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2014 07 ismb personalized medicine
This document discusses how big data in biomedicine can be used to translate over 300 trillion points of data into new drugs and diagnostics. It describes some of the large amounts of data that exist, including over 127 million substances tested in over 740,000 assays totaling over 1.2 billion data points. The document also discusses several ongoing projects using this data, including efforts to find new markers for preeclampsia and research using genome sequencing to study diseases like inflammatory bowel disease.
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2014 07 ismb personalized medicine
- 1. Big Data in Biomedicine: Transla3ng 300 trillion points of data into new drugs and diagnos3cs Atul Bu;e, MD, PhD Chief, Division of Systems Medicine, Departments of Pediatrics, Gene3cs, and, by courtesy, Computer Science, Pathology, and Medicine Center for Pediatric Bioinforma3cs, LPCH Stanford University abu;e@stanford.edu @atulbu;e @ImmPortDB
- 2. Disclosures • Scien'fic founder and advisory board membership – Genstruct – NuMedii – Personalis – Carmenta • Honoraria for talks – Lilly – Pfizer – Siemens – Bristol Myers Squibb – AstraZeneca – Roche – Genentech • Past or present consultancy – Lilly – Johnson and Johnson – Roche – NuMedii – Genstruct – Tercica – Ecoeos – Ansh Labs – Prevendia – Samsung – Assay Depot – Regeneron – Verinata – Geisinger – Covance • Corporate Rela'onships – Northrop Grumman – Aptalis – Thomson Reuters • Speakers’ bureau – None • Companies started by students – Carmenta – Serendipity – NuMedii – S'mulomics – NunaHealth – Praedicat – MyTime – Flipora
- 3. Big Data in Biomedicine
- 5. Nearly 1.4 million microarrays available Doubles every 2-‐3 years Bu;e AJ. Transla3onal Bioinforma3cs: coming of age. JAMIA, 2008.
- 8. 127 million substances x 740,000 assays 1.2 billion points of data within a grid of 100 trillion cells ~250 million ac3ve substances
- 9. 5,178 compounds ·∙ 1,300 off-‐patent FDA-‐approved drugs ·∙ 700 bioac've 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 nomina'ons (n=500+) 15 cell types ·∙ Banked primary cell types ·∙ Cancer cell lines ·∙ Primary hTERT immortalized ·∙ Pa'ent derived iPS cells ·∙ 5 community nominated
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- 12. Protein Cancer markers Transplant Rejec3on markers
- 13. Preeclampsia: large cause of maternal and fetal death • Incidence • 5-‐8% of all pregnancies in the U.S. and worldwide • 4.1 million births in the U.S. in 2009 • Up to 300K cases of preeclampsia annually in the U.S. • Mortality • Responsible for 18% of all maternal deaths in the U.S. • Maternal death in 56 out of every 100,000 live births in US • Neonatal death in 71 out of every 100,000 live births in US • Cost • $20 billion in direct costs in the U.S annually • Average hospital stay of 3.5 days Linda Liu Ma; Cooper Bruce Ling
- 15. New markers for preeclampsia p value 3.49 X 10-‐4 1.79 X 10-‐5 ng/ml p value = 1.92 X 10-‐8 Control N=16 Preeclampsia N=15 Control N=16 Preeclampsia N=17 GA 23-‐34 weeks GA > 34 weeks ng/ml Gesta3onal age (weeks) march of dimes® prematurity research center VERSION: MOD_PRC_LOGO_R7G_082712 at STANFORD University School of Medicine Linda Liu Bruce Ling
- 17. Sequencing Excitement • 454/Roche, Life Technologies • Helicos: $30k genome • Pacific Biosystems: sequence human genome in 15 minutes • Run 'mes in minutes at a cost of hundreds of dollars • Complete Genomics: 80 genomes/day • Ion Torrent and Illumina: ~$1500 per genome • Oxford: USB s'ck
- 18. Lancet, 375:1525, May 1, 2010.
