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Jerry S.H. Lee, PhD
Deputy Director for Cancer Research and Technology
White House Cancer Moonshot Task Force
https://www.whitehouse.gov/the-press-office/2016/10/13/fact-sheet-harnessing-possibilities-science-technology-and-innovation http://whitehouse.gov/cancermoonshot
October 2016
WH OSTP/NSTC
Machine Learning and Artificial Intelligence
WH OVP/NCI
Cancer Moonshot
“…an advantage of machine
learning is that it can be used
even in cases where it is infeasible
or difficult to write down explicit
rules to solve a problem…”
https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf
Keynote at NVIDIA GPU Technology Conference in D.C.
Keynote at NVIDIA GPU Technology Conference in D.C.
https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf
http://venturebeat.com/2015/10/27/deep-learning-startup-enlitic-raises-10m-from-radiology-company-capitol-health/
http://www.ibm.com/watson/health/oncology/
http://hanover.azurewebsites.net/
https://deepmind.com/applied/deepmind-health/
“…to apply machine learning, a
practitioner starts with
a historical data set, which the
practitioner divides into a
training set and a test set…”
https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf
1,685,210
new cases of cancer in the U.S.
595,690
projected deaths due to cancer in the U.S.
2016
15,533,220
cancer survivors in the U.S.
2016
Primary
tumor
(Localized)
Secondary
tumor
(Distant)
“…>90% of deaths are caused by disseminated disease or metastasis…”
5 year Relative Survival Rates (2016 report of 2005-2011 data)
Tumor, Cancer, and Metastasis: Length-scale and Time-scale Matter
Siegel et. al. CA Cancer J Clin, Jan/Feb 2016
(12,000+ patient tumors and increasing)
2006-2015: Building a historical data set
A Decade of Illuminating the Underlying Causes of
Primary Untreated Tumors
2004
New Cancer Test Stirs Hope and Concern
Lancet 2002; 359: 572-577
2002
Nature 2004; 429: 496-497
2004
“What is Water?”: Measurements  Insights
Color (clear, yellow, brown)
Taste (none, metallic, awful)
LOTS of
Quantitative
“Data”
Qualitative Descriptions
Phase (liquid, gas, solid)
Phase change (boil, melt, freeze)
Measurements
Taken
But also LOTS of
disagreements…
Boiling point = 92oC Boiling point = 100oC
“What is Water?”: Standards and Sharing of Data 
New Insights and Understanding
2400m
0m
New Parameter
“Pressure”
LOTS of
Quantitative
and
Reproducible
Data
(Steam Table)
New Understanding
• Phase boundaries
• V/L equilibrium
• Triple Point
(Phase Diagram)
• Define samples and protocols
• Share collected data
Boiling point = 92oC
Boiling point = 100oC
Keynote at NVIDIA GPU Technology Conference in D.C.
Many “Thermometers” (Genomics and Proteomics)
Samples AND Handling Matter!
Nkoy et. al., Arch Pathol Lab Med, April 2010
“…We found that specimens obtained
late in the week are more likely to
be ER/PR negative than specimens
obtained on other weekdays…”
#ofpatienttumorsamples
ARRA $
Rapid Acceleration from Stimulus Funding (2009-2011)
2006 2007 2008 2009 2010 2011
Patient Samples Collected
(Reality)
Patient Samples Collected
(Projected)
Patient Samples Collected
(No ARRA $)
Source: UCSC Cancer Genomic Heatmaps (CopyNumber GISTIC2) [https://genome-cancer.ucsc.edu/] Compiled by Jerry S.H. Lee, PhD, March 2013
Lung squamous:
Lung adeno:
Stomach adeno:
Breast carc:
Ovarian serous:
Kidney clear cell carc:
Prostate adeno:
Colon/rectum adeno:
Head & neck:
Glioblastoma:
343
356
237
866
559
493
171
575
306
563
Total: 5,979
Brain lower grade glioma: 180
Thyroid carc: 401
Uterine corpus end. carc: 492
Liver hep. carc: 97
Kidney pap. cell carc: 103
Bladder carc: 135
Cervical carc: 102
http://cancerimagingarchive.net
Keynote at NVIDIA GPU Technology Conference in D.C.
