancer.  Colorectal and testicular most common in US.
  • SCC is decreasing by adenocarcinoma of the cervix is increasing in women’s 40s, potentially due to changing sexual behaviors
  • Breast cancer is increasing in younger women: maybe etiologic distinct like triple negative and larger racial disparities in younger African American women
  • Increased obesity among younger people is becoming a factor in this increasing incidence of early onset cancers

 

 

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

 

Read Full Post »


Live Conference Coverage AACR 2020 in Real Time: Monday June 22, 2020 Mid Day Sessions

Reporter: Stephen J. Williams, PhD

This post will be UPDATED during the next two days with notes from recordings from other talks

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

 

 

 

 

 

 

 

Register for FREE at https://www.aacr.org/

 

AACR VIRTUAL ANNUAL MEETING II

 

June 22-24: Free Registration for AACR Members, the Cancer Community, and the Public
This virtual meeting will feature more than 120 sessions and 4,000 e-posters, including sessions on cancer health disparities and the impact of COVID-19 on clinical trials

 

This Virtual Meeting is Part II of the AACR Annual Meeting.  Part I was held online in April and was centered only on clinical findings.  This Part II of the virtual meeting will contain all the Sessions and Abstracts pertaining to basic and translational cancer research as well as clinical trial findings.

 

REGISTER NOW

 

Pezcoller Foundation-AACR International Award for Extraordinary Achievement in Cancer Research

The prestigious Pezcoller Foundation-AACR International Award for Extraordinary Achievement in Cancer Research was established in 1997 to annually recognize a scientist of international renown who has made a major scientific discovery in basic cancer research OR who has made significant contributions to translational cancer research; who continues to be active in cancer research and has a record of recent, noteworthy publications; and whose ongoing work holds promise for continued substantive contributions to progress in the field of cancer. For more information regarding the 2020 award recipient go to aacr.org/awards.

John E. Dick, Enzo Galligioni, David A Tuveson

DETAILS

Awardee: John E. Dick
Princess Anne Margaret Cancer Center, Toronto, Ontario
For determining how stem cells contribute to normal and leukemic hematopoeisis
  • not every cancer cell equal in their Cancer Hallmarks
  • how do we monitor and measure clonal dynamics
  • Barnie Clarkson did pivotal work on this
  • most cancer cells are post mitotic but minor populations of cells were dormant and survive chemotherapy
  •  only one cell is 1 in a million can regenerate and transplantable in mice and experiments with flow cytometry resolved the question of potency and repopulation of only small percentage of cells and undergo long term clonal population
  • so instead of going to cell lines and using thousands of shRNA looked at clinical data and deconvoluted the genetic information (RNASeq data) to determine progenitor and mature populations (how much is stem and how much is mature populations)
  • in leukemic patients they have seen massive expansion of a single stem cell population so only need one cell in AML if the stem cells have the mutational hits early on in their development
  • finding the “seeds of relapse”: finding the small subpopulation of stem cells that will relapse
  • they looked in BALL;;  there are cells resistant to l-aspariginase, dexamethasone, and vincristine
  • a lot of OXPHOS related genes (in DRIs) that may be the genes involved in this resistance
  • it a wonderful note of acknowledgement he dedicated this award to all of his past and present trainees who were the ones, as he said, made this field into what it is and for taking it into directions none of them could forsee

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Experimental and Molecular Therapeutics, Drug Development, Cancer Chemistry

Chemistry to the Clinic: Part 1: Lead Optimization Case Studies in Cancer Drug Discovery

How can one continue to deliver innovative medicines to patients when biological targets are becoming ever scarcer and less amenable to therapeutic intervention? Are there sound strategies in place that can clear the path to targets previously considered “undruggable”? Recent advances in lead finding methods and novel technologies such as covalent screening and targeted protein degradation have enriched the toolbox at the disposal of drug discovery scientists to expand the druggable ta

Stefan N Gradl, Elena S Koltun, Scott D Edmondson, Matthew A. Marx, Joachim Rudolph

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Bioinformatics and Systems Biology, Molecular and Cellular Biology/Genetics

Informatics Technologies for Cancer Research

Cancer researchers are faced with a deluge of high-throughput data. Using these data to advance understanding of cancer biology and improve clinical outcomes increasingly requires effective use of computational and informatics tools. This session will introduce informatics resources that support the data management, analysis, visualization, and interpretation. The primary focus will be on high-throughput genomic data and imaging data. Participants will be introduced to fundamental concepts

Rachel Karchin, Daniel Marcus, Andriy Fedorov, Obi Lee Griffith

DETAILS

  • Variant analysis is the big bottleneck, especially interpretation of variants
  • CIVIC resource is a network for curation, interpretation of genetic variants
  • CIVIC curators go through multiple rounds of editors review
  • gene summaries, variant summaries
  • curation follows ACSME guidelines
  • evidences are accumulated, categories by various ontologies and is the heart of the reports
  • as this is a network of curators the knowledgebase expands
  • CIVIC is linked to multiple external informatic, clinical, and genetic databases
  • they have curated 7017 clinical interpretations, 2527 variants, using 2578 papers, and over 1000 curators
  • they are currently integrating with COSMIC ClinVar, and UniProt
  • they are partnering with ClinGen to expand network of curators and their curation effort
  • CIVIC uses a Python interface; available on website

https://civicdb.org/home

The Precision Medicine Revolution

Precision medicine refers to the use of prevention and treatment strategies that are tailored to the unique features of each individual and their disease. In the context of cancer this might involve the identification of specific mutations shown to predict response to a targeted therapy. The biomedical literature describing these associations is large and growing rapidly. Currently these interpretations exist largely in private or encumbered databases resulting in extensive repetition of effort.

CIViC’s Role in Precision Medicine

Realizing precision medicine will require this information to be centralized, debated and interpreted for application in the clinic. CIViC is an open access, open source, community-driven web resource for Clinical Interpretation of Variants in Cancer. Our goal is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations. For more details refer to the 2017 CIViC publication in Nature Genetics.

U24 funding announced: We are excited to announce that the Informatics Technology for Cancer Research (ICTR) program of the National Cancer Institute (NCI) has awarded funding to the CIViC team! Starting this year, a five-year, $3.7 million U24 award (CA237719), will support CIViC to develop Standardized and Genome-Wide Clinical Interpretation of Complex Genotypes for Cancer Precision Medicine.

Informatics tools for high-throughput analysis of cancer mutations

Rachel Karchin
  • CRAVAT is a platform to determine, categorize, and curate cancer mutations and cancer related variants
  • adding new tools used to be hard but having an open architecture allows for modular growth and easy integration of other tools
  • so they are actively making an open network using social media

Towards FAIR data in cancer imaging research

Andriy Fedorov, PhD

Towards the FAIR principles

While LOD has had some uptake across the web, the number of databases using this protocol compared to the other technologies is still modest. But whether or not we use LOD, we do need to ensure that databases are designed specifically for the web and for reuse by humans and machines. To provide guidance for creating such databases independent of the technology used, the FAIR principles were issued through FORCE11: the Future of Research Communications and e-Scholarship. The FAIR principles put forth characteristics that contemporary data resources, tools, vocabularies and infrastructures should exhibit to assist discovery and reuse by third-parties through the web. Wilkinson et al.,2016. FAIR stands for: Findable, Accessible, Interoperable and Re-usable. The definition of FAIR is provided in Table 1:

Number Principle
F Findable
F1 (meta)data are assigned a globally unique and persistent identifier
F2 data are described with rich metadata
F3 metadata clearly and explicitly include the identifier of the data it describes
F4 (meta)data are registered or indexed in a searchable resource
A Accessible
A1 (meta)data are retrievable by their identifier using a standardized communications protocol
A1.1 the protocol is open, free, and universally implementable
A1.2 the protocol allows for an authentication and authorization procedure, where necessary
A2 metadata are accessible, even when the data are no longer available
I Interoperable
I1 (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2 (meta)data use vocabularies that follow FAIR principles
I3 (meta)data include qualified references to other (meta)data
R Reusable
R1 meta(data) are richly described with a plurality of accurate and relevant attributes
R1.1 (meta)data are released with a clear and accessible data usage license
R1.2 (meta)data are associated with detailed provenance
R1.3 (meta)data meet domain-relevant community standards

A detailed explanation of each of these is included in the Wilkinson et al., 2016 article, and the Dutch Techcenter for Life Sciences has a set of excellent tutorials, so we won’t go into too much detail here.

