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Library of Integrated Network-based Cellular Signatures (LINCS)

Library of Integrated Network-based Cellular Signatures (LINCS). Cell types. Phenotypic assays. Perturbations. Inaugural LINCS Consortium Meeting October 27-28, 2011 . LINCS Mission Statement.

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Library of Integrated Network-based Cellular Signatures (LINCS)

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  1. Library of Integrated Network-basedCellular Signatures (LINCS) Cell types Phenotypic assays Perturbations Inaugural LINCS Consortium Meeting October 27-28, 2011

  2. LINCS Mission Statement “To generate coherent, multi-dimensional datasets of perturbation-induced molecular and cellular signatures that can be integrated and analyzed by computational methods to inform a network understanding of biological systems in health and disease, thereby facilitating drug and biomarker development.”

  3. NIH’s Major Research Opportunities • Applying high throughput technologies to understand fundamental biology, and to uncover the causes of specific diseases • Translating basic science discoveries into new and better treatments • Putting science to work for the benefit of health care reform • Encouraging a greater focus on global health • Reinvigorating and empowering the biomedical research community Francis Collins, Science 327, 36-37 (2010)

  4. The LINCS concept and goals • LINCS is based on the idea that normal human biology, pathology and pharmacology are best understood using a systems-level approach • LINCS uses a biological network-based strategy to assess how genetic, drug and related biological perturbations affect cellular states • LINCS paradigm = matrix of: • perturbagens X cell types X phenotypic assays • Overarching goal: to generate a robust approach for perturbing a diversity of cell types, measuring cellular responses, integrating and analyzing data, and visualizing and interrogating the database for a variety of biomedical research applications

  5. The LINCS Approach Reveals: • mechanism-based relationships among the effects of different perturbagens (drug responses and their targets) • associations among responding cellular components (network interactions and structure-function relationships)

  6. Inspiration for and feasibility of LINCS Science 313, 1929-1935 (2006) Nat. Rev. Cancer 7, 54-60 (2007)

  7. Extension of the Connectivity Map concept by LINCS LINCS aims to extend the original Connectivity Map by increasing the dimensionality of: • perturbation conditions • small molecules • gene knockdowns and overexpression • physiological signals (growth factors, cytokines) • cell types • immortalized cell lines • primary cells • stem cells (ESC, iPSC) and their derivatives • cells representative of different disease states • phenotypic assays • molecular and biochemical profiles • cellular features and behavior Outcome: rich datasets from which molecular relationships and network architecture can be computationally derived.

  8. The LINCS Paradigm Data Generation, Analysis, Integration, Visualization Application Cell types Phenotypic assays Functional annotation with existing knowledge Perturbations • Additional dimensions to consider: • biological replicas • dose-response effects • perturbation response kinetics • informative combinations of perturbation agents • influence of genotypic variation among cells

  9. Organization of the LINCS Program Phase 1 Sept., 2010 Broad U54 HMS U54 Informational website Joint data portal Joint U54 project Collaborative projects (R01) External Scientific Panel Joint Working Groups Aug., 2011 Jan., 2011 1A Computational tool development (U01) New assay development (U01) Sept., 2011 1B Oct., 2011 Integrating U54 and U01 projects: 1st joint meeting TBD Phase 2

  10. Challenges for LINCS Phases 1 and 2 • Continue to expand the “matrix” and to develop new technologies, but also: • move beyond large-scale data generation to apply the LINCS knowledge base to understand normal biology and disease states, and • develop new treatments through the prioritization and support of informative driving biological problems that test hypotheses generated by LINCS data

  11. Challenges to Begin Addressing in LINCS Phase 1B • Standardize, integrate and query across coherent datasets derived from diverse phenotypic assays • Discover novel drug targets and elucidate drug mechanisms: pathway and network-based (“systems”) pharmacology • Develop rational approaches to combination chemotherapies • Reconstruct biological networks in health and disease, including integration of related data from other sources • Distinguish efficacious from toxic signatures in different target cell types • Disseminate LINCS resources, concepts, data and network models to the larger research community • Establish new collaborations that facilitate achieving the above goals through informative use cases • Define and meet specific milestones and metrics based on the above goals

