Transcript Systems Biology

The 6th Chinese Conference on Oncology
May 21-23, 2010, Shanghai, China
Cancer Genome Atlas
and Functional
Systems Biology
Wei Zhang, Ph.D.
Professor
Department of Pathology
Director
Cancer Genomics Core Laboratory
M. D. Anderson Cancer Center
Complexity of Cancer
• Cancers have heterogeneous etiology.
One patient’s cancer is different from
another patient’s cancer.
• Cancers have heterogeneous genetic
defects.
• Cancers are results of combinations of
multiple genetic and molecular alterations.
• Personalized medicine
• Targeted therapy
Complexity of Human Genome
• 30-40,000 genes
• 1-10 millions of Single Nucleotide
Polymorphisms (SNPs)
• 10-20 millions of proteins
• One gene
•  different spliced mRNAs
•  different proteins
•  different modified forms of proteins
Genomics and Proteomics
Broad-scope, large-scale measurement
of gene copy number, gene expression,
gene methylation, and protein
expression.
Data interpretation or signal processing
in pursuit of biological understanding.
Agilent Technologies Microarray Portfolio…
RNA
DNA
CGH
Copy
number
• chromosomal
aberrations
• gene copy
number
CH3
ChIP
Methylation Transcription
Factors
• methylation
• protein/DNA
patterns
interactions
• downstream
• transcription
transcriptional • DNA
effects
replication
• DNA repair
Splice
Variants
GX
mRNA
mRNA isoforms
• high sensitivity •
measurements
of transcription •
• correlate
results with
genomic data
splice forms of
specific genes
downstream
effects on
translation
miRNA
RNA interference
• presence of
microRNAs
• knockout
analysis
• correlate results
with transcription
data
What is TCGA?
•
The Cancer Genome Atlas (TCGA). The first phase is a
3-year, 100 million pilot project of the National Cancer Institute
(NCI) and the National Human Genome Research Institute (NHGRI)
focusing on glioblastoma and ovarian cancer. The second phase will
cover 25 major cancer types.
•
TCGA Mission: Increase scientific understanding of the
molecular basis of cancer and apply this information to improve our
ability to diagnose, treat, and prevent cancer.
•
TCGA Purpose: Develop a complete “atlas” of all genomic
alterations involved in cancer.
TCGA Pilot Project Milestones
1
2
Collect/Utilize
tumor tissue
samples and
medical
information from
cancer patients
during treatment.
Catalog and
store samples at
a centralized
facility and send genetic
material
to research centers
involved in the project.
3
Identify genomic
changes associated
with cancer in
individual patients.
4
5
Identify genomic
patterns associated
with the disease,
and use that
information to
inform cancer
diagnosis, treatment,
and prevention.
Make information
available to scientists as it
is produced, to speed
treatment and prevention
research and help doctors
and patients make
treatment decisions.
Graphics credit: The Washington Post, December 14, 2005
How TCGA Functions
Data Management, Bioinformatics,
and Computational Analysis (GDAC)
Technology Development
An integrated database providing access to
all of the information generated by the
TCGA pilot project
Throughout the pilot project, technology
development will enable improvements
to genomic analysis
Cancer Genome
Characterization Centers
Genome Sequencing Centers
High-throughput sequencing of genes
identified through cancer genome
characterization centers
Technologies to investigate and
characterize genes that may be associated
with cancer
Human Cancer Biospecimen
Core Resource
Centralized facility to catalog and store
tumor samples, and distribute genetic
material to TCGA research centers
Our GDAC Center
• Center for Systems Analysis of the Cancer
Regulome
• Directors: Ilya Shmulevich (Institute for Systems
Biology); Wei Zhang (M.D. Anderson Cancer
Center)
• Bioinformatic researchers at MDACC: Da Yang,
Yuexin Liu
• Focuses are on prognosis markers, systems
understanding and functional validation
Copy Number
Methylation
mRNA expression
Tumor Subgroup
•
Distinct Dosage Sensitive
Expression Patterns
Systematic Network
•
•
Co-occurrence Copy
Number Alterations
Bayesian Network
Biomarker
•
•
Prediction Analysis of
Microarray
Top Scoring Pair Algorithm
Consensus Copy Number Altered
Regions
Survival Classification
> 3 yr survival
< 3 yr survival
Top 200 pairs
The Cancer Genome Atlas: Glioblastoma
The Cancer Genome Atlas Research Network, Nature, 2008
Statistical analysis of mutation significance identified Eight Genes as Significantly
Mutated and P53 Mutation Is a Common Event in Primary Glioblastoma.
