Presentation on theme: "Biosystemix, Ltd. Data-Driven Solutions for Clinical Prediction and Functional Discovery CHI, Molecular Medicine Tri-Conference Emerging Company Profile."— Presentation transcript:
Biosystemix, Ltd. Data-Driven Solutions for Clinical Prediction and Functional Discovery CHI, Molecular Medicine Tri-Conference Emerging Company Profile April 19, 2005 Roland Somogyi, Ph.D. Larry D. Greller, Ph.D. Biosystemix, Ltd. email@example.com firstname.lastname@example.org www.biosystemix.com (613)-376-3126
Biosystemix, Ltd. 2 Personalized medicine: The future of therapeutic discovery, practice and business Diseases are complex –Genes and pathways lead to the same symptoms in different ways in different individuals –We must target these specific causes, not the symptoms Some drugs are only effective in specific individuals –Drug targets can be specific for genetic variants of disease –Individual pathway activity fingerprints may determine efficacy Some drugs cause adverse effects in a very small subpopulation –Toxicity due to genetic variants of drug metabolism –Physiological and pathway background patterns may lead to unanticipated side effects
Biosystemix, Ltd. 3 Biosystemix value to customers: Succesful personalized medicine programs ultimately depend on understanding the data and deriving meaningful predictions Biosystemix solutions Clinical data Experimental platforms Personalized medicine predictors Therapeutic markers & targets Signaling pathways & networks Genomics & proteomics Biomedical data Discoveries and models Integrative data mining and predictive modeling You must pass through here
Biosystemix, Ltd. 4 Biosystemix focuses on the opportunity for therapeutic solutions, services and products The predictive models which integrate the knowledge of markers, patterns and pathways associated with disease and therapeutic outcome, will become vital –to personalized therapeutic practice, –target and drug discovery, validation and approval, and –an economic engine for the biomedical industry. Biosystemix provides key technologies and experience in –extracting complex patterns of key markers from genomic and clinical data, –integrating predictive molecular profiles, functional knowledge and clinical outcomes into comprehensive predictive models, and –generating personalized medicine marker, target and model IP in a large variety of disease and biomedical application areas.
Biosystemix, Ltd. 5 An advance in personalized medicine / predictive medicine
Biosystemix, Ltd. 6 A personalized medicine scientific case study: Predicting clinical drug response in MS (multiple sclerosis) Nonlinear & combinatorial predictive models Gene A expression Good responder to interferon Gene B expression Clinical RNA expression profiling data Computational modeling Personalized medicine outcome Gene C expression Poor responder to interferon
Biosystemix, Ltd. 7 Predicting drug response before IFN treatment in MS: Two genes work better than one 10 samples are misclassified by Caspase 10 alone Blue: poor response Red: good response 1d IBIS models 2d IBIS models 15 samples are misclassified by FLIP alone Only 5 samples are misclassified by FLIP and Caspase 10 together Poor response predictive region Good response predictive region
Biosystemix, Ltd. 8 Predicting drug response before IFN treatment in MS: Three genes work better than two The yellow, orange and blue arrows point to samples that are incorrectly classified in the 2d models and correctly classified in the 3d models Note: 3d models pass stringent statistical cross-validation criteria A & B: Views of 3d model predicting good and poor drug responders from the expression of 3 genes B, C & D: All 3 possible 2d predictive models involving the same genes Blue: poor response Red: good response 3d IBIS model 2d IBIS models
