path class 1ppt - center for health informatics and bioinformatics

Introduction to translational and clinical bioinformaticsConnecting complex molecular information to clinically relevant decisions using molecular profiles,Constantin F. Aliferis M.D., Ph.D., FACMIDirector, NYU Center for Health Informatics and BioinformaticsInformatics Director, NYU Clinical and Translational Science Institute Director, Molecular Signatures Laboratory,Associate Professor, Department of Pathology,Adjunct Associate Professor in Biostatistics and Biomedical Informatics, Vanderbilt University,1,Alexander Statnikov Ph.D. Director, Computational Causal Discovery laboratoryAssistant Professor, NYU Center for Health Informatics and Bioinformatics, General Internal Medicine,Goals,Understand spectrum of Bioinformatics and Medical informatics activities Understand basic concepts of clinical/translational BioinformaticsUnderstand basic concepts of molecular profilingIntroduction to high-throughput assays enabling molecular profilingIntroduction to computational data analytics/bioinformatics enabling molecular profilingUnderstand analytic challenges and pitfalls/interpretation issuesDiscuss case study of profiles used to diagnose/treat patientsPerform hands-on development of a molecular profile, finding novel biomarkers and testing profile/markers accuracyDiscussion supported by general literature and heavily grounded on:NYUMC informatics experts/research projects/grants/papers/entities/software systemsCommercially availiable modalities & assays,2,Overview,Session #1: Basic Concepts Session #2: High-throughput assay technologies Session #3: Computational data analytics Session #4: Case study / practical applications Session #5: Hands-on computer lab exercise,3,Session #1: Basic Concepts,Understand spectrum of Bioinformatics and Medical informatics activities - NYUMC informaticsUnderstand basic concepts of clinical/translational BioinformaticsUnderstand basic concepts of molecular profilingALSO:- emails/names/interests- adjustments to plan,4,NYU Center for health Informatics & Bioinformatics: Broad Plan,Health Informatics,Bioinformatics,BPIC (best Practices Integrative Consultation Core/Service,Literature Synthesis & Benchmarking studies,Design and execution of studies,Method-problem “matchmaking”,Microarray Informatics: UpstreamDifferential expression, Pathway inferenceMolecular profiles,Next-gen sequencing informatics : Upstream analysesi. Chi-seqii. RNA seqiii. Epigeneticsiv. Microbiomicsv. micro RNA studiesvi. CNV & splice variation studiesvii. Digital RNAviii. Denovo sequencing & re-sequencingDownstream analyses,Educational informatics,Evidence based medicine, and Information retrieval Informatics,Library Collaborative,Data Integration & Mining:Data warehouse & interfacing with EMR Omics LIMS Genomic EMR Biospecimen managementresearch protocol database systems and management teamData mining serviceData Mining software,Infrastructure & Integrative Methods/Activities,High Performance Computing Facility,MS/PhD (& Post-doc Fellowship) Program,Continuing Education Workshops & tutorialsPaper digestResearch ColloquiumInvited Speakers,Research labs KlugerMolecular SignaturesEBM, IR & ScientometricsComputational Causal Discovery,CTSI,Integrate/Focus Existing Informatics and Increase Collaborations,5,ProteomicsInformatics,CancerCenter,Genetics-Genomics,COEs,Multi-modal & Integrative studies,Current Capabilities: Areas,Health Informatics,Educational informatics,Evidence based medicine, and Information retrieval Informatics,Library Collaborative,Infrastructure & Integrative Methods/Activities,High Performance Computing Facility,MS/PhD (& Post-doc Fellowship) Program,Continuing Education Workshops & tutorialsPaper digestResearch ColloquiumInvited Speakers,Research labs KlugerMolecular SignaturesEBM, IR & ScientometricsComputational Causal Discovery,CTSI,Integrate/Focus Existing Informatics and Increase Collaborations,6,Data Integration & Mining:Data warehouse & interfacing with EMR Omics LIMS Genomic EMR Biospecimen managementresearch protocol database systems and management teamData mining serviceData Mining software,Bioinformatics,BPIC (best Practices Integrative Consultation Core/Service,Literature Synthesis & Benchmarking