personalized medicine and the omics revolution by professor mike snyder
Post on 21-Oct-2014
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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.TRANSCRIPT
Personal Medicine and the Omics Revolution
Michael Snyder
September 3, 2014
Conflicts: Personalis, Genapsys, AxioMx
Health Is a Product of Genome + Environment
Exposome
Health
DNA
Health Is a Product of Genome + Environment
Exposome
Health
DNA
The Cost of DNA Sequencing is Dropping
Human Genome Cost ~$2Khttp://www.genome.gov/
For each person it is packaged into 2 copies of each of 23 chromosomes
From: www.ikkeweer.net
Our DNA Has 6 Billion letters of a 4
Letter Code (Genome)
Genetic Variation Among People: Three
Types
3.7 Million/person
2) Short Indels (Insertions/Deletions 1-100
bp)
GATTTAGATCGCGATAGAGGATTTAGATCTCGATAGAG
1) Single nucleotide variants(SNVs)
GATTTAGATCGCGATAGAGGATTTAGA------TAGAG
300-600K/person
Examples of People Who Have had Their Genomes Sequenced
From: www.genciencia.com
Jim Watson Craig Ventor Ozzy Osbourne
sciencewithmoxie.blogspot.com.au/2010_11_01_archive.html
• Understand and Treat Disease – Cancer– Mystery diseases– Prenatal diagnostics
• Pharmacogenomics – Determining which drug side effects and doses
• Managing Health Care in Healthy Individuals?
Impact of Genomics on Medicine
Cancer Genome Sequencing1) Cancer is a genetic disease: both inherited and
somatic
Vogelstein et al., March Science, 2013
2) 10-20 “driver” mutations
3) Every cancer is unique
4) Sequence genomes (cancer tissue and normal) find genetic changes and suggest possible therapies
Patient with Metastatic Colon Cancer
Chromosome 7: Two amplification regions
Chr 7p arm Chr 7q armGenomic Copy
Number
CEN
EGFR CDK6
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
From Richard Gibbs, Baylor
X
Solving Mystery Diseases: Child With Variety of ConditionsDevelopmentally Delayed, Significant Health Issues
F M
A1
MotherSNVs: 3,125,880Private: 581,754Indels: 723,379
FatherSNVs: 3,119,588Private: 596,691Indels: 750,522
ChildSNVs: 3,118,638Private: 33,158Indels: 673,809
SNVs: Single nucleotide variantsIndels: = Insertions/deletions (~<100bp)
Candidates:TCP10L2, SUPV3L1, PIEZO1DNAH2, NGLY1, FANCA, WFS1
Lessons LearnedOverall success rate for identifying causative mutations is low 25%
What is Needed1) Large database to share information:
Recurrence is key. ClinVar
2) Functional information to determine which variant is likely causative
Fetal DNA Sequencing1) 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
Fetal DNA Sequencing
Srinivasan A, Bianchi DW, Huang H, et al. Am J Hum Genet 2013; 1–10.
Personal Genome Sequencing:
Can genome sequencing of a healthy person be useful in health care?
Sequencing Genomes of Healthy People:Incorporate into Medicine
Genomic
1. Predict risk2. Diagnose3. Monitor4. Treat &5. UnderstandDisease States
GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTCAAGTGACAAGGACAATTACTTGGACCGTAATAGATTTTTTGAGGCTCAGCAAAAAAGAAAATGGAAATTAATTTTGAAGTGCCATTGA….
Family HistoryMedical Tests:Few Tests (<20)
Personalized Medicine: Combine Genomic and Other Omic Information
Genomic Transcriptomic, Proteomic, Metabolomic
1. Predict risk2. Diagnose3. Monitor4. Treat &5. UnderstandDisease States
GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTCAAGTGACAAGGACAATTACTTGGACCGTAATAGATTTTTTGAGGCTCAGCAAAAAAGAAAATGGAAATTAATTTTGAAGTGCCATTGA….
