Download PPT - NIH LINCS Program

NCI Projects Relevant to LINCS
Jennifer Couch, Ph.D.
Daniela Gerhard, Ph.D.
National Cancer Institute, NIH
A Long History of Using Cell Lines
to study disease: NCI 60
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Genes and Genetics
Complex Signaling Networks
Multiple Cellular Processes
Microenvironment
Host Systems
Environmental Factors
Population Dynamics
Initiation
Progression
Time - Progression
Metastasis
Recurrence
Histology Variation
Cancer is Complicated
The ICBP Approach
Data & Information - Clinical, Biological,
Epidemiological
Systems Biology
Centers for
Cancer
Systems
Biology
(CCSB)
Computational
Modeling
Discovery and Knowledge- Basic and translational
ICBP2:Centers for Cancer
Systems Biology
Cancer Stage
Sander
Califano
Plevritis
Cell – cancer
differentiation; single
cell analysis; disease
progression and
resistance
Leukemia,
Lymphoma
Multiscale
analysis of
Genomic and Cell
Networks
(MAGNet), B cell
lymphoma,
glioma
Golub
Integrate
multiple data
types to identify
essential genenew drug targets
Lung Melanoma
Lauffenburger
Quantative modeling of
critical cancer process;
growth, migration, DNA
repair
Brain, lymphoma, Breast
Quaranta
3-D model cell-cell
communication;
microenvironment;
variability of drug
response
Melanoma, breast,
panceas, glio, CLL
Cellular model of
tumor
heterogeneity; cell
phenotype
measurements;
multiscale
Breast, Lung
Clarke
Hormone
responsiveness;
population
exposure risk;
GWAS; drug design
Breast
Friend
Cross disease analysis;
Sage Bionetworks;
network data
integration
Glioma, Ovarian,
colon, Liver,
Meduloblastoma,
Pancreas, Breast, ect
Wong
Tumor Stem cell,
imaging, ME
interations; protein
cDNA array
Breast
Hlatky
Tumor evolution;
quanatative analysis;
cellular population
dynamic modeling
Prostate, Breast
Gray
Modeling Approach
Cancer
heterogeneity;
molecular
signaling and
combinational
therapy
Breast
Huang
Epigenetic and
microRNA
examination of
hormonal –chemo
resistance
Prostate, Ovarian
Breast
Physical Scale
Lawrence Berkeley Labs (Gray)
Model Based Predictions of Responses to RTK Targeted
Therapies in Breast Cancer
Overall: Infer signaling network for cancer subtypes; model response to
MAPK inhibitors, Her2 targeted therapies and P13K targeted therapies
Project 3 :HER-family signaling deregulated in breast cancer
Glucose
• Therapeutic perturbations of a
breast cancer cell line panel show:
GF
GLUT4
RTK
PDK1
Glucose
AS160
PI3K
SHP2/
SHC/SOS/
GRB2
C-Src
HK2
Ras
G6P
Stat3/5
Rac
AKT
ATP
AMP
F6P
Rho
Raf
LKB1
PFK1
cdc42
MEK1,2
SGK3
GADP
F1-6BP
AMPK
MAPK1,2
GSK3b
1,3 BPG
MEKK4
MEKK6
mTOR
3PG
MEK4
MEK6
JNK/
SAPK
p38
PK-M2
2PG
PEP
HIF1a
Pyruvate
LDH
PDH
RSK
TSC2
Rheb
PDHK1
BAD
AcetylCoA
Lactate
FOXO3A
p70S6K
GS
TCA-Cycle
S6
ER
ACL
Glycogen
synthesis
Citrate
AcetylCoA
MalonylC
oA
FAS
CREB
Fos
ACC
RB
Jun
ER
ATF2
AP1
Glycolysis
Fatty Acid
Synthesis
Gluconeog
enesis
Cellular
respiration
Stat3/5
c-Myc
CBX5
• Pathway function and
mechanism of deregulation
differ according to subtype
• Responses are subtype specific
• Responses are not durable and
mechanisms are not
understood
• Therapeutic responses depend on
the chemical and mechanical
microenvironment
ICBP: Efforts to Expand the Field
• Community resources
– Software tools and models
– Data sharing/Data portal
– Biological (ICBP 43 cell lines)
• Educational /Outreach
– Undergraduate opportunities
– Junior PI training and mentoring
– Curricula development
• Meetings/ workshop
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PI meetings
Joint meetings (other consortia, etc.)
