Advancing Innovation and Convergence in Cancer Research: US Federal Cancer Moonshot

8th Taiwan Biosignatures Workshop
October 12th, 2017
Jerry S.H. Lee, Ph.D.
Health Sciences Director
Deputy Director, Center for Strategic Scientific Initiatives (CSSI)
Joint Executive for Data Integration, Center for Biomedical Informatics and Information Technology (CBIIT)
Office of the Director, National Cancer Institute (NCI), National Institutes of Health (NIH)
Advancing Innovation and Convergence in Cancer
Research: US Federal Cancer Moonshot Efforts

06
2005 2018
Joined NCI
Center for Strategic
Scientific Initiatives
(CSSI)
08
Official
“Other Duties
As Assigned”
09
Transitioned to
Deputy Director, CSSI
10 16
Served as Deputy Director for
Cancer Research and Technology
WH Cancer Moonshot Task Force
4/14/16
10/17/16
PhD in Chemical and Biomolecular Engineering
Nuclear and Cellular Mechanics: Implications for Laminopathies and Cancer

Lung Cancer: 76 on deck for 2017

“…it is of critical national importance
that we …double the rate of progress
in the fight against cancer- and put
ourselves on a path to achieve in just
5 years research and treatment gains
that otherwise might take a decade or
more…”
(From Presidential Memo 2016)

U.S. National Cancer Program:
Stakeholders
NCI
$5 B
Private
Industry
$9.2 B
Fed/State
$3.4 B
NPO/Foundations, $0.6 B
~$18 B per year
NCAB Working Group Report, 2010
Deputy Director
Douglas R. Lowy, MD
Director (Soon)
Ned E. Sharpless, MD

Translation Pace: How To Break Out of Current
Paradigm?
 Standards and protocols
 Real-time, public release of data
 Large, multi-disciplinary teams
 Pilot-friendly team environment to share
failures and successes
 Team members with
trans-disciplinary training
Key Needs (from community ‘02)
Turning the Crank… The potential to transform cancer drug
discovery and diagnostics
Paul et. al, Nature Rev. Drug Discovery, March 2010
$150M
Phase I: $273M
Phase II: $319M
Phase III: $314M
$48M
$414M$166M
$94M
~$1.8B/turn
[Basis for CSSI, 2002]

NCI Center for Strategic Scientific Initiatives
(CSSI): Innovation Center (2003  Present)
Dates indicate approval(s) by NCI Board of Scientific Advisors; *Program moved to NCI Division of Cancer Biology
“…to create and uniquely implement exploratory programs focused on the development and integration of advanced
technologies, trans-disciplinary approaches, infrastructures, and standards, to accelerate the creation and
broad deployment of data, knowledge, and tools to empower the entire cancer research continuum in
better understanding and leveraging knowledge of the cancer biology space for patient benefit…”
Mission
2003, 2007, 2011, 2013, 2014
2004, 2008, 2014
2005, 2010, 2015
2005, 2008 2010
2008, 2013* 2011, 2014
Deputy Director
Jerry S.H. Lee, PhD
Director
Douglas R. Lowy, MD
Henry Rodriguez, PhD, MBA

Translational from basic
science to human studies
Translational of new interventions into
the clinic and health decision making
Defining mechanisms,
targets, and lead molecules
New methods of diagnosis,
treatment, and prevention
Delivery of recommended and
timely care to the right patient
True Benefit to society
Controlled studies
leading to effective care

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

(12,000+ patient tumors and increasing)
2006-2015: A Decade of Illuminating the Underlying
Causes of Primary Untreated Tumors
Primary
tumor
(Localized)

“The working group recommends
the initiation of a bold technology-
based project: Human Cancer
Genome Project.”
- National Cancer Advisory Board (NCAB) Working Group on
Biomedical Technology, February 16, 2005
https://deainfo.nci.nih.gov/advisory/ncab/workgroup/archive/sub-bt/NCABReport_Feb05.pdf

