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Precision Medicine in Oncology – an Informatics

Perspective

September 22nd, 2015

Warren Kibbe, PhD

NCI Center for Biomedical Informatics

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1.  Precision Medicine

2.  Pre-clinical models

3.  TCGA

4.  Genomic Data Commons

5.  Cloud Pilots

Slides  are  from  many  sources,  but  special  thanks  to    Drs.  Harold  Varmus,  Doug  Lowy,  Jim  Doroshow,  Lou  Staudt  

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Photo  by  F.  Collins  

President Obama Announces the Precision Medicine Initiative

The East Room, January 30, 2015

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TOWARDS PRECISION MEDICINE (IoM REPORT, NOVEMBER 2011)

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Definition of Precision Oncology

§  Interventions to prevent, diagnose, or treat cancer, based on a molecular and/or mechanistic understanding of the causes, pathogenesis, and/or pathology of the disease. Where the individual characteristics of the patient are sufficiently distinct, interventions can be concentrated on those who will benefit, sparing expense and side effects for those who will not.

Modified  by  D.  Lowy,  M.D.,  from  IoM’s  Toward  Precision  Medicine  report,  2011  

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Understanding Cancer

§ Precision medicine will lead to fundamental understanding of the complex interplay between genetics, epigenetics, nutrition, environment and clinical presentation and direct effective, evidence-based prevention and treatment.

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What is Cancer?

§  Cancer is a disease where cells ‘lose’ the normal controls that enable them to participate as productive, stable members of tissues, organs and organ systems. The process of developing cancer is usually viewed as having three phases §  Initiation, where some of the normal control processes define the cell

type, inter-cellular communication with other cells, and normal responses to cell-cell signaling are altered

§  Proliferation, where cancerous cells ‘take over’ normal processes and begin to proliferate

§  Metastasis, where the cancerous cell leave their normal location and invade other tissues

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Cancer Statistics

In 2015 there will be an estimated

1,700,000 new cancer cases and

600,000 cancer deaths - American Cancer Society 2015

Cancer remains the second most common cause of death in the U.S.

- Centers for Disease Control and Prevention 2015

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Survivorship is on the rise

Today there are 14 million Americans alive with a history of cancer.

It is estimated that by 2024, the population of cancer survivors will increase to almost 19 million: 9.3 million males and 9.6 million females

- American Cancer Society 2015

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The hope

§  We need to do what the HIV/AIDS research community did in the last 30 years, turn a certain death sentence into a managed, chronic disease.

§  Like the HIV/AIDS story for the course of the HIV infection, we believe that cancer has multiple pathways that are altered during the course of the disease, and that targeted therapies hitting multiple involved signaling pathways, metabolic pathways, receptors can block and prevent disease progression

§  Unlike HIV, the course of cancer is more individualized and involves different molecular actors depending on the cancer and the individual

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The frustration

We already know how to remove >50% of the cancer burden in the U.S.

Consistent nutrition, exercise & sleep – estimate(10-30%)

Tobacco control (smoking cessation) – ~25%

HPV and HCV immunization – ~20%

Behavior change is hard. Impacting the behavior of a broad population is harder.

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Precision Medicine is not new: An early example

First disease for which molecular defect identified Single substitution at position 6 of ß-globin chain

Abnormal Hb polymerization upon deoxygenation “I believe medicine is just now entering into a new era when progress will be much more rapid than before, when scientists will have discovered the molecular basis of diseases, and will have discovered why molecules of certain drugs are effective in treatment, and others are not effective.” Linus Pauling 1952 2015: still no good treatment

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The NCI Mission & the PMI for

Oncology

•  NCI Mission: To help people live longer, healthier lives by supporting research to reduce the incidence of cancer and to improve the outlook for patients who develop cancer

•  Precision Medicine Initiative for

Oncology: Significantly expand our efforts to improve cancer treatment through genomics

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What Problems are We Trying to Solve?