Junior Investigator meeting (yearly)
Mathematical Cancer Modeling workshop (2010, 2012)
National Cancer Systems Biology meeting/ AACR
(March 2011)
ICBP 43
http://physics.cancer.gov/about/
Physical Sciences-Oncology Centers (PS-OCs) Network: Cel
Line Project
• Selected human immortalized cell lines
– MCF-10A: non-malignant breast epithelial cells
– MDA-MB-231: metastatic breast cancer cells
• Scope of project was to have each PS-OC conduct their unique physical
science measurements on the cell lines and share and cross-compare
datasets
• Cell lines were propagated by one PS-OC Investigator and distributed to
one site at each of the 12 PS-OCs along with detailed SOP for cell culture
of each cell line
• Requirement to upload cell line data to a pilot data coordination site
• Investigators presented results at Data Jamboree Meeting in January 2011
• Cell line project data will serve as a pilot dataset for building the PS-OC
Data Coordination Center (DCC)
• Currently a Network manuscript is in preparation describing the results of
the project
Cell Line Project Physical Science
Measurements: Molecules to Cells
Genomics
Cornell
• Gene expression analysis in response to
changes in pO2, pH and metabolic load in 3D
alginate
MIT
• Single-cell transcript counting
NU
• TEM and cryo-EM analysis of chromatin structure
and distribution of chromatin-organizing proteins
• Light scattering and TEM roughness nanoscale
measurements
• Deep sequencing-based nucleosome position
analysis
•Metaphase chromosome mechanics
• Magnetic tweezer analysis of chromatin polymer
elasticity
•DNA replication dynamics analysis
Princeton
• Analysis of chromatin and mitochondria as a
function of time and position on stressed
landscapes
•Genomic analysis of cells transported to and from
stressed landscapes
Scripps
• Nuclear dimensions (radius, aspect ratio)
Proteomics
NU
• Proteasome
processing
analysis
USC
• SILAC for MSbased
proteomics
Cell Mechanics/
Morphology
ASU
• Cell CT
• AFM
JHU
• Cadherins via single- molecule force microscopy
(2D/3D)
• Ballistic Intracellular Nanorheology (BIN; 2D/3D)
• Intracellular microrheology for cells in 2D & 3D
MIT
• Cell mass measurements during cell cycle
Cell Surface/
Adhesion
Cornell
• FACS for adhesion receptors
• Adhesion assay to E-selectin
surfaces under shear stress
• Aggregation with human leukocytes
under flow
JHU
• Subcellular release assay to measure
rate of de-adhesion
• Flow chamber assay to measure cellmatrix adhesion strength
Moffitt
• Colony size/morphology in response to altered pH
and pO2
• Cell trace
Princeton
• Cell morphology as a function of time and position on
stressed landscapes
Scripps
• Cell dimensions (radius, aspect ratio)
• Cortical tension
• Cytoplasmic viscosity
•Elasticity (E)
• Cellular complexity/granularity
UCB
• ECM stiffness effects on cell polarity and
spreading
Princeton
• Analysis of cell adhesion as a
function of time and position on
stressed landscapes under flow
conditions
Methodist
• In vitro nanoparticle
•internatlization
The Cancer Target Discovery and Development
(CTD2) Network
LINCS Consortium Kick-Off Meeting
October 28, 2011
Daniela S. Gerhard, Ph.D.
Director, Office of Cancer Genomics
Large Projects Examples of NIH
Investment in Genomic Research
• Therapeutically Applicable Research to Generate Effective
Treatment (TARGET)
• The Cancer Genome Atlas (TCGA)
• Cancer Genome Anatomy Project/Cancer Genome
Characterization Initiative (CGAP/CGCI)
• Genome-wide association studies (GWAS) of common and
complex diseases and follow-up (~60/450 grants are cancerrelated)
Data generated is made publicly available
• ~20% of NIH ARRA funded genomic projects
Molecular Characterization of Cancer
Tissues is Essential but not Sufficient
• Each tumor has hundreds to thousands genomic alterations
– Chromosomal changes: amplifications, deletions, translocations
– Epigenetic changes
– Mutations
• Little is known about the cellular function of most genes, much less how
sequence variants and mutations affect them
– Distinguishing initiating vs. driver vs. passenger mutations
• Drivers are defined as genes involved in tumor maintenance
• Evidence is accumulating that multiple subclones exist within a tumor and
their frequency varies between patients
• As tumors evolve genes essential for survival may be different from those
that were necessary early on
– Genomic alterations result in cancer within specific context
• Cell of origin
• Other molecular alterations in genes that may have synergistic or
antagonistic impact
ARRA Opportunity
Question:
Can a network be formed that would effectively address a
current major scientific challenge: efficient transition from
patient-based large multi-dimensional genomic data  target
validation  small molecule modulators  (therapy, not part
of the initiative)
How to advantage the flood of genomic data and accelerate the
transition to treatments of patients based on the genomic
profile of their cancer?
