Interrogating High-Grade Glioma Regulatory
Networks to Identify :
MASTER REGULATORS OF TUMOR
SUBTYPE AND ASSOCIATED DRIVER
MUTATIONS
Genomics
Cancer
Drugs & Biomarkers
Prevention
Diagnosis
Treatment
Epigenomics
Cell Regulatory Logic
Transcriptomics
Other Omics:
Metabolomics
Proteomics
Glycomics
…
Protein
DNA
Protein
Protein
Protein
Membrane
Clinical Trials
miR1
miR2
miR3
miR1
3’ UTR Gene 1
…
…
3’ UTR Gene 2
Zhao X et al. (2009) Dev Cell. 17(2):210-21.
Mani KM et al. (2008) Mol Syst Biol. 4:169
Palomero 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)
Basso et al. Immunity. 2009 May;30(5):744-52
Klein et al, Cancer Cell, 2010 Jan 19;17(1):28-40.
Sumazin et al. 2011, in press
Phenotype 1 (Normal)
Phenotype 2 (Neoplastic)
A Master Regulator is a gene that is
necessary and/or sufficient to induce a
specific cellular transformation or
differentiation event.
MRx ?
g
2
4
g
2
5
g
2
6
g
1
9
g
2
3
T
F
...
T
F
2
g
2
0
g
2
7
T
F
2
T
F
1
g
2
1
g
2
8
g
2
2
g
2
9
TF1:
Repressed: 5/7
Activated: 5/7
Coverage: 10/18 (55%)
g
1
g
2
g
3
TF2:
Repressed: 1/5
Activated: 1/6
Coverage: 2/18 (11%)
g
4
g
5
Under-expressed in Tumor
Wang K, Saito M, et al. (2009) Nat Biotechnol. 27(9):829-39
Zhao 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. 4:169
Wang K et al. (2006) RECOMB
g
6
g
7
g
8
g
9
g
1
0
Tumor Signature
g
1
1
g
1
2
g
1
3
g
1
4
g
1
5
g
1
6
g
1
7
g
1
8
Over-expressed in Tumor
The CTD2 Network (2010), Nat Biotechnol. 2010 Sep;28(9):904-906.
Floratos A et al. Bioinformatics. 2010 Jul 15;26(14):1779-80
Lefebvre C. et al (2010), Mol Syst. Biol, 2010 Jun 8;6:377
Carro MS et al. (2010) Nature 2010 Jan 21;463(7279):318-25
Mani K et al, (2008) Molecular Systems Biology, 4:169
Mary
Joe
TERT
Tony
MYC
Signal
GSK3
Degradation
MYC
TERT
GSK3
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
IP: C-Myc
1
2
3
STK38 (B09)
NT
1
IP: Mouse IgG
~400 Gene Expression Profiles for Normal and Tumor Related Human B Cells
3
4
M
W
5
1
2
3
4
5
WB: STK38
WB: STK38
WB: MYC
WB: c-Myc
WB: ACTIN
2
NT
1
2
3
STK38 (B11)
4
5
M
W
1
2
3
4
5
WB: STK38
WB: MYC
WB: ACTIN
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)
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
Disease Initiation & Progression
Biomarkers
Pluripotency and Lineage Differentiation
Cell Regulatory
Logic
Therapeutic Targets
Small-molecule
Modulators
Drug MoA and Resistance
Mechanism of
Action
Phenotype 1 (Normal)
Phenotype 2 (Neoplastic)
A Master Regulator is a gene that is
necessary and/or sufficient to induce a
specific cellular transformation or
differentiation event.
MRx ?
g24
g25
g26
g19
g23
TF...
TF2
g20
g27
TF2
TF1
g21
g28
g22
g29
TF1:
Repressed: 5/7
Activated: 5/7
Coverage: 10/18 (55%)
g1
g2
g3
TF2:
Repressed: 1/5
Activated: 1/6
Coverage: 2/18 (11%)
g4
g5
Under-expressed in Tumor
g6
g7
g8
g9
g10
Tumor Signature
g11
g12
g13
g14
g15
g16
g17
g18
Over-expressed in Tumor
1. Carro, M. et al. (2010). "The transcriptional network for mesenchymal transformation of brain tumours." Nature 463(7279): 318-325