“…to conduct this mini–cancer-genome project, a 29-person team, resequenced…11
breast cancer samples and 11 colon cancer samples…then winnowed out more than
99% of the mutations by removing errors…and changes that didn’t alter a protein.
…this yielded a total of 189 “candidate” cancer genes. Although some are familiar…most
had never been found mutated in cancer before. The results…are a ‘treasure trove’…
…the relatively small number of new genes common to the tumors reinforces concerns
about [NIH] The Cancer Genome Atlas…
…despite such doubts, the atlas project gets under way next week. NIH will announce
the three cancers to be studied in the pilot phase…the project is on an extremely
aggressive timeline…”

Disease of Genomic Alterations
 Copy number
 Expression (regulation of)
 Regulation of translation
 Mutations
 Epigenome
First Step(back)- Cancer Genomics:
Taking a Page from Engineers [2005]
• Systematic identification of all genomic changes
• Repeat (<500) for individual cancer
• Replicate for as many cancers as possible
• Make it publically available
Steam table (Reference)

2005
12/13/2005
07/25/2005 E. Lander/L. Hartwell (NCAB Report)

glioblastoma multiforme
(brain)
squamous carcinoma
(lung)
serous
cystadenocarcinoma
(ovarian)
Multiple data types
• Clinical diagnosis
• Treatment history
• Histologic diagnosis
• Pathologic status
• Tissue anatomic site
• Surgical history
• Gene expression
• Chromosomal copy number
• Loss of heterozygosity
• Methylation patterns
• miRNA expression
• DNA sequence
Biospecimen Core
Resource with more than 13
Tissue Source Sites
7 Cancer Genomic
Characterization Centers
3 Genome
Sequencing
Centers
Data Coordinating Center
TCGA: Connecting Multiple Standardized Sources,
Experiments, and Data Types
Three Cancers- Pilot

454
Illumina SOLiD
Helicos
Complete Genomics
Ion-Torrent
Oxford Molecular
PacBioVisigen
LaserGen
IBM
Intelligent Biosystems
NABsysNimblegen
Agilent
Febit
Halycon
ZSGenetics
Raindance
Many “Thermometers”:
Heterogeneity of Platforms

Unanticipated Innovation:
Samples AND Handling Matter!
Nkoy et. al., Arch Pathol Lab Med, April 2010
“Garbage In…Garbage Out”
“…We found that specimens obtained late in the week
(prolonged specimen handling) are more likely to be
ER/PR negative than specimens obtained on other
weekdays (regular specimen handling)…”

#ofpatienttumorsamples
ARRA $
Rapid Acceleration from Stimulus Funding (2009-2011)
2006 2007 2008 2009 2010 2011
Patient Samples Collected
(Reality)
(Projected)
(No ARRA $)

Sample Loss, Time Loss, Money Loss
84% 71%
Overall = 58%

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

Mol Cell Proteomics. 2014 Jul;13(7):1690-1704
CPTAC Due Diligence Study
‒ Scientific implication: effects of pre-analytical
variables associated with TCGA tumors on
protein measurement
‒ TCGA: Cold ischemia (up to 60 min)
‒ Good news: no significant change in protein
levels; change in phosphorylation levels with
biological coherence
Temporal dynamics of phosphorylation
changes resulting from cold ischemia
during surgical procedures.
2011-2012

CPTAC 2: Flagship Characterization Studies
2012 - 2016
Colorectal Cancer Ovarian Cancer
Zhang B, Nature 513, 382–387 (18 Sept 2014) Mertins P, et al, Nature 534, 55–62 (02 June 2016) Zhang, H, et al, Cell 166(3):755-65 (28 Jul 2016)
Breast Cancer

Re-writing Central Dogma (2016)
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