Source: UCSC Cancer Genomic Heatmaps (CopyNumber GISTIC2) [https://genome-cancer.ucsc.edu/] Compiled by Jerry S.H. Lee, PhD, March 2013
Lung squamous:
Lung adeno:
Stomach adeno:
Breast carc:
Ovarian serous:
Kidney clear cell carc:
Prostate adeno:
Colon/rectum adeno:
Head & neck:
Glioblastoma:
343
356
237
866
559
493
171
575
306
563
Total: 5,979
Brain lower grade glioma: 180
Thyroid carc: 401
Uterine corpus end. carc: 492
Liver hep. carc: 97
Kidney pap. cell carc: 103
Bladder carc: 135
Cervical carc: 102
DNA RNA Protein
Central Dogma of Biology
Re-writing Central Dogma “Rule”
On average across 375
tumor samples, ONLY 33%
of DNA/RNA predicted
cancer protein abundance
Zhang, B. et. al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014 Jul 20
patient tumors
genes
Transcriptome Subtypes
Proteome Subtypes
A
B
C
D
E
MSI/CIMP
Invasive
CIN
"…there is great potential for new insights to come
from the combined analysis of cancer proteomic
and genomic data, as proteomic data can now
reproducibly provide information about protein
levels and activities that are difficult or impossible
to infer from genomic data alone…”
Douglas R. Lowy, MD
Acting Director of the National Cancer Institute, National Institutes of Health
Mission
•Make a decade’s worth
of progress in cancer
prevention, diagnosis,
treatment, and care –
ultimately to end cancer
as we know it.
Implementation
Catalyze New Scientific Breakthroughs
Unleash the Power of Data
Accelerate Bringing New Therapies to Patients
Strengthen Prevention and Diagnosis
Improve Patient Access and Care
STRATEGIC GOALS IMPLEMENTATION PATH
FEDERAL
PRIVATE/
NON-PROFIT
PUBLIC-PRIVATE
COLLABORATION
2/1/2016 10/17/2016
https://medium.com/cancer-moonshot
Cancer
CenterPatient
Unable to
Share Primary
Care DataPrimary
Care
Cancer Diagnosis
and Treatment
Cancer
Survivor
Primary
Care
Unable to
Share Cancer
Care Data
Cancer
Relapses
(Months-
Years)
(Months-
Years)
Assumes returning to the same cancer care facility
Without a National Learning
Healthcare System for Cancer
Lost Opportunity to
Learn from Pre-Cancer
Clinical Data
Lost Opportunity to
Learn from Post-Cancer
Treatment Clinical Data
http://www.cancer.gov/brp
NCI Blue Ribbon Panel Report Recommendations
A. Establish a network for direct patient involvement
B. Create a clinical trials network devoted exclusively to
immunotherapy
C. Develop ways to overcome cancer’s resistance to therapy
D. Build a national cancer data ecosystem
E. Intensify research on the major drivers of childhood cancers
F. Minimize cancer treatment’s debilitating side effects
G. Expand use of proven cancer prevention and early detection
strategies
H. Mine past patient data to predict future patient outcomes
I. Develop a 3-D cancer atlas
J. Develop new cancer technologies
Cancer Moonshot Data & Technology Track
Co-Chairs: Dimitri Kusnezov (DOE), DJ Patil (OSTP), and Jerry Lee (OVP)
Members:
• John Scott (DoD)
• Craig Shriver (DoD)
• Cheryll Thomas (CDC)
• Frances Babcock (CDC)
• Teeb Al-Samarrai (DOE)
• Sean Khozin (FDA)
• Alexandra Pelletier (PIF)
• Maya Mechenbier (OMB)
• Henry Rodriguez (NCI)
• Karen Cone (NSF)
• Michael Kelley (VA)
• Louis Fiore (VA)
• Warren Kibbe (NCI)
• Betsy Hsu (NCI)
• Niall Brennan (CMS)
• Thomas Beach (USPTO)
• Claudia Williams (OSTP)
• Vikrum Aiyer (USPTO)
• Tom Kalil (OSTP)
• Kathy Hudson (NIH)
• Dina Paltoo (NIH)
• Al Bonnema (DoD)
• Michael Balint (PIF)
• Kara DeFrias (OVP)
• Greg Pappas (FDA)
• Erin Szulman (OSTP)
• Paula Jacobs (NCI)
Vision:
Enable the creation of a Learning Healthcare System
for Cancer, where as a nation we learn from the
contributed knowledge and experience of every
cancer patient. As part of the Cancer Moonshot, we
want to unleash the power of data to enhance, improve,
and inform the journey of every cancer patient from the
point of diagnosis through survivorship.