TCGA., “Comprehensive Genomic Characterization Defines Human Glioblastoma Genes And Core Pathways,” Nature, 455(23), 10611068, 2008
Genomic and transcriptional aberration analysis detected New Recurrent Focal
Alterations such as Homozygous Deletions involving NF1 and PARK2, and
Amplifications of AKT3.
TCGA., “Comprehensive Genomic Characterization Defines Human Glioblastoma Genes And Core Pathways,” Nature, 455(23), 10611068, 2008
Akt Cell Signaling
Integrins
RTK
IGFBP2
PI3K
ILK
P
P
P
P
P
P
PTEN
P
P
AKT
Proliferation
Survival
Growth
Migration
Metabolism
Akt Isoforms
308
Akt1
PH
Kinase domain
RD
309
Akt2
PH
Kinase domain
Homology
PH
75-84%
Adapted from Cheng GZ et al. 2008.
Kinase domain
90-95%
14q32
474
RD
305
Akt3
473
Chromosome
location
19q13
472
RD
73-79%
1q44
Developmental Roles of Akt1/2/3
Postnatal Survival
Cellular growth
Angiogenesis
Akt1
Embryonic
Development
and Survival
Akt2
Akt3
Neuronal
development
Adapted from Gonzalez and McGraw. Cell Cycle. 2009.
Glucose
homeostasis
Whole body
weight and size
Differential Roles of Akt Isoforms in
Cancer
• Differential roles of Akt1 and Akt2 in breast cancer
(Hutchinson et al. Can Res. 2004; Arboleda et al. Can Res. 2003)
• Akt2 predominant in ovarian cancer (Noske et al. Cancer
Letters. 2007)
• Akt3 important in melanoma (Robertson. Can and Met Rev.
2005)
• Akt activation in glioma correlates with higher tumor
grade (Wang et al. Lab Invest. 2004)
Differential Akt2 and Akt3 Levels in
Oligodendroglioma
AKT1
N
O/AO
AKT2
N
O/AO
AKT3
N
O/AO
Is there a hierarchy in the ability of Akt isoforms
to promote oligodendroglioma development and
progression?
Hypothesis
Kristen
Holmes
Akt3 is the dominant Akt isoform which
preferentially induces Oligodendroglioma
progression
RCAS/tv-a Glial-specific Transgenic Mouse Model
Begemann, M., Uhrbom, L., Rajasekhar, V.K., Fuller, G.N., and E.C. Holland. 2004 Dissecting Gliomagenesis Using Glial-Specific Transgenic Mouse Models. In Zhang, W.
and G.N. Fuller (Ed.) Genomic and Molecular Neuro-Oncology. Sudbury: Jones and Bartlett. p233-278
Histologic Criteria for Oligodendroglioma
Progression
Normal Brain
WHO Grade II
Oligodendroglioma
WHO Grade III
Anaplastic Oligo
Akt3 Promotes Oligodendroglioma
Progression
AKT1
AKT3
AKT2
Tumor
Penetrance
Anaplastic
Oligodendroglioma
GFP
0% (0/22)
0% (0/22)
PDGFB
81% (35/43)
11% (4/35)
PDGFB / Akt1
77% (42/57)
16% (7/42)
PDGFB / Akt2
39% (11/28)
9% (1/11)
PDGFB / Akt3
100% (35/35)
100% (35/35)
Gene Combination
O/AO
N
N O/AO
O/AO
N
Akt3 Promotes Oligodendroglioma
Progression
PDGFB + Akt1
PDGFB + Akt2
PDGFB + Akt3
Challenge
How do we better
understand cancer systems?