"…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

• Mock 510(k) device clearance
documents in targeted proteomics
• Data sharing policies (Amsterdam
Principles)
http://assays.cancer.gov
898 “fit-for-purpose” targeted
assays
(6,584 users/month)
http://antibodies.cancer.gov
349 mAbs available
(2,653 units distributed)

Overarching Structure of CPTAC 3.0
(2016 – 2021)
A. Proteome Characterization Centers
additional cancer types where questions
remain on their proteogenomic complexity
B. Proteogenomic Translational Research Centers
research models and NCI-sponsored clinical trial
C. Proteogenomic Data Analysis Centers
develop innovative tools that process and integrate
data across the entire proteome
Data, assays and resources - community resources
newtreatment-naïve
cancertypes
5-6
Henry Rodriguez
henry.rodriguez@nih.gov

Proteogenomic Translational Research Centers
Structure and Information
Applications must cover BOTH preclinical studies and studies
with clinical biospecimens from NCI-sponsored trials
Preclinical Research Arm
• Comprehensively characterize and quantitatively measure
proteins and their variants along with associated genomics
in preclinical cancer model samples
Clinical Research Arm
• Develop and apply quantitative proteomic assays to cancer-
relevant proteins identified in Preclinical Research Arm or
preliminary data, to NCI-sponsored clinical trial samples
(http://proteomics.cancer.gov/aboutoccpr/fundingopportunities/curr
ent/Reissuance-of-Clinical-Proteomic-Tumor-Analysis-Consortium)

http://cancerimagingarchive.net
• 33,000 total subjects
in the archive
• 67 data sets currently
available
• 21 from The Cancer
Genome Atlas project
• 10 from the Quantitative
Imaging Network
• Clinical trial data from
ECOG-ACRIN and RTOG

• Accelerate progress in cancer, including
prevention & screening
• From cutting edge basic research to wider
uptake of standard of care
• Encourage greater cooperation and
collaboration
• Within and between academia, government,
and private sector
• Enhance data sharing
Goals of the Initiative:
(From Presidential Memo 2016)
Courtesy of Dinah Singer (http://deainfo.nci.nih.gov/advisory/bsa/0316/0905Singer.pdf)

Cancer Moonshot
Federal Task Force
Vice President’s Office
“Blue Ribbon Panel”
Working Groups
NCAB
NCI
Courtesy of Dinah Singer (http://deainfo.nci.nih.gov/advisory/bsa/0316/0905Singer.pdf)

Make a decade’s worth of progress in cancer prevention,
diagnosis, treatment, and care – ultimately to end cancer
as we know it.

https://www.whitehouse.gov/the-press-office/2016/06/28/fact-
sheet-cancer-moonshot-summit-vice-president-biden-announces-new
• 38 announcements
• 12 public sector
• 26 private sector
June 29, 2016 Oct 17, 2016
• 36 announcements
• 8 public sector
• 28 private sector
sheet-vice-president-biden-delivers-cancer-moonshot-report

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

Cancer Moonshot Data & Technology Team
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)

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

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.

NCI Genomic Data Commons
launched at ASCO on June 6, 2016
https://gdc-portal.nci.nih.gov
2.6 PB of legacy data and 1.5 PB of harmonized data.

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

20102001 2015
1 million healthy genomes
Precision Health

Denny and Steventon. BMJ, 2015
Disease
Precision Health Precision Oncology

Disease
Precision Health Precision OncologyReality

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
“…we must increase research and patient
data sharing...imagine what we could do with
global sets of patient data to represent the
great international diversity of populations,
of people, and of cancers…”
Vice President Biden, Vatican, April 2016

TCGA
2004
MATCH
2016
Translational from basic science to human studies
Translational of new data into the
clinic and health decision making
targets, and lead molecules
New methods of diagnosis,
treatment, and prevention
Delivery of recommended and
timely care to the right patient
True Benefit to society
Controlled studies
leading to effective care
MPACT
LungMAP
ALCHEMIST
2004