Priorities Areas and Ongoing Activities
Priority Area A: Enabling a seamless data environment [If you build it…]
 MVP CHAMPION and DAVINCI
Priority Area B: Unlocking science through open [Make it easy AND
computational and storage platforms relevant to use…]
 NCI GDC and APOLLO
Priority Area C: Workforce development using open [They will come…]
and connected data
 BD-STEP
Million Veteran Program Computational Health Analytics for
Medical Precision to Improve Outcomes Now
(MVP CHAMPION)
Department of Veterans Affairs (VA) and the Department of Energy (DOE) are announcing a new five-year
collaboration to apply the most powerful computational assets at the DOE’s National Labs to nearly half a million
veterans' records from one of the world's largest research cohorts -- the Million Veteran Program
Department of Defense And Department of Veterans Affairs
INfrastructure for Clinical Intelligence (DAVINCI)
COL Albert Bonnema, Chief, Information Delivery Division
NCI Genomic Data Commons
launched by Vice President Biden
at ASCO on June 6, 2016
NCI Genomic Data Commons
 The GDC went live with approximately 4.1 PB of data.
 2.6 PB of legacy data and 1.5 PB of harmonized data.
 577,878 files about 14,194 cases (patients)
 42 cancer types, across 29 primary sites.
 10 major data types
 Raw Sequencing Data, Raw Microarray Data, to Copy Number Variation, Simple Nucleotide Variation
and Gene Expression
 Data are derived from 17 different experimental strategies
 Major ones being RNA-Seq, WXS, WGS, miRNA-Seq, Genotyping Array and Expression Array
https://gdc-portal.nci.nih.gov
At the June 29th Cancer Moonshot Summit, Foundation Medicine
announced the release of 18,000 genomic profiles to the NCI GDC
GDC Content
GDC
 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 ~3,000 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
~56,000 cases
Mission
•Make a decade’s worth
of progress in cancer
prevention, diagnosis,
treatment, and care –
ultimately to end cancer
as we know it.
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
2013 2014 2015 2016 2017 2018 2019 2020
#oftumorsamples
Projected
Reality
Moonshot?
(Agency and International
Collaborations)
2009
2016
https://medium.com/cancer-moonshot/
Col. Craig Shriver, MD
Jennifer Lee, MD Henry Rodriguez,
PhD, MBA
Patients with
new or recurrent
cancer diagnosis
Veterans
Active Duty &
DoD Beneficiaries
Civilians
Consents to
VA/DoD/NCI
APOLLO
research
program
The American
Genome Center
Co-enroll
MVP
Proteogenomics
Characterization
(~8,000 patients)
CPTAC PCC
+ MCC PRO / IHC
Residual tissue for CLIA-approved
targeted sequencing (CATS)
VA ORD
and
NCI-
sponsored
Clinical
Trials
NCI CTEP/CPTAC PTRC
VA Hospitals
Murtha Cancer
Center
Clinical Phenotype
& outcomes
Data aggregation, analysis, and sharing to
rapidly improve outcomes for active duty,
beneficiaries, veterans, and civilians
Murtha Cancer
Center
VA Hospitals
Adaptive Learning
Healthcare System
Clinical Data
Research Data
APOLLO – Applied Proteogenomics OrganizationaL Learning and Outcomes consortium
DaVINCI
Registry
DPALS CATS
Keynote at NVIDIA GPU Technology Conference in D.C.
