Cancer Moonshot, Data Sharing, Genomic Data Commons, NCI Cloud Pilots, Cancer Research Data Ecosystem, technology advances, chemotherapy advances, MATCH, NCI Cancer Moonshot Blue Ribbon Panel Recommendations

1. Cancer Moonshot,
Data Sharing,
Genomic Data Commons
October 26th, 2016
Warren Kibbe, PhD
Warren.kibbe@nih.gov
@wakibbe

2. 2
To develop the knowledge base
that will lessen the burden of
cancer in the United States and
around the world.
NCI Mission

3. 3
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

4. 4
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.

5. Cancer Moonshot: Genomic Data Commons, Data sharing, Ecosystem
 The era of precision oncology is predicated on the
integration of research, care, and molecular medicine
and the availability of data for modeling, risk analysis,
and optimal care
The Genomic Data Commons is a
completely new way of making
scientific data available to the cancer
research community

6. 6
Cancer Data Sharing & Data Commons
• Making data available for discovery, validation,
new therapies
• Working toward a National Learning Health
System for Cancer
• Maximizing the impact, reuse, and
reproducibility of cancer research
• We need to change the incentives for data
sharing
Reduce the risk, improve early detection, outcomes and survivorship in cancer

7. 7
FAIR –
Making data
Findable,
Accessible,
Attributable,
Interoperable,
Reusable,
and provide Recognition
Force11 white paper
https://www.force11.org/group/fairgroup/fairprinciples

8. 8
NIH Genomic Data Sharing Policy
https://gds.nih.gov/
Went into effect January 25, 2015
NCI guidance:
http://www.cancer.gov/grants-training/grants-
management/nci-policies/genomic-data
Requires public sharing of genomic data sets

9. 9
Changing the conversation around data sharing
 How do we find data, software, standards?
 How can we make data, annotations, software, metadata accessible?
 How do we reuse data standards
 How do we make more data machine readable?
NIH Data Commons
NCI Genomic Data Commons
Data commons co-locate data, storage and computing infrastructure, and
commonly used tools for analyzing and sharing data to create an
interoperable resource for the research community.
*Robert L. Grossman, Allison Heath, Mark Murphy, Maria Patterson, A Case for Data Commons Towards Data Science as a
Service, to appear. Source of image: Interior of one of Google’s Data Center, www.google.com/about/datacenters/.

10. Cancer Research Data Ecosystem – Cancer Moonshot BRP
Well characterized
research data sets Cancer cohorts Patient data
EHR, Lab Data, Imaging,
PROs, Smart Devices,
Decision Support
Learning from every
cancer patient
Active research
participation
Research information
donor
Clinical Research
Observational studies
Proteogenomics
Imaging data
Clinical trials
Discovery
Patient engaged
Research
Surveillance
Big Data
Implementation research
SEERGDC

11. 11
Cancer Research Data Commons Ecosystem
Genomic
Data Commons
Data Standards
Validation and Harmonization
Imaging
Data Commons
Proteomics
Data Commons
Clinical Data
Commons
(Cohorts / Indiv.)
SEER
(Populations)
Data Contributors and Consumers
Researchers PatientsClinician
s
Institutions

12. 12
(10,000+ patient tumors and increasing)
Courtesy of P. Kuhn (USC)
2006-2015:
A Decade of Illuminating the
Underlying Causes of Primary
Untreated Tumors Omics
Characterization

13. 13
1997 20152001 20051998 20142002 20061999 2003 20112000 2004 2007 20122008 20132009 2010
10/23/2001
(~5 yrs old)
4/21/1997
1/9/2007
(~10 yrs old)
iPod (10GB max)
WinAMP(mp3)
iPhone (EDGE, 16 GB max)
9/16/1999
(~3 yrs old)
802.11b WiFi
4/3/2010
(~13 yrs old)
iPad (EDGE, 64 GB max)
4/23/2005
(~8 yrs old)
9/26/2006
(~9 yrs old)
7/15/2006
2/7/2007
Google
Drive
4/24/2012
(~15 yrs old)7/11/2008
(~11 yrs old)
iPhone 3G
(16 GB max)
9/12/2012
(~15 yrs old)
iPhone5 (LTE, 128 GB max)
Google
Baseline
7/14/2014
(~17 yrs old)
3/9/2015
(~18 yrs old)
Digital technology is changing rapidly