How Will This Help the Patients? (2026)
Proteogenomics
Characterization Centers
(PCC)
2016 2026
Translational from basic science to human studies Translational of new data into the
clinic and health decision making
targets, and lead
molecules
New methods of
diagnosis, treatment,
and prevention
Delivery of
recommended and timely
care to the right patient
True Benefit to
society
Controlled studies
leading to effective
care
VA
DoD
Proteogenomics Translational
Research Centers (PTRC)

REGION 1 REGION 2 REGION 4
REGION 3
VA Medical Centers Regional / Corporate Data
Warehousing and Analytical Environment
RDW
V20
V19
V18
V22
V21
MOSS
Farm
RDW
V12
V15
V16
V17
V23
MOSS
Farm RDW
V1
V2
V3
V4
V5
MOSS
Farm
MOSS Farm
• Performance Point Services
• Excel Services
• Reporting Services
• Analysis Services
• Collaboration Services
• Team Foundation Services
RDW
V6
V7
V8
V9V10
V11
MOSS
Farm
CDW
SAS
Grid
VINCI
Apps
PMAS
GIS
MOSS
Farm
Enterprise
Courtesy of Ross Fletcher (DC VAMC)

MCC Military Clinical Trials Network
Naval Medical Center
Portsmouth, VA
Clinical Trials
Increased Access
Referral Center
High cost/low volume
Genetics Counseling
Telehealth technologies
Training &Education
Distributed learning/fellowships
Standardized Clinical
Practice Guidelines
Evidenced-based clinical
practice & research
Patient Outreach
Education and information
MCC Membership
Murtha Cancer
Center
Naval Medical Center
San Diego, CA
Womack Army
Medical Center
Ft Bragg, NC
Keesler Air Force
Medical Center
Biloxi, MS
Lackland Air Force
Medical Center
San Antonio, TX
MCC Clinical Trials Network
Medical Treatment Facilities
MHS
Courtesy of Craig Shriver (DoD)

https://medium.com/cancer-moonshot/
Col. Craig Shriver, MD
Jennifer Lee, MD Henry Rodriguez,
PhD, MBA
5/26/16

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

 Basic and Translational Science
o DoD and NCI will share protocols and materials to standardize proteogenomic characterization of
~2,000 patient tumor cases.
Key Areas Covered by MOA:
 Clinical Science
o VA, DoD, and NCI will utilize new and existing targeted clinical grade genetic/genomic assays, with
appropriately paired proteomic assays, to test proteogenomic profiling of ~6,000 patients receiving
molecularly matched therapies in NCI-sponsored trials and DoD/VA cooperative study programs.
 Learning Healthcare System
o VA and DoD will extend clinical science results by leveraging existing electronic health records to
define and implement VA system-wide best practices and share lessons learned/evidence-based
medicine to help inform DoD policy development and NCI community practice.
 Data Analysis and Work Force Development
o VA, DoD, and NCI will develop capacities to allow end-to-end analysis of tri-agency generated datasets
that will provide a fertile environment for novel data integration and interpretation as well as the
training of the next generation of proteogenomic data, physician, and population scientists.

Patient
Pre-Cancer Dx
VA/DoD Datasets
[Molecular,
Operational,
and e-Health Records]
APOLLO: Basic and Translational Area
(Draft)
Research Grade Proteogenomic Profiling
(Frozen Tissue + Blood)
Recurrence
of Disease
Standard
of Care
Treatment
Clinical Grade Molecular Profiling
(FFPE Tissue + Blood)
1o Cancer Dx
Standard
Diagnostic Lab
Tests
(Tissue, Blood,
Imaging)
Tx Monitor 2o Dx
DoD Murtha
Cancer Center
VA Hospitals
TCGA/CPTAC
DoD/VA/CPTAC Assay
Assume following
NCCN guidelines
(no targeted tx yet)Assume following
NCCN guidelines
1o Disease
Survivor
Clinical Path
Research Path Assume either metastasis
or new 1o disease