Systems biology
Systems Biology
Systems biology is an emerging field that aims at system-level
understanding of biological systems.
Unlike molecular biology which focus on molecules, such as
sequence of nucleotide acids and proteins, systems biology
focus on systems that are composed of molecular components.
Although systems are composed of matters, the essence of
system lies in dynamics and it cannot be described merely by
enumerating components of the system. At the same time, it is
misleading to believe that only system structure, such as
network topologies, is important without paying sufficient
attention to diversities and functionalities of components. Both
structure of the system and components play indispensable
role forming symbiotic state of the system as a whole.
- H Kitano
Probabilistic Boolean network
US Patent # 7,257,563 (Shmulevich, Dougherty, and Zhang)
1. Shmulevich I, Dougherty ER, Kim S, and Zhang
W. Probabilistic Boolean network: a rule-based
uncertainty model for gene regulatory networks.
Bioinformatics 18:261-274, 2002.
2. Shmulevich I, Dougherty ER, and Zhang W. Gene
perturbation and intervention in probabilistic
Boolean network. Bioinformatics 18:1319-1331,
2002.
3. Shmulevich I, Lahdesmaki H, Dougherty ER,
Astola J, Zhang W. Proc. Natl. Acad. Sci. USA 100
(16) 2003.
• Such relationships should also be validated
experimentally.
• The networks built from our models should
provide valuable theoretical guidance to
experiments.
Cancer tissues need nutrients.
Gliomas are highly angiogenic.
Expression of VEGF is often
elevated.
VEGF protein is secreted outside the
cells and binds to its receptor on the
endothelial cells to promote their
growth.
FGF7
VEGF
Member of fibroblast
growth factor family
PTK7
GRB2
FSHR
Tyrosine kinase
receptor
• The protein products of all four genes are part
of signal transduction pathways that involve
surface tyrosine kinase receptors.
• These receptors, when activated, recruit a
number of adaptor proteins to relay the signal to
downstream molecules
• GRB2 is one of the most crucial adaptors that
have been identified.
• GRB2 is also a target for cancer intervention
because of its link to multiple growth factor
signal transduction pathways.
Follicle-stimulating
hormone receptor
GRB2
•
Molecular studies have
demonstrated that activation of
protein tyrosine kinase receptorGRB-2 complex activates ras-MAP
kinase-NFB pathway to complete
the signal relay from outside the
cells to the nucleus.
•
GNB2 is a ras family member.
GNB2
MAP kinase 1
c-rel
•
GNB2 influences MAP
kinase 1, which in turn
influences c-rel, an NFB
component.
IGFBP-2 in Glioma Progression
•
Up-regulation of IGFBP2 is one of the most consistent and
distinctive gene expression changes in high-grade gliomas
(Fuller et al., 1999)
IGFBP2 is a poor prognosis factor
Rembrandts Data
All gliomas
TCGA Data
Glioblastomas
IGFBP-2 Promotes Motility & Invasion
•
IGFBP2 activates expression of invasion enhancing genes
and promotes glioma invasion in vitro (Hua Wang et al.,
Cancer Res., 2003)
Regulated matrix
degradation
MMP2
CD10
TIMP-1
IGFBP2
Bcl-2
PUMA
p21/WAF1
XRCC2
survival
Guiding migration
Fibronectin
Thrombospondin 2
TGF beta R
Bradykinin R B2
Thrombin R
Integrin a 5
Integrin a 6
Filamin A
Vinculin
invasion
ILK-FAK-PI3K-AKT
Centaurin
Actin stress fiber
Cytoskeleton reorganization
Migration & survival
Hua Wang, PhD
First Prize poster
Competition at MDACC
Trainee Recognition Day
George Wang, M.D., Ph.D.
Limei Hu, M.D., M.S.
Resident at Mt Sinai Med Ctr.
Proc Natl Acad Sci USA
104(28):11736-41, 2007
IGFBP2 is an Oncogene
Sarah Dunlap (now Sarah Smith)
First prize in 2007 Trainee Recognition
Day at MDACC
American Legion Auxiliary Fellowship
NIH Training grant
Pharmacoinformatics fellowship
IGFBP2
• A review of the
literature showed
that Cazals et al.