studies,Design and execution of studies,Method-problem “matchmaking”,Microarray Informatics: UpstreamDifferential expression, Pathway inferenceMolecular profiles,Next-gen sequencing informatics : Upstream analysesi. Chi-seqii. RNA seqiii. Epigeneticsiv. Microbiomicsv. micro RNA studiesvi. CNV & splice variation studiesvii. Digital RNAviii. Denovo sequencing & re-sequencingDownstream analyses,ProteomicsInformatics,CancerCenter,Genetics-Genomics,COEs,Multi-modal & Integrative studies,Current & Future capabilities,Health Informatics,Educational informatics,Evidence based medicine, and Information retrieval Informatics,Library Collaborative,Content management, medical simulations,Filter Medline according to content and quality Filter Web for health advice quality Predict future citations of articles Classify individual citations as instrumental or not Identify special types of articles Construct citation histories & Analyze impact of articles Integrate and manage queries and related content Combine and optimize knowledge source searches New “find a researcher”“Find a collaborator”,Apply, evaluate, refine next-gen IR methods,Data warehouse needs; software acquisition; implementation OMICS LIMS needs capture; vendor product assessment; funds; sofwtare purchase and implementation; integration with billing and EMRBiospecimen managementResearch protocol database system (eVelos)Data base management teamData mining serviceData mining engine: faculty; funds; prototype; implementation; evaluation,7,Data Integration & Mining:Data warehouse & interfacing with EMR Omics LIMS Genomic EMR Biospecimen managementresearch protocol database systems and management teamData mining serviceData Mining software,Current & Future capabilities,Infrastructure & Integrative Methods/Activities,High Performance Computing Facility,MS/PhD (& Post-doc Fellowship) Program,Continuing Education Workshops & tutorialsPaper digestResearch ColloquiumInvited Speakers,Research labs KlugerMolecular SignaturesEBM, IR & ScientometricsComputational Causal Discovery,CTSI,Integrate/Focus Existing Informatics and Increase Collaborations,(supported by rest of objectives),Sequencing server; hectar1; hectar2; Funds; needs; grants; personnel post; specs; room/networking/access; Personnel hires; hw install; licenses; BP; launch,Kluger TF /Regulation studies; high-throughput outcome prediction, specialized clustering methods Molecular Signatures development of molecular signatures for diagnosis outcome prediction and personalized medicine, discovery of diagnostic/imaging biomarkers and putative drug targets , deployment of signatures, automated software, new methodsEBM, IR studies of causal validity of bioinformatics discovery methods, multiplicity studies, automated software, active learning/experiment number minimization,Formal Training in Biomedical Informatics at pre and post-doctoral levels,Continuing Education Workshops & tutorials Paper digest Research Colloquium Invited Speakers,Faculty and Staff career development; Informatics Affiliates; Working Collaborations with Courant, Polytechnic, NYC Informatics and other non-NYUMC entities,8,Current & Future capabilities,Bioinformatics,BPIC (best Practices Integrative Consultation Core/Service,Literature Synthesis & Benchmarking studiesMethod-problem “matchmaking”Design and execution of studies Study publication assistance,Microarray Informatics: Experiment design, assay execution, differential expression, pathway mapping, pathway-specific testing (GSEA/GSA), de novo pathway discovery, phylogeny, clustering, hybrid experimental/observational designs; SNP arrays; ChIP-on-ChIP analyses, aCGH, tiled arrays, etc,Sequencing Informatics: Chip-Seq analysis, digital gene expression, de novo sequence assembly & reassembly, CNV analysis, epigenomic studies, microbiomics,Proteomics Informatics: platform-specific pre-processing, differential abundance, peptide-protein mapping, protein identification, de novo protein interaction network inference, protein modification and structure studies,Cancer Center,Area-specific (Disease, Assay) Informatics,Genetics-Genomics,COEs,9,Multi-modal Integrative and Higher-level Informatics: Molecular Signatures multiplicity studies, TS/DBN designs, automated software, active learning/experiment