- 19. Credit: Euan Ashley, Russ Altman, Steve Quake, Lancet
- 20. • Study published in 2008 in Inflammatory Bowel Disease • Crohn’s Disease and Ulcera've Coli's • Inves'gated 9 loci in 700 Finnish IBD pa'ents • We record 100+ items – GWAS, non-‐GWAS papers – Disease, Phenotype – Popula'on, Gender – Alleles and Genotypes – p-‐value (and confidence) – Odds ra'o (and confidence) – Technology, Study design – Gene'c model • Mapped to UMLS concepts Rong Chen Optra Systems
- 21. • Study published in 2008 in Inflammatory Bowel Disease • Crohn’s Disease and Ulcera've Coli's • Inves'gated 9 loci in 700 Finnish IBD pa'ents • We record 100+ items – GWAS, non-‐GWAS papers – Disease, Phenotype – Popula'on, Gender – Alleles and Genotypes – p-‐value (and confidence) – Odds ra'o (and confidence) – Technology, Study design – Gene'c model • Mapped to UMLS concepts
- 22. • Study published in 2009 in Rheumatology • Ankylosing spondyli's • Inves'gated 8 SNPs in IL23R in 2000 UK case-‐ control pa'ents • Tables can be rotated • NLP is hard
- 23. • Study published in 2009 in Rheumatology • Ankylosing spondyli's • Inves'gated 8 SNPs in IL23R in 2000 UK case-‐ control pa'ents • Tables can be rotated • NLP is hard
- 24. • Study published in 2009 in Rheumatology • Ankylosing spondyli's • Inves'gated 8 SNPs in IL23R in 2000 UK case-‐ control pa'ents • Tables can be rotated • NLP is hard
- 26. What are the alleles for rs1004819?
- 27. Alleles for rs1004819 are C and T ~11% of records reported genotypes in the nega3ve strand
- 31. Number of papers curated Number of records Dis3nct SNPs Diseases and phenotypes ~19,000 ~1.6 million ~473,000 ~7,400 Rong Chen Anil Patwardhan Michael Clark Optra Systems Personalis VARIMED: Variants Informing Medicine Chen R, Davydov EV, Sirota M, Bu;e AJ. PLoS One. 2010 October: 5(10): e13574.
- 33. Diseases and Traits • Risk factors are associated with an increased likelihood of developing a given diseases • Smoking à chronic obstruc've pulmonary disease • Risk factors are iden'fied for diseases through large scale epidemiological studies, which are resource intensive • GWAS have iden'fied gene'c variants for thousands of diseases and traits • If traits and diseases share the same associated gene'c variants, could the trait be used to suggest risk factors for disease? Li L, Ruau DJ, Patel CJ, Weber SC, Chen R, Tatonej NP, Dudley JT, Bu;e AJ. Science Transla3onal Medicine, 2014, 6(234). Li Li
- 34. EMR Cohort Identify significantdisease-trait genetic associations and clinically validate using EMR data Gene counts > 3 Disease (n=201) Varimed TF-IDF weighing Cosine distance Random shuffling Trait (n=85) Disease (n=69) Trait (n=249) Disease-Trait Pair (n=120) p < 1e-8 Disease modules (n=8) Gene3cs Module Clinical Valida3on Novel predictions (n=26) T q ≤ 0.01 D Published findings (n=94) T D Trait modules (n=7) Complications Diagnostic tests Risk factors 1st dx After dxBefore dx 1st dx Li Li
- 35. Assessing significance of disease-‐trait (D-‐T) pair • Each gene within individual disease or trait by taking into account the frequency of the gene: Term Frequency–Inverse Document Frequency • 2-‐idf(i, j) = 2(i, j) × idfi, = ni, j/(∑k nk, j) x log(D/Di) which adjusted the score of 6(i, j) by taking into account the popularity level of the gene i. • e.g, 154 D+T, 28 genes in Alzheimer's disease and 5 genes in ESR, CR1 was in common • s-‐idf (AD)=1/28 x log(154/2,10)=0.067 • s-‐idf (ESR)=1/5 x log(154/2,10)=0.377 • D-‐T distance score was calculated using Cosine distance to evaluate similarity between all pairs. • Randomly sampling all the genes across all the traits, and calculated the D-‐T similarity, repeated 1,000 'mes and generated the q value based on the number of the samplings. ∑∑ ∑ == = × × = • =− n i i n i i n i ii TD TD TD TD TDsimilarityine 1 2 1 2 1 )()( ),(cos = 0.9274524 Li L, Ruau DJ, Patel CJ, Weber SC, Chen R, Tatonej NP, Dudley JT, Bu;e AJ. Science Transla>onal Medicine, 2014, 6(234). Li Li
