This document summarizes Atul Butte's presentation on discovering new drugs and diagnostics from large amounts of biomedical data. Some key points: - Over 300 trillion data points are now available from sources like gene expression studies which could help find new treatments. - Butte's research has used data mining of gene expression profiles to identify potential new drug targets, such as a protein called Gene A which may be involved in diabetes and obesity. - Further studies in mouse models and human samples supported the role of Gene A, and an antibody against it showed promise in reducing glucose levels and fat inflammation. - Butte argues that more can be done to enable scientists to directly use and analyze existing biomedical data, which

1. Discovering new drugs and
diagnostics from 300 trillion
points 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

2. 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

4. Kilo
Mega
Giga
Tera
Peta
Exa
Zetta

8. 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.”

10. Total 1.2 million microarrays available
Doubles every 2-3 years
Butte AJ. Translational Bioinformatics:
coming of age. JAMIA, 2008.

14. Available Cancer Types # Cases Shipped by BCR # Cases with Data
Date 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

28. Validation methods are increasingly
commoditized

31. Translational Pipeline
Clinical and Molecular Measurements

Translational Question or Trial

Statistical/Computational methods

Validating drug or biomarker

32. Translational Pipeline
Clinical and Molecular Measurements

Translational Question or Trial

Statistical/Computational methods

Validating drug or biomarker
Commodity
Commodity

33. We are used to starting
computer, IT, and Internet
companies in garages...

34. We are used to starting
computer, IT, and Internet
companies in garages...
Potentials for starting a
“garage biotech”?

35. 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)

36. 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.

37. 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.

38. 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

39. 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.

40. 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.

41. Keiichi Kodama
Kyoko Toda
Gene A is higher in high fat diet
Gene A is expressed in mouse fat infiltrate
Gene A
Kodama K, Horikoshi M, ..., Maeda S, Kadowaki T, Butte AJ. PNAS, 2012.

42. 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.

43. 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.

44. 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.

45. 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.

46. 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.

47. 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.

48. 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.

49. 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

50. Funded post-doctoral
positions in
Translational
Bioinformatics
available
Contact Atul Butte
abutte@stanford.edu

51. 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

52. 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
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• Alex Skrenchuk