Atul Butte's AAPS big data workshop presentation 6/2015

Open Big Data in Biomedicine

Atul Butte, MD, PhDDirector, Institute for

Computational Health ScienceUniversity of California, San Francisco

[email protected] @atulbutte

@ImmPortDB

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

KiloMegaGigaTeraPetaExa

Zetta

Big Data in Biomedicine

Already nearly 1.7 million microarrays publicly-availableDoubles every 2-3 years

Butte AJ. Translational Bioinformatics: coming of age. JAMIA, 2008.

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

170 million substances x1.1 million assays

More than a billion measurements within a grid of 190 trillion cells

122 million meet Lipinski 51 million active substances

• 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

Keiichi Kodama

Rela

tive

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# 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.

Keiichi Kodama

Rela

tive

freq

uenc

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# 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.

Keiichi Kodama

Rela

tive

freq

uenc

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# of positive RNA microarray experiments (out of 130)

Intersect 130 T2D microarray experiments

TCF7L2PPARG

IDELEPR

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

Keiichi Kodama

Close collaboration with Dr. Takashi Kadowaki, Momoko Horikoshi, Kazuo Hara, University of Tokyo

Rela

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# 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.

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.

Keiichi KodamaKyoko Toda

Gene A is higher in high fat dietGene A is expressed in mouse fat infiltrate

Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.

Gene A knockout has reduced infiltrate in fat

Keiichi KodamaKyoko Toda

• Mac-2 stain

Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.

Gene A knockout has increased insulin sensitivity

Keiichi KodamaKyoko Toda

• No change in weight gain

Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.

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.

Keiichi KodamaMomoko 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.

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.

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.

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.

Longer-term trial of anti-CD44 as a prototype therapy fortype 2 diabetes

Kodama K, …, Butte AJ. Diabetes, 2015 Mar;64(3):867-75.

Keiichi KodamaKyoko Toda

Shojiroh MorinagaSatoru Yamada

Anti-CD44 for 4 weeks reduces fasting glucose and improves insulin sensitivity

Kodama K, …, Butte AJ. Diabetes, 2015 Mar;64(3):867-75.

Keiichi KodamaKyoko Toda

Shojiroh MorinagaSatoru Yamada

Kodama K, …, Butte AJ. Diabetes, 2015 Mar;64(3):867-75.

Keiichi KodamaKyoko Toda

Shojiroh MorinagaSatoru Yamada

Anti-CD44 for 4 weeks slows weight gain and reduces intake

Anti-CD44 for 4 weeks reduces adipose inflammation and hepatic steatosis

Kodama K, …, Butte AJ. Diabetes, 2015 Mar;64(3):867-75.

Keiichi KodamaKyoko Toda

Shojiroh MorinagaSatoru Yamada

Kodama K, …, Butte AJ. Diabetes, 2015.

Keiichi KodamaKyoko Toda

Shojiroh MorinagaSatoru Yamada

bit.ly/immport

The next big open data: clinical trials

bit.ly/1b4sa7b

Institute for Computational Health Sciences

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

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