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- 38. Categoriza3ons for known D-‐T pairs and discover poten3al confounders in GWAS studies 38 pairs 27 pairs 28 pairs 93 pairs T D Gene3c Variants TD Gene3c Variants Timing of Disease Progression Risk Factor Consequence T D Gene3c Variants Diagnos3c Test Li Li
- 39. Diagnos3c tests where traits occur at the same 3me as disease onset An3body 3ter Hepa<<s B vaccine response Png et al, Hum Mol Genet, 2011 Even though this GWAS did not explicitly par'cipants with the autoimmune diseases above, our approach inferred known rela'onships between diseases and traits based on their shared gene'c architecture T D Gene3c Variants Diagnos3c Test Li Li
- 40. Significant genes shared between an3body 3ter and 16 autoimmune diseases Disease Common Genes Genes Shared q-‐value Alopecia areata 4 BTNL2; C6orf10; RDBP; TNXB <0.001 Ankylosing spondyli's 2 BTNL2; LOC100507436 0.001 Asthma 4 BTNL2; C6orf10; HLA-‐DPA1; NOTCH4; <0.001 Biliary liver cirrhosis 3 BTNL2; C6orf10; HLA-‐DPB1 0.003 Chronic hepa''s b 2 HLA-‐DPA1; HLA-‐DPB1 <0.001 HIV infec'on 7 C6orf10; HLA-‐C; LOC100507436; NOTCH4; PRRC2A; RDBP; TNXB <0.001 Membranous nephropathy 15 AGPAT1; BAG6; BTNL2; C6orf10; EHMT2; GPANK1; LY6G5B; LY6G6C; NOTCH4; PRRC2A; RDBP; RNF5; SLC44A4; TNXB; ZBTB12 <0.001 Mul'ple sclerosis 7 AGPAT1; BAG6; BTNL2; C6orf10; EHMT2; NOTCH4; TNXB <0.001 Neonatal lupus 3 BAG6; C6orf10; ZBTB12 <0.001 Primary biliary cirrhosis 3 BTNL2; C6orf10; HLA-‐DPB1 0.005 Rheumatoid arthri's 20 AGPAT1; BAG6; BTNL2; C6orf10; EHMT2; GPANK1; HLA-‐C; HLA-‐DPA1; HLA-‐DPB1; LOC100507436; LY6G5B; LY6G6C; LY6G6F; NOTCH4; PRRC2A; RDBP; RNF5; SLC44A4; TNXB; ZBTB12 <0.001 Systemic lupus erythematosus 9 BAG6; BTNL2; C6orf10; GPANK1; HLA-‐DPB1; NOTCH4; PRRC2A; TNXB; ZBTB12 <0.001 Systemic sclerosis 3 HLA-‐DPA1; HLA-‐DPB1; NOTCH4 <0.001 Type 1 diabetes 5 BAG6; BTNL2; C6orf10; HLA-‐C; HLA-‐DPB1 0.001 Vi'ligo 6 AGPAT1; BTNL2; NOTCH4; RNF5; SLC44A4; TNXB <0.001 Wegener's granulomatosis 2 HLA-‐DPA1; HLA-‐DPB1 <0.001 Li Li
- 41. Risk factors where traits occur prior to the disease onset and may accompany disease Trait Disease Common Genes Genes Shared q-‐value Smoking Chronic obstruc've pulmonary disease 3 AGPHD1; CHRNA3; RAB4B <0.001 Gene3cs Variants Known clinical study: Smoking is the primary risk factor for COPD although lixle was known the pathogenesis between smoking and COPD. Pauwels et al, 2001, Vestbo et al 2012 In GWAS study: Six GWAS studies are related to COPD in VARIMED and their COPD cohorts all are from smoking pa'ents. Cho et al, 2012, Pillai SG, 2010, Wang et al 2010, Cho et al, 2010, lambrechts et al, 2010, Pillai SG, 2009 As COPD occurs ayer smoking, the variants associated with COPD could be influenced by smoking, and the gene'c variants for COPD could be unmasked if smoking confounder is excluded in GWAS. Smoking COPD Li Li
- 42. Gene3c Variants Consequence where traits occur aqer the disease onset Trait Common Genes Genes Shared q-‐value Alanine aminotransferase levels 1 C12orf51 0.001 Cholesterol levels 3 ALDH2; BRAP; C12orf51 0.001 HDL cholesterol levels 2 C12orf51; OAS3 <0.001 Known clinical study: High HDL criterion was observed with triple frequency in the ADS group, high cholesterol diet was associated with ADS pa'ents , and ALT levels have been seen to increase with daily alcohol intake in pa'ents who developed ADS. Kahl et al, 2010; imhof et al, 2001, Gross GA, 1994 In GWAS study: 3 genes for cholesterol levels reported by Kato et al. and 2 genes for ALT and HDL-‐C reported by Young et al. could be biased by alcohol effect as the authors did not perform alcohol intake adjustment or controlled for drinking habits on these genes in their GWAS studies. Kato et al, 2011; Kamatani et al, 2010 The GWAS to iden'fy concrete gene'c variants for these three clinical measurements should be performed in pa'ents without ADS as a confounder Alcohol dependence syndrome (ADS) ALT HDL-‐C ADS Li Li