number minimization Integrating clinical lab, text, imaging and high throughput data in CTs/prospective studies or exploratory retrospective ones,Summary Contacts (Until Centralized Consultation Service is Launched),Management of Clinical and protocol data James Robinson Educational Informatics Mark Triola Next-Gen Information Retrieval Lawrence Fu, Constantin Aliferis, TBD Informatics for Data Mining Alexander Statnikov, Constantin Aliferis Data Integration & Warehousing John Chelico, Ross Smith, Constantin Aliferis High Performance Computing Constantin Aliferis, Ross Smith Best Practices in Bioinformatics Constantin Aliferis, Alexander Statnikov Sequencing Informatics Upstream:Stuart Brown, Alexander Alekseyenko, Yuval Kluger, Jinhua Wang, TBD, TBD Downstream: Alexander Alekseyenko, Yuval Kluger, Jinhua Wang, Alexander Statnikov, Constantin Aliferis Microarray Informatics Jiri Zafadil, Yuval Kluger, Jinhua Wang, Constantin Aliferis, Alexander Statnikov Cancer Informatics Yuval Kluger, Jinhua Wang, Stuart Brown, Jiri Zafadil, Constantin Aliferis Proteomics Informatics Stuart Brown, Jinhua Wang, Constantin Aliferis,Alexander Statnikov, TBD General Tools Stuart Brown Specialized applications (Genetics, Regulation, Pathways) Stuart Brown, Yuval Kluger, Alexander Statnikov, Constantin Aliferis Molecular Signatures development, biomarker discovery, Multi-modal and Integrative studies Constantin Aliferis, Alexander Statnikov, Yuval Kluger,10,Molecular Signatures,Definition = computational or mathematical models that link high-dimensional molecular information to phenotype of interest,11,12,Molecular SignaturesGene markersNew drug targets,Molecular Signatures: Main Uses,Direct benefits: Models of disease phenotype/clinical outcome & estimation of the model performanceDiagnosisPrognosis, long-term disease managementPersonalized treatment (drug selection, titration) (“predictive” models)Ancillary benefits 1: Biomarkers for diagnosis, or outcome predictionMake the above tasks resource efficient, and easy to use in clinical practiceHelps next-generation molecular imagingLeads for potential new drug candidatesAncillary benefits 2: Discovery of structure & mechanisms (regulatory/interaction networks, pathways, sub-types)Leads for potential new drug candidates,13,Molecular Signatures,The FDA calls them “in vitro diagnostic multivariate index assays”1. “Class II Special Controls Guidance Document: Gene Expression Profiling Test System for Breast Cancer Prognosis”:addresses device classification2. “The Critical Path to New Medical Products”:- identifies pharmacogenomics as crucial to advancing medical product development and personalized medicine. 3. “Draft Guidance on Pharmacogenetic Tests and Genetic Tests for Heritable Markers” & “Guidance for Industry: Pharmacogenomic Data Submissions”identifies 3 main goals (dose, ADEs, responders),define IVDMIA,encourages “fault-free” sharing of pharmacogenomic data,separates “probable” from “valid” biomarkers,focuses on genomics (and not other omics),14,Increased Clinical Trial sample efficiency, and decreased costs or both, using placebo responder signatures ;In silico signature-based candidate drug screening;Drug “resurrection”Establishing existence of biological signal in very small sample situations where univariate signals are too weak; Assess importance of markers and of mechanisms involving thoseChoosing the right animal model?,15,Less Conventional Uses of Molecular Signatures,Agendia,Clarient,Prediction Sciences,Veridex,LabCorp,University Genomics,Genomic Health,BioTheranostics,Applied Genomics,Power3,Correlogic Systems,Recent molecular mignatures available for patient care,16,Molecular signatures in the market (examples),17,MammaPrint,Developed by Agendia ()70-gene signature to stratify women with breast cancer that hasnt spread into “low risk” and “high risk” for recurrence of the diseaseIndependently validated in 1,000 patientsSo far performed 12,000 testsCost of the test is $3,200In February, 2007 the FDA cleared the MammaPrint test for marketing in the U.S. for node negative women under 61 years of age with tumors of less than 5 cm.TIME Magazines 2007 “medical invention of the year”.,18,CupPrint,Developed by Agendia ()500-gene (1900 probes) signature to