Patient and
Providers
Initiate system for
specific learning or
research activities
time
Ongoing
clinical care Research activities
Usual care continues
time
time
Ongoing collection of data [genomics, proteomics, medical imaging]
Courtesy of Lou Fiore
Keynote at NVIDIA GPU Technology Conference in D.C.
APOLLO
DAVINCI
7/17/2016
“…proteogenomics, which is -- as I used a metaphor
-- it’s like the genes are the full roster of a
basketball team….but the winning strategy comes
from finding out who their starting lineup is. The
proteins are the starters you're going to play against
-- the five you are going to have to defend against
I’m pleased to say, Mr. Prime Minister, that we've
signed three memorandums of understanding
between our two nations …we're going to be able to
share patient histories, proteogenomics and clinical
phenotypes data -- data on various proteins and
genetic characteristics of almost 60,000 patients in
Australia and the United States with full privacy
protections…
And I predict that you're going to see this repeated
around the world.”
- Vice President Biden, Australia
https://www.whitehouse.gov/the-press-office/2016/07/16/fact-
sheet-victoria-comprehensive-cancer-center-vice-president-biden
9/19/2016
http://mashable.com/2016/09/19/joe-biden-cancer-moonshot-social-good-summit/#RdZcawT8ZGqu
International Proteogenomic Moonshot Programs
Macquarie University
Garvan Institute of Medical Research
Bioplatforms Australia
University of British Columbia
Chang Gung University
Academia Sinica
Korea Institute of Science and Technology
Swiss Federal Institute of Technology – Zurich
Leibniz Institute for Analytical Sciences
McGill University
University of Victoria
Fudan University
Shanghai Institute of Materia Medica, CAS
Children’s Medical Research Institute
U.S. National Cancer Institute
(CPTAC)
Applied Proteogenomics OrganizationaL Learning and Outcomes
APOLLO Leadership Meeting
August 29, 2016
Keynote at NVIDIA GPU Technology Conference in D.C.
Where the Nation Heals Its HeroesSM
Tissue Repository
The Joint Pathology Center (JPC) maintains
the largest tissue repository in the world
with over
55 million slides representing
3.4 million cases from
all over the world!
COL Clayton D. Smith
Director, JPC
Where the Nation Heals Its HeroesSM
MODERNIZATION
Tissue Repository
Phase 1 – Digitization
Where the Nation Heals Its HeroesSM
MODERNIZATION
Tissue Repository
Phase 2 – Deep Learning
Squamous Cell
Carcinoma
Secondary tumor
Primary tumor
“…the emergence of a secondary cancer is so rare,
at least relative to the number of tumor cells set
free into circulation, that as few as 0.01% of
circulating cells ultimately survive to form new
viable distant colonies
…one-cubic-centimeter tumor mass can contain a
billion cells and shed a million cells per day into
circulation…”
G.S. Mack and A. Marshall Lost in Migration Nature Biotech, 2010
M0 M1 M1
Adapted from and courtesy of Jacob Scott, M.D., Moffitt PS-OC
Plasma
Water 91%
Proteins 7%
Metabolites (trace)
Cell-free DNA (trace)
Cellular Components
White Blood Cells 2-3%
Platelets 2-3%
Red Blood Cells 90%
Circulating tumor cells (trace)
Courtesy of Luiz Diaz (JHU)
Sources of Circulating “Needles” (Normal and Cancer Patients)
Bone Marrow GI Tract Skin
Tumor
Fetal DNA
Finding the Right “Needle” at the Right “Time” of Disease
10/17/16
A no-cell-left-behind approach
From slide to CTC in
“High Definition” ™
 9216 images/slides
 25 GB/slide
10 Million cells analyzed
Courtesy of Peter Kuhn (USC)
Keynote at NVIDIA GPU Technology Conference in D.C.
