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From a Trillion Points of Data into
Discoveries, Diagnostics, and
New Insights in Health and Disease
atul.butte@ucsf.edu
@atulbutte
Atul Butte, MD, PhD
Director, Bakar Computational
Health Sciences Institute
Distinguished Professor of Pediatrics, UCSF
Chief Data Scientist, University of California Health
Conflicts of Interest
• Scientific founder and
advisory board membership
– Genstruct
– NuMedii
– Personalis
– Carmenta
• Honoraria for talks
– Lilly
– Pfizer
– Siemens
– Bristol Myers Squibb
– AstraZeneca
– Roche
– Genentech
– Warburg Pincus
• Past or present consultancy
– Lilly
– Johnson and Johnson
– Roche
– NuMedii
– Genstruct
– Tercica
– Ecoeos
– Helix
– Ansh Labs
– Prevendia
– Samsung
– Assay Depot
– Regeneron
– Verinata
– Pathway Diagnostics
– Geisinger Health
– Covance
– Wilson Sonsini Goodrich & Rosati
– Orrick
– 10X Genomics
– Medgenics
– GNS Healthcare
– Gerson Lehman Group
– Coatue Management
• Corporate Relationships
– Northrop Grumman
– Aptalis
– Allergan
– Astellas
– Thomson Reuters
– Intel
– SAP
– SV Angel
– Progenity
– Illumina
• Speakers’ bureau
– None
• Companies started by students
– Carmenta
– Serendipity
– Stimulomics
– NunaHealth
– Praedicat
– MyTime
– Flipora
– Tumbl.in
Kilo
Mega
Giga
Tera
Peta
Exa
Zetta
@chr1sa
bit.ly/endscience
@affymetrix
bit.ly/genedata
bit.ly/geobrca
Public big data = retroactive crowd-sourcing
Yes, even a high-school student can
use public data to design a new
diagnostic test!
The Cancer Genome Atlas
• 14 thousand cases
• 39 types of cancers
• 13 types of data: molecular, clinical, sequencing
5,178 compounds
· 1,300 off-patent FDA-approved drugs
· 700 bioactive tool compounds
· 2,000+ screening hits (MLPCN and others)
3,712 genes (shRNA + cDNA)
· targets/pathways of FDA-approved drugs (n=900)
· candidate disease genes (n=600)
· community nominations (n=500+)
15 cell types
· Banked primary cell types
· Cancer cell lines
· Primary hTERT immortalized
· Patient derived iPS cells
· 5 community nominated
227 million substances x
1.3 million assays
More than a billion measurements
within a grid of 300 trillion cells
71 million meet Lipinski 5
1.2 million active substances
http://www.nap.edu/catalog.php?record_id=13284
Credit: Painting by Alexander Roslin (public domain, through Wikipedia)
Credit: Harvard University Libraries and Google Books
Credit: Stanford Lane Medical Library and Google Books
39 Cancer of the buccal cavity
40 Cancer of stomach and liver
41 Cancer of peritoneum, intestines, rectum
...
44 Cancer of skin
45 Cancer of other organs or
not specified
Lung is an “other organ”
Brain is an “other organ”
...
189 Visitation from God
Marina Sirota
Protein
Cancer markers
Haeberle H, Dudley JT, …,
Butte AJ, Contag CH. Neoplasia, 2012.
Cancer markers
Transplant Rejection markers
Chen R, …, Butte AJ.
PLoS Computational Biology, 2010.
Preeclampsia: large cause of maternal and fetal death
• Incidence
• 5-8% of all pregnancies in the U.S. and worldwide
• 4.1 million births in the U.S. in 2009
• Up to 300K cases of preeclampsia annually in the U.S.
• Mortality
• Responsible for 18% of all maternal deaths in the U.S.
