1. Presented by Meeyoung Park
Network modeling links breast cancer susceptibility and centrosome dysfunction
Pujana et al.
Nature genetics, 2007
Presented by Meeyoung Park
Feb. 29, 2008

2. Outline
Introduction
Methods & Results
Discussion
Conclusion
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3. Motivation
Most genes and their products interact in complex cellular networks, the properties which might be altered in cancer cells.
Modeling the functional interrelationships between genes and/or proteins may be required for a deeper understanding of cancer molecular mechanisms.
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4. Objective
Modeling of global macromolecular networks to identify cancer genes and their products.
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5. Outline
Introduction
Methods & Results
Discussion
Conclusion
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6. A network modeling strategy
Macromolecular networks can be modeled on the basis of global correlations observed among:
Transcriptional profiling compendia, protein-protein interaction or ‘interactome’ networks, and genomewide phenotypic profiling data sets
Comparisons of ‘interolog’ data sets from different organisms.
Interolog : A pair of molecular interactions X-Y and X'- Y‘.
( X-Y and X'-Y' are in two different organisms.)
X is an interolog of X' while Y is an interolog of Y'.
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7. Modeling macromolecular networks
Strategy
Integrates coexpression profiles in human
Integrates functional associations derived from various functional ‘omic’ data sets obtained in humans and model organisms.
Reference genes/proteins
BRCA1 and BRCA2
identified by high-penetrance mutations
ATM and CHEK2
identified by low-penetrance mutations
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Figure 1. Outline for the generation of the BRCA-centered Network (BCN) model

9. Coexpression profiling
Data
9,214 human genes in 101 samples and three cell lines
Pearson correlation coefficient (PCC)
Between each of the reference genes and all of the genes on the array
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10. Functional associations
Literature interaction (LIT-Int) network
To determine the likelihood of predicting functional associations
Curated published data from the scientific literature on protein interactions
103 proteins and 129 functional associations.
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11. LIT-Int network for ATM, BRCA1, BRCA2 and CHEK2
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12. PCC > 0.4 captures 36% of the LIT-Int functional associations.
b) PDF of transcriptional PCC values between gene pairs for each of the four reference genes
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13. Potential functional associations
Expression intersection (XPRSS-Int)
Generate the XPRSS-Int of the four coexpression sets
164 genes (PCC) > 0.4
15 are present in
LIT-Int data set
C) XPRSS-Int of the four reference coexpression sets using PCC > 0.4. LIT-Int genes included in the XPRSS-Int set are shown.
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14. Potential functional associations
Evaluate the significance of the XPRSS-Int
Randomly chosen sets do not overlap in coexpression levels.
Results indicates that P < 0.005
d) Distribution of the coexpression intersection for randomly chosen sets of four genes and comparison with the XPRSS-Int set using PCC > 0.4.
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15. XPRSS-Int and reference genes
Functionally related in shared characteristics:
An enrichment of Gene Ontology (GO) terms
Evolutionary conservation of coexpression patterns (Orthologs of XPRSS-Int and reference genes)
Significant coexpression among 33 XPRSS-Int genes was observed when an expression data set used for the analysis of breast tumor cell lines.
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16. The functional significance of the XPRSS-Int set
Expression changes in breast tumors
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17. BRCA-centered network modeling
Integrated data
Gene expression profiling similarity above a given threshold.
Saccharomyces cerevisiae and C. elegans microarray profiles (6,174 and 18,451 genes, respectively)
Phenotypic similarity for 661 early embryogenesis C. elegans genes above a specific threshold.
Genetic interactions for 1,347 S. cerevisiae genes.
Protein physical interactions
binary interactions, complex co-memberships and biochemical interactions (protein modification)) for 3,458 S. cerevisiae, 4,588 C. elegans (WI6 data set), 7,198 Drosophila melanogaster and 10,305 Homo sapiens proteins.
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18. A BRCA-centered network model
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19. Evaluation of BCN connectivity
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20. (c) GO terms annotations reveal functional clusters contained in distinct omic data sets used to generate the BCN.
C. elegans tac-1: functional associations of the C. elegans tac-1 gene and TAC-1 protein with connections to BCN genes/proteins.
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21. (d) Five criteria were integrated to estimate the overall functional significance of XPRSS-Int genes/proteins relative to breast cancer reference genes/proteins. XPRSS-Int genes are clustered according to the number of criteria they match (from 5 to 0) and then ordered within clusters according to their average PCC value for BRCA1 (PCC-BRCA1).
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22. Bioinformatic Analysis
GO annotations from NetAffix(Affymetrix)
Only grade 3 (poorly differentiated) tumors were used to study gene expression in BRCA1 mutation tumors
P values for differential expression were determined by two-tailed Student's t-test (P < 0.10)
The BRCA1mut coexpression network was generated with the Graphviz graph visualization package
Orthologs were defined by reciprocal BLASTP best hit (P < 10-6) or from the literature
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23. Predictions based on the BCN model
HMMR
encodes the hyaluronan-mediated motility receptor (HMMR, also known as RHAMM).
It has the highest PCC coexpression value relative to BRCA1 (0.9)
It is known that HMMR may have a potential role in centrosome function in conjunction with BRCA1.
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24. Centrosome
The main microtubule organizing center (MTOC) of the animal cell
Centriole
From
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25. Role of the centrosome in cell cycle progression
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From

26. New BRCA functional associations
HMMR-centered interactome map
Yeast two-hybrid screens
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27. Experimentally Testing BCN predictions
Coimmunoprecipitation
(b) Endogenous coimmunoprecipitation of CSPG6 and BRCA1, and SMC1L1 and BRCA1 in 293 cells. (c) Endogenous coimmunoprecipitation of HMMR and BRCA1 in non- synchronized HeLa S3 cells. (d) Cell cycle synchronization and coimmunoprecipitation assays in HeLa S3 cells.
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28. Co-Immunoprecipitation
Co-immunoprecipitation (Co-IP) is a popular technique for protein interaction discovery.
Co-IP is conducted in essentially the same manner as an IP.
However, in a co-IP the target antigen precipitated by the antibody “co-precipitates” a binding partner/protein complex from a lysate,
i.e., the interacting protein is bound to the target antigen, which becomes bound by the antibody that becomes captured on the Protein A or G gel support.
The assumption that is usually made when associated proteins are co-precipitated is that these proteins are related to the function of the target antigen at the cellular level. This is only an assumption, however, that is subject to further verification.
Traditional IP
Co- IP
From
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29. HMMR-BRCA1 and centrosome dysfunction
Overexpression of HMMR and/or its biochemical modification resulting in centrosome amplification are early somatic molecular events that contribute to breast tumorigenesis.
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30. HMMR and breast cancer risk
Functional association investigation
Study of HMMR haplotype-tagging SNPs in 923 individually matched case-control pairs.
Found a significant association between genetic variation in HMMR and early-onset breast cancer.
HMMR may be a previously unknown susceptibility gene for breast cancer within diverse human populations.
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32. Outline
Introduction
Methods & Results
Discussion
Conclusion
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33. Discussion
Network modeling points to functional associations for genes/proteins.
The observation supports the idea that the HMMR-centered interactome network described has a role in genomic stability and breast tumorigenesis.
Genetic analysis supports HMMR as a newly defined breast cancer susceptibility gene, thereby delineating a genetic link between risk of breast cancer and centrosome dysfunction.
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34. Conclusion
The network modeling strategy is applicable to other types of cancer.
It will help to discover more cancer-associated genes and to generate a ‘wiring diagram’ of functional interactions between their products.
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35. Thank You !