interrogating high-grade glioma regulatory networks to identify :
TRANSCRIPT
MASTER REGULATORS OF TUMOR SUBTYPE AND ASSOCIATED DRIVER MUTATIONS
Interrogating High-Grade Glioma Regulatory Networks to Identify :
Systems BiologyGenomics
Epigenomics
Cell Regulatory Logic
ProteinProtein
ProteinMembrane
ProteinDNA
Transcriptomics
Other Omics: Metabolomics Proteomics Glycomics …
Drugs & Biomarkers
Clinical Trials
PreventionDiagnosisTreatment
Cancer
POST-TRANSLATIONAL INTERACTIONS
TRANSCRIPTIONAL INTERACTIONS
Zhao X et al. (2009) Dev Cell. 17(2):210-21.Mani KM et al. (2008) Mol Syst Biol. 4:169Palomero T et al., Proc Natl Acad Sci U S A 103, 18261 (Nov 28, 2006).Margolin AA et al., Nature Protocols; 1(2): 662-671 (2006)Margolin AA et al., BMC Bioinformatics 7 Suppl 1, S7 (2006).Basso K et al. (2005), Nat Genet.;37(4):382-90. (Apr. 2005)
Wang K, Saito M, et al. (2009) Nat Biotechnol. 27(9):829-39Zhao X et al. (2009) Dev Cell. 17(2):210-21.Wang K et al. (2009) Pac Symp Biocomput. 2009:264-75.Mani KM et al. (2008) Mol Syst Biol. 4:169Wang K et al. (2006) RECOMB
POST-TRANSCRIPTIONAL INTERACTIONS
Basso et al. Immunity. 2009 May;30(5):744-52Klein et al, Cancer Cell, 2010 Jan 19;17(1):28-40. Sumazin et al. 2011, in press
MASTER REGULATORS AND MECHANISM OF ACTION
The CTD2 Network (2010), Nat Biotechnol. 2010 Sep;28(9):904-906.Floratos A et al. Bioinformatics. 2010 Jul 15;26(14):1779-80Lefebvre C. et al (2010), Mol Syst. Biol, 2010 Jun 8;6:377Carro MS et al. (2010) Nature 2010 Jan 21;463(7279):318-25Mani K et al, (2008) Molecular Systems Biology, 4:169
MINDy: Reverse Engineering of Post Translational Modulators of Transcriptional Regulation
JoeMary
Tony
MYC TERT
GSK3
Degradation
SignalMYC
TERTGSK3
Post-Translational Network Validation (MINDy)
WB: STK38
WB: c-Myc
IP:
C-M
yc
IP:
Mo
use
IgG
STK38 (serine-threonine kinase 38, NDR1)
1) Protein-Protein interaction with MYC
2) STK38 silencing in ST486 decreases MYC stability
3) MYC mRNA is not affected
3) MYC targets are consistently affected
1
2
NT STK38 (B11)
1 1 2 2 3 3 4 4 5 5 MW
WB: STK38
WB: ACTIN
WB: MYC
3
~400 Gene Expression Profiles for Normal and Tumor Related Human B Cells
Wang K, Saito M, et al. (2009) Nat. Biotechnol. 27(9):829-39
Cancer B Cell interactome (BCi) Breast Cancer Cell interactome (BCCi) T-ALL interactome (TALLi) AML Prostate Cancer interactome (Pci mouse/human) Glioblastoma Multiforme interactome (GBMi) Ovarian Non-small-cell Lung Cancer Colon Cancer Hepatocellular Carcinoma Neuroblastoma NET
Stem Cells Mouse EpiSC and ESC Human ESC Germ Cell Tumors (Pluripotency, Lineage Differentiation)
Neurodegenerative Disease Human and Mouse Motor Neuron (ALS) Human and Mouse whole brain (Alzheimer’s)
Available Transcriptional Interactomes:
Interactomes are generated from primary tissue profiles and thus reflect cell regulation in vivo, in the presence of all relevant paracrine, endocrine, and contact signals
Interrogating the Assembly Manual of the Cell
Cell RegulatoryLogic
Pluripotency and Lineage Differentiation
Drug MoA and Resistance
Disease Initiation & Progression
Mechanism of Action
Biomarkers
Therapeutic Targets
Small-molecule Modulators
MARINa: Master Regulator Inference algorithm
Over-expressed in TumorUnder-expressed in Tumor
A Master Regulator is a gene that is necessary and/or sufficient to induce a specific cellular transformation or differentiation event.