• Maternal death in 56 out of every 100,000 live births in US
• Neonatal death in 71 out of every 100,000 live births in US
• Cost
• $20 billion in direct costs in the U.S. annually
• Average hospital stay of 3.5 days
Linda Liu
Bruce Ling
Matt Cooper
New blood markers for preeclampsia
Linda Liu
Bruce Ling
Matt Cooper
@MarchofDimes
bit.ly/preeclamp
Need a
diagnostic for
preeclampsia
Public big data
available
March of Dimes
Center for
Prematurity
Research
Data analyzed,
diagnostic
designed
SPARK grant
($50k)
Life Science
Angels, other
seed investors
($2 million)
@CarmentaBio
progenity.com
bit.ly/carm_prog
@MatthewHerper
bit.ly/newdrug1
Joel Dudley
Marina Sirota
Rat colonoscopy Rat model of
Inflammatory
Bowel Disease
Inflammatory Bowel
Disease
After Topiramate
Science Translational Medicine 2011, bit.ly/scitmtop
Anti-epileptic drug topiramate works against a
rat model of inflammatory bowel disease
Cancer Discovery 2013, 3:1.
Psychiatric Drug Imipramine Shows Significant Activity
Against Small Cell Lung Cancer
Vehicle control Imipramine
p53/Rb/p130
triple knockout
model of SCLC
Mice dosed after
tumor formation
Joel Dudley
Nadine Jahchan
Julien Sage
Alejandro Sweet-Cordero
Joel Neal
@NuMedii
Mazen Nasrallah
Peter Marinkovich
Mårten Winge
Unpublished
Unpublished
Need more drugs
for more diseases
Public big data
available
NIH funding
Data analyzed,
method designed
Company launched,
ARRA, StartX,
Stanford license,
first deal
Claremont Creek,
Lightspeed ($3.5
million)
@NuMedii
The next big open data: clinical trials
Download 300+ studies today
Drug repositioning, new patient subsets,
digital comparative effectiveness, more!
immport.org
Sanchita Bhattacharya
Elizabeth Thomson
ImmPort redistributes data from
major NIAID-funded programs and more
Data from 300+ trials and studies already released, involving:
• Immune Tolerance Network (ITN)
• Atopic Dermatitis Research Network (ADRN)
• Clinical Trials in Organ Transplantation (CTOT) and in Children (CTOT-C)
• Population Genetics Analysis Program
• Protective Immunity for Special Populations
• Human Immunology Project Consortium
• HLA Region Genomics in Immune-mediated Diseases
• Modeling Immunity for Biodefense
• Reagent Development for Innate Immune Receptors
• Adjuvant Development Program
• Innate Immune Receptors and Adjuvant Discovery Program
• Maintenance of Macaque Specific Pathogen-Free Breeding Colonies
• Non-human Primate Transplantation Tolerance Cooperative Study Group
Collaborations with The Bill and Melinda Gates Foundation and March of Dimes
and the NIH Accelerating Medicines Partnership (AMP)
De-identified raw clinical study data is released to the public along with genetic,
gene expression, and flow cytometry measurements, in open formats
Hundreds of user downloads per month
255 255
291 290 301 301 309 309 309 309 318 318 318
141 148
109 116 109 99 101 106 107 102 104 105
120
ImmPort Study Summary
Cumulative Number of DAIT-
funded studies shared
Cumulative Number of Private
Studies in ImmPort
Immunol Res. 2014 May;58(2-3):234-9.
Reanalyzing RAVE
• Rituximab in ANCA-Associated Vasculitis (RAVE) trial of new approach to
the induction of remission
– randomized
– double-blind
– double-dummy
– active-controlled
– non-inferiority
Reproduce CD19+ B-cell depletion
using publicly released clinical trials data
Nasrallah M, …, Butte AJ. Arthritis Research & Therapy (2015) 17:262.
RAVE re-analysis
• 63 of the 99 patients in the rituximab group (64%) reached the
primary end point, as compared with 52 of 98 in the control group
(53%).
• The treatment difference of 11% points between the groups met the
criterion for non-inferiority (P<0.001).
