Cell types
Library of Integrated Network-based
Cellular Signatures (LINCS)
Perturbations
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.”
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)
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
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)
Inspiration for and feasibility of LINCS
Science 313, 1929-1935 (2006)
Nat. Rev. Cancer 7, 54-60 (2007)
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
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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
Cell types
Data
Generation,
Analysis,
Integration,
Visualization
Application
Perturbations
Additional dimensions to consider:
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biological replicas
dose-response effects
perturbation response kinetics
informative combinations of perturbation agents
influence of genotypic variation among cells
Functional
annotation
with existing
knowledge
Organization of the LINCS Program
Phase 1
Sept.,
2010
1A
1B
Broad
HMS
U54
U54
Jan.,
2011
Joint Working
Groups
Aug.,
2011
Informational website
Joint data portal
Sept.,
2011
Oct.,
2011
Phase 2
External Scientific
Panel
Joint U54
project
Computational tool
development (U01)
Collaborative
projects (R01)
New assay
development (U01)
Integrating U54 and U01 projects: 1st joint meeting
TBD
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
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”)
LINCS & next-gen genetic association studies
Cases + Controls
Genotype or
Sequence
Induce
iPS Lines
Genetic variants
LINCS Database
Differentiate
Cellular models
of disease
Associations among human genetic
variants/disease signatures/
clinical phenotypes &
LINCS perturbations/cellular signatures
Profile
Molecular and
Cellular Signatures
Mechanistic hypotheses about
disease causation and leads to
novel therapeutic targets
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)
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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
Potential synergies with other NIH programs
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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)
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
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
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)