(1999) indeed
demonstrated that
NFB activated
the IGFBP2
promoter in lung
alveolar epithelial
cells.
NFB
c-Myc
-561
AP2
NFB
+1
• Higher NFB activity
in IGFBP2
overexpressing cells
was also found.
IGFBP2
TNFR2
ILK
High IGFBP2 expressing Clones have high NFkB activity
NFB
5
Relative Luciferase Activity
• Our real-time PCR
data showed that
in stable IGFBP2overexpressing cell
lines, IGFBP2
indeed enhances
ILK expression.
• In addition,
IGFBP2 contains
an RGD domain,
implying its
interaction with
integrin molecules.
• ILK is in the
integrin signal
transduction
pathway.
4
3
2
1
0
parental
p65/p50
p50/p50
Non-specific
OCT1
c1
c4
c5
c8
ILK is elevated in high-grade glioma
and correlates with shorter survival
RGE mutational substitution on IGFBP2
IGF binding
domains
Thyroglobulin type-1 motif
(TG domain)
COOH
NH2
DXXD
motif
RGD domain
R
G
D 306
5’ TCCAGGGAGCCCCCACCATCCGGGGGGACCCCGAGTGTCATCTCTTCT 3’
GAA
E 306
D306E-IGFBP2
(RGE mutant)
Integrin Linked Kinase
Integrins
IGFBP2
Ligand
R
G
D
PI3K
PIP3
1
Receptor
Tyrosine
Kinase
P
PH
ILK
GSK3
P
Akt
Ser 473
U
Cyclin D
U
U
Nucleus
IKKα
IĸB
Target genes
NFĸB
Proliferation
P
IĸB
NFĸB
NFĸB
Integrin Binding is Required for IGFBP2mediated Progression
PDGFB
IGFBP2
50
45
Grade III Incidence (%)
40
35
30
25
20
15
PDGFB
PDGFB
IGFBP2(RGE)
10
5
0
GFP
n=22
n=42
n=50
RCAS Combination
n=50
n=32
IGFPB2 Drives Progression via ILK
PDGFB
IGFBP2
50%
Grade III Incidence (%)
45%
PDGFB
ILK
40%
35%
30%
25%
PDGFB
ILK-KD
20%
15%
PDGFB
PDGFB
ILK-KD
IGFBP2
10%
5%
0%
n=42
n=50
n=28
RCAS Combination
n=26
n=22
IGFBP2-ILK-AKT pathway
• Critical for cancer development
and progression
• Opportunities for drug
development
Future Cancer Biology
• Systems understanding to cancer
and cancer therapeutics
• Predictive instead of reactive
medicine
• TCGA is making a major impact
on individualized medicine
Acknowledgment
•
•
•
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•
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•
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•
NIH/NCI GDAC Center grant (Shmulevich/Zhang)
NIH/NCI RO1 CA098503 (Zhang/Fuller)
NIH/NCI NIH R01 CA141432 (Zhang/Fuller)
NIGMS/NIH R01 GM072855 (Shmulevich/Zhang)
Goldhirsh Foundation (Zhang)
James S McDonald Foundation (Zhang)
National Foundation for Cancer Research (Zhang/Hamilton)
NFCR Hope Fund (Zhang)
Anthony Bullock III Research fund (Zhang/Fuller/Sawaya)
The Oreffice Foundation (Zhang/Fuller/Sawaya)
Commonwealth Foundation for Cancer Research (Zhang/Trent)
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NIH/NCI NIH R01 CA098570 (Zhang/Pollock, completed)
NIH R21 GM070600 (Shmulevich/Zhang/Kauffman, completed)
Department of Defense (Zhang, completed)
Texas Higher Education Coordinating Board ARP and ATP grants (Zhang/Fuller, Zhang/Holland,
completed)
RGK Foundation (Zhang, completed)
•
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NIH/NCI P30 CA016672-28 (CCSG)
Tobacco Settlement Fund
Kadoorie Foundation
Goodwin Fund