  • for outside vendors to access their data, vendors would need a signed Material Transfer Agreement but NCI had formulated a framework to facilitate sharing of data using a DIACOM standard for imaging data

Monday, June 22

1:30 PM – 3:01 PM EDT

Virtual Educational Session

Experimental and Molecular Therapeutics, Cancer Chemistry, Drug Development, Immunology

Engineering and Physical Sciences Approaches in Cancer Research, Diagnosis, and Therapy

The engineering and physical science disciplines have been increasingly involved in the development of new approaches to investigate, diagnose, and treat cancer. This session will address many of these efforts, including therapeutic methods such as improvements in drug delivery/targeting, new drugs and devices to effect immunomodulation and to synergize with immunotherapies, and intraoperative probes to improve surgical interventions. Imaging technologies and probes, sensors, and bioma

Claudia Fischbach, Ronit Satchi-Fainaro, Daniel A Heller

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Survivorship

Exceptional Responders and Long-Term Survivors

How should we think about exceptional and super responders to cancer therapy? What biologic insights might ensue from considering these cases? What are ways in which considering super responders may lead to misleading conclusions? What are the pros and cons of the quest to locate exceptional and super responders?

Alice P Chen, Vinay K Prasad, Celeste Leigh Pearce

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Tumor Biology, Immunology

Exploiting Metabolic Vulnerabilities in Cancer

The reprogramming of cellular metabolism is a hallmark feature observed across cancers. Contemporary research in this area has led to the discovery of tumor-specific metabolic mechanisms and illustrated ways that these can serve as selective, exploitable vulnerabilities. In this session, four international experts in tumor metabolism will discuss new findings concerning the rewiring of metabolic programs in cancer that support metabolic fitness, biosynthesis, redox balance, and the reg

Costas Andreas Lyssiotis, Gina M DeNicola, Ayelet Erez, Oliver Maddocks

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

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Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM

Reporter: Stephen J. Williams, PhD

New Drugs on the Horizon: Part 3
Introduction

Andrew J. Phillips, C4 Therapeutics

  • symposium brought by AACR CICR and had about 30 proposals for talks and chose three talks
  • unfortunately the networking event is not possible but hope to see you soon in good health

ABBV-184: A novel survivin specific T cell receptor/CD3 bispecific therapeutic that targets both solid tumor and hematological malignancies

Edward B Reilly
AbbVie Inc. @abbvie

  • T-cell receptors (TCR) can recognize the intracellular targets whereas antibodies only recognize the 25% of potential extracellular targets
  • survivin is expressed in multiple cancers and correlates with poor survival and prognosis
  • CD3 bispecific TCR to survivn (Ab to CD3 on T- cells and TCR to survivin on cancer cells presented in MHC Class A3)
  • ABBV184  effective in vivo in lung cancer models as single agent;
  • in humanized mouse tumor models CD3/survivin bispecific can recruit T cells into solid tumors; multiple immune cells CD4 and CD8 positive T cells were found to infiltrate into tumor
  • therapeutic window as measured by cytokine release assays in tumor vs. normal cells very wide (>25 fold)
  • ABBV184 does not bind platelets and has good in vivo safety profile
  • First- in human dose determination trial: used in vitro cancer cell assays to determine 1st human dose
  • looking at AML and lung cancer indications
  • phase 1 trial is underway for safety and efficacy and determine phase 2 dose
  • survivin has very few mutations so they are not worried about a changing epitope of their target TCR peptide of choice

The discovery of TNO155: A first in class SHP2 inhibitor

Matthew J. LaMarche
Novartis @Novartis

  • SHP2 is an intracellular phosphatase that is upstream of MEK ERK pathway; has an SH2 domain and PTP domain
  • knockdown of SHP2 inhibits tumor growth and colony formation in soft agar
  • 55 TKIs there are very little phosphatase inhibitors; difficult to target the active catalytic site; inhibitors can be oxidized at the active site; so they tried to target the two domains and developed an allosteric inhibitor at binding site where three domains come together and stabilize it
  • they produced a number of chemical scaffolds that would bind and stabilize this allosteric site
  • block the redox reaction by blocking the cysteine in the binding site
  • lead compound had phototoxicity; used SAR analysis to improve affinity and reduce phototox effects
  • was very difficult to balance efficacy, binding properties, and tox by adjusting stuctures
  • TNO155 is their lead into trials
  • SHP2 expressed in T cells and they find good combo with I/O with uptick of CD8 cells
  • TNO155 is very selective no SHP1 inhibition; SHP2 can autoinhibit itself when three domains come together and stabilize; no cross reactivity with other phosphatases
  • they screened 1.5 million compounds and got low hit rate so that is why they needed to chemically engineer and improve on the classes they found as near hits

Closing Remarks

 

Xiaojing Wang
Genentech, Inc. @genentech

Follow on Twitter at:

@pharma_BI

@AACR

@CureCancerNow

@pharmanews

@BiotechWorld

@HopkinsMedicine

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Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Reporter: Stephen J. Williams, PhD

 Minisymposium: Evaluating Cancer Genomics from Normal Tissues through Evolution to Metastatic Disease

Oncologic therapy shapes the fitness landscape of clonal hematopoiesis

April 28, 2020, 4:10 PM – 4:20 PM

Presenter/Authors
Kelly L. Bolton, Ryan N. Ptashkin, Teng Gao, Lior Braunstein, Sean M. Devlin, Minal Patel, Antonin Berthon, Aijazuddin Syed, Mariko Yabe, Catherine Coombs, Nicole M. Caltabellotta, Mike Walsh, Ken Offit, Zsofia Stadler, Choonsik Lee, Paul Pharoah, Konrad H. Stopsack, Barbara Spitzer, Simon Mantha, James Fagin, Laura Boucai, Christopher J. Gibson, Benjamin Ebert, Andrew L. Young, Todd Druley, Koichi Takahashi, Nancy Gillis, Markus Ball, Eric Padron, David Hyman, Jose Baselga, Larry Norton, Stuart Gardos, Virginia Klimek, Howard Scher, Dean Bajorin, Eder Paraiso, Ryma Benayed, Maria Arcilla, Marc Ladanyi, David Solit, Michael Berger, Martin Tallman, Montserrat Garcia-Closas, Nilanjan Chatterjee, Luis Diaz, Ross Levine, Lindsay Morton, Ahmet Zehir, Elli Papaemmanuil. Memorial Sloan Kettering Cancer Center, New York, NY, University of North Carolina at Chapel Hill, Chapel Hill, NC, University of Cambridge, Cambridge, United Kingdom, Dana-Farber Cancer Institute, Boston, MA, Washington University, St Louis, MO, The University of Texas MD Anderson Cancer Center, Houston, TX, Moffitt Cancer Center, Tampa, FL, National Cancer Institute, Bethesda, MD