2. Lefebvre C. et al. (2009). "A Human B Cell Interactome Identifies MYB and FOXM1 as Regulators of Germinal Centers." Mol Syst Biol, in press
3. Lim, W. et al. (2009). "Master Regulators Used As Breast Cancer Metastasis Classifier." Pac Symp Biocomp 14: 492-503
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
PNGES
MGES
PROGES
Malignant gliomas belonging to the mesenchymal sub-class express genes linked to the
most aggressive properties of glioblastoma (migration, invasion and angiogenesis).
Master Regulators control >75% of the Mesenchymal
Signature of High-Grade Glioma
Mes signature genes
Activator
Repressor
Biochemical Validation of ARACNe Inferred
Targets of Stat3, C/EBPb, FosL2, and bHLH-B2
Hierarchical Regulatory Module
b
GFP
c
Vector
Stat3C
C/EBPβ
Stat3C+C/EBPβ
Vector
Stat3C+C/EBPβ
25mm
CTGF
CTGF
GFP/CTGF
GFP/CTGF
Cola51/DAPI
50
Vector
Stat3C+C/EBPβ
***
40
Cola51/DAPI
20
***
10
25mm
YKL40/DAPI
0
YKL40/DAPI
Mitogens
-
Cola51/DAPI
0
YKL40/DAPI
+
e
400
f
Vector
b
Stat3C+C/EBPβ
Cola51/DAPI
200
100
YKL40/DAPI0
Mitogens
70
60
50
40
**
*
30
- 20
+
***
10
Doublecortin
0
f
shStat3
Cola51/DAPI
shC/EBPβ
Cola51/DAPI
shCtr 1200
shStat3
1000
shC/EBPβ
shStat3+shC/EBPβ
60
50
40
30
20
800
600
400
*** **
200
0
Mitogens
10
0
g
shStat3 + shC/EBPβ
Cola51/DAPI
c
shCtr
shStat3
***
shC/EBPβ
shStat3+shC/EBPβ
300
200mm
shCtr
20
Fibronectin/DAPI
βIIITubulin
e
40
shStat3 + shC/EBPβ
Fibronectin/DAPI
Cola51/DAPI
30
60
20
18
16
14
12
10
8
6
4
2
0
Col5A1+cells (%)
25mm
shC/EBPβ
80
Fibronectin+cells (%)
shStat3
d
Fibronectin/DAPI
mRNA relative level
shCtr
Fibronectin/DAPI
mRNA relative level
a
**
Relative mRNA level
Ctgf + cells (%of GFP+ cells)
100
shCtr
shStat3
shC/EBPβ
shCtr
shStat3
shC/EBPβ
Vector
d
Stat3C+C/EBPβ
shCtr
***
shStat3
shCebpb
shStat3+shCebpb
12
***
-
YKL40 +cells (%)
Stat3C+C/EBP
GFP
mRNA relative level
Vector
a
10
8
6
4
2
Gfap
0
**
+
**
**
Mouse Survival Data
100
Cumulative Survival
**
80
60
40
20
0
40 60 80 100 120
Days post-injection
Control Vector
Stat3C/EBPbStat3-/C/EBPb-
Human Survival Data
Mouse immunohistochemistry
Carro, M. et al. (2010). Nature 463(7279): 318-325
Mesenchymal Signature
Proneural Signature
Proliferative Signature
TF
MES+
FOSL2
45
RUNX1
37
ZNF238
0
CEBPD 27
STAT3
26
BHLHB2 25
MYCN
0
FOSL1
23
ELF4
21
LZTS1
20
CEBPB 20
THRA
0
OLIG2
0
HLF
0
ZNF291
1
SATB1
0
ZNF217 12
MSRB2
0
PKNOX2 0
CUTL2
0
MLL
1
SNAPC1 0
MYT1L
0
HMGB2
0
CREBL2 0
PHTF2
0
TCF3
2
PTTG1
3
E2F6
0
E2F8
0
SMAD4
0
ZNF207
0
KNTC1
0
FOXM1
0
E2F1
0
MES0
0
37
0
0
0
25
0
0
0
0
9
12
7
2
17
0
5
1
0
4
0
0
0
0
0
0
1
0
0
0
0
0
0
0
PN+
0
0
10
0
0
0
4
0
1
0
0
55
46
43
32
27
0
27
24
24
22
0
20
0
7
0
1
0
0
0
0
0
0
0
0
PN3
5
0
6
3
5
0
2
7
3
3
2
1
1
1
0
27
0
0
0
1
21
0
7
0
6
5
5
5
4
3
3
1
0
0
Prolif+
0
0
1
0
0
1
0
0
3
0
0
1
0
0
1
0
2
0
0
0
0
7
0
57
0
26
21
37
24
42
23
22
35
24
23
Prolif2
0
0
1
1
0
0
0
0
1
0
4
8
16
11
1
0
3
7
0
6
0
0
0
21
0
0
0
0
0
0
1
0
0
0
E 1 2 3
Sun (3)
6
5
4
22
1.9x10-7
10
TCGA (1)
10
8
Phillips (2)
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`
Distinct Programs with significant overlap across distinct datasets
G1
G2
G5
G3
G6
G9
Glioblastoma:
G4
G7
G10