Phenotype 2 (Neoplastic)Phenotype 1 (Normal)
MRx ?
1. Carro, M. et al. (2010). "The transcriptional network for mesenchymal transformation of brain tumours." Nature 463(7279): 318-3252. Lefebvre C. et al. (2009). "A Human B Cell Interactome Identifies MYB and FOXM1 as Regulators of Germinal Centers." Mol Syst Biol, in press3. Lim, W. et al. (2009). "Master Regulators Used As Breast Cancer Metastasis Classifier." Pac Symp Biocomp 14: 492-503
TF2: Repressed: 1/5 Activated: 1/6 Coverage: 2/18 (11%)
TF1: Repressed: 5/7 Activated: 5/7 Coverage: 10/18 (55%)
Tumor Signature
Unsupervised clustering of 176 high grade tumors by expression of 108 genes that are positively or negatively associated with survival reveals 3 tumors classes (Proneural (PN), Mesenchymal (Mes) and Proliferative (Prolif).
Phillips et al., Cancer Cell, 2006
Malignant gliomas belonging to the mesenchymal sub-class express genes linked to the most aggressive properties of glioblastoma (migration, invasion and angiogenesis).
Mesenchymal Subtype of High-Grade Gliomas
MGES
PNGES
PROGES
Mes signature genesActivatorRepressor
Identification of a mesenchymal regulatory module
Biochemical Validation of ARACNe Inferred Targets of Stat3, C/EBPb, FosL2, and bHLH-B2
Master Regulators control >75% of the Mesenchymal Signature of High-Grade Glioma
Hierarchical Regulatory Module
In Vitro Validation
02468
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ells)
Vector
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Vector Stat3C+C/EBPGFP GFP
CTGF CTGF
GFP/CTGF GFP/CTGF
25mm**
Figure 8
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ells (%
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shCtrshStat3shC/EBPβshStat3+shC/EBPβ
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shCtr shStat3 shC/EBPβ shStat3 + shC/EBPβFibronectin/DAPIFibronectin/DAPIFibronectin/DAPI Fibronectin/DAPI
Cola51/DAPICola51/DAPICola51/DAPI Cola51/DAPI
YKL40/DAPIYKL40/DAPIYKL40/DAPI YKL40/DAPI
Cola51/DAPICola51/DAPICola51/DAPI Cola51/DAPI
YKL40/DAPIYKL40/DAPIYKL40/DAPI YKL40/DAPI
shCtr shStat3 shC/EBPβ shStat3 + shC/EBPβ
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Control VectorStat3-C/EBPb-Stat3-/C/EBPb-
Human Survival Data
Mouse Survival Data
Mouse immunohistochemistry
In Vivo Validation
Carro, M. et al. (2010). Nature 463(7279): 318-325
Distinct Programs with significant overlap across distinct datasets
Phillips (2)
Sun (3)
TCGA (1)
22
10
5
6
8
4
101.9x10-7
E 1 2 3
TCGA dataset on different networksTF MES+ MES- PN+ PN- Prolif+ Prolif-
FOSL2 45 0 0 3 0 2RUNX1 37 0 0 5 0 0ZNF238 0 37 10 0 1 0CEBPD 27 0 0 6 0 1STAT3 26 0 0 3 0 1
