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Translational Bioinformatics 2010: The Year in Review
Russ B. Altman, MD, PhDStanford University
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Goals
• Provide an overview of the major scientific events, trends and publications in translational bioinformatics
• Create a “snapshot” of what seems to be important in March, 2010 for the amusement of future generations.
• Marvel at the progress made and the opportunities ahead.
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Process
1. Think about what has had early impact
2. Think about sources to trust
3. Solicit advice from colleagues
4. Surf online resources
5. Select papers to highlight in ~2 slides and some to highlight in < 1 slide.
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Caveats
• Considered 2009 to present
• Focused on human biology and clinical implications: molecules, clinical data, informatics.
• Considered both data sources and informatics methods (and combination)
• Tried to avoid simply following crowd mentality.
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Final list
• ~70 semi-finalist papers
• 24 presented here (briefly!)
• This talk and semi-finalist bibliography will be made available on the conference website.
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Thanks!• George Hripcsak
• Brian Athey
• Peter Tarczy-Hornoch
• Alain Laederach
• Soumya Raychaudhuri
• Yves Lussier
• Dan Masys
• Emidio Capriotti
• Andrea Califano
• Liping Wei
• Atul Butte
• Nick Tatonetti
• Joel Dudley
• Gill Omenn
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Public Health Translational Informatics
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“Geographic dependence, surveillance, and origins of the 2009 Influenza A (H1N1) Virus” (Trifonov et al, NEJM)
• Goal: understand the origin and recent history of new strains from viral DNA sequences.
• Method: Sequence analysis and comparison of eight key influenza genes in current and historical samples.
• Result: Evolutionary map of recombination events leading to current H1N1 variant.
• Conclusion: Aggressive sampling of multiple species may allow us to anticipate novel flu in the future.
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Whole or Mostly Whole Genome Sequencing
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“Exome sequencing identifies the cause of a mendelian disorder” (Ng et al, Nat. Gen.)
• Goal: find the cause of Miller syndrome.
• Miller syndrome = facial and limb anomalies.
• Method: exon-only sequencing of 4 affected individuals in three kindreds.
• Result: DHODH gene (enzyme for pyrimidine synthesis) mutations in these and 3 other families.
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Miller Syndrome
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Mutations in DHODH
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“Analysis of genetic inheritance in a family quartet by whole-genome sequencing” (Roach et al, Science Express)
• Goal: understand relationship between rare disease and corresponding genetic changeas.
• Miller syndrome & cilia dyskinesia = both recessive.
• Method: whole genome sequencing of parents and 2 affected sibs.
• Result: 4 genes identified with SNPs explaining pattern of inheritence (CES1, DHODH, DNAH5, KIAA056)
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Recombination landscape defined
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“Whole-genome sequencing in a patient with Charcot-Marie-Tooth Neuropathy” (Lupski et al, NEJM)
• Goal: understand relationship between rare disease and corresponding genetic changes.
• CMT neuropathy = recessive, demyelinating disease.
• Method: whole genome sequencing of (big!) family (parents, 4 affected sibs, 4 unaffected sibs). Negative for previous CMT common screens.
• Result: causative alleles in gene SH3TC2, het
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Y169H & R954X alleles in affected
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Genetic associations and mechanisms (!)
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“Autoimmune disease classification by inverse association with SNP alleles” (Sirota et al, PLoS Genetics)
• Goal: Compare genetic variation profiles across six autoimmune diseases.
• MS, AS, ATD, RA, CD, T1D + 5 non-autoimmne
• Method: Cluster diseases based on allele occurrences from GWAS studies.
• Result: RS/AS cluster separates from MS/ATD cluster with someone “opposite” allele profile. May yield information about disease-specific differences.
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Y169H & R954X alleles in affected
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“Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions” (Raychaudhuri et al, PLoS Genetics)
• Goal: Map associations to potential mechanisms using literature mining.
• Method: Test associated disease regions with medical literature, looking for connectionss = pathways
• Result: Able to filter candidate mutations in Crohn’s disease and schizophrenia, and map them to subset of mutations for which there is a biological pathway related to the disease.
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9 rare causative variants create signal in GWAS
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Results for Crohn’s & Schizophrenia
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“Rare variants create synthetic genome-wide associations” (Dickson et al, PLoS Biology)
• Goal: understand the impact of rare variants on common SNP association studies.
• Method: Simulation of effect of LD between rare SNPs and common ones
• Result: Correlations are not possible but inevitable, so GWAS may work for wrong reason. F/U sequencing is key.
• Many positive GWAS studies, especially with differential results in geographically disperse populations, may be affected by this phenomenon.
