Library of Integrated Network-basedCellular Signatures (LINCS)

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Inaugural LINCS Consortium MeetingOctober 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

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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)