14. 14
$640M
(FY74)
$5.21 B
(FY16)
Cancer therapy is changing

15. 1518
Application of Cancer Genomics is changing

16. 16
Precision Oncology
Trials Launched
2014:
MPACT
Lung MAP
ALCHEMIST
Exceptional Responders
2015:
NCI-MATCH
2017:
Pediatric-MATCH
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
 Latest match-rate is 25% with 24 open
arms
 Biopsy results in 13 days, average from
June-Sept 2016

17. 17
Vice President’s
Cancer Moonshot
How do we enable meaningful,
patient-centered and patient-level
data sharing for cancer and
promote access to clinical trials for
all Americans?

18. 18
Cancer Moonshot Outline
• Genomic Data Commons June 6, 2016
• Vice President’s Cancer Moonshot Summit – June 29, 2016
• Rethinking Clinical Trial Search
- Development of Application Programming Interface (API) to NCI’s Clinical Trials
Reporting Program, for use by:
- NCI’s Cancer.gov website
- Third party innovators providing clinical trial content to their communities
• Blue Ribbon Panel recommendations – accepted by the National Cancer Advisory
Board on September 7th, 2016
• Cancer Moonshot Task Force and BRP recommendations sent to President on October
17th, 2016 https://www.cancer.gov/research/key-initiatives/moonshot-cancer-
initiative/milestones
• https://cancer.gov/brp

19. 19
https://cancer.gov/brp

20. Blue Ribbon Panel Recommendations
 Network for Direct Patient Engagement
 Cancer Immunotherapy Translational Science Network
 Therapeutic Target Identification to Overcome Drug Resistance
 A National Cancer Data Ecosystem for Sharing and Analysis
 Fusion Oncoproteins in Childhood Cancers
 Symptom Management Research
 Prevention and Early Detection – Implementation of Evidence-based Approaches
 Retrospective Analysis of Biospecimens from Patients Treated with Standard of Care
 Generation of 4D Human Tumor Atlas
 Development of New Enabling Cancer Technologies

21. 21
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, Attributable,
Interoperable, Reusable, and Provide
Recognition.
The GDC is part of the NIH Big Data to
Knowledge (BD2K) initiative and an
example of the NIH Commons
Genomic Data Commons
Microattribution, nanopublications, tracking the use of
data, annotation of data, use of algorithms, supports
the data /software /metadata life cycle to provide
credit and analyze impact of data, software, analytics,
algorithm, curation and knowledge sharing
Force11 white paper
https://www.force11.org/group/fairgroup/fairprinciples

22. NCI Genomic Data Commons
 The GDC went live on June 6, 2016 with approximately 4.1 PB
of data.
 This includes: 2.6 PB of legacy data;
 and 1.5 PB of “harmonized” data.
 577,878 files about 14194 cases (patients), in 42 cancer types,
across 29 primary sites.
 10 major data types, ranging from Raw Sequencing Data, Raw
Microarray Data, to Copy Number Variation, Simple Nucleotide
Variation and Gene Expression.
 Data are derived from 17 different experimental strategies, with
the major ones being RNA-Seq, WXS, WGS, miRNA-Seq,
Genotyping Array and Expression Array.
 Foundation Medicine announced the release of 18,000
genomic profiles to the GDC at the Cancer Moonshot Summit.

