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Library of Integrated Network-basedCellular Signatures (LINCS)
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Inaugural LINCS Consortium Meeting
October 27-28, 2011
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.”
• 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
NIH’s Major Research Opportunities
Francis Collins, Science 327, 36-37 (2010)
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
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)
The LINCS Approach Reveals:
Inspiration for and feasibility of LINCS
Nat. Rev. Cancer 7, 54-60 (2007)
Science 313, 1929-1935 (2006)
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.
The LINCS Paradigm
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Additional dimensions to consider: • biological replicas• dose-response effects• perturbation response kinetics• informative combinations of perturbation agents• influence of genotypic variation among cells
DataGeneration,Analysis,Integration,VisualizationApplication
Functional annotation
with existing knowledge
Organization of the LINCS Program
Computational tool development (U01)
New assaydevelopment (U01)
Sept., 2011
BroadU54
HMSU54
Phase 1Sept., 2010
Phase 2 TBD
Informational websiteJoint data portal
Joint U54project
Collaborative projects (R01)
Aug., 2011
Oct., 2011 Integrating U54 and U01 projects: 1st joint meeting
Joint WorkingGroups
External ScientificPanel
Jan., 20111A
1B
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
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
Potential Goals of LINCS Phase 2
• 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”)
Continue Phase 1 goals but extend LINCS to:
LINCS & next-gen genetic association studies
LINCS Database
Mechanistic hypotheses about disease causation and leads to
novel therapeutic targets
Associations among human genetic variants/disease signatures/
clinical phenotypes & LINCS perturbations/cellular signatures
Cases + Controls
Genetic variants
Genotype or Sequence
iPS Lines
Cellular modelsof disease
Molecular andCellular Signatures
Differentiate
Induce
Profile
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
• 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)
Potential synergies with other NIH programs
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 developing a strategic template for how to optimally generate and apply network-based cellular signatures in biomedical research
• LINCS will be scalable to more biological systems than are included in the initial program
• LINCS is providing coordination and establishing best practices across related research projects
• LINCS is establishing methods for integrating disparate data types for understanding bionetworks
• 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
Summary of the LINCS Vision (cont’d.)
LINCS Implementation Group
Co-Chairs:Alan Michelson (NHLBI)Mark Guyer (NHGRI)
Working Group Coordinators:Ajay Pillai (NHGRI)Jennie Larkin (NHLBI)
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)
Working Group Members:
Leslie Adams (NHGRI)David Balshaw (NIEHS)Maureen Beanan (NIAID)Arthur L. Castle (NIDDK)Hemin Chin (NEI)Jennifer Couch (NCI)