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9 rare causative variants create signal in GWAS
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“In Silico functional profiling of human disease-associated and polymorphic amino acid substitutions” (Mort et al, Human Mutation)
• Goal: Understand how variation in proteins leads to complex disease phenotypes.
• Method: Compare amino acid substitutions associated with disease and neutral, looking for differences in protein chemical features.
• Results: Associated UMLS disease areas with different sets of predictive protein features
• Conclusion: The types of proteins used in different disease areas are sensitive to different types of mutations.
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Network biomedicine
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“Exploring the human genome with functional maps” (Huttenhower et al, Genome Research)
• Goal: Systems-level understanding of genetic contributions to human phenotypes.
• Method: Bayesian integration of 30K experiments on 25K genes. Creation of data-driven functional maps weighted by reliability for individual functional categories.
• Result: 200 context-specific interaction networks. Experimentally validated 5 novel predictions for genes involved in macroautophagy.
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5 query genes + 1 context
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“Genome-wide identification of post-translational modulators of transcription factor activity in human B cells” (Wang et al, Nat. Biotech.)
• Goal: Understand TF regulation via proteins.
• Method: Mutual information analysis to identify protein modulators of TF function on chosen targets.
• Result: Able to detect molecules that transduce signal from TF to target either as positive modulator (create correlation) or negative modulator (destroy correlation). Successful application to MYC to find ~50 significant modulators, experimentally verified.
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“MiR-204 suppresses tumor invasion by regulating networks of cell adhesion and extracellular matrix remodeling ” (Lee et al, PLoS Comp. Bio, in press)
• Goal: Identify microRNA regulators of cancer and opportunities for new therapies
• Method: Integrate expression, genetics, and cancer molecular phenotypes.
• Result: 18 validated targets of miR-204, experimental evidence showing that miRNA-204 replacement reduces tumor aggressiveness.
• Conclusion: Integrated analysis of miRNA with experimental validation yields new cancer leads.
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Drugs and Genes and their relationships
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“Drug discovery using chemical systems biology: repositioning the safe medicine comtan to treat multidrug and extensively drug resistant Tuberculosis” (Kinnings et al, PLoS Comp. Bio)
• Goal: Identify off-targets of major pharmaceuticals to find new uses for old drugs.
• Method: Use protein structure to characterize binding site of drug, and then look for cryptic similar sites in other proteins, including TB proteome.
• Result: Comtan (for Parkinson’s) binds InhA in TB, & inhibits TB growth--they also found evidence that Parkinson’s patients improve with TB treatment!
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“Generating genome-scale candidate gene lists for pharmacogenomics” (Hansen et al, Clin. Pharm. & Ther.)
• Goal: Identify genes likely to modulate drug response.
• Method: Associate drugs with network representation of genetic interactions, rank genes based on likelihood of interacting with drugs.
• Result: AUC of 82% on independent test set. Novel gene candidates for warfarin, gefitinib, carboplatin and gemcitabine.
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“Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets” (Suthram et al, PLoS Comp. Bio.)
• Goal: Create molecular relationships between diseases, use this to find new drug opportunities.
• Method: Define 4600 co-expressed functional modules, and cluster diseases using these.
• Result: A novel disease clustering, and functional modules including known drug targets that participate in many diseases.
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“Predicting new molecular targets for known drugs” (Keiser et al, Nature)
• Goal: Find new uses for old drugs
• Method: Represent drug targets by the company they keep: the drugs that bind them. Compare the list of drugs for similarity. Targets with similar lists may have cross-reactivity. Find drugs that are most similar with a new list. Careful statistics.
• Result: An off-target network that relates drugs to new targets. 5 potent new associations, e.g. Prozac as beta-blocker, Vadilex as serotonin blocker.
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Infrastructure for translational
bioinformatics
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“Ontology-driven indexing of public datasets for translational bioinformatics” (Shah et al, BMC Bioinf.)
• Goal: develop infrastructure for applying controlled descriptors to datasets.
• Method: Annotate and index multiple biomedical data resources with UMLS concepts, create index, and federate these together.
• Result: Integration of multiple data sources with controlled vocabulary allowing powerful searches across data sets.
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“A recent advance in the automatic indexing of the biomedical literature” (Neveol et al, J. Biomed. Info.)
• Goal: Move towards automated indexing of Medline articles
• Method: Combine methods of NLP & machine learning to assign heading/subheading pairs.
• Results: Best combination 48% precision, 30% recall. Integrated into MTI tool for NLM curators.
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“Cloud computing: a new business paradigm for biomedical informatics” (Rosenthal et al, J. Biomed. Inf.)