identify primary site of 49 different types of carcinomas as well as other types of cancer such as sarcoma and melanoma. Several independent validation studies,19,ColoPrint,In development & validation by Agendia ()Multi-gene expression signature to determine the risk for recurrence in colorectal cancer patientsPlanning to seek FDA approvalReferences:/category/coloprint//issues/7_34/features/141935-1.html/downloads/PressreleaseColoPrintfinalJuly10th2007.pdf,20,Oncotype DX,Development synopsisMain reference: Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med. 2004; 351(27):2817-26.Developed by Genomic Health ( )21-gene signature to predict whether a woman with localized, ER+ breast cancer is at risk of relapseIndependently validated in 1,000 patientsSo far performed 55,000 testsCost of the test is $3,650Reimbursement. Information about reimbursement for molecular signatures from Aetna: /cpb/medical/data/300_399/0352.html Oncotype DX did not undergo FDA review. Here is an article that mentions FDA review of Oncotype DX (slightly outdated): /cgi/content/full/303/5665/1754 The following paper shows the health benefits and cost-effectiveness benefits of using Oncotype DX: /cgi-bin/abstract/114124513/ABSTRACT,21,CancerType ID,Developed by AviaraDX ()92-gene signature to classify 39 tumor typesSignature developed by GA/KNN“Compressed version” of CupPrint,22,Breast Cancer Index,Developed by AviaraDX ()Uses 7 genes (combines 5-gene MGI signature and 2-gene H/I signature)Stratifies breast cancer patients into groups with low or high risk of cancer recurrence and good or poor response to endocrine therapy. Validated in thousands of patients (treated & untreated),23,GeneSearch Breast Lymph Node (BLN) Assay,Developed by Veridex (), a Johnson & Johnson companyTest to detect if breast cancer has spread to the lymph nodesThe GeneSearch BLN uses real-time reverse transcriptase-polymerase chain reaction (RT-PCR) to detect ammoglobin (MG) and cytokeratin 19 (CK 19) in lymph nodes.FDA approvedFeatured in TIMEs 2007 Top 10 Medical Breakthroughs list,24,MammoStrat,Developed by Applied Genomics ()The test is based on 5 biomarkers.The test is used to classify individual patients as having an AGI-defined high-, moderate-, or low-risk of breast cancer recurrence following surgical removal of their primary tumor and treatment with tamoxifen alone.Independently validated in 1000 patients,25,NuroPro,Developed by Power3 (/)Early detection of neurodegenerative diseases: Alzheimers disease, ALS (Lou Gehrigs disease), and Parkinsons disease.Validation study in progress.Based on 59 proteins.,26,BC-SeraPro,Developed by Power3 (/)Test for diagnosis of breast cancer (breast cancer case vs. control).Validation study in progress.Based on 22 proteins.Uses linear discriminant analysis; outputs a probability score.,27,Key ingredients for developing a molecular signature,Well-defined clinical problem &access to patients,High-throughput assays,Computational &Biostatistical Analysis,Molecular Signature,28,Challenges in Computational Analysis of omics data for development of molecular signatures,Relatively easy to develop a predictive model + even easier to believe that a model is good when it is not false sense of securitySeveral problems exist: some theoretical and some practicalOmics data has many special characteristics and is tricky to analyze!,29,OvaCheck,Developed by Correlogic ()Blood test for the early detection of epithelial ovarian cancer Failed to obtain FDA approval Looks for subtle changes in patterns among the tens of thousands of proteins, protein fragments and metabolites in the bloodSignature developed by genetic algorithmSignificant artifacts in data collection & analysis questioned validity of the signature:Results are not reproducibleData collected differently for different groups of patients/nature/journal/v429/n6991/full/429496a.html,30,A,B,C,D,E,F,Figure from Baggerly et al (Bioinformatics, 2004),Problem with OvaCheck,31,32,Molecular SignaturesGene markersNew drug targets,33,Brief History of main “omics” technology: gene expression microarrays,1988: Edwin Southern files UK patent applications for in situ synthesized, oligo-nucleotide microarrays1991: Stephen Fodor and colleagues publish photolithographic array fa