Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
LPBI Group’s decision to publish the Table of Contents of this Report does not imply endorsement of the Report
Aviva Lev-Ari, PhD, RN, Founder 1.0 & 2.0 LPBI Group
Guest Reporter: MIKE WOOD
Marketing Executive BIOTECH FORECASTS
ABOUT BIOTECH FORECASTS
BIOTECH FORECASTS is a full-service market research and business- consulting firm primarily focusing on healthcare, pharmaceutical, and biotechnology industries. BIOTECH FORECASTS provides global as well as medium and small Pharmaceutical and Biotechnology businesses with unmatched quality of “Market Research Reports” and “Business Intelligence Solutions”. BIOTECH FORECASTS has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions, and achieve sustainable growth in their respective market domain.
CAR T-cell therapy as a part of adoptive cell therapy (ACT), has become one of the most rapidly growing and promising fields in the Immuno-oncology. As compared to the conventional cancer therapies, CAR T-cell therapy is the single-dose solution for the treatment of various cancers, significantly for some lethal forms of hematological malignancies.
CAR T-cell therapy mainly involves the use of engineered T-cells, the process starts with the extraction of T-cells through leukapheresis, either from the patient (autologous) or a healthy donor (allogeneic). After the expression of a synthetic receptor (Chimeric Antigen Receptor) in the lab, the altered T-cells are expanded to the right dose and administered into the patient’s body. where they target and attach to a specific antigen on the tumor surface, to kill the cancerous cells by igniting the apoptosis.
The global CAR T-cell therapy market was valued at $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027.
Factors that drive the market growth involve, (1)Increased in fundingfor R&D activities pertaining to cell and gene therapy. By H1 2020 cell and gene therapy companies set new records in the fundraising despite the pandemic crisis. For Instance, by June 2020 totaled $1,452 Million raised in Five IPOs including, Legend Biotech ($487M), Passage Bio ($284M), Akouos ($244M), Generation Bio ($230M), and Beam Therapeutics ($207M), which is 2.5 times the total IPO of 2019.
Moreover, in 2019 cell therapy companies specifically have raised $560 million of venture capital, including Century Therapeutics ($250M), Achilles Therapeutics Ltd. ($121M in series B), NKarta Therapeutics Inc. ($114M), and Tmunity Therapeutics ($75M in Series B).
(2)Increased in No. of Approved Products, By July 2020, there are a total of 03 approved CAR T-cell therapy products, including KYMRIAH®, YESCARTA®, and the most recently approved TECARTUS™ (formerly KTE-X19). Furthermore, two CAR T-cell therapies BB2121, and JCAR017 are expected to get the market approval by the end of 2020 or in early 2021.
Other factors that boost the market growth involves; (3) increase in government support, (4) ethical acceptance of Cell and Gene therapy for cancer treatment, (5) rise in the prevalence of cancer, and (6) an increase in awareness regarding the CAR T-cell therapy.
However, high costs associated with the treatment (KYMRIAH® cost around $475,000, and YESCARTA® costs $373,000 per infusion), long production hours, obstacles in treating solid tumors, and unwanted immune responses & potential side effects might hamper the market growth.
The report also presents a detailed quantitative analysis of the current market trends and future estimations from 2020 to 2027.
The forecasts cover 2 Approach Types, 5 Antigen Types, 5 Application Types, 4 Regions, and 14 Countries.
The report comes with an associated file covering quantitative data from all numeric forecasts presented in the report, as well as with a Clinical Trials Data File.
KEY FINDINGS
The report has the following key findings:
The global CAR T-cell therapy market accounted for $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027.
By approach type the autologous segment was valued at $655.26 million in 2019 and is estimated to reach $ 3,324.52 million by 2027, registering a CAGR of 22.51% from 2020 to 2027.
By approach type, the allogeneic segment exhibits the highest CAGR of 32.63%.
Based on the Antigen segment CD19 was the largest contributor among the other segments in 2019.