• Goal: Examine fit of BMI to cloud computing.
• Method: Focus on specific component technologies used by the field in different types of tasks.
• Result: Clouds require careful analysis and attention to the migration path from current infrastructure to future.
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“Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery” (Barnes et al, Nat. Rev. Drug. Disc.)
• There are substantial challenges facing pharmaceutical industry (failed new drugs, slow pipeline).
• Opportunity for pre-competitive collaboration and engagement with public domain.
• Propose new areas for collaboration, and highlight cultural shifts that will be needed.
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PROPOSED INITIATIVES
• Disease knowledge: Curating gene-disease associations, shared pathways, imaging repositories
• Target pharmacology: redefine druggability, catalog of targets/phenotypes, share data on known molecules
• Drug safety: adverse event signatures, Pgx data (!), ADME models
• Knowledge management: literature mining, patent mining, data standards
• Pharmaceutical infrastructure: gene indices/nomenclature, robust web service standards, data storage cooperatives.
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Warnings and Causes for Hope
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“An agenda for personalized medicine” (Ng et al, Nature)
• Goal: Compare direct-to-consumer (DTC) services.
• Method: Compare analyses from two DTC companies for 13 diseases on 5 individuals.
• Result: Raw data very accurate. Interpretation vary significantly. For 7 diseases, 50% or less of predictions agree.
• Conclusion: Focus on high risk, strong effect, direct measures. Focus on PGx. Monitor outcomes.
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“Back to the future: why randomized controlled trials cannot be the answer to pharmacogenomics and personalized medicine” (Frueh, Pharmacogenomics)
• Question: RCTs are the gold standard, shouldn’t they be required for personalized medicine interventions?
• Answer: No. Not based on “averages” (by definition), better to use case-control, retrospective and other mechanisms.
• Conclusion: Insistence on RCT level evidence will unnecessarily hinder the roll out of personalized medicine.
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“Computing has changed biology--biology education must catch up” (Pevzner et al, Science)
• Education Forum piece
• Computation is now essential to biology
• Undergraduate biology education has not changed
• New course proposed for all biology undergrads: “Algorithmic, mathematical, and statistical concepts in Biology”
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“Distilling free-form natural laws from experimental data” (Schmidt & Lipson, Science)
• Goal: Define algorithmically what makes a correlation in observed data important and insightful.
• Method: Propose a principle for identifying nontriviality: candidate equations should predict connections between dynamics of subcomponents of the system.
• Result: Example in undergraduate physics, recovered well-known physical laws (Hamiltonian, Lagrange, Equation of Motion)
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“A statistical dynamics approach to the study of human health data: resolving population scale diurnal variation in laboratory data” (Albers & Hripcsak, Physics Letters A)
• Goal: Apply statistical physics and information theory to clinical chemistry measurements.
• Method: 2.5 million data points over 20 years, look at time delay mutual information. Focus on creatinine.
• Result: Creatinine is initially measured twice a day at Columbia, and then every morning. Yesterday’s measurement predicts today’s.
• Conclusion: Sophisticated dynamic modeling methods (that physicists use )are applicable to biological systems.
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2008 Crystal ball...Sequencing makes a comeback (watch out microarrays....)
Translational science projects will create astounding data sets (hopefully available) to catalyze research
GWAS will continue to proliferate
Consumer-oriented genetics will create demand for online resources for interpretation
Difficult decisions about when/how to bring new molecular diagnostics to practice.
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2008 Crystal ball...Sequencing makes a comeback (watch out microarrays....)
Translational science projects will create astounding data sets (hopefully available) to catalyze research
GWAS will continue to proliferate
Consumer-oriented genetics will create demand for online resources for interpretation
Difficult decisions about when/how to bring new molecular diagnostics to practice.
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2009 Crystal ball...
Focus on mechanism in interpreting genetic associations
More sophisticated mechanisms to find signal in GWAS, including data integration
Cellular dynamics of expression, metabolites, proteins
Multiple human & cancer genome sequences
Consumer sequencing (vs. genotyping)
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2009 Crystal ball...
Focus on mechanism in interpreting genetic associations
More sophisticated mechanisms to find signal in GWAS, including data integration
Cellular dynamics of expression, metabolites, proteins
Multiple human & cancer genome sequences
Consumer sequencing (vs. genotyping)
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2010 Crystal ball... Clinical records will be linked to genomics to make discoveries.
More emphasis on drugs and ancestry in DTC companies
Whole genome sequencing for a cohort with a common disease (cancer already here?)
Consumer sequencing (vs. genotyping)
Semantics in literature mining for knowledge discovery
Cloud computing will contribute to one biomedical discovery.