BHLHB2 25 0 0 5 1 0MYCN 0 25 4 0 0 0FOSL1 23 0 0 2 0 0ELF4 21 0 1 7 3 0
LZTS1 20 0 0 3 0 1CEBPB 20 0 0 3 0 0
THRA 0 9 55 2 1 4OLIG2 0 12 46 1 0 8HLF 0 7 43 1 0 16
ZNF291 1 2 32 1 1 11SATB1 0 17 27 0 0 1ZNF217 12 0 0 27 2 0MSRB2 0 5 27 0 0 3
PKNOX2 0 1 24 0 0 7CUTL2 0 0 24 0 0 0
MLL 1 4 22 1 0 6SNAPC1 0 0 0 21 7 0MYT1L 0 0 20 0 0 0
HMGB2 0 0 0 7 57 0CREBL2 0 0 7 0 0 21PHTF2 0 0 0 6 26 0TCF3 2 0 1 5 21 0
PTTG1 3 1 0 5 37 0E2F6 0 0 0 5 24 0E2F8 0 0 0 4 42 0
SMAD4 0 0 0 3 23 0ZNF207 0 0 0 3 22 1KNTC1 0 0 0 1 35 0FOXM1 0 0 0 0 24 0E2F1 0 0 0 0 23 0
Me
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Pro
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1Cancer Genome Atlas Research Network, Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008 Oct 23;455(7216):1061-8
2 Phillips, H.S., et al., Molecular subclasses of high-grade glioma predict prognosis, delineate a pattern of disease progression, and resemble stages in neurogenesis. Cancer Cell, 2006. 9(3): p. 157-73.
3 Sun, L., et al., Neuronal and glioma-derived stem cell factor induces angiogenesis within the brain. Cancer Cell, 2006. 9(4): p. 287-300`
The Bottleneck HypothesisG1 G2 G3 G4
G5 G6 G7 G8
G10 G11 G12
Patient X
MolecularPhenotype
E.g. GBM subtypes
G9
X
Y Z
W
V
MasterRegulatorModule(s)
DiseaseStratificationBiomarkers
Therapeutic Targets
…
= EGFR= PDGFRA= p16= p53= PTEN= MDM2= MDM4= MYC= NF1= ERBB2= RB1= CDK4
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
Glioblastoma: Carro MS et al. The transcriptional
network for mesenchymal transformation of brain tumours. Nature. 2010 Jan 21;463(7279):318-25.
Master Regulators: C/EBP + Stat3
Diffuse Large B Cell Lymphoma: Compagno M et al. Mutations of
multiple genes cause deregulation of NF-kappaB in diffuse large B-cell lymphoma. Nature. 2009 Jun 4;459(7247):717-21
Master Regulator: Nf-kB pathway
GC-Resistance in T-ALL: Real PJ et al. Gamma-secretase
inhibitors reverse glucocorticoid resistance in T cell acute lymphoblastic leukemia. Nat Med. 2009 Jan;15(1):50-8.
Master Regulator: NOTCH1 pathway
Inhibitors of C/EBP Activity
MGES
STAT3
C/EBP
MnM1
M2
MnM1
M2
(c) MINDy Analysis
Comp1
Compn
Comp1
Compn
(a) Protein Binding Assays
(b) High Throughput Screening
Gene ID Modul ator11130 ZWINT3148 HMGB22146 EZH25984 RFC4890 CCNA26790 AURKA1894 ECT27298 TYMS780 DDR151512 GTSE129899 GPSM229097 CNIH45902 RANBP1998 CDC4210549 PRDX423228 PLCL24862 NPAS29308 CD8351285 RASL121389 CREBL2
Collaboration with: S. Schreiber (a) B. Stockwell (b) A. Iavarone and A. Lasorella (a, b, c)
MINDy Modulators of miRNA activity
maturemiRNA
miR
target
Mod
Sumazin et al. Cell, 2011 Oct 14;147(2):370-81.