In retrospect, do any measured factors
predict response?
Mazen Nasrallah
Nasrallah M, …, Butte AJ. Arthritis Research & Therapy (2015) 17:262.
Nasrallah M, …, Butte AJ. Arthritis Research & Therapy (2015) 17:262.
Granularity index higher in rituximab-treated
subjects with remission
SSC
1 2
Granulocyte Subpopulations and Treatment Outcomes
Panel A: representative bi-dimensional dot-plot of granulocyte sub-
populations identified by ImmPortFLOCK on the basis of FSC and SSC.
A1: Hypogranular granulocytes with an SSC of low or positive (2 or 3).
A2: Hyper granular granulocytes with an SSC of high (4).
Panel B: granularity index at day 0 among patients receiving rituximab or
cyclophosphamide, stratified by treatment outcome (failure: red,
success: blue). Data distribution is shown as a boxplot, with mean ±
SEM represented by dots and small error bars. CYC: cyclophosphamide,
RTX: rituximab. A Welch two-sided t-test was used to calculate
significance.
Nasrallah M, …, Butte AJ. Arthritis Research & Therapy (2015) 17:262.
ANCA-
associated
Vasculitis
Profiled
Therapy
~ 54% of patients
Non-profiled
Therapy
~46% of patients
Treat with
Rituximab
~ 30% of patients
Remission Rate ~ 83%
Treat with
Cyclophosphamide
~24% of patients
Remission Rate ~ 66%
Do not treat with
Cyclophosphamide
Failure rate ~ 67%
Do not treat with
Rituximab
Failure rate ~ 70%
GI ≤ -9.25%
OR
GI ≥ 47.6%
GI ≤ -9.25% GI ≥ 47.6%
Treat with either Rituximab or
Cyclophosphamide
according to best clinical judgement
Average Remission Rate ~ 60%
Non-profiled
Therapy
100% of patients
NO
Proposed
Method
Current
Method
Measure the Granularity Index
(GI)
YES
Mazen Nasrallah
bioRxiv bit.ly/10kimmu
http://10kimmunomes.org/
The 10,000 Immunome Project: From the control groups
of 242 manually curated experiments
Kelly Zalocusky
Sanchita Bhattacharya
@ImmPortDB
• Founded 2015
• 49 affiliated faculty members from UCSF’s four
top-ranked schools
– 5 in National Academy of Medicine
– 1 in National Academy of Science
– 2 in the American Society for Clinical Investigation
– 3 NIH Director’s Awards
– 2 Sloan Foundation fellows
– 1 HHMI faculty scholar
– 1 MacArthur Foundation fellow
– 1 Chan/Zuckerberg faculty fellow
Build the strongest team in the world in
biomedical computation and health data analytics
• Academic affinity home for faculty and staff
• Research and development (and spin out technologies)
• Develop new educational plans
• Bring the best new computational and informatics faculty members
to UCSF
• Organize infrastructure and operations
• Build and use our new data assets for precision medicine
University of California
• 10 campuses and 3 national labs
• ~200,000 employees, ~250,000 students/yr
UC Health
• 18 health professional schools (6 med schools)
• Train half the medical students and residents in
California
• ~$2 billion NIH funding
• $11.4 billion clinical operating revenue
• 5000 faculty physicians, 12000 nurses
• UCSF and UCLA are in US News top 10
• 5 NCI Comprehensive Cancer Centers, 5 NIH CTSA
UC Health Data Analytics Platform
Combining healthcare data from across the
six University of California medical schools and systems
Health Data Warehouse
The next big data: clinical data
Source: American Diabetes Association Standards of Medical Care in Diabetes
Medication Strategies for First-Time Type 2 Diabetes Patients
Tom Peterson
Medication Strategies for First-Time Type 2 Diabetes Patients
Tom Peterson
Medication Strategies for First-Time Type 2 Diabetes Patients
Tom Peterson
1,640 Unique Medication Trajectories for Treating T2D at UCSF
Tom Peterson
6,543 Unique Medication Trajectories for Treating T2D UC-Wide
Tom Peterson
Lisa Dahm
Ayan Patel
Performance for Predicting Medication Class
Increase Within 90 Days from Metformin
Tom Peterson
Hanna Paik
Jae Hyun
Hanna Paik
Jae Hyun
What is Big Data in Biomedicine?