http://pivot.usc.edu ~7,000 patient draws from 100 patients since 2009
10/18/16: Blood Profiling Atlas Face 2 Face
10/26/16
http://occ-data.org/bpa
2016 20212006
Primary
Tumor
Secondary
Tumor
20,000
200,000 patientsPre-Moonshot
50,000
100,000
15,000
APOLLO
DAVINCI
2016 20212006
Primary
Tumor
Secondary
Tumor
15,000
20,000
200,000 patientsPost-Moonshot
50,000
100,000
73
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 PatientsCliniciansInstitutions
Blood Profiling Atlas
Commons
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)
$640M
(FY74)
$5.21 B
(FY16)
Big Data Scientist Training Enhancement Program
(BD-STEP)
Graduates of BD-STEP would:
• have skillsets to perform next-generation patient-
centered outcomes research by manipulating and
analyzing large-scale, multi-element, patient data sets
to develop novel disease signatures or unique
performance-based clinical benchmarks
• have an understanding of real-time, performance-
driven health care delivery in the VA systems
Michelle Berny-Lang, NCIConnie Lee, VHA/EES
2017 Potential
Partners:
BD-STEP Sites and Fellows: 2016-2017
Time 
11/24/2014
Clinical Sciences
Physical Sciences
Life Sciences
Keynote at NVIDIA GPU Technology Conference in D.C.
Thanks!
jlee@ovp.eop.gov

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Keynote at NVIDIA GPU Technology Conference in D.C.

  • 1. Jerry S.H. Lee, PhD Deputy Director for Cancer Research and Technology White House Cancer Moonshot Task Force
  • 3. “…an advantage of machine learning is that it can be used even in cases where it is infeasible or difficult to write down explicit rules to solve a problem…” https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf
  • 8. “…to apply machine learning, a practitioner starts with a historical data set, which the practitioner divides into a training set and a test set…” https://www.whitehouse.gov/sites/default/files/whitehouse_files/microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf
  • 9. 1,685,210 new cases of cancer in the U.S. 595,690 projected deaths due to cancer in the U.S. 2016
  • 11. Primary tumor (Localized) Secondary tumor (Distant) “…>90% of deaths are caused by disseminated disease or metastasis…” 5 year Relative Survival Rates (2016 report of 2005-2011 data) Tumor, Cancer, and Metastasis: Length-scale and Time-scale Matter Siegel et. al. CA Cancer J Clin, Jan/Feb 2016
  • 12. (12,000+ patient tumors and increasing) 2006-2015: Building a historical data set A Decade of Illuminating the Underlying Causes of Primary Untreated Tumors
  • 13. 2004 New Cancer Test Stirs Hope and Concern Lancet 2002; 359: 572-577 2002 Nature 2004; 429: 496-497 2004
  • 14. “What is Water?”: Measurements  Insights Color (clear, yellow, brown) Taste (none, metallic, awful) LOTS of Quantitative “Data” Qualitative Descriptions Phase (liquid, gas, solid) Phase change (boil, melt, freeze) Measurements Taken But also LOTS of disagreements… Boiling point = 92oC Boiling point = 100oC
  • 15. “What is Water?”: Standards and Sharing of Data  New Insights and Understanding 2400m 0m New Parameter “Pressure” LOTS of Quantitative and Reproducible Data (Steam Table) New Understanding • Phase boundaries • V/L equilibrium • Triple Point (Phase Diagram) • Define samples and protocols • Share collected data Boiling point = 92oC Boiling point = 100oC
  • 18. Samples AND Handling Matter! Nkoy et. al., Arch Pathol Lab Med, April 2010 “…We found that specimens obtained late in the week are more likely to be ER/PR negative than specimens obtained on other weekdays…”
  • 19. #ofpatienttumorsamples ARRA $ Rapid Acceleration from Stimulus Funding (2009-2011) 2006 2007 2008 2009 2010 2011 Patient Samples Collected (Reality) Patient Samples Collected (Projected) Patient Samples Collected (No ARRA $)
  • 20. Source: UCSC Cancer Genomic Heatmaps (CopyNumber GISTIC2) [https://genome-cancer.ucsc.edu/] Compiled by Jerry S.H. Lee, PhD, March 2013 Lung squamous: Lung adeno: Stomach adeno: Breast carc: Ovarian serous: Kidney clear cell carc: Prostate adeno: Colon/rectum adeno: Head & neck: Glioblastoma: 343 356 237 866 559 493 171 575 306 563 Total: 5,979 Brain lower grade glioma: 180 Thyroid carc: 401 Uterine corpus end. carc: 492 Liver hep. carc: 97 Kidney pap. cell carc: 103 Bladder carc: 135 Cervical carc: 102