Analysis of TCGA data for GBM and Ovarian Cancer including matched gene and miRNA expression profiles
for 422 and 587 samples
Modulation of miRNA activity on targets 7,000 Sponge modulators, participating in 248,000 miR-
mediated mRNA-mRNA interactions 148 Non-sponge modulators affecting more than 100
miRs (using only experimentally validated miRs targets) 17/430 are RNA-binding proteins or a component of the
spliceosome
Novel miR-program mediated regulatory network
13 miR-mediated regulators of PTENG1
G2
G3
G4
G5
G6G8
G9
G10
G11
G13
G14 – G 563
PTEN
G7
564 node, 111 core sub-graph
p < 5 x 10-23
p < 2 x 10-10
13 miR-mediated regulators of PTEN
Silencing PTEN mPR regulators affects SNB19 cell growth rate
Pro
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ng
e
Days
PTEN over expression and silencing effects on SNB19 cell growth rate
Pro
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e
A B
Days
Days Days
Silencing PTEN mPR regulators affects SF188 cell growth rate
PTEN over expression and silencing effects on SF188 cell growth rate
C D
Pro
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Targeted Drug Development
Hit compound
Compound Mechanism
of Action
Biomarkers:Response/Efficacy
Molecular Target(s)Clinical Trials
CTD2 Network
Broad Inst.S.Schreiber
CSHLS.Power
ColumbiaA.Califano
Dana FarberB.Hahn
UT South-westernM.Reich
Current emphasis on genes harboring genetic and epigenetic alterations may not be sufficient We should also focus on Master Regulator and Master Integrator genes
Current approach to biomarker discovery should be re-evaluated in a molecular interaction network context. It is not the genes/proteins that change the most but rather those that change
most consistently. (mRNA is not informative)
From GWAS (Genome-Wide Association Studies) to NBAS (Network-Based Association Studies) Califano A, Butte A, Friend S, Ideker T, and Schadt EE, Integrative Network-based
Association Studies: Leveraging cell regulatory models in the post-GWAS era, Nat. Genetics, in press. Accessible in Nature Preceedings: http://precedings.nature.com/documents/5732/version/1
One disease – One target – One drug Multi-target combinations Optimal combination of drugs selected from a repertoire of safe, target-specific
compounds using predictive tools. Identification of genetic dependencies (addictions) from Ex Vivo Models Identification of candidate therapeutic agents from In Vitro mechanistic models.
Conclusions and Reflections
Acknowledgements
Funding Sources: NCI, NIAID, NIH Roadmap
Califano Lab Experimental Gabrielle Rieckhof, Ph.D. (Exec
Director) Mariano Alvarez, Ph.D. Brygida Bisikirska, Ph.D. Xuerui Yang, Ph.D. Yao Shen, Ph.D. Presha Rajbhandari, M.A. (Sr. Res. Worker) Jorida Coku, M.A. (Staff Associate) Hesed Kim, (Staff Associate) Sergey Pampou, Ph.D.
A. Iavarone & A. Lasorella (CU) Maria Stella Carro
K. Aldape (MD Anderson)
R. Dalla Favera (CUMC) Katia Basso Ulf Klein
R. Chaganti (MSKCC)
M. White & J. Minna (UTSW)
J. Silva (CU)
C. Abate-Shen & M. Shen (CU)
D. Felsher (Stanford)
Califano Lab (Computational) Mukesh Bansal, Ph.D. Archana Iyer, Ph.D. Celine Lefebvre, Ph.D. Yishai Shimoni, Ph.D. Maria Rodriguez-Martinez, Ph.D. Antonina Mitrofanova, Ph.D. Jose’ Morales, Ph.D. Paola Nicoletti, Ph.D. Pavel Sumazin, Ph.D. Gonzalo Lopez, Ph.D. James Chen, (GRA) Hua-Sheng Chu (GRA) Wei-Jen Chung (GRA) In Sock Jang (GRA) William Shin (GRA) Jiyang Yu (GRA) Wei-Jen Chung (GRA) Alex Lachman (GRA) Pradeep Bandaru M.A. Manjunath Kustagi (Programmer)
Software Development Aris Floratos, Ph.D. (Exec. Director) Ken Smith, Ph.D. Min Yu, (Programmer)