atul butte's aaps big data workshop presentation 6/2015
TRANSCRIPT
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
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
freq
uenc
y
# 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
y
# 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
y
# 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
tive
freq
uenc
y
# 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
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