Algorithms?
Programmers?
Databases?
High-performance computers?
Mobile?
What is Big Data in Biomedicine?
Predicting the disease before it strikes
Explaining the rare disease that defies experts
Finding drugs for diseases lacking attention
Making sure we do the right thing for patients
An amazing platform for biomedical innovation
Big Data in Biomedicine is…
Hope
Big Data in Biomedicine is
UC Clinical Data Warehouse Team
Executive Team
• Atul Butte
• Joe Bengfort
• Michael Pfeffer
• Tom Andriola
• Chris Longhurst
Steering Committee
• Lisa Dahm
• Mohammed Mahbouba
• David Dobbs
• Kent Andersen
• Ralph James
• Jennifer Holland
• Eugene Lee
ETL Team
• Albert Dugan
• Tony Choe
• Michael Sweeney
• Timothy Satterwhite
• Ayan Patel
• Niranjan Wagle
• Ralph James
• Joseph Dalton
Data Harmonization
• Dana Ludwig
• Daniella Meeker
Data Quality
• Momeena Ali
• Jodie Nygaard
Business Analyst
• Ankeeta Shukla
Hardware
• Sandeep Chandra
• Jeff Love
• Scott Bailey
• Kwong Law
• Pallav Saxena
Support
• Elizabeth Engel
• Jack Stobo
• Michael Blum
• Sam Hawgood
Collaborators
• Alejandro Sweet-Cordero, Julien Sage /
Pediatric Oncology
• Elizabeth Thomson, Patrick Dunn / Northrop
Grumman
• Gabe Rosenfeld, Quan Chen / NIAID
• Andrei Goga / UCSF Oncology
• Mallar Bhattacharya / UCSF Pulmonary
• Minnie Sarwal / UCSF Nephrology
• Geoffrey Gurtner / UCSF Surgery
• Roberta Diaz Brinton / Arizona
• Carol Bult / Jackson Labs
• David Stevenson, Gary Shaw / Stanford
Neonatology
• Takashi Kadowaki, Momoko Horikoshi, Kazuo
Hara, Hiroshi Ohtsu / U Tokyo
• Kyoko Toda, Satoru Yamada, Junichiro Irie /
Kitasato Univ and Hospital
• Shiro Maeda / RIKEN
• Mark Davis, C. Garrison Fathman /
Immunology
• Russ Altman, Steve Quake / Stanford
Bioengineering
• Euan Ashley, Joseph Wu / Stanford Cardiology
• Mike Snyder, Carlos Bustamante, Anne Brunet
/ Stanford Genetics
• Jay Pasricha / Stanford Gastroenterology
• Rob Tibshirani, Brad Efron / Stanford Statistics
• Hannah Valantine, Kiran Khush/ Stanford
Cardiology
• Mark Musen, Nigam Shah / National Center for
Biomedical Ontology
• Sam So, Ken Weinberg, David Miklos /
Stanford Oncology
Support
Admin and Tech Staff
• Mary Lyall
• Mounira Kenaani
• Kevin Kaier
• Boris Oskotsky
• Mae Moredo
• Ada Chen
• University of California, San Francisco
• Priscilla Chan and Mark Zuckerberg
• NIH: NIAID, NLM, NIGMS, NCI, NHLBI, OD; NIDDK, NHGRI, NIA, NCATS, NICHD
• California Governor’s Office of Planning and Research
• March of Dimes
• Juvenile Diabetes Research Foundation
• Howard Hughes Medical Institute, California Institute for Regenerative Medicine
• Hewlett Packard, L’Oreal, Progenity
• Scleroderma Research Foundation
• Clayville Research Fund, PhRMA Foundation, Stanford Cancer Center, Bio-X, SPARK
• Tarangini Deshpande
• Kimayani Butte
• Talmadge King and Mark Laret
• Sam Hawgood and Keith Yamamoto
• Isaac Kohane

