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Personalized Medicine and the Omics Revolution by Professor Mike Snyder

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Personalized medicine is expected to benefit from the combination of genomic information with the global monitoring of molecular components and physiological states. To ascertain whether this can be achieved, we determined the whole genome sequence of an individual at high accuracy and performed an integrated Personal Omics Profiling (iPOP) analysis, combining genomic, transcriptomic, proteomic, metabolomic, and autoantibodyomic information, over a 38-month period that included healthy and two virally infected states. Our iPOP analysis of blood components revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways across healthy and disease conditions. Importantly, genomic information was also used to estimate medical risks, including Type 2 Diabetes, whose onset was observed during the course of our study. Our study demonstrates that longitudinal personal omics profiling can relate genomic information to global functional omics activity for physiological and medical interpretation of healthy and disease states.

Meet the speaker, Professor Michael Snyder (Stanford):

Michael Snyder is the Stanford Ascherman Professor, Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. He received his Ph.D. from the California Institute of Technology and postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks. Seminal findings from the Snyder laboratory include; the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species. He has also combined different state-of–the-art omics technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine. He is a co-founder of several biotechnology companies including; Protometrix (now part of Life Technologies), Affomix (now part of Illumina), Excelix, and Personalis, and he presently serves on the board of a number of companies.

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Personalized Medicine and the Omics Revolution by Professor Mike Snyder

