Presented at the DOE BAASiC Predictive Medicine workshop Sept 22-23, 2015.

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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
6
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
26
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
32
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
36
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
38
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