Genome
Transcriptome(mRNA, miRNA, isoforms, edits)
Proteome
Metabolome
PersonalOmicsProfile
Autoantibody-ome
Microbiome (Gut, Urine, Nasal, Tongue, Skin)
Personal “Omics” Profiling (POP)
Cytokines
Epigenome
Genome
Transcriptome(mRNA, miRNA, isoforms, edits)
Proteome
Metabolome
PersonalOmicsProfile
Autoantibody-ome
Personal “Omics” Profiling (POP)
Cytokines
Epigenome
Initially 40K
Molecules/Measure-
ments
Now Billions!Microbiome (Gut, Urine,
Nasal, Tongue, Skin)
Personal Omics Profile53 months; 80 Timepoints; 6 Viral Infections
/
/
Chen et al., Cell 2012
Genome Sequence (Ilumina, Complete Genomics)
Predict Type 2 Diabetes
Rong Chen and Atul Butte
0% 100%
HbA1c (%) 6.4 6.7 4.9 5.4 5.3 4.7 (Day Number) (329) (369) (476) (532) (546) (602)
RSVHRVLIFESTYLE CHANGE
Glucose levels
Integrated Analysis of Proteome, Transcriptome, Metabolome Dynamics: Overall trend
george mias RSV
Dynamical Outcomes for Integrated Analysis of Proteome, Transcriptome, Metabolome
george mias RSV 18 days
Platelet Plug Formation
Glucose Regulation of Insulin Secretion
Study of 12 Healthy People7 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
Dewey et al JAMA 2014
One individual had a BRCA1 deletion/frameshift mutation—no known family history!
Many Unaddressed Challenges1) Interpreting regulatory/non protein coding
regions
2) DNA Methylation
3) Microbiome
4) Exposome
Possible Phenotypic Consequences of Differentially Methylated Regions?
DNA Methylation: Epigenetic Changes
Personal Omics Profile53 months; 80 Timepoints; 6 Viral Infections
/
/
Chen et al., Cell 2012
*
Nasal microbes--Top 25 most abundant microbial species
HealthyFever Recovery
57 58 58b 5943
43_2
HealthyFever Recovery
Gut microbiome temporal profiles-- At the family level analyzed by RTG
Healthy
Wenyu Zhao, George Weinstock
AliveCor Measures ECG
Other Data Types: Sensors
71Moves App
71
BasisMeasures Heart RateSleepStress
Genome
Transcriptome(mRNA, miRNA, isoforms, edits)
Proteome
Metabolome
PersonalOmicsProfile
Autoantibody-ome
Microbiome (Gut, Urine, Nasal, Tongue, Skin)
Personal “Omics” Profiling (POP)~60 Prediabetics
Cytokines
Epigenome
Sensors
T1pre
Weight gain Maintain peak
weight
Weight loss Maintain
T2peak
T3post
T4followup
Overfeeding Study20 Individuals
30days
60days
7days
90days
• 17 participants have completed study so far• Gained an average of 7.1 pounds
• Blood sampling at indicated timepoints
Genome (1TB)
Transcriptome (0.7TB)(mRNA, miRNA, isoforms, edits)
Proteome (0.02 TB)
Metabolome (0.02 TB)
PersonalOmicsProfileTotal =5.76TB/
Sample + 1 TB
GenomeAutoantibody-ome
Microbiome (2TB)
Big Data Handling and Storage
Cytokines
Epigenome (3TB)
Devices (2 GB/year)
The Future?Genomic Sequencing
1. Predict risk2. Early Diagnose3. Monitor4. Treat
Omes and Other Information: Home Sensors
http://www.baby-connect.com/
GGTTCCAAAAGTTTATTGGATGCCGTTTCAGTACATTTATCGTTTGCTTTGGATGCCCTAATTAAAAGTGACCCTTTCAAACTGAAATTCATGATACACCAATGGATATCCTTAGTCGATAAAATTTGCGAGTACTTTCAAAGCCAAATGAAATTATCTATGGTAGACAAAACATTGACCAATTTCATATCGATCCTCCTGAATTTATTGGCGTTAGACACAGTTGGTATATTTA….
iPS Cells
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.
4) Every person’s complex disease profile is different and following many components longitudinally may provide valuable information.
5) You are responsible for your own health
Data at: snyderome.stanford.edu
New Online Professional Certificate Program
For more information visit http://geneticscertificate.stanford.edu
The Personal Omics Profiling ProjectRui 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 VariationLinfeng 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
Further Information:Stanford Genetics and Genomics Certificate
Program
Learn about genetics, genomics and personalized medicine
http://geneticscertificate.stanford.edu/certificate-overview.php
• 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
DNA Methylation
5 methylC