24. 24

25. 25

26. Genomic Data Commons Data Portal
https://gdc.cancer.gov

27. The NCI Genomic Data Commons User Interface
Home Page

28. The NCI Genomic Data Commons User Interface
Sample Browser

29. 29
Clinical data Biospecimen
data
Molecular data Files
uploaded
The NCI Genomic Data Commons User Interface
Data Submission Dashboard

30. Development of the NCI Genomic Data Commons (GDC)
To Foster the Molecular Diagnosis and Treatment of Cancer
GDC
Bob Grossman PI
Univ. of Chicago
Ontario Inst. Cancer Res.
Leidos
Institute of Medicine
Towards Precision Medicine
2011

31. The Mutational Burden of Human Cancer
Mike Lawrence and Gaddy Getz, Broad Institute: Nature 2014
Increasing genomic
complexity
Childhood
cancers
Known Carcinogens

32. 32
Example
publication
from
TCGA

35. Discovery of Cancer Drivers With 2% Prevalence
Lung adeno.
+ 2,900
Colorectal
+ 1,200
Ovarian
+ 500
Lawrence et al, Nature 2014
Power Calculation for Cancer Driver Discovery
Need to resequence >100,000 tumors to
identify all cancer drivers at >2% prevalence

36. GDC Infrastructure and Functionality
Data
Submitters
Open
Access
Users
Controlled
Access
Users
eRA
Commons
& dbGaP
Open Access
Data
Metadata+Data
Storage
Reporting
System
Harmonization
GDC Users GDC System Components
Data
Submission
Data Security
System
APIsDigital ID
System
Controlled
Access Data

37. Exome-seq
Whole genome-seq
RNA-seq
Copy number
Genome
alignment
Genome
alignment
Genome
alignment
Data
segmentation
1° processing
Mutations
Mutations +
structural variants
Digital gene
expression
Copy number
calls
2° processing
Oncogene vs.
Tumor suppressor
Translocations
Relative RNA levels
Alternative splicing
Gene amplification/
deletion
3° processing
GDC Data Harmonization
Multiple data types and levels of processing

38. Mutect2
pipeline
GDC Data Harmonization
Open Source, Dockerized Pipelines

39. Recovery
rate
(% true
positives) A0F0
SomaticSniper 81.1% 76.5%
VarScan 93.9% 84.3%
MuSE 93.1% 87.3%
All Three 96.4% 91.2%
GDC variant calling
pipelines
Wash U
Baylor
Broad
GDC Data Harmonization
Multiple pipelines needed to recover all variants

40. GDC Content
 TCGA 11,353 cases
 TARGET 3,178 cases
Current
 Foundation Medicine 18,000 cases
 Cancer studies in dbGAP ~4,000 cases
Coming soon
 NCI-MATCH ~5,000 cases
 Clinical Trial Sequencing Program ~3,000 cases
Planned (1-3 years)
 Cancer Driver Discovery Program ~5,000 cases
 Human Cancer Model Initiative ~1,000 cases
 APOLLO – VA-DoD ~8,000 cases
~58,000 cases

41. Towards a Cancer Knowledge System
 Continue genomic investigations of cancer
• Need > 100,000 cases analyzed
• Embrace all genomic platforms
• Relationship of relapse and primary biopsies
 Incorporate associated clinical annotations
• Clinical trial data
• Observational, longitudinal standard-of-care data
• N-of-1 clinical data
 Promote and curate biological investigations of cancer genetic
variants
• Driver vs. passenger mutations
• Multiple phenotypic assays
• Alterations in regulatory pathways – proteomics
• Mechanisms of therapeutic resistance
• Functional genomic investigations
 Integrative models for high-dimensional data
Genomic
Data
Commons

42. GDC
Utility of a Cancer Knowledge System
Identify
low-frequency
cancer drivers
Define genomic
determinants of response
to therapy
Compose clinical trial
cohorts sharing
targeted genetic lesions
Cancer
information
donors
Genomic
Data
Commons

43. 43
Support the Precision Medicine Initiative
• Expand data model to include
other data (e.g. imaging and
proteomics)
• Allow easy publication of
persistent links to data,
annotations, algorithms, tools,
workflows
• Measure usage and impact
• Change incentives for public
contributions
The Genomic Data Commons and Cloud Pilots