  12. Potential Goals of LINCS Phase 2 Continue Phase 1 goals but extend LINCS to: • Compare and establish the relevance of drug effects on cultured cells vs. in vivo tissues • Contribute signatures that classify diseases by common molecular criteria: a new taxonomy of disease • Contribute to novel biomarkers for molecular diagnostics, disease stratification, risk assessment and assays for response to therapy • Contribute to molecular criteria for subject recruitment and surrogate endpoints in clinical trials • Convert signatures determined by destructive assays to biosensors suitable for inclusion in chip-based drug screening microsystems in living cells • Align RNAi-based, small molecule and cytokine perturbations with the effects of naturally occuring human genetic variation to decipher disease mechanisms (“next-gen genetic association studies”)

  13. LINCS & next-gen genetic association studies Cases + Controls Genotype or Sequence Induce Genetic variants iPS Lines Differentiate Associations among human genetic variants/disease signatures/ clinical phenotypes & LINCS perturbations/cellular signatures Cellular models of disease LINCS Database Profile Molecular and Cellular Signatures Mechanistic hypotheses about disease causation and leads to novel therapeutic targets

  14. Potential for future LINCS relationships • Explore future interactions with: • Categorical Institutes: testing hypotheses and extending analyses to specific cells and diseases • National Center for Advancing Translational Sciences (NCATS) • Drug targets and mechanisms • Network-based drug discovery • Drug efficacy and toxicity testing • Pharmacogenetics and personalized therapies • LINCS signatures as biomarkers for disease stratification, diagnosis and prognosis • Network signatures as markers for clinical trials

  15. Potential synergies with other NIH programs • Molecular Libraries Program (Common Fund) • Protein Capture Reagents (Common Fund) • Metabolomics Program (Common Fund) • Single Cell Analysis Program (Common Fund) • Chip-based Microphysiological Systems (NIH-DARPA-FDA collaboration) • ENCODE (NHGRI) • Next-gen Genetic Association Studies (NHLBI, NHGRI) • National Centers for Systems Biology (NIGMS) • Integrative Cancer Biology Program (NCI) • Tox21 Program (NIEHS-NCGC collaboration) • Large-scale DNA sequencing in population cohorts and case-control studies (multiple Institutes)

  16. Summary of the LINCS Vision • LINCS is generating high-dimensional data that will provide mechanistic insights into disease etiology and the identification of novel drug targets • LINCS is establishing methods for integrating disparate data types for understanding bionetworks • LINCS is developing a strategic template for how to optimally generate and apply network-based cellular signatures in biomedical research • LINCS is providing coordination and establishing best practices across related research projects • LINCS will be scalable to more biological systems than are included in the initial program

  17. Summary of the LINCS Vision (cont’d.) • LINCS signatures have multiple potential applications: • Network understanding of normal and disease states • Discovery of new drugs and their targets • Pathway and network-based pharmacology • Pathway and network-based diagnostics • Biomarkers for disease classification and for design of clinical trials

  18. LINCS Implementation Group Co-Chairs: Alan Michelson (NHLBI) Mark Guyer (NHGRI) Working Group Coordinators: Ajay Pillai (NHGRI) Jennie Larkin (NHLBI) Working Group Members: Leslie Adams (NHGRI) David Balshaw (NIEHS) Maureen Beanan (NIAID) Arthur L. Castle (NIDDK) Hemin Chin (NEI) Jennifer Couch (NCI) Rina Das (NCI) Weiniu Gan (NHLBI) Tina Gatlin (NHGRI) Q. Max Guo (NIAAA) Michael Huerta (NIMH) Jerry Li (NCI) Peter Lyster (NIGMS) Ronald Margolis (NIDDK) Mary Ellen Perry (DPCPSI) Robert Riddle (NINDS) Lillian Shum (NIDCR) Lois Winsky (NIMH)

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