Discovering new drugs and diagnostics from 300 trillionpoints of data

Atul Butte, MD, PhD

Chief, Division of Systems Medicine, Department of Pediatrics,Department of Medicine, and, by courtesy, Computer Science

Center for Pediatric Bioinformatics, LPCH

Stanford University

abutte@stanford.edu

@atulbutte

@ImmPortDB

Disclosures• Scientific founder and

advisory board membership– Genstruct

– NuMedii

– Personalis

– Carmenta

• Past or present consultancy– Lilly

– Johnson and Johnson

– Roche

– NuMedii

– Genstruct

– Tercica

– Ansh Labs

– Prevendia

– Samsung

– Assay Depot

– Regeneron

– Verinata

– Geisinger

• Honoraria– Lilly

– Pfizer

– Siemens

– Bristol Myers Squibb

• Speakers’ bureau– None

• Companies started by students– Carmenta

– Serendipity

– NuMedii

– Stimulomics

– NunaHealth

– Praedicat

– Flipora

KiloMegaGigaTeraPetaExa

Zetta

John Holdren, Director of the Office of Science and Technology Policy, “has directed Federal agencies with more than $100M in R&D expenditures to develop plans to make the published results of federally funded research freely available to the public within one year of publication and requiring researchers to better account for and manage the digital data resulting from federally funded scientific research.”

Total 1.2 million microarrays available

Doubles every 2-3 years

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

Available Cancer Types # Cases Shipped by BCR # Cases with DataDate Last Updated (mm/dd/yy)

Acute Myeloid Leukemia [LAML] 200 200 6/24/2013

Adrenocortical carcinoma [ACC] 80 0

Bladder Urothelial Carcinoma [BLCA] 201 184 7/5/2013

Brain Lower Grade Glioma [LGG] 296 271 7/3/2013

Breast invasive carcinoma [BRCA] 1007 961 7/5/2013

Cervical squamous cell carcinoma and endocervical adenocarcinoma [CESC] 163 163 7/5/2013

Colon adenocarcinoma [COAD] 439 425 6/28/2013

Esophageal carcinoma [ESCA] 63 63 7/5/2013

Glioblastoma multiforme [GBM] 514 510 6/28/2013

Head and Neck squamous cell carcinoma [HNSC] 427 376 7/3/2013

Kidney Chromophobe [KICH] 66 66 7/5/2013

Kidney renal clear cell carcinoma [KIRC] 512 512 7/3/2013

Kidney renal papillary cell carcinoma [KIRP] 158 144 6/28/2013

Liver hepatocellular carcinoma [LIHC] 152 128 7/3/2013

Lung adenocarcinoma [LUAD] 500 499 7/3/2013

Lung squamous cell carcinoma [LUSC] 500 494 7/5/2013

Lymphoid Neoplasm Diffuse Large B-cell Lymphoma[DLBC] 18 18 7/3/2013

Mesothelioma [MESO] 0 0

Ovarian serous cystadenocarcinoma [OV] 572 570 7/5/2013

Pancreatic adenocarcinoma [PAAD] 71 62 7/3/2013

Pheochromocytoma and Paraganglioma [PCPG] 0 0

Prostate adenocarcinoma [PRAD] 248 201 7/5/2013

Rectum adenocarcinoma [READ] 169 168 6/28/2013

Sarcoma [SARC] 111 75 7/5/2013

Skin Cutaneous Melanoma [SKCM] 357 336 7/5/2013

Stomach adenocarcinoma [STAD] 343 325 7/3/2013

Testicular Germ Cell Tumors [TGCT] 0 0

Validation methods are increasingly commoditized

Translational Pipeline

Clinical and Molecular Measurements

Translational Question or Trial

Statistical/Computational methods

Validating drug or biomarker

Translational Pipeline

Clinical and Molecular Measurements

Translational Question or Trial

Statistical/Computational methods

Validating drug or biomarker

Commodity

Commodity

We are used to starting computer, IT, and Internet

companies in garages...

We are used to starting computer, IT, and Internet

companies in garages...

Potentials for starting a“garage biotech”?

Type 2 Diabetes Mellitus

• Affects 20 million in US, 170 million world-wide

• Leading cause of kidney failure, blindness, amputation

• Major risk factor for heart disease, stroke, birth defects

• 12% of all US health-care dollars

• Prevalence in children born after the year 2000 expected to reach 30%

• Many drugs available to elicit more insulin secretion, heighten insulin response, lower glucagon secretion

• New drugs still needed and used: DPP-4 inhibitors (2008)

Keiichi Kodama

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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

Rel

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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

Rel

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# of positive RNA microarray experiments (out of 130)

Intersect 130 T2D microarray experiments

TCF7L2PPARG

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

Keiichi Kodama

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

Rel

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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 experiments

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

Keiichi Kodama

Kyoko Toda

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

Gene A

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

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.

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.

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 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.

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)

• Osteopontin knockout previously associated with insulin sensitivity (Nomiyama, Bruemmer, JCI 2007)

• Anti-CD44 in development for multiple cancers

• CD44 is a complicated receptor

Ponta, Sherman, Herrlich. Nature Reviews Molecular Cell Biology, 2003.

Six Lessons Learned

• Sufficient data already exists to impact medicine– More is better, but we see no reason to wait for perfect data or annotations

• Even imperfect data and annotations have high utility– Requiring perfection slows secondary use

• It’s not just about infrastructure, it’s about using it– Already too many tools. Those who build tools use them first!

• Enable yourself as a scientist first, then enable others– Build and they will come… won’t work if no utility or findings

• Sticks seem to work better than carrots– Continue exponential growth, more transparency

• Need to train students to initiate science with data– High school higher education career changers

Funded post-doctoral positions in Translational Bioinformatics available

Contact Atul Butte

abutte@stanford.edu

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

• Scleroderma Research Foundation

• Clayville Research Fund

• PhRMA Foundation

• Stanford Cancer Center, Bio-X

• Tarangini Deshpande

• Alan Krensky, Harvey Cohen

• Hugh O’Brodovich

• Isaac Kohane

Admin and Tech Staff

• Susan Aptekar

• Camilla Morrison

• Alex Skrenchuk