  • 23. Source: UCSC Cancer Genomic Heatmaps (CopyNumber GISTIC2) [https://genome-cancer.ucsc.edu/] Compiled by Jerry S.H. Lee, PhD, March 2013 Lung squamous: Lung adeno: Stomach adeno: Breast carc: Ovarian serous: Kidney clear cell carc: Prostate adeno: Colon/rectum adeno: Head & neck: Glioblastoma: 343 356 237 866 559 493 171 575 306 563 Total: 5,979 Brain lower grade glioma: 180 Thyroid carc: 401 Uterine corpus end. carc: 492 Liver hep. carc: 97 Kidney pap. cell carc: 103 Bladder carc: 135 Cervical carc: 102
  • 24. DNA RNA Protein Central Dogma of Biology
  • 25. Re-writing Central Dogma “Rule” On average across 375 tumor samples, ONLY 33% of DNA/RNA predicted cancer protein abundance Zhang, B. et. al. Proteogenomic characterization of human colon and rectal cancer. Nature. 2014 Jul 20
  • 26. patient tumors genes Transcriptome Subtypes Proteome Subtypes A B C D E MSI/CIMP Invasive CIN
  • 27. "…there is great potential for new insights to come from the combined analysis of cancer proteomic and genomic data, as proteomic data can now reproducibly provide information about protein levels and activities that are difficult or impossible to infer from genomic data alone…” Douglas R. Lowy, MD Acting Director of the National Cancer Institute, National Institutes of Health
  • 28. Mission •Make a decade’s worth of progress in cancer prevention, diagnosis, treatment, and care – ultimately to end cancer as we know it.
  • 29. Implementation Catalyze New Scientific Breakthroughs Unleash the Power of Data Accelerate Bringing New Therapies to Patients Strengthen Prevention and Diagnosis Improve Patient Access and Care STRATEGIC GOALS IMPLEMENTATION PATH FEDERAL PRIVATE/ NON-PROFIT PUBLIC-PRIVATE COLLABORATION 2/1/2016 10/17/2016
  • 31. Cancer CenterPatient Unable to Share Primary Care DataPrimary Care Cancer Diagnosis and Treatment Cancer Survivor Primary Care Unable to Share Cancer Care Data Cancer Relapses (Months- Years) (Months- Years) Assumes returning to the same cancer care facility Without a National Learning Healthcare System for Cancer Lost Opportunity to Learn from Pre-Cancer Clinical Data Lost Opportunity to Learn from Post-Cancer Treatment Clinical Data
  • 32. http://www.cancer.gov/brp NCI Blue Ribbon Panel Report Recommendations A. Establish a network for direct patient involvement B. Create a clinical trials network devoted exclusively to immunotherapy C. Develop ways to overcome cancer’s resistance to therapy D. Build a national cancer data ecosystem E. Intensify research on the major drivers of childhood cancers F. Minimize cancer treatment’s debilitating side effects G. Expand use of proven cancer prevention and early detection strategies H. Mine past patient data to predict future patient outcomes I. Develop a 3-D cancer atlas J. Develop new cancer technologies
  • 33. Cancer Moonshot Data & Technology Track Co-Chairs: Dimitri Kusnezov (DOE), DJ Patil (OSTP), and Jerry Lee (OVP) Members: • John Scott (DoD) • Craig Shriver (DoD) • Cheryll Thomas (CDC) • Frances Babcock (CDC) • Teeb Al-Samarrai (DOE) • Sean Khozin (FDA) • Alexandra Pelletier (PIF) • Maya Mechenbier (OMB) • Henry Rodriguez (NCI) • Karen Cone (NSF) • Michael Kelley (VA) • Louis Fiore (VA) • Warren Kibbe (NCI) • Betsy Hsu (NCI) • Niall Brennan (CMS) • Thomas Beach (USPTO) • Claudia Williams (OSTP) • Vikrum Aiyer (USPTO) • Tom Kalil (OSTP) • Kathy Hudson (NIH) • Dina Paltoo (NIH) • Al Bonnema (DoD) • Michael Balint (PIF) • Kara DeFrias (OVP) • Greg Pappas (FDA) • Erin Szulman (OSTP) • Paula Jacobs (NCI)
  • 34. Vision: Enable the creation of a Learning Healthcare System for Cancer, where as a nation we learn from the contributed knowledge and experience of every cancer patient. As part of the Cancer Moonshot, we want to unleash the power of data to enhance, improve, and inform the journey of every cancer patient from the point of diagnosis through survivorship.
