Atul Butte's AAPS big data workshop presentation 6/2015
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Atul Butte's AAPS big data workshop presentation 6/2015
- 1. Open Big Data in Biomedicine Atul Butte, MD, PhD Director, Institute for Computational Health Science University of California, San Francisco atul.butte@ucsf.edu @atulbutte @ImmPortDB
- 2. Disclosures • 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 – Ansh Labs – Prevendia – Samsung – Assay Depot – Regeneron – Verinata – Pathway Diagnostics – Geisinger Health – Covance – Wilson Sonsini Goodrich & Rosati – 10X Genomics – Medgenics – GNS Healthcare – Gerson Lehman Group – Coatue Management • Corporate Relationships – Northrop Grumman – Aptalis – Thomson Reuters – Intel – SAP – SV Angel • Speakers’ bureau – None • Companies started by students – Carmenta – Serendipity – NuMedii – Stimulomics – NunaHealth – Praedicat – MyTime – Flipora
- 6. Already nearly 1.7 million microarrays publicly-available Doubles every 2-3 years Butte AJ. Translational Bioinformatics: coming of age. JAMIA, 2008.
- 11. 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
- 12. 170 million substances x 1.1 million assays More than a billion measurements within a grid of 190 trillion cells 122 million meet Lipinski 5 1 million active substances
- 13. • One example of a microarray experiment with diabetes and control samples • 187 genes differentially expressed Any one experiment does not yield clear disease-causal factors
- 15. Keiichi Kodama Relativefrequency # of positive RNA microarray experiments (out of 130) Intersect 130 T2D microarray experiments Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 16. Keiichi Kodama Relativefrequency # of positive RNA microarray experiments (out of 130) Intersect 130 T2D microarray experiments Most of the 25000 genes in the genome are positive in few T2D microarray experiments Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 17. Keiichi Kodama Relativefrequency # of positive RNA microarray experiments (out of 130) Intersect 130 T2D microarray experiments TCF7L2 PPARG IDE LEPR Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012. The 186 best known drug targets or genes with DNA variants (from GWAS) are positive in more experiments
- 18. Keiichi Kodama Close collaboration with Dr. Takashi Kadowaki, Momoko Horikoshi, Kazuo Hara, University of Tokyo Relativefrequency # of positive RNA microarray experiments (out of 130) Intersect 130 T2D microarray experiments A Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 19. Keiichi Kodama Gene A changes the most in adipose tissue and islet cell experiments Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 20. Keiichi Kodama Kyoko Toda Gene A is higher in high fat diet Gene A is expressed in mouse fat infiltrate Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 21. Gene A knockout has reduced infiltrate in fat Keiichi Kodama Kyoko Toda • Mac-2 stain Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 22. Gene A knockout has increased insulin sensitivity Keiichi Kodama Kyoko Toda • No change in weight gain Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 23. Keiichi Kodama Inflammatory infiltrate in human fat Protein of Gene A • Paraffin-embedded omental adipose tissue from an obese 57 year woman, BMI 36.9 kg/m2 • Analyzed for Protein A immunoreactivity Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 24. Keiichi Kodama Momoko Horikoshi Serum soluble Gene A protein correlates with human HbA1c and insulin resistance • n = 55 non-diabetics • 60.3 years of age ± 15, 36 males, 19 females • BMI 23.2 ± 4.3 kg/m2 Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 25. Keiichi Kodama Therapeutic antibody against Gene A reduces fat inflammatory infiltrate in mouse Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 26. Keiichi Kodama Therapeutic antibody against Gene A reduces glucose • C57BL6/6J fed high-fat diet for 18 weeks • Intraperitoneal injection of rat anti-mouse anti-A antibody (n=8) or isotype control (n=8) • 100 μg at day 0 and 50 μg at day 1-7 Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.
- 27. Keiichi Kodama • Gene A is CD44 (Hyaluronic Acid Receptor) • Anti-CD44 in development for multiple cancers • CD44 is a complicated receptor Ponta, Sherman, Herrlich. Nature Reviews Molecular Cell Biology, 2003.
- 28. Longer-term trial of anti-CD44 as a prototype therapy for type 2 diabetes Kodama K, …, Butte AJ. Diabetes, 2015 Mar;64(3):867-75. Keiichi Kodama Kyoko Toda Shojiroh Morinaga Satoru Yamada
- 29. Anti-CD44 for 4 weeks reduces fasting glucose and improves insulin sensitivity Kodama K, …, Butte AJ. Diabetes, 2015 Mar;64(3):867-75. Keiichi Kodama Kyoko Toda Shojiroh Morinaga Satoru Yamada
- 30. Kodama K, …, Butte AJ. Diabetes, 2015 Mar;64(3):867-75. Keiichi Kodama Kyoko Toda Shojiroh Morinaga Satoru Yamada Anti-CD44 for 4 weeks slows weight gain and reduces intake
- 31. Anti-CD44 for 4 weeks reduces adipose inflammation and hepatic steatosis Kodama K, …, Butte AJ. Diabetes, 2015 Mar;64(3):867-75. Keiichi Kodama Kyoko Toda Shojiroh Morinaga Satoru Yamada
- 32. Kodama K, …, Butte AJ. Diabetes, 2015. Keiichi Kodama Kyoko Toda Shojiroh Morinaga Satoru Yamada
- 33. bit.ly/immport The next big open data: clinical trials
- 35. bit.ly/1b4sa7b
- 36. Institute for Computational Health Sciences
- 37. Collaborators • Jeff Wiser, Patrick Dunn, Mike Atassi / Northrop Grumman • Ashley Xia and Quan Chen / NIAID • Takashi Kadowaki, Momoko Horikoshi, Kazuo Hara, Hiroshi Ohtsu / U Tokyo • Kyoko Toda, Satoru Yamada, Junichiro Irie / Kitasato Univ and Hospital • Shiro Maeda / RIKEN • Alejandro Sweet-Cordero, Julien Sage / Pediatric Oncology • Mark Davis, C. Garrison Fathman / Immunology • Russ Altman, Steve Quake / Bioengineering • Euan Ashley, Joseph Wu, Tom Quertermous / Cardiology • Mike Snyder, Carlos Bustamante, Anne Brunet / Genetics • Jay Pasricha / Gastroenterology • Rob Tibshirani, Brad Efron / Statistics • Hannah Valantine, Kiran Khush/ Cardiology • Ken Weinberg / Pediatric Stem Cell Therapeutics • Mark Musen, Nigam Shah / National Center for Biomedical Ontology • Minnie Sarwal / Nephrology • David Miklos / Oncology
- 38. Support • Lucile Packard Foundation for Children's Health • NIH: NIAID, NLM, NIGMS, NCI; NIDDK, NHGRI, NIA, NHLBI, NCATS • March of Dimes • Hewlett Packard • Howard Hughes Medical Institute • California Institute for Regenerative Medicine • Luke Evnin and Deann Wright (Scleroderma Research Foundation) • Clayville Research Fund • PhRMA Foundation • Stanford Cancer Center, Bio-X, SPARK • Tarangini Deshpande • Sam Hawgood • Keith Yamamoto • Isaac Kohane Admin and Tech Staff • Mary Lyall • Mounira Kenaani • Kevin Kaier • Boris Oskotsky