Patient
1o Cancer Dx Datasets
Research/Clinical Grade
Profiling of 1o tumor,
Treatment, Operational,
and e-Health Records
APOLLO: Clinical Science Area
(Draft)
Clinical Grade
Genomic Profile
(Tissue + Blood)
Failed
Targeted Tx
Genomic
Targeted
Treatment
Research Grade
Protein Panel
(Tissue + Blood)
2o Cancer Dx
Standard
Diagnostic Lab
Tests
(Tissue, Blood,
Imaging)
Tx Monitor NCI MATCH
DoD Murtha
Cancer Center
VA Hospitals
DoD/VA
Assuming targeted tx
here only to allow
inclusion in NCI-MATCH
Assume following
NCCN guidelines
2o Disease
Survivor
Clinical Path
Research Path
CPTAC Assay
Assume either metastasis
or new 1o disease
If patient fails
targeted treatment,
rapid iteration with
proteogenomics
data
Assume NCI MATCH
for ease of discussion
DoD Murtha
Cancer Center
VA Hospitals

Pre-Cancer VA/DoD
Datasets
Molecular, Operational,
and Health Datasets
1o Tumor
Profiling
Recurrence
Profiling
VA Hospitals DoD Murtha
Cancer Center
SOC
Tx
DAVINC
I
Targeted
Treatment
NCI MATCH
Clinical Path
Research Path
1o Disease
Survivor
Failed
Targeted Tx
3 potential
scenarios
Patients
2o Disease
Survivor
1o Tumor
Profiling
1o Tumor
Profiling
SOC
Tx
SOC
Tx
proteogenomics iteration as feasible
Recurrence
Profiling
Targeted
Treatment
proteogenomics iteration as feasible
Feedback to help next
active duty, beneficiaries,
veterans, or civilian patient
Proteogenomics
Research Grade
Proteogenomics
Research Grade
Proteogenomics
Research Grade
APOLLO: Learning Healthcare System Area
(Draft)

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
sheet-victoria-comprehensive-cancer-center-vice-president-biden

https://tinyurl.com/zr955sr
9/19/2016

http://proteomics.cancer.gov
9/16/2017

Secondary tumor
Primary tumor
8/31/16

Finding the Right “Needle” at the Right “Time” of Disease

Sources of Circulating “Needles” (Normal and Cancer Patients)
Bone Marrow GI Tract Skin
Tumor
Fetal DNA

May 3 2016
https://www.fnih.org/news/announcements/biomarkers-consortium-evaluate-effectiveness-biopsies-colorectal-cancer-patients

https://medium.com/cancer-moonshot/blood-
profiling-atlas-in-cancer
10/17/2016

Parkinson et al., Clin Cancer Res 20:1428, 2014.

Lauren Leiman
Executive Director
lauren@bloodpac.org
http://bloodpac.org

88
National Cancer Data 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

Modified from Abernethy et. al. JCO 2010

1997 20172002 2007 2012
10/23/2001
(~4 yrs old)
1/9/2007
(~10 yrs old)
iPod (10GB max)
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
UberX
7/1/2012
(~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
3/9/2015
(~18 yrs old)
Apple
ResearchKit
AI beats
human at Go
3/15/2016
(~19 yrs old)
HTC VR Headset
4/5/2016
(~19 yrs old)
7/14/2014
(~17 yrs old)
Next Gen
5/1/1997
AOL Instant
Messenger
4/21/1997
WinAMP(mp3)
4/28/2003
(~6 yrs old)
iTunes
Music Store

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

Acknowledgements/Thanks to the
“Secret Ingredients”
Clinical Sciences
Physical Sciences
Life Sciences

Learn More About Us…
http://cssi.cancer.gov
Jerry S.H. Lee, PhD
jerry.lee@nih.gov
@NCI_CSSI
@jleePSOC

Advancing Innovation and Convergence in Cancer Research: US Federal Cancer Moonshot

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