44. 44
PMI – Oncology, the GDC and the Cloud Pilots Goals
 Support precision medicine-focused clinical research
 Enable researchers to deposit well-annotated
(Interoperable) genomic data sets with the GDC
 Provide a single source (and single dbGaP access
request!) to Find and Access these data
 Enable effective analysis and meta-analysis of these data
without requiring local downloads – data Reuse
 Understand Contributions, Assess value through usage,
and give Attribution to all users

45. 45
PMI – Oncology, the GDC and the Cloud Pilots Goals
 Provide a data integration platform to allow multiple data
types, multi-scalar data, temporal data from cancer models
and patients through open APIs
 Work with the Global Alliance for Genomics and Health
(GA4GH) to define the next generation of secure,
flexible, meaningful, interoperable, lightweight
interfaces – open APIs
 Engage the cancer research community in evaluating
the open APIs for ease of use and effectiveness

46. Cancer Research Data Ecosystem – Cancer Moonshot BRP
Well characterized
research data sets Cancer cohorts Patient data
EHR, Lab Data, Imaging,
PROs, Smart Devices,
Decision Support
Learning from every
cancer patient
Active research
participation
Research information
donor
Clinical Research
Observational studies
Proteogenomics
Imaging data
Clinical trials
Discovery
Patient engaged
Research
Surveillance
Big Data
Implementation research
SEERGDC

47. GDC Acknowledgements
NCI Center for Cancer Genomics Univ. of Chicago
Bob Grossman
Allison Heath
Mike Ford
Zhenyu Zhang
Ontario Institute for Cancer Research
Lou Staudt
Zhining Wang
Martin Ferguson
JC Zenklusen
Daniela Gerhard
Deb Steverson
Vincent Ferretti
'Francois Gerthoffert
JunJun Zhang
Leidos Biomedical Research
Mark Jensen
Sharon Gaheen
Himanso Sahni
NCI NCI CBIIT
Tony Kerlavage
Tanya Davidsen

48. CGC Pilot Team Principal Investigators
• Gad Getz, Ph.D - Broad Institute - http://firecloud.org
• Ilya Shmulevich, Ph.D - ISB - http://cgc.systemsbiology.net/
• Deniz Kural, Ph.D - Seven Bridges – http://www.cancergenomicscloud.org
NCI Project Officer & CORs
• Anthony Kerlavage, Ph.D –Project Officer
• Juli Klemm, Ph.D – COR, Broad Institute
• Tanja Davidsen, Ph.D – COR, Institute for Systems Biology
• Ishwar Chandramouliswaran, MS, MBA – COR, Seven Bridges Genomics
GDC Principal Investigator
• Robert Grossman, Ph.D - University of Chicago
• Allison Heath, Ph.D - University of Chicago
• Vincent Ferretti, Ph.D - Ontario Institute for Cancer Research
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.
• Martin Ferguson, Ph.D.

49. 49
Rethinking Clinical Trials Search
Thanks to: PIFs, various NCI teams (CBIIT, CCCT, OCPL)

50. 50
Improving Cancer Clinical Trials Search
 Engage Presidential Innovation Fellows
 Create an Application Programming Interface (API) for clinical trials
 Engage with third-party innovators to guide the API development
process
 Utilize the API on NCI’s website, cancer.gov
*An API is a set of protocols that provides communication between a software application
and a computer operating system or between applications

51. 51
 The API (https://clinicaltrialsapi.cancer.gov/v1/) makes publicly available
trial registration information from NCI’s clinical trial data base (Clinical Trial
Reporting Program, CTRP) available to third-party innovators
 Enables innovators to build new tools tailored to the clinical trial search need
of their users
 Expands opportunities for patients and doctors to locate cancer trials
 Cancer.gov now utilizes the API (http://trials.cancer.gov) , enhancing
clinical trial searching on the NCI website
 The API source code is publicly available on GitHub
(https://github.com/presidential-innovation-fellows/clinical-trials-search)
Cancer Clinical Trials API

52. 52

54. 54
Questions?
Warren Kibbe, Ph.D.
Warren.kibbe@nih.gov
@wakibbe

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