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Atul Butte's presentation at the From Data to Discovery symposium at Westat

  • 1. From a Trillion Points of Data into Discoveries, Diagnostics, and New Insights in Health and Disease atul.butte@ucsf.edu @atulbutte Atul Butte, MD, PhD Director, Bakar Computational Health Sciences Institute Distinguished Professor of Pediatrics, UCSF Chief Data Scientist, University of California Health
  • 2. Conflicts of Interest • Scientific founder and advisory board membership – Genstruct – NuMedii – Personalis – Carmenta • Honoraria for talks – Lilly – Pfizer – Siemens – Bristol Myers Squibb – AstraZeneca – Roche – Genentech – Warburg Pincus • Past or present consultancy – Lilly – Johnson and Johnson – Roche – NuMedii – Genstruct – Tercica – Ecoeos – Helix – Ansh Labs – Prevendia – Samsung – Assay Depot – Regeneron – Verinata – Pathway Diagnostics – Geisinger Health – Covance – Wilson Sonsini Goodrich & Rosati – Orrick – 10X Genomics – Medgenics – GNS Healthcare – Gerson Lehman Group – Coatue Management • Corporate Relationships – Northrop Grumman – Aptalis – Allergan – Astellas – Thomson Reuters – Intel – SAP – SV Angel – Progenity – Illumina • Speakers’ bureau – None • Companies started by students – Carmenta – Serendipity – Stimulomics – NunaHealth – Praedicat – MyTime – Flipora – Tumbl.in
  • 3.
  • 9. Public big data = retroactive crowd-sourcing
  • 10. Yes, even a high-school student can use public data to design a new diagnostic test!
  • 11. The Cancer Genome Atlas • 14 thousand cases • 39 types of cancers • 13 types of data: molecular, clinical, sequencing
  • 12. 5,178 compounds · 1,300 off-patent FDA-approved drugs · 700 bioactive tool compounds · 2,000+ screening hits (MLPCN and others) 3,712 genes (shRNA + cDNA) · targets/pathways of FDA-approved drugs (n=900) · candidate disease genes (n=600) · community nominations (n=500+) 15 cell types · Banked primary cell types · Cancer cell lines · Primary hTERT immortalized · Patient derived iPS cells · 5 community nominated
  • 13. 227 million substances x 1.3 million assays More than a billion measurements within a grid of 300 trillion cells 71 million meet Lipinski 5 1.2 million active substances
  • 15. Credit: Painting by Alexander Roslin (public domain, through Wikipedia)
  • 16. Credit: Harvard University Libraries and Google Books
  • 17. Credit: Stanford Lane Medical Library and Google Books 39 Cancer of the buccal cavity 40 Cancer of stomach and liver 41 Cancer of peritoneum, intestines, rectum ... 44 Cancer of skin 45 Cancer of other organs or not specified Lung is an “other organ” Brain is an “other organ” ... 189 Visitation from God
  • 18.
  • 19.
  • 21.
  • 23. Cancer markers Haeberle H, Dudley JT, …, Butte AJ, Contag CH. Neoplasia, 2012.
  • 24. Cancer markers Transplant Rejection markers Chen R, …, Butte AJ. PLoS Computational Biology, 2010.