  1. 1. Personal Medicine and the Omics Revolution Michael Snyder September 3, 2014 Conflicts: Personalis, Genapsys, AxioMx
  2. 2. Health Is a Product of Genome + Environment Exposome Health DNA
  3. 3. Health Is a Product of Genome + Environment Exposome Health DNA
  4. 4. The Cost of DNA Sequencing is Dropping Human Genome Cost ~$2K http://www.genome.gov/
  5. 5. For each person it is packaged into 2 copies of each of 23 chromosomes Our DNA Has 6 Billion letters of a 4 Letter Code (Genome) From: www.ikkeweer.net
  6. 6. Genetic Variation Among People: Three Types 1) Single nucleotide variants (SNVs) GATTTAGATCGCGATAGAG GATTTAGATCTCGATAGAG 3.7 Million/person 2) Short Indels (Insertions/Deletions 1-100 bp) GATTTAGATCGCGATAGAG GATTTAGA------TAGAG 300-600K/person
  7. 7. Examples of People Who Have had Their Genomes Sequenced Jim Watson Craig Ventor Ozzy Osbourne From: www.genciencia.com sciencewithmoxie.blogspot.com.au/2010_11_01_archive.html
  8. 8. Impact of Genomics on Medicine • Understand and Treat Disease – Cancer – Mystery diseases – Prenatal diagnostics • Pharmacogenomics – Determining which drug side effects and doses • Managing Health Care in Healthy Individuals?
  9. 9. Cancer Genome Sequencing 1) Cancer is a genetic disease: both inherited and somatic 2) 10-20 “driver” mutations 3) Every cancer is unique 4) Sequence genomes (cancer tissue and normal) find genetic changes and suggest possible therapies Vogelstein et al., March Science, 2013
  10. 10. Patient with Metastatic Colon Cancer Chromosome 7: Two amplification regions EGFR CDK6 Genomic Chr 7p arm Chr 7q arm Copy Number CEN
  11. 11. Solving Mystery Diseases: Dizygotic Twins: Dopamine Responsive Dystonia • Constantly sick, colicky, failed to meet milestones “floppy”; MRI showed some abnormalities • Children diagnosed with dystonia • Trial of L-DOPA showed dramatic improvement in 2 days • Sequenced genomes-found mutation in SPR Gene • Administered dopamine + seratonin precursor X From Richard Gibbs, Baylor
  12. 12. Solving Mystery Diseases: Child With Variety of Conditions Developmentally Delayed, Significant Health Issues F M A1 Mother SNVs: 3,125,880 Private: 581,754 Indels: 723,379 Father SNVs: 3,119,588 Private: 596,691 Indels: 750,522 Child SNVs: 3,118,638 Private: 33,158 Indels: 673,809 SNVs: Single nucleotide variants Indels: = Insertions/deletions (~<100bp) Candidates: TCP10L2, SUPV3L1, PIEZO1 DNAH2, NGLY1, FANCA, WFS1
  13. 13. Lessons Learned Overall success rate for identifying causative mutations is low 25% What is Needed 1) Large database to share information: Recurrence is key. ClinVar 2) Functional information to determine which variant is likely causative
  14. 14. Fetal DNA Sequencing 1) Cell free fetal DNA can be detected in maternal blood as early a 4-5 weeks gestation 2) 4-10% circulating DNA is fetal  increases with pregnancy 3) Targeted detection of mutations 4) Whole genome sequencing routinely used to detect trisomies: Down’s (Chr. 21), Chromosome 18 and Chromosome 13. 99% sensitivity 5) Taking over from aminocentesis
  15. 15. Fetal DNA Sequencing Srinivasan A, Bianchi DW, Huang H, et al. Am J Hum Genet 2013; 1–10.
  16. 16. Personal Genome Sequencing: Can genome sequencing of a healthy person be useful in health care?
  17. 17. Sequencing Genomes of Healthy People: Incorporate into Medicine Genomic 1. Predict risk 2. Diagnose 3. Monitor 4. Treat & 5. Understand Disease States GGTTCCAAAAGTTTATTGGATGCCGTTTCA GTACATTTATCGTTTGCTTTGGATGCCCTA ATTAAAAGTGACCCTTTCAAACTGAAATTC ATGATACACCAATGGATATCCTTAGTCGAT AAAATTTGCGAGTACTTTCAAAGCCAAATG AAATTATCTATGGTAGACAAAACATTGACC AATTTCATATCGATCCTCCTGAATTTATTG GCGTTAGACACAGTTGGTATATTTCAAGTG ACAAGGACAATTACTTGGACCGTAATAGAT TTTTTGAGGCTCAGCAAAAAAGAAAATGGA AATTAATTTTGAAGTGCCATTGA…. Family History Medical Tests: Few Tests (<20)
  18. 18. Personalized Medicine: Combine Genomic and Other Omic Information Genomic Transcriptomic, Proteomic, Metabolomic 1. Predict risk 2. Diagnose 3. Monitor 4. Treat & 5. Understand Disease States GGTTCCAAAAGTTTATTGGATGCCGTTTCA GTACATTTATCGTTTGCTTTGGATGCCCTA ATTAAAAGTGACCCTTTCAAACTGAAATTC ATGATACACCAATGGATATCCTTAGTCGAT AAAATTTGCGAGTACTTTCAAAGCCAAATG AAATTATCTATGGTAGACAAAACATTGACC AATTTCATATCGATCCTCCTGAATTTATTG GCGTTAGACACAGTTGGTATATTTCAAGTG ACAAGGACAATTACTTGGACCGTAATAGAT TTTTTGAGGCTCAGCAAAAAAGAAAATGGA AATTAATTTTGAAGTGCCATTGA….
  19. 19. Personal “Omics” Profiling (POP) Genome Epigenome Transcriptome (mRNA, miRNA, isoforms, edits) Proteome Cytokines Metabolome Personal Omics Profile Autoantibody-ome Microbiome (Gut, Urine, Nasal, Tongue, Skin)
  20. 20. Personal “Omics” Profiling (POP) Genome Epigenome Transcriptome (mRNA, miRNA, isoforms, edits) Proteome Cytokines Metabolome Personal Omics Profile Autoantibody-ome Initially 40K Molecules/ Measure-ments Now Billions! Microbiome (Gut, Urine, Nasal, Tongue, Skin)
  21. 21. Personal Omics Profile 53 months; 80 Timepoints; 6 Viral Infections / / Chen et al., Cell 2012