Biosystemix, Ltd. 9 Reference S. Baranzini 1, P. Mousavi 2, J. Rio 3, S. Caillier 1, A. Stillman 1, P. Villoslada 4, M. Wyatt 1, M. Comabella 3, L. Greller 5, R. Somogyi 5, X. Montalban 3, J. Oksenberg 1 Classification and prediction of response to IFNß using gene expression profiling the supervised computational methods. (2004) PLoS Biol 3(1): e2 1 Department of Neurology, School of Medicine, University of California at San Francisco 2 School of Computing, Queens University, Kingston, Ontario, Canada. 3 Department of Neuroimmunology, Hospital Vall dHebron, Barcelona, Spain 4 Department of Neurology, Clinica Universitaria de Navarra, University of Navarra, Spain, 5 Biosystemix Ltd., Sydenham, Ontario, Canada
Biosystemix, Ltd. 10 What have we found? Combinatorial 3d models predicting IFN response outcome in MS achieve high accuracy and statistical validation scores. These predictive models provide valuable diagnostic/prognostic answers in complex diseases for which no markers exist –Next step is in-depth clinical validation Single genes and pairs do not achieve high predictive accuracy Finding the nonlinear and combinatorial patterns at the root of these models requires advanced data mining –Conventional statistics not effective here
Biosystemix, Ltd. 11 Gene function and pathway discovery through gene network reverse engineering
Biosystemix, Ltd. 12 IFN gamma receptor heterodimers activate Jak2 SOS1 and Grb2 complex activates RAS/MAPK pathway leading to FOS activation Literature quote: …interferon-inducible stat2: stat1 heterodimer preferentially binds in vitro to a consensus element found in the promoters of a subset of interferon-stimulated genes Jak2 phosphorylates only Stat1 resulting in Stat1 homodimer formation and GAS (cis element) activation of Interferon gamma induced genes Predicting the molecular mechanisms underlying differential drug response: Data-driven, computational reverse engineering reconstructs signaling pathways directly from clinical MS gene expression data Red lines: Gene interactions in good responders Red lines: Gene interactions in good responders Green lines: Gene interactions in poor responders Green lines: Gene interactions in poor responders
Biosystemix, Ltd. 13 What made it possible? Setting the stage with thorough experimental design –Careful clinical study design and patient recruitment –Sufficient number of high quality, clinical blood and RNA sample A solid foundation of precisions measurements –Quantitiave, gene expression RT-PCR assays Reverse transcription – polymerase chain reaction Combines stringent hybridization with amplification –Only the best assays should be used for clinical applications Providing the edge with advanced computational analysis –Nonlinear and combinatorial methods for pattern recognition –Higher-dimensional predictive modeling and statistical validation In the words of by Kaminski and Achiron, highlighting the Baranzini study in PLoS Med 2(2): e33:. –However, the importance of Baranzini and colleagues study lies not in its mechanistic insights, but in its clinical relevance. The careful design of the experiment, the use of reproducible real-time PCR instead of microarrays, the meticulous analysis, and the previous observations support the notion that PBMCs express clinically relevant gene expression signatures in MS and probably in other organ-confined diseases.
Biosystemix, Ltd. 14 Data-driven predictive models provide opportunities for better medical practice Step 1: Diagnosis of the disease –Specific form of a disease is not apparent in superficial symptoms –Higher-dimensional diagnostic models based on in-depth patient profiling Molecular and physiological fingerprints distinguish forms of a disease. Step 2: Prognosis of the outcome –Complex prognostic models based on in-depth profiling data can enable reliable choices for timing of therapeutic interference Step 3: Therapeutic choice –Therapeutic decision models based on detailed patient state information will significantly increase the probability of successsful treatment Step 4: Therapeutic discovery –Data from personalized medicine studies will be used in the data-driven discovery of new disease mechanisms and pathways for individually-targeted intervention.