Abstract
Recent studies among healthy individuals show evidence of somatic mutations in leukemia-associated genes, referred to as clonal hematopoiesis (CH). To determine the relationship between CH and oncologic therapy we collected sequential blood samples from 525 cancer patients (median sampling interval time = 23 months, range: 6-53 months) of whom 61% received cytotoxic therapy or external beam radiation therapy and 39% received either targeted/immunotherapy or were untreated. Samples were sequenced using deep targeted capture-based platforms. To determine whether CH mutational features were associated with tMN risk, we performed Cox proportional hazards regression on 9,549 cancer patients exposed to oncologic therapy of whom 75 cases developed tMN (median time to transformation=26 months). To further compare the genetic and clonal relationships between tMN and the proceeding CH, we analyzed 35 cases for which paired samples were available. We compared the growth rate of the variant allele fraction (VAF) of CH clones across treatment modalities and in untreated patients. A significant increase in the growth rate of CH mutations was seen in DDR genes among those receiving cytotoxic (p=0.03) or radiation therapy (p=0.02) during the follow-up period compared to patients who did not receive therapy. Similar growth rates among treated and untreated patients were seen for non-DDR CH genes such as DNMT3A. Increasing cumulative exposure to cytotoxic therapy (p=0.01) and external beam radiation therapy (2×10-8) resulted in higher growth rates for DDR CH mutations. Among 34 subjects with at least two CH mutations in which one mutation was in a DDR gene and one in a non-DDR gene, we studied competing clonal dynamics for multiple gene mutations within the same patient. The risk of tMN was positively associated with CH in a known myeloid neoplasm driver mutation (HR=6.9, p<10-6), and increased with the total number of mutations and clone size. The strongest associations were observed for mutations in TP53 and for CH with mutations in spliceosome genes (SRSF2, U2AF1 and SF3B1). Lower hemoglobin, lower platelet counts, lower neutrophil counts, higher red cell distribution width and higher mean corpuscular volume were all positively associated with increased tMN risk. Among 35 cases for which paired samples were available, in 19 patients (59%), we found evidence of at least one of these mutations at the time of pre-tMN sequencing and in 13 (41%), we identified two or more in the pre-tMN sample. In all cases the dominant clone at tMN transformation was defined by a mutation seen at CH Our serial sampling data provide clear evidence that oncologic therapy strongly selects for clones with mutations in the DDR genes and that these clones have limited competitive fitness, in the absence of cytotoxic or radiation therapy. We further validate the relevance of CH as a predictor and precursor of tMN in cancer patients. We show that CH mutations detected prior to tMN diagnosis were consistently part of the dominant clone at tMN diagnosis and demonstrate that oncologic therapy directly promotes clones with mutations in genes associated with chemo-resistant disease such as TP53.

  • therapy resulted also in clonal evolution and saw changes in splice variants and spliceosome
  • therapy promotes current DDR mutations
  • clonal hematopoeisis due to selective pressures
  • mutations, variants number all predictive of myeloid disease
  • deferring adjuvant therapy for breast cancer patients with patients in highest MDS risk group based on biomarkers, greatly reduced their risk for MDS

5704 – Pan-cancer genomic characterization of patient-matched primary, extracranial, and brain metastases

Presenter/AuthorsOlivia W. Lee, Akash Mitra, Won-Chul Lee, Kazutaka Fukumura, Hannah Beird, Miles Andrews, Grant Fischer, John N. Weinstein, Michael A. Davies, Jason Huse, P. Andrew Futreal. The University of Texas MD Anderson Cancer Center, TX, The University of Texas MD Anderson Cancer Center, TX, Olivia Newton-John Cancer Research Institute and School of Cancer Medicine, La Trobe University, AustraliaDisclosures O.W. Lee: None. A. Mitra: None. W. Lee: None. K. Fukumura: None. H. Beird: None. M. Andrews: ; Merck Sharp and Dohme. G. Fischer: None. J.N. Weinstein: None. M.A. Davies: ; Bristol-Myers Squibb. ; Novartis. ; Array BioPharma. ; Roche and Genentech. ; GlaxoSmithKline. ; Sanofi-Aventis. ; AstraZeneca. ; Myriad Genetics. ; Oncothyreon. J. Huse: None. P. Futreal: None.

Abstract: Brain metastases (BM) occur in 10-30% of patients with cancer. Approximately 200,000 new cases of brain metastases are diagnosed in the United States annually, with median survival after diagnosis ranging from 3 to 27 months. Recently, studies have identified significant genetic differences between BM and their corresponding primary tumors. It has been shown that BM harbor clinically actionable mutations that are distinct from those in the primary tumor samples. Additional genomic profiling of BM will provide deeper understanding of the pathogenesis of BM and suggest new therapeutic approaches.
We performed whole-exome sequencing of BM and matched tumors from 41 patients collected from renal cell carcinoma (RCC), breast cancer, lung cancer, and melanoma, which are known to be more likely to develop BM. We profiled total 126 fresh-frozen tumor samples and performed subsequent analyses of BM in comparison to paired primary tumor and extracranial metastases (ECM). We found that lung cancer shared the largest number of mutations between BM and matched tumors (83%), followed by melanoma (74%), RCC (51%), and Breast (26%), indicating that cancer type with high tumor mutational burden share more mutations with BM. Mutational signatures displayed limited differences, suggesting a lack of mutagenic processes specific to BM. However, point-mutation heterogeneity revealed that BM evolve separately into different subclones from their paired tumors regardless of cancer type, and some cancer driver genes were found in BM-specific subclones. These models and findings suggest that these driver genes may drive prometastatic subclones that lead to BM. 32 curated cancer gene mutations were detected and 71% of them were shared between BM and primary tumors or ECM. 29% of mutations were specific to BM, implying that BM often accumulate additional cancer gene mutations that are not present in primary tumors or ECM. Co-mutation analysis revealed a high frequency of TP53 nonsense mutation in BM, mostly in the DNA binding domain, suggesting TP53 nonsense mutation as a possible prerequisite for the development of BM. Copy number alteration analysis showed statistically significant differences between BM and their paired tumor samples in each cancer type (Wilcoxon test, p < 0.0385 for all). Both copy number gains and losses were consistently higher in BM for breast cancer (Wilcoxon test, p =1.307e-5) and lung cancer (Wilcoxon test, p =1.942e-5), implying greater genomic instability during the evolution of BM.
Our findings highlight that there are more unique mutations in BM, with significantly higher copy number alterations and tumor mutational burden. These genomic analyses could provide an opportunity for more reliable diagnostic decision-making, and these findings will be further tested with additional transcriptomic and epigenetic profiling for better characterization of BM-specific tumor microenvironments.