G8
G11
Therapeutic
Targets
=
=
=
=
=
=
=
=
=
=
=
=
EGFR
PDGFRA
p16
p53
PTEN
MDM2
MDM4
MYC
NF1
ERBB2
RB1
CDK4

G12
Diffuse Large B Cell Lymphoma:

Patient X
G1
G2
G3
G4
G5
G6
G7
G8
G9
G10
G11
G12
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
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
X

W
Y
GC-Resistance in T-ALL:
Z
V
Master
Regulator
Module(s)


Molecular
Phenotype
E.g. GBM subtypes
…
Disease
Stratification
Biomarkers
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
(c) MINDy Analysis
(a) Protein Binding Assays
M2
M1
Mn
M2
M1
Mn
C/EBP
Comp1
Comp1
STAT3
Compn
Compn
MGES
Collaboration with:
S. Schreiber (a)
B. Stockwell (b)
A. Iavarone and A. Lasorella (a, b, c)
(b) High Throughput Screening
Gene ID Modulator
11130
ZWINT
3148
HMGB2
2146
EZH2
5984
RFC4
890
CCNA2
6790
AURKA
1894
ECT2
7298
TYMS
780
DDR1
51512
GTSE1
29899
GPSM2
29097
CNIH4
5902
RANBP1
998
CDC42
10549
PRDX4
23228
PLCL2
4862
NPAS2
9308
CD83
51285
RASL12
1389
CREBL2
mature
miRNA
Mod
miR
target
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
G1
G14 – G 563
564 node, 111 core sub-graph
G2
G3
G13
PTEN
G4
G11
G5
G10
G9
p < 5 x 10-23
p < 2 x 10-10
G8
G7
G6
PTEN over expression and silencing
effects on SNB19 cell growth rate
B
Silencing PTEN mPR regulators affects
SNB19 cell growth rate
Proliferation fold change
Proliferation fold change
A
Days
D
Silencing PTEN mPR regulators affects
SF188 cell growth rate
Proliferation fold change
PTEN over expression and silencing
effects on SF188 cell growth rate
Proliferation fold change
C
Days
Days
Days
C/EBP
STAT3
bHLHB2
FOSL2
RUNX1
Hit compound
Clinical Trials
Molecular Target(s)
CTD2
Network
Biomarkers:
Response/Efficacy
Compound
Mechanism
of Action

Current emphasis on genes harboring genetic and epigenetic alterations may not
be sufficient


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)


We should also focus on Master Regulator and Master Integrator genes
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.

Califano Lab (Computational)
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
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)
JiyangYu (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)

Califano Lab Experimental
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Gabrielle Rieckhof, Ph.D. (Exec Director)
Mariano Alvarez, Ph.D.
Brygida Bisikirska, Ph.D.
XueruiYang, 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)
Funding Sources: NCI, NIAID, NIH Roadmap