Precision  Medicine    Ini5a5ve  in  Oncology  

•  For most of its 70-year history, systemic cancer treatment has relied on therapies that are marginally more toxic to malignant cells than to normal tissues

•  Molecular molecules that predict benefit, response, or resistance in the clinic have been lacking for many cancers

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Proposed Solution to these Problems

Use genomics and other high throughput technologies including

imaging to identify, create predictive signatures, and

target molecular vulnerabilities of individual cancers

Precision  Medicine    Ini5a5ve  in  Oncology  

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Precision Oncology in Practice

Nature Rev. Clin. Oncol. 11:649-662 (2014)

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Precision Oncology Trials Launched 2014: MPACT Lung MAP ALCHEMIST Exceptional Responders 2015: NCI-MATCH ALK Inhibitor MET Inhibitor

NCI-MATCH: Features [Molecular Analysis for Therapy Choice]

• Foundational treatment/discovery trial; assigns therapy based on molecular abnormalities, not site of tumor origin for patients without available standard therapy •  Regulatory umbrella for phase II drugs/studies from > 20 companies; single agents or combinations

• Available nationwide (2400 sites)

• Accrual began mid-August 2015

NCI MATCH

• Conduct  across  2400  NCI-­‐supported  sites  • Pay  for  on-­‐study  and  at  progression  biopsies  • Ini5al  es5mate:  screen  3000  pa5ents  to  complete  

 20  phase  II  trials  

1CR,  PR,  SD,  and  PD  as  defined  by  RECIST  2Stable  disease  is  assessed  relaSve  to  tumor  status  at  re-­‐iniSaSon  of  study  agent  3Rebiopsy;  if  addiSonal  mutaSons,  offer  new  targeted  therapy  

,2  

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NCI-MATCH: Initial Ten Studies

Agents and targets below grey line are pending final regulatory review; economies of scale—larger number of agents/genes, fewer overall patients to screen

Agent(s) Molecular Target(s) Estimated Prevalence

Crizotinib ALK Rearrangement (non-lung adenocarcinoma) 4% Crizotinib ROS1 Translocations (non-lung adenocarcinoma) 5% Dabrafenib and Trametinib BRAF V600E or V600K Mutations (non-melanoma) 7%

Trametinib BRAF Fusions, or Non-V600E, Non-V600K BRAF Mutations (non-melanoma)

2.8%

Afatinib EGFR Activating Mutations (non-lung adenoca) 1 – 4% Afatinib HER2 Activating Mutations (non-lung adenoca) 2 – 5% AZD9291 EGFR T790M Mutations and Rare EGFR Activating Mutations (non-

lung adenocarcinoma) 1 – 2%

TDM1 HER2 Amplification (non breast cancer) 5% VS6063 NF2 Loss 2% Sunitnib cKIT Mutations (non GIST) 4%

≈ 35%

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MATCH Assay: Workflow for 10-12 Day Turnaround

Tissue Fixation Path Review

Nucleic Acid Extraction

Library/Template Prep

Sequencing , QC Checks

Clinical Laboratory aMOI

Verification

Biopsy Received at Quality Control Center

1 DAY

1 DAY

1 DAY 1 DAY

3 DAYS

10-12 days

Tumor content >70%

Centralized Data Analysis

DNA/RNA yields >20 ng

Library yield >20 pM Test fragments Total read Reads per BC Coverage NTC, Positive, Negative Controls aMOIs Identified

Rules Engine Treatment Selection

3-5 DAYS

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The Components of the Precision Medicine Initiative for Oncology

•  Dramatically expand the NCI-MATCH

umbrella to include new trials, new agents, new genes, and new drug combinations

•  Understand and overcome resistance to therapy through molecular analysis and development of new cancer models

•  Increase genomics-based preclinical studies, especially in the area of immunotherapy, through the creation of patient-derived pre-clinical models and non-invasive tumor profiling

•  Establish the first national cancer database integrating genomic information with clinical response and outcome: to accelerate understanding of cancer and improve its treatment

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PMI-O: Expanding Genomically-Based Cancer Trials (FY16-FY20)