  • 35. Priorities Areas and Ongoing Activities Priority Area A: Enabling a seamless data environment [If you build it…]  MVP CHAMPION and DAVINCI Priority Area B: Unlocking science through open [Make it easy AND computational and storage platforms relevant to use…]  NCI GDC and APOLLO Priority Area C: Workforce development using open [They will come…] and connected data  BD-STEP
  • 36. Million Veteran Program Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP CHAMPION) Department of Veterans Affairs (VA) and the Department of Energy (DOE) are announcing a new five-year collaboration to apply the most powerful computational assets at the DOE’s National Labs to nearly half a million veterans' records from one of the world's largest research cohorts -- the Million Veteran Program
  • 37. Department of Defense And Department of Veterans Affairs INfrastructure for Clinical Intelligence (DAVINCI) COL Albert Bonnema, Chief, Information Delivery Division
  • 38. NCI Genomic Data Commons launched by Vice President Biden at ASCO on June 6, 2016
  • 39. NCI Genomic Data Commons  The GDC went live with approximately 4.1 PB of data.  2.6 PB of legacy data and 1.5 PB of harmonized data.  577,878 files about 14,194 cases (patients)  42 cancer types, across 29 primary sites.  10 major data types  Raw Sequencing Data, Raw Microarray Data, to Copy Number Variation, Simple Nucleotide Variation and Gene Expression  Data are derived from 17 different experimental strategies  Major ones being RNA-Seq, WXS, WGS, miRNA-Seq, Genotyping Array and Expression Array https://gdc-portal.nci.nih.gov
  • 40. At the June 29th Cancer Moonshot Summit, Foundation Medicine announced the release of 18,000 genomic profiles to the NCI GDC
  • 41. GDC Content GDC  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 ~3,000 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 ~56,000 cases
  • 42. Mission •Make a decade’s worth of progress in cancer prevention, diagnosis, treatment, and care – ultimately to end cancer as we know it.
  • 43. 0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000 2013 2014 2015 2016 2017 2018 2019 2020 #oftumorsamples Projected Reality Moonshot? (Agency and International Collaborations) 2009 2016
  • 44. https://medium.com/cancer-moonshot/ Col. Craig Shriver, MD Jennifer Lee, MD Henry Rodriguez, PhD, MBA
  • 45. Patients with new or recurrent cancer diagnosis Veterans Active Duty & DoD Beneficiaries Civilians Consents to VA/DoD/NCI APOLLO research program The American Genome Center Co-enroll MVP Proteogenomics Characterization (~8,000 patients) CPTAC PCC + MCC PRO / IHC Residual tissue for CLIA-approved targeted sequencing (CATS) VA ORD and NCI- sponsored Clinical Trials NCI CTEP/CPTAC PTRC VA Hospitals Murtha Cancer Center Clinical Phenotype & outcomes Data aggregation, analysis, and sharing to rapidly improve outcomes for active duty, beneficiaries, veterans, and civilians Murtha Cancer Center VA Hospitals Adaptive Learning Healthcare System Clinical Data Research Data APOLLO – Applied Proteogenomics OrganizationaL Learning and Outcomes consortium DaVINCI Registry DPALS CATS
  • 47. Patient and Providers Initiate system for specific learning or research activities time Ongoing clinical care Research activities Usual care continues time time Ongoing collection of data [genomics, proteomics, medical imaging] Courtesy of Lou Fiore