  • 25. Preeclampsia: large cause of maternal and fetal death • Incidence • 5-8% of all pregnancies in the U.S. and worldwide • 4.1 million births in the U.S. in 2009 • Up to 300K cases of preeclampsia annually in the U.S. • Mortality • Responsible for 18% of all maternal deaths in the U.S. • Maternal death in 56 out of every 100,000 live births in US • Neonatal death in 71 out of every 100,000 live births in US • Cost • $20 billion in direct costs in the U.S. annually • Average hospital stay of 3.5 days Linda Liu Bruce Ling Matt Cooper
  • 26.
  • 27. New blood markers for preeclampsia Linda Liu Bruce Ling Matt Cooper @MarchofDimes bit.ly/preeclamp
  • 28. Need a diagnostic for preeclampsia Public big data available March of Dimes Center for Prematurity Research Data analyzed, diagnostic designed SPARK grant ($50k) Life Science Angels, other seed investors ($2 million) @CarmentaBio progenity.com bit.ly/carm_prog
  • 30.
  • 31. Joel Dudley Marina Sirota Rat colonoscopy Rat model of Inflammatory Bowel Disease Inflammatory Bowel Disease After Topiramate Science Translational Medicine 2011, bit.ly/scitmtop Anti-epileptic drug topiramate works against a rat model of inflammatory bowel disease
  • 32. Cancer Discovery 2013, 3:1. Psychiatric Drug Imipramine Shows Significant Activity Against Small Cell Lung Cancer Vehicle control Imipramine p53/Rb/p130 triple knockout model of SCLC Mice dosed after tumor formation Joel Dudley Nadine Jahchan Julien Sage Alejandro Sweet-Cordero Joel Neal @NuMedii
  • 35. Need more drugs for more diseases Public big data available NIH funding Data analyzed, method designed Company launched, ARRA, StartX, Stanford license, first deal Claremont Creek, Lightspeed ($3.5 million) @NuMedii
  • 36. The next big open data: clinical trials Download 300+ studies today Drug repositioning, new patient subsets, digital comparative effectiveness, more! immport.org Sanchita Bhattacharya Elizabeth Thomson
  • 37. ImmPort redistributes data from major NIAID-funded programs and more Data from 300+ trials and studies already released, involving: • Immune Tolerance Network (ITN) • Atopic Dermatitis Research Network (ADRN) • Clinical Trials in Organ Transplantation (CTOT) and in Children (CTOT-C) • Population Genetics Analysis Program • Protective Immunity for Special Populations • Human Immunology Project Consortium • HLA Region Genomics in Immune-mediated Diseases • Modeling Immunity for Biodefense • Reagent Development for Innate Immune Receptors • Adjuvant Development Program • Innate Immune Receptors and Adjuvant Discovery Program • Maintenance of Macaque Specific Pathogen-Free Breeding Colonies • Non-human Primate Transplantation Tolerance Cooperative Study Group Collaborations with The Bill and Melinda Gates Foundation and March of Dimes and the NIH Accelerating Medicines Partnership (AMP) De-identified raw clinical study data is released to the public along with genetic, gene expression, and flow cytometry measurements, in open formats Hundreds of user downloads per month 255 255 291 290 301 301 309 309 309 309 318 318 318 141 148 109 116 109 99 101 106 107 102 104 105 120 ImmPort Study Summary Cumulative Number of DAIT- funded studies shared Cumulative Number of Private Studies in ImmPort
  • 38. Immunol Res. 2014 May;58(2-3):234-9.
  • 39.
  • 40. Reanalyzing RAVE • Rituximab in ANCA-Associated Vasculitis (RAVE) trial of new approach to the induction of remission – randomized – double-blind – double-dummy – active-controlled – non-inferiority
  • 41.
  • 42.
  • 43. Reproduce CD19+ B-cell depletion using publicly released clinical trials data Nasrallah M, …, Butte AJ. Arthritis Research & Therapy (2015) 17:262.
  • 44. RAVE re-analysis • 63 of the 99 patients in the rituximab group (64%) reached the primary end point, as compared with 52 of 98 in the control group (53%). • The treatment difference of 11% points between the groups met the criterion for non-inferiority (P<0.001). In retrospect, do any measured factors predict response? Mazen Nasrallah Nasrallah M, …, Butte AJ. Arthritis Research & Therapy (2015) 17:262.