•  Accelerate Launch of NCI-Pediatric MATCH •  Broaden the NCI-MATCH Umbrella:

ü  Expand/add new Phase II trials to explore novel clinical signals—mutation/disease context

ü  Add new agents for new trials, and add new genes to panel based on evolving evidence

ü  Add combination targeted agent studies ü  Perform Whole Exome Sequencing, RNAseq,

and proteomic studies on quality-controlled biopsy specimens—extent of research based on resource availability

ü  Add broader range of hematologic malignancies •  Perform randomized Phase II studies or hand-off to

NCTN where appropriate signals observed •  Apply genomics resources to define new predictive

markers in novel immunotherapy trials •  Expand approach to ‘exceptional responders’: focus

on mechanisms of response/resistance in pilot studies

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PMI-O: Understanding and overcoming resistance to therapy (FY16-FY20)

•  Create a repository of molecularly analyzed

samples of resistant disease •  Expand the use of tumor profiling methods

such as circulating tumor cells (CTCs) and fragments of tumor DNA in blood to understand and monitor disease progression

•  Develop new cancer models to identify the heterogeneity of resistance mechanisms

•  Use preclinical modeling to determine the effectiveness of new combinations of novel molecularly targeted investigational agents

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Mechanisms of Resistance

To Targeted Cancer Therapeutics

Primary or Acquired

Resistance

Epigenetic DNA Damage Repair

Drug Efflux

Drug Inactivation

EMT: Micro-

environment

Cell Death Inhibition

Drug Target Alteration

•  Broad range of mechanisms •  Until recently, tools to interrogate possibilities in

vivo quite limited •  Resistance to single agents inevitable: 1º or

acquired; requires combinations but data to provide molecular rationale for the combination (both therapy & toxicity) not often available

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Develop a Cancer Knowledge System. Establish a national database that integrates genomic information with clinical response and outcomes as a resource.

PMI-O: Informa5cs  Goal  

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Develop molecular, imaging, pathology, and clinical signatures that predict therapeutic response, outcomes, and tumor resistance

PMI-O: Informa5cs  Goal  

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Build multi-scale, predictive computational biology models for understanding cancer biology and informing therapy. Develop detailed cancer pathway models to create targeted combination therapies in cancer. This approach has transformed HIV therapy and has the potential to do the same in cancer

PMI-O: Informa5cs  Goal  

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The Cancer Genomic Data Commons (GDC) is an existing effort to standardize and simplify submission of genomic data to NCI and follow the principles of FAIR -Findable, Accessible, Interoperable, Reusable. The GDC is part of the NIH Big Data to Knowledge (BD2K) initiative and an example of the NIH Data Commons

Genomic Data Commons

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The  Genomic  Data  Commons    

FacilitaSng  the  idenSficaSon  of  molecular  subtypes  of  cancer  and  potenSal  drug  targets  

NCI Cancer Genomic Data Commons (GDC)

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Genomic Data Commons (GDC) – Rationale §  TCGA and many other NCI funded cancer genomics projects each

currently have their own Data Coordinating Centers (DCCs) §  BAM data and results stored in many different repositories; confusing

to users, inefficient, barrier to research §  GDC will be a single repository for all NCI cancer genomics data

§  Will include new, upcoming NCI cancer genomics efforts §  Store all data including BAMs §  Harmonize the data as appropriate

§  Realignment to newest human genome standard §  Recall all variants using a standard calling method §  Define data sharing standards and common data elements

§  Will be the authoritative reference data set §  Will need to scale to 200+ petabytes

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Genomic Data Commons (GDC)

§  First step towards development of a knowledge system for cancer §  Foundation for a genomic precision medicine platform §  Consolidate all genomic and clinical data from:

§  TCGA, TARGET, CGCI, Genomic NCTN trials, future projects

§  Project initiated Spring of 2014 §  Contract awarded to University of Chicago §  PI: Dr. Robert Grossman §  Go live date: Mid 2016 §  Not a commercial cloud