- 43. 27 novel pairs Trait Disease Common Genes Genes Shared q-‐value Mean corpuscular volume Acute lymphoblas3c leukemia 1 IKZF1 0.001 Mean cell hemoglobin concentra3on Alcohol dependence 1 ALDH2 0.005 Platelet count Alcohol dependence 1 C12orf51 0.007 Lung func'on Alopecia areata 1 AGER 0.008 Erythrocyte sedimenta3on rate Alzheimer's disease 1 CR1 0.004 Prostate-‐Specific an'gen levels Basal cell carcinoma 1 CLPTM1L 0.004 Eye color Chronic lymphocy'c leukemia 1 IRF4 0.006 Freckles Chronic lymphocy'c leukemia 1 IRF4 0.008 Blood pressure Esophageal cancer 3 ALDH2, C12orf51, PLCE1 0.009 Factor vii coagulant ac'vity Esophageal cancer 1 ADH4 0.008 Serum magnesium levels Gastric cancer 3 MUC1; THBS3; TRIM46 <0.001 Prostate-‐Specific an'gen levels Glioma 1 TERT 0.005 Alpha linolenic acid levels Glucose intolerance 1 FADS1 0.01 Alanine aminotransferase levels Hypertension 1 C12orf51 0.003 Serum transferrin levels Hypertension 1 HFE 0.005 Smoking Kawasaki disease 1 RAB4B 0.003 Prostate-‐Specific an'gen levels Lung cancer 2 CLPTM1L; TERT 0.001 Homocysteine levels Melanoma 1 C16orf55 0.01 Protein c levels Melanoma 2 NCOA6; PIGU <0.001 Transferrin receptor levels Metabolic syndrome 3 APOA5; BUD13; ZNF259 <0.001 PR interval Open-‐Angle glaucoma 1 CAV1 0.002 PR interval Restless legs syndrome 1 MEIS1 0.003 Bone mineral density Sudden cardiac arrest 1 ESR1 0.006 Acenocoumarol maintenance dosage Systemic lupus erythematosus 2 ITGAM; ITGAX 0.004 Platelet count Tes'cular cancer 1 BAK1 0.003 Prostate-‐Specific an'gen levels Tes'cular cancer 2 CLPTM1L; TERT <0.001 Alkaline phosphatase levels Venous thromboembolism 1 ABO 0.008 Li Li
- 44. Independent pa3ent cohort valida3on: clinical data warehouses • STRIDE: clinical data warehouse, has ICD9 diagnoses codes, CPT procedure codes, and lab results on over 1.7 million pediatric and adult pa'ents at Stanford Hospital and Clinic, independent cohort 1/1/2005 to 7/15/2012 • Collabora'ons also with Columbia University and Mount Sinai School of Medicine to validate findings • Time frame for analysis: within one year before the 1st disease diagnosis or within one year ayer the 1st disease diagnosis 1st Dx Target disease (case) Non-‐target disease (control) lab lab 1 year 1 year Li Li
- 45. Serum magnesium levels and gastric cancer Li Li
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- 48. Digital compara3ve effec3veness Find precision subsets If entry criteria are same, outcome measures are same, and comparable studies, can perform “meta-‐trial”
- 50. Take Home Points • Personalized medicine ≥ DNA. Will include other clinical, molecular, and environment measures. • We need new inves'gators who can imagine basic ques'ons to ask of these repositories of clinical and genomic measurements. • Bioinforma'cs is not just about building tools. We know our tools; we should use them first. Don’t be afraid to test your ideas.
- 51. Funded post-‐doctoral posi3ons in Transla3onal Bioinforma3cs Contact Atul Bu;e abu;e@stanford.edu
- 52. 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 / Gene'cs • Jay Pasricha / Gastroenterology • Rob Tibshirani, Brad Efron / Sta's'cs • Hannah Valan'ne, Kiran Khush/ Cardiology • Ken Weinberg / Pediatric Stem Cell Therapeu'cs • Mark Musen, Nigam Shah / Na'onal Center for Biomedical Ontology • Minnie Sarwal / Nephrology • David Miklos / Oncology
- 53. Support • Lucile Packard Founda'on for Children's Health • NIH: NIAID, NLM, NIGMS, NCI; NIDDK, NHGRI, NIA, NHLBI, NCATS • March of Dimes • Hewlex Packard • Howard Hughes Medical Ins'tute • California Ins'tute for Regenera've Medicine • Luke Evnin and Deann Wright (Scleroderma Research Founda'on) • Clayville Research Fund • PhRMA Founda'on • Stanford Cancer Center, Bio-‐X, SPARK • Tarangini Deshpande • Alan Krensky, Harvey Cohen • Hugh O’Brodovich • Isaac Kohane Admin and Tech Staff • Susan Aptekar • Jen Cory • Boris Oskotsky