  22. 22. Genome Sequence (Ilumina, Complete Genomics) Predict Type 2 Diabetes 0% 100% Rong Chen and Atul Butte 160 150 140 130 120 110 100 90 ++)%"#)"# +"%# ""#)"+#)"#%""0# %)")#"#200%""2#50%)"3#00&"3"5#0 &)4"0#0 '""4#50 ')5"#00 ("5"5#0 (6)"0#0 )6"5"0# HRV Infection RSV Infection Life Style Change Glucose (mg/dL) Day Number (Relative to 1st Infection) 80 -150 Glycated HgA1c (%): (Day Number) 6.4 (329) 6.7 (369) 4.9 (476) 5.4 (532) 5.3 (546) 4.7 HbA1c (%) 6.4 6.7 4.9 5.4 5.3 ( 6 402.7) (Day Number) (329) (369) (476) (532) (546) (602) HRV RSV LIFESTYLE CHANGE Glucose levels
  23. 23. Integrated Analysis of Proteome, Transcriptome, Metabolome Dynamics: Overall trend george mias RSV
  24. 24. Dynamical Outcomes for Integrated Analysis of Proteome, Transcriptome, Metabolome Glucose Regulation of Insulin Secretion george mias RSV 18 days Platelet Plug Formation
  25. 25. Study of 12 Healthy People 7 Asian, 5 European Median 5 reportable disease risk associations (ACMG + others) per individual (range 2-6) 3 Followup diagnostic tests (range 0-10) Cost $400-$1400 per individual 54 minutes per variant One individual had a BRCA1 deletion/frameshift mutation—no known family history! Dewey et al JAMA 2014
  26. 26. Many Unaddressed Challenges 1) Interpreting regulatory/non protein coding regions 2) DNA Methylation 3) Microbiome 4) Exposome
  27. 27. Possible Phenotypic Consequences of Differentially Methylated Regions? DNA Methylation: Epigenetic Changes
  28. 28. Personal Omics Profile 53 months; 80 Timepoints; 6 Viral Infections / / * Chen et al., Cell 2012
  29. 29. Nasal microbes --Top 25 most abundant microbial species Fever Recovery Healthy
  30. 30. Gut microbiome temporal profiles 18% 2% 1% 13% 26% 14% 10% 11% 5% -- At the family level analyzed by RTG 26% 1% 15% 0% 12% 20% 3% 11% 3% 9% 50% 6% 14% 1% 1% 3% 1% 6% 15% 3% 46% 9% 9% 7% 1% 1% 2% 11% 10% 4% 38% 1% 0% 7% 1% 6% 28% 9% 6% 4% 33% 0% 1% 1% 7% 33% 8% 9% 5% 3% 43 57 58 58b 59 43_2 Fever Recovery Healthy 18% 2% 1% 13% 26% 14% 10% 11% 5% Bacteroidaceae Clostridiaceae Erysipelotrichaceae Eubacteriaceae Lachnospiraceae Porphyromonadaceae Prevotellaceae Rikenellaceae Ruminococcaceae 2% 18% Others 1% 13% 26% 14% 10% 11% 5% Bacteroidaceae Clostridiaceae Erysipelotrichaceae Eubacteriaceae Lachnospiraceae Porphyromonadaceae Prevotellaceae Healthy Wenyu Zhao, George Weinstock
  31. 31. AliveCor Other Data Types: Sensors Measures ECG 71 Moves App 71 Basis Measures Heart Rate Sleep Stress
  32. 32. Personal “Omics” Profiling (POP) ~60 Prediabetics Genome Epigenome Transcriptome (mRNA, miRNA, isoforms, edits) Proteome Cytokines Metabolome Personal Omics Profile Autoantibody-ome Microbiome (Gut, Urine, Nasal, Tongue, Skin) Sensors
  33. 33. Overfeeding Study 20 Individuals 30 days 60 days 7 days 90 days • 17 participants have completed study so far • Gained an average of 7.1 pounds • Blood sampling at indicated timepoints
  34. 34. Big Data Handling and Storage Genome (1TB) Epigenome (3TB) Transcriptome (0.7TB) (mRNA, miRNA, isoforms, edits) Proteome (0.02 TB) Cytokines Metabolome (0.02 TB) Personal Omics Profile Total = 5.76TB/Sa mple + 1 TB Genome Autoantibody-ome Microbiome (2TB) Devices (2 GB/year)
  35. 35. The Future? Genomic Sequencing Omes and Other Information: Home Sensors 1. Predict risk 2. Early Diagnose 3. Monitor 4. Treat GGTTCCAAAAGTTTATTGGATGCCGT TTCAGTACATTTATCGTTTGCTTTGG ATGCCCTAATTAAAAGTGACCCTTTC AAACTGAAATTCATGATACACCAATG GATATCCTTAGTCGATAAAATTTGCG AGTACTTTCAAAGCCAAATGAAATTA TCTATGGTAGACAAAACATTGACCAA TTTCATATCGATCCTCCTGAATTTAT TGGCGTTAGACACAGTTGGTATATTT A…. iPS Cells http://www.baby-connect.com/
  36. 36. Conclusions 1) Omics technologies are revolutionizing science and medicine. 2) Personal sequencing and profiling can provide valuable insights into medicine 3) Integrated omics analysis can provide a detailed physiological perspective for what is occurring. 1) Every person’s complex disease profile is different and following many components longitudinally may provide valuable information. 2) You are responsible for your own health Data at: snyderome.stanford.edu
  37. 37. New Online Professional Certificate Program For more information visit http://geneticscertificate.stanford.edu
  38. 38. The Personal Omics Profiling Project Rui Chen, George Mias, Hugo Lam, Jennifer Li-Pook-Than, Lihua Jiang, …, Russ Altman, Atul Butte, Euan Ashley, Tom Quertermous, Mark Gerstein, Kari Nadeau, Hua Tang, Phyllis Snyder GTEx and Human Variation Linfeng Wu, Lihua Jiang, Maya Kasowski, Fabian Grubert, Anshul Kundaje, Sophia K. Judith Zaugg Phase 2 HMP: Wenyu Zhou, Kim Kukurba, Brian Pienning, Colleen Craig, Lihua Jiang, Sid Mitra, George Weinstock, Tracey McLaughlin Methylome: Dan Xie, Volodymyr Kuleshov, Rui Chen, Dmitry Pushkarev, Konrad Karczewski, Alan Boyle, Tim Blauwkamp, Michael Kertesz
  39. 39. Further Information: Stanford Genetics and Genomics Certificate Program Learn about genetics, genomics and personalized medicine http://geneticscertificate.stanford.edu/certificate-overview. php
  40. 40. DNA Methylation • Associated with gene silencing • Affected by nutrition, aging etc. Deep Sequencing: two time points analyzed • ~19,000 non CG disruption allele differential methylated CGs • 539 allele differential methylated regions (DMRs) • Identified well known regions: H19, GNAS • Identified many novel regions 5 methylC

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