  • are there genomic signatures different in brain mets versus non metastatic or normal?
  • 32 genes from curated databases were different between brain mets and primary tumor
  • frequent nonsense mutations in TP53
  • divergent clonal evolution of drivers in BMets from primary
  • they were able to match BM with other mutational signatures like smokers and lung cancer signatures

5707 – A standard operating procedure for the interpretation of oncogenicity/pathogenicity of somatic mutations

Presenter/AuthorsPeter Horak, Malachi Griffith, Arpad Danos, Beth A. Pitel, Subha Madhavan, Xuelu Liu, Jennifer Lee, Gordana Raca, Shirley Li, Alex H. Wagner, Shashikant Kulkarni, Obi L. Griffith, Debyani Chakravarty, Dmitriy Sonkin. National Center for Tumor Diseases, Heidelberg, Germany, Washington University School of Medicine, St. Louis, MO, Mayo Clinic, Rochester, MN, Georgetown University Medical Center, Washington, DC, Dana-Farber Cancer Institute, Boston, MA, Frederick National Laboratory for Cancer Research, Rockville, MD, University of Southern California, Los Angeles, CA, Sunquest, Boston, MA, Baylor College of Medicine, Houston, TX, Memorial Sloan Kettering Cancer Center, New York, NY, National Cancer Institute, Rockville, MDDisclosures P. Horak: None. M. Griffith: None. A. Danos: None. B.A. Pitel: None. S. Madhavan: ; Perthera Inc. X. Liu: None. J. Lee: None. G. Raca: None. S. Li: ; Sunquest Information Systems, Inc. A.H. Wagner: None. S. Kulkarni: ; Baylor Genetics. O.L. Griffith: None. D. Chakravarty: None. D. Sonkin: None.AbstractSomatic variants in cancer-relevant genes are interpreted from multiple partially overlapping perspectives. When considered in discovery and translational research endeavors, it is important to determine if a particular variant observed in a gene of interest is oncogenic/pathogenic or not, as such knowledge provides the foundation on which targeted cancer treatment research is based. In contrast, clinical applications are dominated by diagnostic, prognostic, or therapeutic interpretations which in part also depends on underlying variant oncogenicity/pathogenicity. The Association for Molecular Pathology, the American Society of Clinical Oncology, and the College of American Pathologists (AMP/ASCO/CAP) have published structured somatic variant clinical interpretation guidelines which specifically address diagnostic, prognostic, and therapeutic implications. These guidelines have been well-received by the oncology community. Many variant knowledgebases, clinical laboratories/centers have adopted or are in the process of adopting these guidelines. The AMP/ASCO/CAP guidelines also describe different data types which are used to determine oncogenicity/pathogenicity of a variant, such as: population frequency, functional data, computational predictions, segregation, and somatic frequency. A second collaborative effort created the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets to provide a harmonized vocabulary that provides an evidence-based ranking system of molecular targets that supports their value as clinical targets. However, neither of these clinical guideline systems provide systematic and comprehensive procedures for aggregating population frequency, functional data, computational predictions, segregation, and somatic frequency to consistently interpret variant oncogenicity/pathogenicity, as has been published in the ACMG/AMP guidelines for interpretation of pathogenicity of germline variants. In order to address this unmet need for somatic variant oncogenicity/pathogenicity interpretation procedures, the Variant Interpretation for Cancer Consortium (VICC, a GA4GH driver project) Knowledge Curation and Interpretation Standards (KCIS) working group (WG) has developed a Standard Operating Procedure (SOP) with contributions from members of ClinGen Somatic Clinical Domain WG, and ClinGen Somatic/Germline variant curation WG using an approach similar to the ACMG/AMP germline pathogenicity guidelines to categorize evidence of oncogenicity/pathogenicity as very strong, strong, moderate or supporting. This SOP enables consistent and comprehensive assessment of oncogenicity/pathogenicity of somatic variants and latest version of an SOP can be found at https://cancervariants.org/wg/kcis/.

  • best to use this SOP for somatic mutations and not rearangements
  • variants based on oncogenicity as strong to weak
  • useful variant knowledge on pathogenicity curated from known databases
  • the recommendations would provide some guideline on curating unknown somatic variants versus known variants of hereditary diseases
  • they have not curated RB1 mutations or variants (or for other RBs like RB2? p130?)

 

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Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

Reporter: Stephen J. Williams, PhD

 

Session VMS.ET04.01 – Novel Targets and Therapies

Targeting chromatin remodeling-associated genetic vulnerabilities in cancer: PBRM1 defects are synthetic lethal with PARP and ATR inhibitors

Presenter/AuthorsRoman Merial Chabanon, Daphné Morel, Léo Colmet-Daage, Thomas Eychenne, Nicolas Dorvault, Ilirjana Bajrami, Marlène Garrido, Suzanna Hopkins, Cornelia Meisenberg, Andrew Lamb, Theo Roumeliotis, Samuel Jouny, Clémence Astier, Asha Konde, Geneviève Almouzni, Jyoti Choudhary, Jean-Charles Soria, Jessica Downs, Christopher J. Lord, Sophie Postel-Vinay. Gustave Roussy, Villejuif, France, The Francis Crick Institute, London, United Kingdom, Institute of Cancer Research, London, United Kingdom, Sage Bionetworks, Seattle, WA, Institute of Cancer Research, London, United Kingdom, Institute of Cancer Research, London, United Kingdom, Institut Curie, Paris, France, Université Paris-Sud/Université Paris-Saclay, Le Kremlin-Bicêtre, France, Gustave Roussy Cancer Campus and U981 INSERM, ATIP-Avenir group, Villejuif, FranceDisclosures R.M. Chabanon: None. D. Morel: None. L. Colmet-Daage: None. T. Eychenne: None. N. Dorvault: None. I. Bajrami: None. M. Garrido: None. S. Hopkins: ; Fishawack Group of Companies. C. Meisenberg: None. A. Lamb: None. T. Roumeliotis: None. S. Jouny: None. C. Astier: None. A. Konde: None. G. Almouzni: None. J. Choudhary: None. J. Soria: ; Medimmune/AstraZeneca. ; Astex. ; Gritstone. ; Clovis. ; GSK. ; GamaMabs. ; Lilly. ; MSD. ; Mission Therapeutics. ; Merus. ; Pfizer. ; PharmaMar. ; Pierre Fabre. ; Roche/Genentech. ; Sanofi. ; Servier. ; Symphogen. ; Takeda. J. Downs: None. C.J. Lord: ; AstraZeneca. ; Merck KGaA. ; Artios. ; Tango. ; Sun Pharma. ; GLG. ; Vertex. ; Ono Pharma. ; Third Rock Ventures. S. Postel-Vinay: ; Merck KGaA. ; Principal investigator of clinical trials for Gustave Roussy.; Boehringer Ingelheim. ; Principal investigator of clinical trials for Gustave Roussy.; Roche. ; Principal investigator of clinical trials for Gustave Roussy. Benefited from reimbursement for attending symposia.; AstraZeneca. ; Principal investigator of clinical trials for Gustave Roussy.; Clovis. ; Principal investigator of clinical trials for Gustave Roussy.; Bristol-Myers Squibb. ; Principal investigator of clinical trials for Gustave Roussy.; Agios. ; Principal investigator of clinical trials for Gustave Roussy.; GSK.AbstractAim: Polybromo-1 (PBRM1), a specific subunit of the pBAF chromatin remodeling complex, is frequently inactivated in cancer. For example, 40% of clear cell Renal Cell Carcinoma (ccRCC) and 15% of cholangiocarcinoma present deleterious PBRM1 mutations. There is currently no precision medicine-based therapeutic approach that targets PBRM1 defects. To identify novel, targeted therapeutic strategies for PBRM1-defective cancers, we carried out high-throughput functional genomics and drug screenings followed by in vitro and in vivo validation studies.
Methods: High-throughput siRNA-drug sensitization and drug sensitivity screens evaluating > 150 cancer-relevant small molecules in dose-response were performed in Pbrm1 siRNA-transfected mouse embryonic stem cells (mES) and isogenic PBRM1-KO or -WT HAP1 cells, respectively. After identification of PBRM1-selective small molecules, revalidation was carried out in a series of in-house-generated isogenic models of PBRM1 deficiency – including 786-O (ccRCC), A498 (ccRCC), U2OS (osteosarcoma) and H1299 (non-small cell lung cancer) human cancer cell lines – and non-isogenic ccRCC models, using multiple clinical compounds. Mechanistic dissection was performed using immunofluorescence, RT-qPCR, western blotting, DNA fiber assay, transcriptomics, proteomics and DRIP-sequencing to evaluate markers of DNA damage response (DDR), replication stress and cell-autonomous innate immune signaling. Preclinical data were integrated with TCGA tumor data.
Results: Parallel high-throughput drug screens independently identified PARP inhibitors (PARPi) as being synthetic lethal with PBRM1 defects – a cell type-independent effect which was exacerbated by ATR inhibitors (ATRi) and which we revalidated in vitro in isogenic and non-isogenic systems and in vivo in a xenograft model. PBRM1 defects were associated with increased replication fork stress (higher γH2AX and RPA foci levels, decreased replication fork speed and increased ATM checkpoint activation), R-loop accumulation and enhanced genomic instability in vitro; these effects were exacerbated upon PARPi exposure. In patient tumor samples, we also found that PBRM1-mutant cancers possessed a higher mutational load. Finally, we found that ATRi selectively activated the cGAS/STING cytosolic DNA sensing pathway in PBRM1-deficient cells, resulting in increased expression of type I interferon genes.
Conclusion: PBRM1-defective cancer cells present increased replication fork stress, R-loop formation, genome instability and are selectively sensitive to PARPi and ATRi through a synthetic lethal mechanism that is cell type-independent. Our data provide the pre-clinical rationale for assessing PARPi as a monotherapy or in combination with ATRi or immune-modulating agents in molecularly-selected patients with PBRM1-defective cancers.