§  Data will be freely available for download subject to data access requirements

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The  NCI  Cancer  Genomics    Cloud  Pilots  

 Understanding how to meet the research

community’s need to analyze large-scale cancer genomic and clinical data

Slide courtesy of Deniz Kural, Seven Bridges Genomics

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NCI Cloud Pilots

The  Broad    PI:  Gad  Getz  

InsStute  for  Systems  Biology    PI:  Ilya  Shmulevich  

Seven  Bridges  Genomics    PI:  Deniz  Kural  

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NCI GDC and the Cloud Pilots

§  Working together to build common APIs §  Working with the Global Alliance for Genomics and Health (GA4GH) to

define the next generation of secure, flexible, meaningful, interoperable, lightweight interfaces

§  Competing on the implementation, collaborating on the interface §  Aligned with BD2K and serving as a part of the NIH Commons and

working toward shared goals of FAIR (Findable, Accessible, Interoperable, Reusable)

§  Exploring and defining sustainable precision medicine information infrastructure

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Information problem(s) we intend to solve with the Precision Medicine Initiative for Oncology § Establish a sustainable infrastructure for cancer genomic

data – through the GDC § Provide a data integration platform to allow multiple data

types, multi-scalar data, temporal data from cancer models and patients §  Under evaluation, but it is likely to include the GDC, TCIA,

Cloud Pilots, tools from the ITCR program, and activities underway at the Global Alliance for Genomics and Health

§ Support precision medicine-focused clinical research

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NCI Precision Medicine Informatics Activities

§ As we receive additional funding for Precision Medicine, we plan to: §  Expand the GDC to handle additional data types

§  Include the learning from the Cloud Pilots into the GDC

§  Scale the GDC from 10PB to hundreds of petabytes

§  Include imaging by interoperating between the GDC and the Quantitative Imaging Network TCIA repository

§  Expand clinical trials tooling from NCI-MATCH to NCI-MATCH Plus

§  Strengthen the ITCR grant program to explicitly include precision medicine-relevant proposals

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Bridging Cancer Research and Cancer Care

§ Making clinical research relevant in the clinic § Supporting the virtuous cycle of clinical research informing

care, and back again § Providing decision support tools for precision medicine

CGC  Pilot  Team  Principal  Inves5gators    •  Gad  Getz,  Ph.D  -­‐  Broad  InsStute  -­‐  h_p://firecloud.org        •  Ilya  Shmulevich,  Ph.D  -­‐  ISB  -­‐  h_p://cgc.systemsbiology.net/      •  Deniz  Kural,  Ph.D  -­‐  Seven  Bridges  –    h_p://www.cancergenomicscloud.org      

NCI  Project  Officer  &  CORs  •  Anthony  Kerlavage,  Ph.D  –Project  Officer  •  Juli  Klemm,  Ph.D  –  COR,  Broad  InsStute  •  Tanja  Davidsen,  Ph.D  –  COR,  InsStute  for  Systems  Biology    •  Ishwar  Chandramouliswaran,  MS,  MBA  –  COR,  Seven  Bridges  Genomics  

GDC  Principal  Inves5gator  •  Robert  Grossman,  Ph.D  -­‐    University  of  Chicago  

 

Cancer  Genomics  Project  Teams    

NCI  Leadership  Team  •  Doug  Lowy,  M.D.  •  Lou  Staudt,  M.D.,  Ph.D.  •  Stephen  Chanock,  M.D.  •  George  Komatsoulis,  Ph.D.  •  Warren  Kibbe,  Ph.D.  

Center  for  Cancer  Genomics  Partners  •  JC  Zenklusen,  Ph.D.  •  Daniela  Gerhard,  Ph.D.  •  Zhining  Wang,  Ph.D.  •  Liming  Yang,  Ph.D.  •  MarSn  Ferguson,  Ph.D.  

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 QuesSons?  

 

Warren  A.  Kibbe  

warren.kibbe@nih.gov  

Thank  you  

www.cancer.gov www.cancer.gov/espanol