  • 50. 7/17/2016 “…proteogenomics, which is -- as I used a metaphor -- it’s like the genes are the full roster of a basketball team….but the winning strategy comes from finding out who their starting lineup is. The proteins are the starters you're going to play against -- the five you are going to have to defend against I’m pleased to say, Mr. Prime Minister, that we've signed three memorandums of understanding between our two nations …we're going to be able to share patient histories, proteogenomics and clinical phenotypes data -- data on various proteins and genetic characteristics of almost 60,000 patients in Australia and the United States with full privacy protections… And I predict that you're going to see this repeated around the world.” - Vice President Biden, Australia https://www.whitehouse.gov/the-press-office/2016/07/16/fact- sheet-victoria-comprehensive-cancer-center-vice-president-biden
  • 52. International Proteogenomic Moonshot Programs Macquarie University Garvan Institute of Medical Research Bioplatforms Australia University of British Columbia Chang Gung University Academia Sinica Korea Institute of Science and Technology Swiss Federal Institute of Technology – Zurich Leibniz Institute for Analytical Sciences McGill University University of Victoria Fudan University Shanghai Institute of Materia Medica, CAS Children’s Medical Research Institute U.S. National Cancer Institute (CPTAC)
  • 53. Applied Proteogenomics OrganizationaL Learning and Outcomes APOLLO Leadership Meeting August 29, 2016
  • 55. Where the Nation Heals Its HeroesSM Tissue Repository The Joint Pathology Center (JPC) maintains the largest tissue repository in the world with over 55 million slides representing 3.4 million cases from all over the world! COL Clayton D. Smith Director, JPC
  • 56. Where the Nation Heals Its HeroesSM MODERNIZATION Tissue Repository Phase 1 – Digitization
  • 57. Where the Nation Heals Its HeroesSM MODERNIZATION Tissue Repository Phase 2 – Deep Learning Squamous Cell Carcinoma
  • 59. “…the emergence of a secondary cancer is so rare, at least relative to the number of tumor cells set free into circulation, that as few as 0.01% of circulating cells ultimately survive to form new viable distant colonies …one-cubic-centimeter tumor mass can contain a billion cells and shed a million cells per day into circulation…” G.S. Mack and A. Marshall Lost in Migration Nature Biotech, 2010
  • 60. M0 M1 M1 Adapted from and courtesy of Jacob Scott, M.D., Moffitt PS-OC
  • 61. Plasma Water 91% Proteins 7% Metabolites (trace) Cell-free DNA (trace) Cellular Components White Blood Cells 2-3% Platelets 2-3% Red Blood Cells 90% Circulating tumor cells (trace) Courtesy of Luiz Diaz (JHU)
  • 62. Sources of Circulating “Needles” (Normal and Cancer Patients) Bone Marrow GI Tract Skin Tumor Fetal DNA
  • 63. Finding the Right “Needle” at the Right “Time” of Disease
  • 65. A no-cell-left-behind approach From slide to CTC in “High Definition” ™  9216 images/slides  25 GB/slide 10 Million cells analyzed Courtesy of Peter Kuhn (USC)
  • 67. http://pivot.usc.edu ~7,000 patient draws from 100 patients since 2009
  • 68. 10/18/16: Blood Profiling Atlas Face 2 Face
  • 73. 73 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 PatientsCliniciansInstitutions Blood Profiling Atlas Commons
  • 74. 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)
  • 76. Big Data Scientist Training Enhancement Program (BD-STEP) Graduates of BD-STEP would: • have skillsets to perform next-generation patient- centered outcomes research by manipulating and analyzing large-scale, multi-element, patient data sets to develop novel disease signatures or unique performance-based clinical benchmarks • have an understanding of real-time, performance- driven health care delivery in the VA systems Michelle Berny-Lang, NCIConnie Lee, VHA/EES 2017 Potential Partners:
  • 77. BD-STEP Sites and Fellows: 2016-2017