1057 – Targeting MTHFD2 using first-in-class inhibitors kills haematological and solid cancer through thymineless-induced replication stress

Presenter/AuthorsThomas Helleday. University of Sheffield, Sheffield, United KingdomDisclosures T. Helleday: None.AbstractSummary
Thymidine synthesis pathways are upregulated pathways in cancer. Since the 1940s, targeting nucleotide and folate metabolism to induce thymineless death has remained first-line anti-cancer treatment. Recent discoveries that showing cancer cells have rewired networks and exploit unique enzymes for proliferation, have renewed interest in metabolic pathways. The cancer-specific expression of MTHFD2 has gained wide-spread attention and here we describe an emerging role for MTHFD2 in the DNA damage response (DDR). The folate metabolism enzyme MTHFD2 is one of the most consistently overexpressed metabolic enzymes in cancer and an emerging anticancer target. We show a novel role for MTHFD2 being essential for DNA replication and genomic stability in cancer cells. We describe first-in-class nanomolar MTHFD2 inhibitors (MTHFD2i), with protein co-crystal structures demonstrating binding in the active site of MTHFD2 and engaging with the target in cells and tumours. We show MTHFD2i reduce replication fork speed and induce replication stress, followed by S phase arrest, apoptosis and killing of a range of haematological and solid cancer cells in vitro and in vivo, with a therapeutic window spanning up to four orders of magnitude compared to non-transformed cells. Mechanistically, MTHFD2i prevent thymidine production leading to mis-incorporation of uracil into DNA and replication stress. As MTHFD2 expression is cancer specific there is a potential of MTHFD2i to synergize with other treatments. Here, we show MTHFD2i synergize with dUTPase inhibitors as well as other DDR inhibitors and demonstrate the mechanism of action. These results demonstrate a new link between MTHFD2-dependent cancer metabolism and replication stress that can be exploited therapeutically.
Keywords
MTHFD2, one-carbon metabolism, folate metabolism, DNA replication, replication stress, synthetic lethal, thymineless death, small-molecule inhibitor, DNA damage response

 

 

1060 – Genetic and pharmacologic inhibition of Skp2, an E3 ubiquitin ligase and RB1-target, has antitumor activity in RB1-deficient human and mouse small cell lung cancer (SCLC)

Presenter/Authors
Hongling ZhaoVineeth SukrithanNiloy IqbalCari NicholasYingjiao XueJoseph LockerJuntao ZouLiang ZhuEdward L. Schwartz. Albert Einstein College of Medicine, Bronx, NY, Albert Einstein College of Medicine, Bronx, NY, Albert Einstein College of Medicine, Bronx, NY, University of Pittsburgh Medical Center, Pittsburgh, PA, Albert Einstein College of Medicine, Bronx, NY
Disclosures
 H. Zhao: None. V. Sukrithan: None. N. Iqbal: None. C. Nicholas: None. Y. Xue: None. J. Locker: None. J. Zou: None. L. Zhu: None. E.L. Schwartz: None.
Abstract
The identification of driver mutations and their corresponding targeted drugs has led to significant improvements in the treatment of non-small cell lung cancer (NSCLC) and other solid tumors; however, similar advances have not been made in the treatment of small cell lung cancer (SCLC). Due to their aggressive growth, frequent metastases, and resistance to chemotherapy, the five-year overall survival of SCLC is less than 5%. While SCLC tumors can be sensitive to first-line therapy of cisplatin and etoposide, most patients relapse, often in less than 3 months after initial therapy. Dozens of drugs have been tested clinically in SCLC, including more than 40 agents that have failed in phase III trials.
The near uniform bi-allelic inactivation of the tumor suppressor gene RB1 is a defining feature of SCLC. RB1 is mutated in highly aggressive tumors, including SCLC, where its functional loss, along with that of TP53, is both required and sufficient for tumorigenesis. While it is known that RB1 mutant cells fail to arrest at G1/S in response to checkpoint signals, this information has not led to effective strategies to treat RB1-deficient tumors, and it has been challenging to develop targeted drugs for tumors that are driven by the loss of gene function.
Our group previously identified Skp2, a substrate recruiting subunit of the SCF-Skp2 E3 ubiquitin ligase, as an early repression target of pRb whose knockout blocked tumorigenesis in Rb1-deficient prostate and pituitary tumors. Here we used genetic mouse models to demonstrate that deletion of Skp2 completely blocked the formation of SCLC in Rb1/p53-knockout mice (RP mice). Skp2 KO caused an increased accumulation of the Skp2-degradation target p27, a cyclin-dependent kinase inhibitor, and we confirmed this was the mechanism of protection in the RP-Skp2 KO mice by using the knock-in of a mutant p27 that was unable to bind to Skp2. Building on the observed synthetic lethality between Rb1 and Skp2, we found that small molecules that bind to and/or inhibit Skp2 induced apoptosis and inhibited SCLC cell growth. In a panel of SCLC cell lines, growth inhibition by a Skp2 inhibitor was not correlated with sensitivity/resistance to etoposide. Targeting Skp2 also had in vivo antitumor activity in mouse tumors and human patient-derived xenograft models of SCLC. Using the genetic and pharmacologic approaches, antitumor activity was seen in vivo in established SCLC primary lung tumors, in liver metastases, and in chemotherapy-resistant tumors. The identification and validation of an actionable target downstream of RB1 could have a broad impact on treatment of SCLC and other advanced tumors with mutant RB1, for which there are currently no targeted therapies available.