Biosystemix, Ltd. 15 Biosystemix currently provides its expertise and services to partners in predictive medicine and genomics Immunogenomics –S2K, Genome Canada / Genome Quebec-funded multi-center consortium Infectious diseases –HIV –SARS –HTLV Transplant rejection –Immune Tolerance Network, NIH/NIAID-funded multi-center consortium Autoimmune diseases –Allergy –Diabetes –UCSF, Department of Neurology Predicting drug response in multiple sclerosis Cancer –Queens University, Ontario Cancer Institute Predicting good and poor outcomes in Follicular Lymphoma Toxicogenomics –University of Michigan Inference of pathways involved in toxicity from gene expression data
Biosystemix, Ltd. 16 Biosystemix sees growing opportunities in personalized medicine Growing market for diagnostic and prognostic products –Marker sets, assay kits and hardware for more effective diagnostic/prognostic profiling Information products –Computational models linking complex diagnostic/prognostic patterns to outcomes –Web-based, personalized medicine tools for use by physicians and patients Product linkage –A drug may only be effectively applied if linked to a prognostic test Patent and regulatory approval for product sets that are only effective in combination May be required in the future by regulatory agencies for specifically-targeted drugs –Opportunity for extracting value from generic drugs Novel combinations of generic drugs to match individual patient need Combinations and predictive models generating these combinations constitute valuable IP Creating new markets –Providing new tools and therapies where they are currently non-existent or unreliable
Biosystemix, Ltd. 17 Larry D. Greller, Ph.D. Biosystemix CSO, Co-Founding Director Parvin Mousavi, Ph.D. Assist. Prof. Queens University School of Computing Sergio Baranzini, Ph.D. Assist. Prof. Neurology University of California San Francisco Acknowledgements
Biosystemix, Ltd. 18 Caspase 10 FLIP Caspase 2 Poor response predictive region Good response predictive region Linking genes and pathways to predict therapeutic outcome in a complex disease
Biosystemix, Ltd. 20 A collaborative, predictive medicine study in MS Investigational Groups: –Sergio Baranzini, Jorge Oksenberg: UCSF –Xavier Montalban: Hospital Vall dHebron (Barcelona, Spain) –Parvin Mousavi, Larry Greller, Roland Somogyi: Biosystemix, Ltd. Multiple Sclerosis: –Autoimmune, neuroinflammatory CNS disease –Primary therapy: interferon-beta (IFN ) treatment Study Design: –RNA isolated from peripheral blood mononuclear cells after IFN treatment at 6 time points (0, 3, 6, 9, 12, 18 and 24 months) –70 genes measured by kRTPCR –52 patients –33 good responders –19 bad responders
Biosystemix, Ltd. 21 High-quality molecular and physiological profiling –Study design to capture key components of medical outcomes –Study design to assist better post-hoc discovery of outcome- predictive profiles –Adequate samples for statistical support Data management and integration –Making different assay types commensurable –Standards for data integration Data-driven computational discovery and modeling –Complex outcome-predictive patterns –Predictive models for clinical decision support –Mechanistic discovery for novel intervention strategies Scientific challenges in personalized medicine
Biosystemix, Ltd. 22 The need for data-driven models for tuning therapies to individual need Complex predictive models Clinical assay intensity C Gene expression A Therapeutic compound X Compound cocktail Y Drug dose Z Protein abundance B Diagnostic and prognostic profiling Computational modeling Personalized medicine therapy
Biosystemix, Ltd. 23 Effective inference and modeling for personalized medicine must deal with biological complexity Interaction networks Nonlinearity Combinatorics 12345678910 0 5 15 20 25 30 35 k Log10 (C(N,k)) N = 10 N = 1000 N = 100 N = 10,000 k=4 Curse of dimensionality e.g. 400 million million combinations from 10,000 genes
Biosystemix, Ltd. 24 Data mining Personalized medicine: The ultimate application of systems biology Genetic variation characterization Predictive modeling Clinical testing Laboratory validation Target & marker discovery Clinical assay data RNA, protein, metabolite profiling Personalized medicine Drugs, diagnostics & predictive models Systems Biology Computational analysis and Bioinformatics Biomedical validation
Biosystemix, Ltd. 25 1.More than a vision It will be difficult … Personalized medicine and integrative biology is technologically challenging …but its tractable. Many technological components are there – they now need to work together 2.The devil is in the details Thorough and integrative scientific study design High quality assay technology and execution Advanced computational data mining and predictive modeling 3.It all depends on people and technologies working together Integration of biomedical, physical, and math/statistical/computational sciences Acceptance of new technologies by regulatory bodies and medical practioners Support of R&D and commercialization by businesses community Recipes for success
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