  • 45. Nasrallah M, …, Butte AJ. Arthritis Research & Therapy (2015) 17:262. Granularity index higher in rituximab-treated subjects with remission SSC 1 2 Granulocyte Subpopulations and Treatment Outcomes Panel A: representative bi-dimensional dot-plot of granulocyte sub- populations identified by ImmPortFLOCK on the basis of FSC and SSC. A1: Hypogranular granulocytes with an SSC of low or positive (2 or 3). A2: Hyper granular granulocytes with an SSC of high (4). Panel B: granularity index at day 0 among patients receiving rituximab or cyclophosphamide, stratified by treatment outcome (failure: red, success: blue). Data distribution is shown as a boxplot, with mean ± SEM represented by dots and small error bars. CYC: cyclophosphamide, RTX: rituximab. A Welch two-sided t-test was used to calculate significance.
  • 46. Nasrallah M, …, Butte AJ. Arthritis Research & Therapy (2015) 17:262. ANCA- associated Vasculitis Profiled Therapy ~ 54% of patients Non-profiled Therapy ~46% of patients Treat with Rituximab ~ 30% of patients Remission Rate ~ 83% Treat with Cyclophosphamide ~24% of patients Remission Rate ~ 66% Do not treat with Cyclophosphamide Failure rate ~ 67% Do not treat with Rituximab Failure rate ~ 70% GI ≤ -9.25% OR GI ≥ 47.6% GI ≤ -9.25% GI ≥ 47.6% Treat with either Rituximab or Cyclophosphamide according to best clinical judgement Average Remission Rate ~ 60% Non-profiled Therapy 100% of patients NO Proposed Method Current Method Measure the Granularity Index (GI) YES Mazen Nasrallah
  • 47. bioRxiv bit.ly/10kimmu http://10kimmunomes.org/ The 10,000 Immunome Project: From the control groups of 242 manually curated experiments Kelly Zalocusky Sanchita Bhattacharya @ImmPortDB
  • 48.
  • 49.
  • 50.
  • 51. • Founded 2015 • 49 affiliated faculty members from UCSF’s four top-ranked schools – 5 in National Academy of Medicine – 1 in National Academy of Science – 2 in the American Society for Clinical Investigation – 3 NIH Director’s Awards – 2 Sloan Foundation fellows – 1 HHMI faculty scholar – 1 MacArthur Foundation fellow – 1 Chan/Zuckerberg faculty fellow
  • 52. Build the strongest team in the world in biomedical computation and health data analytics • Academic affinity home for faculty and staff • Research and development (and spin out technologies) • Develop new educational plans • Bring the best new computational and informatics faculty members to UCSF • Organize infrastructure and operations • Build and use our new data assets for precision medicine
  • 53. University of California • 10 campuses and 3 national labs • ~200,000 employees, ~250,000 students/yr UC Health • 18 health professional schools (6 med schools) • Train half the medical students and residents in California • ~$2 billion NIH funding • $11.4 billion clinical operating revenue • 5000 faculty physicians, 12000 nurses • UCSF and UCLA are in US News top 10 • 5 NCI Comprehensive Cancer Centers, 5 NIH CTSA
  • 54. UC Health Data Analytics Platform Combining healthcare data from across the six University of California medical schools and systems Health Data Warehouse
  • 55. The next big data: clinical data
  • 56.
  • 57. Source: American Diabetes Association Standards of Medical Care in Diabetes
  • 58. Medication Strategies for First-Time Type 2 Diabetes Patients Tom Peterson
  • 59. Medication Strategies for First-Time Type 2 Diabetes Patients Tom Peterson
  • 60. Medication Strategies for First-Time Type 2 Diabetes Patients Tom Peterson
  • 61. 1,640 Unique Medication Trajectories for Treating T2D at UCSF Tom Peterson
  • 62. 6,543 Unique Medication Trajectories for Treating T2D UC-Wide Tom Peterson Lisa Dahm Ayan Patel
  • 63. Performance for Predicting Medication Class Increase Within 90 Days from Metformin Tom Peterson
  • 66.