ARRA Cancer Target Discovery and
Development (CTD2) Network Centers
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Broad Institute, Cambridge, Massachusetts
PI: Stuart Schreiber, Ph.D.
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Cold Spring Harbor Laboratory, Long Island, New York
PI: Scott Powers, Ph.D., co-PI: Scott Lowe, Ph.D.
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Columbia University, New York, New York
PI: Andrea Califano, Ph.D.
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Dana-Farber Cancer Institute, Boston, Massachusetts
PIs: William Hahn, M.D., Ph.D., L. Chin, M.D. and R. DePinho, M.D.
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University of Texas Southwestern Medical Center, Dallas, Texas
PI: Michael Roth, Ph.D., co-PIs: M. White, Ph.D., J. Minna, M.D.
http://ocg.cancer.gov/programs/ctdd.asp
ARRA CTD2 Network
• Each application included up to 3 mature projects
• Functional network formed rapidly
– Component centers share results “in real time” (precompetitive)
• Established an ethos of data and resource sharing with scientific community
upon validation
– IT WG developed file formats for data sharing
compatible with Cancer Data Standards Registry and
Repository (caDSR) within caBIG
• Enabled experiments, using new data generated by the molecular
characterization projects to identify candidate targets, small molecule
modulators and mechanisms: one example was ovarian cancer
Cancer Target Discovery and Development
(CTD2) Network
probe acquired ***
dependencies via RNA
determine relevance
(STK33; TBK1) ***
probe acquired dependencies via
proteins ***
cancer genomics
small-molecule probes
(acquired dependency small-molecule probe
kit in >400 genotyped cell lines)
cancer patients
probe acquired
dependencies via network
analyses
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cancer genomics-based mouse
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models
small-molecule drugs
determine relevance
(STAT3; C/EBP in GBM)
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Decode the relationship of cancer genotype to acquired cancer dependencies and identify small
molecules that target the dependencies (*Broad; *CSHL; *Columbia; *DFCI; *Dallas)
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Summary of the CTD2 Network’s caOv Results
The power of the network: made rapid progress by sharing data, working together and
taking advantage of complementary, non-overlapping expertise to carry out the
experiments. Each Center contributed:
– Identified candidate signature to stratify patients into best and worst prognostic groups
– Identified candidate targets for therapeutic development
• Confirmed a subset of candidates by in vitro and ex vivo experiments
– Identified candidate small molecules for a subset of confirmed targets
– Plan to generate mouse models for in vivo screening of other candidate genes within a
specific genetic context
– Experiments are ongoing
Critical lesson: collaborative efforts to integrate several methods can yield exponential
gains relative to the incremental gains achieved through improving any single
method (united they are more than a sum of parts)
CTD2 Network Research
Mission
• Shift current research paradigms in translation pathway of
patient-derived multidimensional genetic data to the clinic and
utilize novel concepts, approaches and methodologies
• Develop research that will exert a sustained influence on the
field
• Develop a pre-competitive culture to ensure sharing of data,
methods (analytical, experimental) and reagents within the
network and the scientific community at large
Goals for the New Network
• Accelerate the translation of patient genomic data into clinical
application
– Innovate the integration of computational mining large scale genomic
data analyses
• Make tools available through web
– Identify and confirm new therapeutic target candidates
– Identify and confirm novel modulators within specific cancer context
(cellular or mutational) in vitro (cell lines) or in vivo (cancer models)
• Small, stereochemically “interesting“ molecules
– Use of novel organist chemistry – molecules more “natural products-like”
– Mature molecules: optimize activity, structure activity relationship, systematic variation
of stereochemistry
• siRNAs
– Multi-expertise team
– Share models and reagents with the scientific community
– Share data and methods with the scientific community through the
web
• As genomic data become available from TARGET, TCGA etc.,: be
nimble, flexible and open to new opportunities
The Cancer Target Discovery and Development
(CTD2) Network
LINCS Consortium Kick-Off Meeting
October 28, 2011
Daniela S. Gerhard, Ph.D.
Director, Office of Cancer Genomics