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Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Early Detection and ctDNA 1:35 – 3:55 PM

Reporter: Stephen J. Williams, PhD

Introduction
Alberto Bardelli

  • circulating tumor DNA has been around but with NGS now we can have more specificity in analyzing ctDNA
  • interest lately in using liquid biopsy to gain insight on tumor heterogeneity versus single needle biopsy of the solid tumor
  • these talks will however be on ctDNA as a diagnostic and therapeutic monitoring modality

Prediction of cancer and tissue of origin in individuals with suspicion of cancer using a cell-free DNA multi-cancer early detection test
David Thiel 

@MayoClinic

  • test has a specificity over 90% and intended to used along with guideline
  • The Circulating  Cell-free Genome Atlas Study (clinical trial NCT02889978) (CCGA) study divided into three substudies: highest performing assay, refining assay, validation of assays
  • methylation based assays worked better than sequencing (bisulfite sequencing)
  • used a machine learning algorithm to help refine assay
  • prediction was >90%; subgroup for high clinical suspicion of cancer
  • HCS sensitivity was 100% and specificity very high; but sensitivity on training set was 40% and results may have been confounded by including kidney cancer
  • TOO tissue of origin was predicted in greater than 99% in both training and validation sets

A first-of-its-kind prospective study of a multi-cancer blood test to screen and manage 10,000 women with no history of cancer

  • DETECT-A study: prospective interventional study; can multi blood test be used prospectively and can lead to a personalized care; can the screen be used to complement current therapy?
  • 10,000 women aged 65-75;  these women could not have previous cancer and conducted through Geisinger Health Network; multi test detects DNA and protein and standard of care screening
  • the study focused on safety so a committee was consulted on each case, and used a diagnostic PET-CT
  • blood test alone not good but combined with protein and CT scans much higher (5 fold increase) detection for breast cancer

Nickolas Papadopoulos

@HopkinsMedicine

Discussant
David Huntsman

  • there are mutiple opportunities yet at same time there are still challenges to utilize these cell free tests in therapeutic monitoring, diagnostic, and screening however sensitivities for some cancers are still too low to use in large scale screening however can supplement current screening guidelines
  • we have to ask about false positive rate and need to concentrate on prospective studies
  • we must consider how tests will be used, population health studies will need to show improved survival

 

Phylogenetic tracking and minimal residual disease detection using ctDNA in early-stage NSCLC: A lung TRACERx study
Chris Abbosh @ucl

  • TRACERx study in collaboration with Charles Swanton.
  • multiplex PCR to track 200 SNVs: correlate tumor tissue biopsy with ctDNA
  • spike in assay shows very good sensitivity and specificity for SNVs variants tracked, did over 400 TRACERx libraries
  • sensitivity increases when tracking more variants but specificity does go down a bit
  • tracking variants can show evidence of subclonal dynamics and evolution and copy number deletion events;  they also show neoantigen editing or changing of their neoantigens
  • this assay can detect low variants in a reproducible manner

The TRACERx (TRAcking Cancer Evolution through therapy (Rx)) lung study is a multi-million pound research project taking place over nine years, which will transform our understanding of non-small cell lung cancer (NSCLC) and take a practical step towards an era of precision medicine. The study will uncover mechanisms of cancer evolution by analysing the intratumour heterogeneity in lung tumours from approximately 850 patients and tracking its evolutionary trajectory from diagnosis through to relapse. At £14 million, it’s the biggest single investment in lung cancer research by Cancer Research UK, and the start of a strategic UK-wide focus on the disease, aimed at making real progress for patients.

Led by Professor Charles Swanton at UCL, the study will bring together a network of experts from different disciplines to help integrate clinical and genomic data and identify patients who could benefit from trials of new, targeted treatments. In addition, it will use a whole suite of cutting edge analytical techniques on these patients’ tumour samples, giving unprecedented insight into the genomic landscape of primary and metastatic tumours and the impact of treatment upon this landscape.

In future, TRACERx will enable us to define how intratumour heterogeneity impacts upon cancer immunity throughout tumour evolution and therapy. Such studies will help define how the clinical evaluation of intratumour heterogeneity can inform patient stratification and the development of combinatorial therapies incorporating conventional, targeted and immune based therapeutics.

Intratumour heterogeneity is increasingly recognised as a major hurdle to achieve improvements in therapeutic outcome and biomarker validation. Intratumour genetic diversity provides a substrate for tumour adaptation and evolution. However, the evolutionary genomic landscape of non-small cell lung cancer (NSCLC) and how it changes through the disease course has not been studied in detail. TRACERx is a prospective observational study with the following objectives:

Primary Objectives

  • Define the relationship between intratumour heterogeneity and clinical outcome following surgery and adjuvant therapy (including relationships between intratumour heterogeneity and clinical disease stage and histological subtypes of NSCLC).
  • Establish the impact of adjuvant platinum-containing regimens upon intratumour heterogeneity in relapsed disease compared to primary resected tumour.

Key Secondary Objectives

  • Develop and validate an intratumour heterogeneity (ITH) ratio index as a prognostic and predictive biomarker in relation to disease-free survival and overall survival.
  • Infer a complete picture of NSCLC evolutionary dynamics – define drivers of genomic instability, metastatic progression and drug resistance by identifying and tracking the dynamics of somatic mutational heterogeneity, and chromosomal structural and numerical instability present in the primary tumour and at metastatic sites. Individual tumour phylogenetic tree analysis will:
    • Establish the order of somatic events in relation to genomic instability onset and metastatic progression
    • Decipher genetic “bottlenecking” events following metastasis and drug therapy
    • Establish dynamics of tumour evolution during the disease course from early to late stage NSCLC.
  • Initiate a longitudinal biobank of circulating tumour cells (CTCs) and circulating-free tumour DNA (cfDNA) to develop analytical methods for the early detection and monitoring of tumour evolution over time.
  • Develop a longitudinal tissue resource to serve as a platform to assess the relationship between genetic intratumour heterogeneity and the host immune response.
  • Define relationships between intratumour heterogeneity and targeted/cytotoxic therapeutic outcome.
  • Use a lung cancer specific gene panel in a certified Good Clinical Practice (GCP) laboratory environment to define clonally dominant disease drivers to address the role of clonal driver dominance in targeted therapeutic response and to guide stratification of lung cancer treatment and future clinical study inclusion (paired primary-metastatic site comparisons in at least 270 patients with relapsed disease).

 

 

Utility of longitudinal circulating tumor DNA (ctDNA) modeling to predict RECIST-defined progression in first-line patients with epidermal growth factor receptor mutation-positive (EGFRm) advanced non-small cell lung cancer (NSCLC)
Martin Johnson

 

Impact of the EML4-ALK fusion variant on the efficacy of lorlatinib in patients (pts) with ALK-positive advanced non-small cell lung cancer (NSCLC)
Todd Bauer

 

From an interview with Dr. Bauer at https://www.lungcancernews.org/2019/08/14/making-headway-with-lorlatinib/

Lorlatinib, a smallmolecule inhibitor of ALK and ROS1, was granted accelerated U.S. Food and Drug Administration approval in November 2018 for patients with ALK-positive metastatic NSCLC whose disease has progressed on crizotinib and at least one other ALK inhibitor or whose disease has progressed on alectinib or ceritinib as the first ALK inhibitor therapy for metastatic disease. Todd M. Bauer, MD, a medical oncologist and senior investigator at Sarah Cannon Research Institute/Tennessee Oncology, PLLC, in Nashville, has been very involved with the development of lorlatinib since the beginning. In the following interview, Dr. Bauer discusses some of lorlatinib’s unique toxicities, as well as his first-hand experiences with the drug.

