NCI Projects Relevant to LINCS Jennifer Couch, Ph.D. Daniela Gerhard, Ph.D. National Cancer Institute, NIH
Dec 18, 2015
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
Cancer is Complicated
• Genes and Genetics• Complex Signaling Networks• Multiple Cellular Processes • Microenvironment• Host Systems• Environmental Factors• Population Dynamics
Time - Progression
His
tolo
gy V
aria
tion
Initiation Progression Metastasis Recurrence
Systems BiologyComputational
Modeling
Data & Information - Clinical, Biological, Epidemiological
Discovery and Knowledge- Basic and translational
Centers for Cancer
Systems Biology (CCSB)
The ICBP Approach
Initiation
Growth
Micro-environme
nt
Progression
Metastasis
Treatment
Regression
Recurrence
GrayCancer heterogeneity; molecular signaling and combinational therapyBreast
Lauffenburger Quantative modeling of critical cancer process; growth, migration, DNA repairBrain, lymphoma, Breast
Computer
simulations
Statistical mining
Game theory
Bayesian networks
Boolean Models
Markov chains
Differential
Equations
Popu
latio
n/
envi
ronm
ent
Org
anis
mTi
ssue
/ org
an
Mic
ro-
envi
ronm
ent
Cell
(com
pone
nts)
Inte
ract
ions
Mol
ecul
es
GolubIntegrate multiple data types to identify essential gene- new drug targetsLung Melanoma
HuangEpigenetic and microRNA examination of hormonal –chemo resistanceProstate, Ovarian Breast
PlevritisCell – cancer differentiation; single cell analysis; disease progression and resistanceLeukemia, Lymphoma
FriendCross disease analysis; Sage Bionetworks; network data integrationGlioma, Ovarian, colon, Liver, Meduloblastoma, Pancreas, Breast, ect
Sander3-D model cell-cell communication; microenvironment; variability of drug responseMelanoma, breast, panceas, glio, CLL
ClarkeHormone responsiveness; population exposure risk; GWAS; drug designBreast
WongTumor Stem cell, imaging, ME interations; protein cDNA arrayBreastHlatky
Tumor evolution; quanatative analysis; cellular population dynamic modelingProstate, Breast
QuarantaCellular model of tumor heterogeneity; cell phenotype measurements; multiscaleBreast, Lung
ICBP2:Centers for Cancer Systems Biology
Cancer Stage
Modeling Approach Physical Scale
CalifanoMultiscale analysis of Genomic and Cell Networks (MAGNet), B cell lymphoma, glioma
Glucose
GLUT4
G6P
F6P
F1-6BPGADP
1,3 BPG
3PG
2PG PEP Pyruvate
Glucose
HK2
PFK1
PK-M2
Acetyl-CoA
PDH
TCA-Cycle
AKT
AS160
AMPK
RTK
HIF1a
PDHK1
Lactate
LDH
c-Myc
CitrateAcetyl-
CoA
MalonylCoA
Fatty Acid Synthesis
ACL
ACC
FAS
LKB1AMPATP
GF
SHP2/SHC/SOS/
GRB2
Ras
MEK1,2
RSK
PI3K
PDK1
GSK3b
TSC2
MAPK1,2
mTOR
Rheb
p70S6K
S6
Raf
BAD
Fos Jun
CREB
AP1
ATF2RB
Glycolysis
Gluconeogenesis
Cellular respiration
Rac
Rho
cdc42
MEKK4
MEK4
MEKK6
MEK6
JNK/SAPK p38
C-Src
Stat3/5
Stat3/5
SGK3
GS
Glycogen synthesis
FOXO3A
ER
ER
CBX5
• Therapeutic perturbations of a breast cancer cell line panel show:
• 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
Lawrence Berkeley Labs (Gray)Model Based Predictions of Responses to RTK Targeted
Therapies in Breast CancerOverall: 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
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– 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: Cell 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
MIT
Genomics Proteomics
NU
ASU CornellCornell
• 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
Moffitt
MIT
UCB
JHU
• Cell CT• AFM
• Colony size/morphology in response to altered pH and pO2• Cell trace
• Cell mass measurements during cell cycle
• Gene expression analysis in response to changes in pO2, pH and metabolic load in 3D alginate
• FACS for adhesion receptors• Adhesion assay to E-selectin surfaces under shear stress• Aggregation with human leukocytes under flow
• Subcellular release assay to measure rate of de-adhesion • Flow chamber assay to measure cell-matrix adhesion strength
• Single-cell transcript counting
NU
• Proteasome processing analysis
Princeton• Cell morphology as a function of time and position on stressed landscapes
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
Princeton
• Analysis of cell adhesion as a function of time and position on stressed landscapes under flow conditions
Scripps
Scripps • Nuclear dimensions (radius, aspect ratio)
• Cell dimensions (radius, aspect ratio)• Cortical tension• Cytoplasmic viscosity•Elasticity (E)• Cellular complexity/granularity
USC
• SILAC for MS-based proteomics
• ECM stiffness effects on cell polarity and spreading
Methodist
• In vitro nanoparticle •internatlization
Cell Mechanics/Morphology
Cell Surface/Adhesion
JHU• Cadherins via single- molecule force microscopy (2D/3D)• Ballistic Intracellular Nanorheology (BIN; 2D/3D)• Intracellular microrheology for cells in 2D & 3D
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 cancer-related)
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 OpportunityQuestion:
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
• Broad Institute, Cambridge, MassachusettsPI: Stuart Schreiber, Ph.D.
• Cold Spring Harbor Laboratory, Long Island, New YorkPI: Scott Powers, Ph.D., co-PI: Scott Lowe, Ph.D.
• Columbia University, New York, New YorkPI: Andrea Califano, Ph.D.
• Dana-Farber Cancer Institute, Boston, MassachusettsPIs: William Hahn, M.D., Ph.D., L. Chin, M.D. and R. DePinho, M.D.
• University of Texas Southwestern Medical Center, Dallas, TexasPI: 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” (pre-competitive)
• 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
cancer genomics small-molecule probesprobe acquired dependencies via proteins
probe acquired dependencies via RNA
probe acquired dependencies via network
analyses
determine relevance (STK33; TBK1)
determine relevance (STAT3; C/EBP in GBM)
(acquired dependency small-molecule probe kit in >400 genotyped cell lines)
cancer genomics-based mouse models
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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small-molecule drugs *cancer patients
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