  • 67. What is Big Data in Biomedicine?
  • 69. Predicting the disease before it strikes Explaining the rare disease that defies experts Finding drugs for diseases lacking attention Making sure we do the right thing for patients An amazing platform for biomedical innovation Big Data in Biomedicine is…
  • 70. Hope Big Data in Biomedicine is
  • 71. UC Clinical Data Warehouse Team Executive Team • Atul Butte • Joe Bengfort • Michael Pfeffer • Tom Andriola • Chris Longhurst Steering Committee • Lisa Dahm • Mohammed Mahbouba • David Dobbs • Kent Andersen • Ralph James • Jennifer Holland • Eugene Lee ETL Team • Albert Dugan • Tony Choe • Michael Sweeney • Timothy Satterwhite • Ayan Patel • Niranjan Wagle • Ralph James • Joseph Dalton Data Harmonization • Dana Ludwig • Daniella Meeker Data Quality • Momeena Ali • Jodie Nygaard Business Analyst • Ankeeta Shukla Hardware • Sandeep Chandra • Jeff Love • Scott Bailey • Kwong Law • Pallav Saxena Support • Elizabeth Engel • Jack Stobo • Michael Blum • Sam Hawgood
  • 72. Collaborators • Alejandro Sweet-Cordero, Julien Sage / Pediatric Oncology • Elizabeth Thomson, Patrick Dunn / Northrop Grumman • Gabe Rosenfeld, Quan Chen / NIAID • Andrei Goga / UCSF Oncology • Mallar Bhattacharya / UCSF Pulmonary • Minnie Sarwal / UCSF Nephrology • Geoffrey Gurtner / UCSF Surgery • Roberta Diaz Brinton / Arizona • Carol Bult / Jackson Labs • David Stevenson, Gary Shaw / Stanford Neonatology • Takashi Kadowaki, Momoko Horikoshi, Kazuo Hara, Hiroshi Ohtsu / U Tokyo • Kyoko Toda, Satoru Yamada, Junichiro Irie / Kitasato Univ and Hospital • Shiro Maeda / RIKEN • Mark Davis, C. Garrison Fathman / Immunology • Russ Altman, Steve Quake / Stanford Bioengineering • Euan Ashley, Joseph Wu / Stanford Cardiology • Mike Snyder, Carlos Bustamante, Anne Brunet / Stanford Genetics • Jay Pasricha / Stanford Gastroenterology • Rob Tibshirani, Brad Efron / Stanford Statistics • Hannah Valantine, Kiran Khush/ Stanford Cardiology • Mark Musen, Nigam Shah / National Center for Biomedical Ontology • Sam So, Ken Weinberg, David Miklos / Stanford Oncology
  • 73. Support Admin and Tech Staff • Mary Lyall • Mounira Kenaani • Kevin Kaier • Boris Oskotsky • Mae Moredo • Ada Chen • University of California, San Francisco • Priscilla Chan and Mark Zuckerberg • NIH: NIAID, NLM, NIGMS, NCI, NHLBI, OD; NIDDK, NHGRI, NIA, NCATS, NICHD • California Governor’s Office of Planning and Research • March of Dimes • Juvenile Diabetes Research Foundation • Howard Hughes Medical Institute, California Institute for Regenerative Medicine • Hewlett Packard, L’Oreal, Progenity • Scleroderma Research Foundation • Clayville Research Fund, PhRMA Foundation, Stanford Cancer Center, Bio-X, SPARK • Tarangini Deshpande • Kimayani Butte • Talmadge King and Mark Laret • Sam Hawgood and Keith Yamamoto • Isaac Kohane