For further reading: Solomon B, Besse B, Bauer T, et al. Lorlatinib in Patients with ALK-positive non-small-cell lung cancer: results from a global phase 2 study. Lancet. 2018;19(12):P1654-1667.

Abstract

BACKGROUND: Lorlatinib is a potent, brain-penetrant, third-generation inhibitor of ALK and ROS1 tyrosine kinases with broad coverage of ALK mutations. In a phase 1 study, activity was seen in patients with ALK-positive non-small-cell lung cancer, most of whom had CNS metastases and progression after ALK-directed therapy. We aimed to analyse the overall and intracranial antitumour activity of lorlatinib in patients with ALK-positive, advanced non-small-cell lung cancer.

METHODS: In this phase 2 study, patients with histologically or cytologically ALK-positive or ROS1-positive, advanced, non-small-cell lung cancer, with or without CNS metastases, with an Eastern Cooperative Oncology Group performance status of 0, 1, or 2, and adequate end-organ function were eligible. Patients were enrolled into six different expansion cohorts (EXP1-6) on the basis of ALK and ROS1 status and previous therapy, and were given lorlatinib 100 mg orally once daily continuously in 21-day cycles. The primary endpoint was overall and intracranial tumour response by independent central review, assessed in pooled subgroups of ALK-positive patients. Analyses of activity and safety were based on the safety analysis set (ie, all patients who received at least one dose of lorlatinib) as assessed by independent central review. Patients with measurable CNS metastases at baseline by independent central review were included in the intracranial activity analyses. In this report, we present lorlatinib activity data for the ALK-positive patients (EXP1-5 only), and safety data for all treated patients (EXP1-6). This study is ongoing and is registered with ClinicalTrials.gov, number NCT01970865.

FINDINGS: Between Sept 15, 2015, and Oct 3, 2016, 276 patients were enrolled: 30 who were ALK positive and treatment naive (EXP1); 59 who were ALK positive and received previous crizotinib without (n=27; EXP2) or with (n=32; EXP3A) previous chemotherapy; 28 who were ALK positive and received one previous non-crizotinib ALK tyrosine kinase inhibitor, with or without chemotherapy (EXP3B); 112 who were ALK positive with two (n=66; EXP4) or three (n=46; EXP5) previous ALK tyrosine kinase inhibitors with or without chemotherapy; and 47 who were ROS1 positive with any previous treatment (EXP6). One patient in EXP4 died before receiving lorlatinib and was excluded from the safety analysis set. In treatment-naive patients (EXP1), an objective response was achieved in 27 (90·0%; 95% CI 73·5-97·9) of 30 patients. Three patients in EXP1 had measurable baseline CNS lesions per independent central review, and objective intracranial responses were observed in two (66·7%; 95% CI 9·4-99·2). In ALK-positive patients with at least one previous ALK tyrosine kinase inhibitor (EXP2-5), objective responses were achieved in 93 (47·0%; 39·9-54·2) of 198 patients and objective intracranial response in those with measurable baseline CNS lesions in 51 (63·0%; 51·5-73·4) of 81 patients. Objective response was achieved in 41 (69·5%; 95% CI 56·1-80·8) of 59 patients who had only received previous crizotinib (EXP2-3A), nine (32·1%; 15·9-52·4) of 28 patients with one previous non-crizotinib ALK tyrosine kinase inhibitor (EXP3B), and 43 (38·7%; 29·6-48·5) of 111 patients with two or more previous ALK tyrosine kinase inhibitors (EXP4-5). Objective intracranial response was achieved in 20 (87·0%; 95% CI 66·4-97·2) of 23 patients with measurable baseline CNS lesions in EXP2-3A, five (55·6%; 21·2-86·3) of nine patients in EXP3B, and 26 (53·1%; 38·3-67·5) of 49 patients in EXP4-5. The most common treatment-related adverse events across all patients were hypercholesterolaemia (224 [81%] of 275 patients overall and 43 [16%] grade 3-4) and hypertriglyceridaemia (166 [60%] overall and 43 [16%] grade 3-4). Serious treatment-related adverse events occurred in 19 (7%) of 275 patients and seven patients (3%) permanently discontinued treatment because of treatment-related adverse events. No treatment-related deaths were reported.

INTERPRETATION: Consistent with its broad ALK mutational coverage and CNS penetration, lorlatinib showed substantial overall and intracranial activity both in treatment-naive patients with ALK-positive non-small-cell lung cancer, and in those who had progressed on crizotinib, second-generation ALK tyrosine kinase inhibitors, or after up to three previous ALK tyrosine kinase inhibitors. Thus, lorlatinib could represent an effective treatment option for patients with ALK-positive non-small-cell lung cancer in first-line or subsequent therapy.

  • loratinib could be used for crizotanib resistant tumors based on EML4-ALK variants present in ctDNA

Reference:
1. Updated efficacy and safety data from the global phase III ALEX study of alectinib (ALC) vs crizotinib (CZ) in untreated advanced ALK+ NSCLCJ Clin Oncol 36, 2018 (suppl; abstr 9043).

Discussion

Corey Langer

 

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Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on COVID-19 and Cancer 9:00 AM

Reporter: Stephen J. Williams, PhD

 

COVID-19 and Cancer

Introduction

Antoni Ribas
UCLA Medical Center

  • Almost 60,000 viewed the AACR 2020 Virtual meeting for the April 27 session
  • The following speakers were the first cancer researchers treating patients at the epicenters of the pandemic even though nothing was known about the virus

 

The experience of treating patients with cancer during the COVID-19 pandemic in China
Li Zhang, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

  • reporting a retrospective study from three hospitals from Wuhan
  • 2.2% of Wuhan cancer patients were COVID positive; most were lung cancers and most male; 35% were stage four
  • most have hospital transmission of secondary infection; had severe events when admitted
  • 74% were prescribed antivirals like ganciclovir and others; iv IgG was given to some
  • mortailtiy rate of 26%; by April 4 54% were cured and discharged; median time of infection to severe event was 7 days; clinical presentation SARS sepsis, and shock
  • by day 10 in lung cancer patients, see lung path but after supportive therapy improved
  • cancer patients at stage four who did not receive therapy were at higher risk
  • cancer patients who had received chemo in last 14 days had higher risk of infection
  • they followed up with cancer patients on I/O inhibitors;  it seemed there was only one patient that contracted COVID19 so there may not be as much risk with immune checkpoint inhibitors

 

TERAVOLT (Thoracic cancERs international coVid 19 cOLlaboraTion): First results of a global collaboration to address the impact of COVID-19 in patients with thoracic malignancies