The Acute lymphocytic leukemia (ALL) segment generated the highest revenue and is expected to continue its dominance in the future, followed by the Diffuse large B-cell lymphoma (DLBCL) segment.
North America dominated the global CAR T-cell therapy market in 2019 and is projected to continue its dominance in the future.
China is expected to grow the highest in the Asia-Pacific region during the forecast period.
TOPICS COVERED
The report covers the following topics:
Market Drivers, Restraints, and Opportunities
Porters Five Forces Analysis
CAR T-Cell Structure, Generations, Manufacturing, and Pricing Models
Top Winning Strategies, Top Investment Pockets
Analysis of by Approach Type, Antigen Type, Application, and Region
51 Company Profiles, Product Portfolio, and Key Strategies
Approved Products Profiles, and list of Expected Approvals
COVID-19 Impact on the Cell and Gene Therapy Industry
CAR T-cell therapy clinical trials analysis from 1997 to 2019
Market analysis and forecasts from 2020 to 2027
FORECAST SEGMENTATION
By Approach Type
Autologous
Allogeneic
By Antigen Type
CD19
CD20
BCMA
MSLN
Others
By Application
Acute lymphoblastic leukemia (ALL)
Diffuse large B-Cell lymphoma (DLBCL)
Multiple Myeloma (MM)
Acute Myeloid Leukemia (AML)
Other Cancer Indications
By Region
North America: USA, Canada, Mexico
Europe: UK, Germany, France, Spain, Italy, Rest of Europe
Asia-Pacific: China, Japan, India, South Korea, Rest of Asia-Pacific
LAMEA: Brazil, South Africa, Rest of LAMEA
Contact at info@biotechforecasts.com for any Queries or Free Report Sample
Noninvasive blood test can detect cancer 4 years before conventional diagnosis
Reporter : Irina Robu, PhD
Several international researchers at Fudan University and at Singlera Genomics have developed a noninvasive blood test, PanSeer that can detect whether a patient with five common type of cancers such as stomach, esophageal, colorectal, lung and liver cancer; four years before the condition can be diagnosed by the current methods. Early detection is significant for the reason that the survival of cancer patients increases when the disease is identified at early stages, as the tumor can be surgically removed or treated with suitable drugs. Yet, only a partial number of early screening tests exist for a few cancer types.
The blood test detected cancer in 91 percent of samples from individuals who have been asymptomatic when the samples were collected, but only diagnosed with cancer one to four years later. It was found that the test can accurately detect cancer in 88 percent from samples of 113 patience who were diagnosed. The blood test also detects cancer free samples 95 percent of the time.
What is clear is that the study is unique, in that the scientists had access to blood samples from patients who were asymptomatic but not diagnosed yet. This permitted the researchers to design a test that can find a cancer marker much earlier than conventional diagnosis. The sample were collected as part of 10-year longitudinal study started in 2007 by Fudan University in China.
The researchers highlight that the PanSeer assay is improbable to predict which patients will later go on to develop cancer. As a substitute, it is most possible identifying patients who already have cancerous growths, but continue to be asymptomatic for current detection methods. The team decided that further large-scale longitudinal studies are needed to confirm the potential of the test for the early detection of cancer in pre-diagnosis individuals.
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 (FromCancer 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.
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 Cancer. Each 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
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.
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.
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:
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
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
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
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
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.
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).
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.
Part I
Historical Perspective of Cancer Demographics, Etiology, and Progress in Research
Chapter 1: The Occurrence of Cancer in World Populations
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.
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.
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:
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.
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.
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:
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
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.
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:
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
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
Some clinicians feel we may be overdiagnosing and overtreating certain cancers, especially the indolent disease
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
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
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
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
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
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
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
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
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
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.
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.
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
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
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
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
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
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
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
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
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
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
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
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
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
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?)
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 Zhao, Vineeth Sukrithan, Niloy Iqbal, Cari Nicholas, Yingjiao Xue, Joseph Locker, Juntao Zou, Liang Zhu, Edward 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.
