Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Atul Butte's presentation to the Association of Medical School Pediatric Department Chairs #AMSPDC

6,348 views

Published on

Atul Butte's presentation to the Association of Medical School Pediatric Department Chairs #AMSPDC on March 3, 2018.

Some pre-publication data slides have been removed from this deck.

Published in: Health & Medicine
  • I recovered from bulimia. You can too! learn more... ◆◆◆ http://scamcb.com/bulimiarec/pdf
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

Atul Butte's presentation to the Association of Medical School Pediatric Department Chairs #AMSPDC

  1. 1. Translating a Trillion Points of Data into Therapies, Diagnostics, and New Insights into Disease atul.butte@ucsf.edu @atulbutte Atul Butte, MD, PhD Director, Institute for Computational Health Sciences UCSF Distinguished Professor of Pediatrics University of California, San Francisco
  2. 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. 3. Kilo Mega Giga Tera Peta Exa Zetta
  4. 4. @chr1sa bit.ly/endscience
  5. 5. @affymetrix
  6. 6. bit.ly/genedata
  7. 7. bit.ly/geobrca
  8. 8. Public big data = retroactive crowd-sourcing
  9. 9. Yes, even a high-school student can use public data to design a new diagnostic test!
  10. 10. http://www.nap.edu/catalog.php?record_id=13284
  11. 11. Marina Sirota
  12. 12. 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
  13. 13. New blood markers for preeclampsia Linda Liu Bruce Ling Matt Cooper @MarchofDimes bit.ly/preeclamp
  14. 14. 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
  15. 15. @nanopore @wired Whitehead Institute
  16. 16. Credit: Euan Ashley, Russ Altman, Steve Quake, Lancet
  17. 17. Credit: Rong Chen, Optra Systems, and Personalis, Inc. Important genome differences “locked up” in publications
  18. 18. Credit: Rong Chen, Optra Systems, and Personalis, Inc. Collect the “big data” of findings across publications to analyze the “big data” of the genome
  19. 19. Credit: Rong Chen, Optra Systems, and Personalis, Inc.
  20. 20. Credit: Rong Chen, Optra Systems, and Personalis, Inc.
  21. 21. Maybe the genome can be used to suggest (promote?) preventative health strategies? Credit: Rong Chen, Alex Morgan, Joel Dudley, Lancet
  22. 22. Credit: Rong Chen, Alex Morgan, Joel Dudley, Lancet
  23. 23. Credit: Rong Chen, Alex Morgan, Joel Dudley, Lancet
  24. 24. Nicholas Volker
  25. 25. Genetic sequencing for cancer and neurological diseases will soon become routine @FoundationATCG @PersonalisInc
  26. 26. Need to use genomes to predict disease Publications available for curation Stanford donor funding Science curated, methods designed Company launched, Stanford license MDV, Lightspeed, Abingworth ($20 million) Same 3 plus Wellington Shields ($22 million) Series C ($33 million)
  27. 27. @MatthewHerper bit.ly/newdrug1 b
  28. 28. 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
  29. 29. Bin Chen Wei Wei Li Ma Bin Yang Mei-Sze Chua Samuel So Gastroenterology, 2017
  30. 30. 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
  31. 31. 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
  32. 32. • 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
  33. 33. 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
  34. 34. The next big data: clinical data
  35. 35. What could we do with clinical data? • Clinical researcher at UCLA could run a genome wide association study across UC Health • Mobile health researcher at UCSD can enable patients to contribute data for research • Community activist and researcher UC Merced can study environmental factors contributing to health and disease • Transplant patient at UC Irvine can download all their data across UC Health • Data scientist at UC Santa Barbara can model development of Alzheimer's disease and build a multi-modal predictor • App designer at UC Riverside can show patients their choices with chronic disease • CMO at UCSF can build predictive models for readmission, test, share across UC Health • AI researcher at UC Berkeley can build deep-learning models for image-based diagnostics • Health services researcher at UC Davis can build predictive models for drug efficacy, and maybe enable pay-for-performance • Cancer genomics researcher at UCSC can study all our clinical cancer genomes
  36. 36. What is Big Data in Biomedicine?
  37. 37. Algorithms? Programmers? Databases? High-performance computers? Mobile? What is Big Data in Biomedicine?
  38. 38. 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…
  39. 39. Hope Big Data in Biomedicine is
  40. 40. Lessons Learned on the Way • We are only as junior as we want to be • Set the level of your peers as high as you can • If we don’t communicate more and share more, we are going to be invisible • Data-science, informatics, entrepreneurship doesn’t come accidently; invest • Innovate beyond your university: industry and startups. Where is our R&D? • Shoot for changing the world – H-index and impact factor are fun games, but they’re just games – Solve real-world problems, address real-world needs, not just easy ones • Every patient should be in a trial, we should be learn from every patient • We have to cheer our colleagues to get more total funding into pediatrics • You determine your future, not NIH
  41. 41. Lessons Learned on the Way • We are only as junior as we want to be • Set the level of your peers as high as you can • If we don’t communicate more and share more, we are going to be invisible • Data-science, informatics, entrepreneurship doesn’t come accidently; invest • Innovate beyond your university: industry and startups. Where is our R&D? • Shoot for changing the world – H-index and impact factor are fun games, but they’re just games – Solve real-world problems, address real-world needs, not just easy ones • Every patient should be in a trial, we should be learn from every patient • We have to cheer our colleagues to get more total funding into pediatrics • You determine your future, not NIH
  42. 42. 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
  43. 43. 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
  44. 44. Support Admin and Tech Staff • Mary Lyall • Mounira Kenaani • Kevin Kaier • Boris Oskotsky • Mae Moredo • Ada Chen • University of California, San Francisco • 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 • Luke Evnin and Deann Wright (Scleroderma Research Foundation) • Clayville Research Fund • PhRMA Foundation • Stanford Cancer Center, Bio-X, SPARK • Tarangini Deshpande • Kimayani Butte • Sam Hawgood, Keith Yamamoto • Isaac Kohane

×