Marina Chiara Garassino

@marinagarassino
Fondazione IRCCS Istituto Nazionale dei Tumori

Dr Marina Chiara Garassino is the Chief of the Thoracic Oncology Unit at Istituto Nazionale dei Tumori, Milan, Italy. She leads the strategy for clinical and translational research in advanced and locally advanced NSCLC, SCLC, mesothelioma and thymic malignancies. Istituto Nazionale dei Tumori in Milan is the most important comprehensive cancer in Italy and one of the most important in Europe. As a medical oncologist, she has done research in precision medicine and in immuno-oncology. Her main research interests have been mainly development of new drugs and therapeutical strategies and biomarkers. She has contributed to over 150 peer-reviewed publications, including publications as first or last author in the New England Journal of Medicine, Lancet Oncology, Journal of Clinical Oncology, Annals of Oncology. She has delivered many presentations at international congresses,  including  AACR, ASCO, ECCO, ESMO, WCLC. Her education includes a degree and further specialization in Medical Oncology at Università degli Studi in Milan. She achieved a Master Degree in Oncology management at University of Economics “Luigi Bocconi”. She completed her training with an ESMO Clinical fellowship in 2009 at Christie’s Hospital in Manchester (UK). She was a member of the EMA SAG (Scientific Advisory Group). She is serving as ESMO Council member as the Chair of the National Societies Committee. She was the ESMO National Representative for Italy for 5 years (2011-2017). She is serving on several ESMO Committees (Public Policy extended Committee, Press Committee, Women for Oncology Committee, Lung Cancer faculty, Membership Committee).She used to be an active member of the Young Oncologist Committee. She’s serving on both ESMO, WCLC and ASCO annual congress Lung Cancer Track (2019, and 2020), Chair of ESMO National Societies, from 2019. She is the founder and president of Women for Oncology Italy.

  • 2 million confirmed cases but half of patients are asymptomatic and not tested; pooled prevalance of COVID in cancer patients in Italy was 2%; must take them as high risk patients
  • they were not prepared for pandemic lasting for months instead of days; March 15 in middle of outbreak they started TERAVOLT registry; by March 26 had IRB approval; they are accruing 17 new patients per week; Ontario also joined in and has become worldwide (21 countries involved);  in registry they also included radiologic exams and COVID testing result
  • most patients were males and many smokers; 75% had SCLC; 83% of cases had one comorbility like hypertension and one third had at least one comorbility; 73.9% of patients were on treatment (they see this in their clinic: 30% on chemo or TKI alone; other patients were just on folowup
  • most of symptoms overlap with symptoms of lung cancer like pneumonia and pneumonitits and multi organ failure; most were hospitalized
  • unexpected high mortality among lung cancer patients with COVID19; this mortality seems due to COVID and not to cancer;
  • study had some limitations like short followup and some surgical cases so some bias may be present
  • she stresses don’t go it alone and make your own registry JOIN A REGISTRY

 

Outcome of cancer patients infected with COVID-19, including toxicity of cancer treatments
Fabrice Barlesi @barlesi
Gustave Roussy Cancer Campus

Professor Fabrice Barlesi
 As a specialist in lung cancer, precision medicine and cancer immunology, Prof. Fabrice Barlesi is a major contributor to research in the field of novel oncological therapies. He was apppointed General Director of Gustave Roussy in January 2020.
Fabrice Barlesi is Professor of Medicine at the University of Aix-Marseille. He has been head of the Multidisciplinary Oncology and Innovative Therapies Department of the Nord Hospital in Marseille (Marseille Public Hospitals) and the Marseille Centre for Early Trials in Oncology (CLIP2) which were established by him. He holds a doctorate in Sciences and Management with methods of analysis of health care systems, together with an ESSEC (international business school) master’s degree in general hospital management.
Professor Barlesi was also a co-founder of the Marseille Immunopôle French Immunology network, which aims to coordinate immunological expertise in the Aix-Marseille metropolitan area. In this context, he has organised PIONeeR (Investment in the future RHU 2017), the major international Hospital-University research project whose objective is to improve understanding of resistance to immunotherapy – anti-PD1(L1) – in lung cancer and help to prevent and overcome it. He was also vice-chair of the PACA (Provence, Alps and Côte d’Azur) Region Cancer Research Directorate.
Professor Barlesi is the author and co-author of some 300 articles in international journals and specialist publications. In 2018, the European Society of Medical Oncology (ESMO) and the International Association for the Study of Lung Cancer (IASLC) awarded him the prestigious Heine H. Hansen prize. He appears in the 2019 world list of most influential researchers (Highly cited researchers, Web of Science Group).
  • March 14 started protective measures and at peak had increased commited beds at highest rate
  • 12% of cancer patients tested positive for COVID; (by RTPCR); they curated data across different chemo regimens used
  • they retrospectively collected data; primary endpoint was clinical worsening; median of disease 13 days;
  • they actually had more breast cancer patients and other solid malignancies; 23% of covid cases no symptoms; 83% finally did have the symptoms after followup; diarhea actually in 10% of cases so clinics are seeing this as a symptom
  • CT scan showed 66% cases had pneumonitits like display; 25% patients were managed as outpatient
  • 24% patients worsened during treatment but 75% were able to go home (treated at home or well)
  • I/O did not have negative outcome and you can use these drugs without increasing risk to COVID
  • although many clinical trials have been hindered they are actively recruiting for COVID-cancer studies
  • outcomes with respect to death and symptoms are comparable to worldwide stats

Adapting oncologic practice to COVID19 outbreak: From outpatient triage to risk assessment for specific treatment in Madrid, Spain
Carlos Gomez-Martin
Octubre University Hospital

  • MOST slides were DO NOT POST so as requested data will not be shown; this study will be published soon
  • Summary is that Spain is seeing statistics like other European countries and similar results
  • Tocilizumab, the IL6 antagonists had been suggested as a treatment for cytokine storm and they are involved in a trial with this agent; results will be published

Experience in using oncology drugs in patients with COVID-19

Paolo A. Ascierto
Istituto Nazionale Tumori IRCCS Fondazione Pascale

  • giving surgery only for patients at highest risk of cancer mortality so using neoadjuvant therapy more often
  • telemedicine is a viable strategy for patient consult
  • for metastatic melanoma they are given highest priority for treatment
  • they are conducting a tocilizumab clinical trial and have accrued over 300 patients
  • results are in press so please look for publication soon
  • also can use TNF inhibitor, JAK inhibitor, IL1 inhibitor to treat cytokine storm

COVID-19 and cancer: Flattening the curve but widening disparities
Louis P. Voigt
Memorial Sloan Kettering Cancer Center

  • Sloan has performed about 5000 COVID tests;  78 patients needed hospitilization; 15 died; 40% still in ICU
  • they do see many African American patients
  • mortality rates in US (published) have been around 50-60 % for cancer patients with COVID; Sloan prelim results are lower but still accruing data

Patients with cancer appear more vulnerable to SARS-COV-2: A multi-center study during the COVID-19 outbreak
Hongbing Cai
Zhongnan Hospital of Wuhan University

  • metastatic cancer showed much higher risk than non cancer but non metastatic showed increased risk too
  • main criteria of outcome was ICU admission
  • patients need to be isolated and personalized treatment plans need to be made
  • many comparisons were between non cancer and cancer which was clearest significance; had not looked at cancer types or stage grade or treatment
  • it appears that there are more questions right now than answers so data collection is a priority

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For other Articles on the Online Open Access Journal on COVID19 and Cancer please see

https://pharmaceuticalintelligence.com/coronavirus-portal/

Opinion Articles from the Lancet: COVID-19 and Cancer Care in China and Africa

Actemra, immunosuppressive which was designed to treat rheumatoid arthritis but also approved in 2017 to treat cytokine storms in cancer patients SAVED the sickest of all COVID-19 patients

The Second in a Series of Virtual Town Halls with Leading Oncologist on Cancer Patient Care during COVID-19 Pandemic: What you need to know

Responses to the COVID-19 outbreak from Oncologists, Cancer Societies and the NCI: Important information for cancer patients

 

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