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Network modeling links breast cancer susceptibility and centrosome dysfunction

Network modeling links breast cancer susceptibility and centrosome dysfunction. Pujana et al. Nature genetics, 2007 Presented by Meeyoung Park Feb. 29, 2008. Outline. Introduction Methods & Results Discussion Conclusion. Motivation.

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Network modeling links breast cancer susceptibility and centrosome dysfunction

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  1. 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

  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.

  4. Objective • Modeling of global macromolecular networksto identify cancer genes and their products.

  5. Outline • Introduction • Methods & Results • Discussion • Conclusion

  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'.

  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

  8. 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

  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.

  11. LIT-Int network for ATM, BRCA1, BRCA2 and CHEK2

  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

  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.

  14. Potential functional associations • Randomly chosen sets do not overlap in coexpression levels. • Results indicates that P < 0.005 • Evaluate the significance of the XPRSS-Int d) Distribution of the coexpression intersection for randomly chosen sets of four genes and comparison with the XPRSS-Int set using PCC > 0.4.

  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.

  16. The functional significance of the XPRSS-Int set Expression changes in breast tumors

  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.

  18. A BRCA-centered network model

  19. Evaluation of BCN connectivity

  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.

  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).

  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

  23. Predictions based on the BCN model • HMMR • encodes the hyaluronan-mediated motilityreceptor(HMMR, also known as RHAMM). • It has the highest PCCcoexpression value relative to BRCA1 (0.9) • It is known that HMMR may have a potential role in centrosome function in conjunctionwith BRCA1.

  24. Centrosome • The main microtubule organizing center (MTOC) of the animal cell Centriole From http://micro.magnet.fsu.edu

  25. Role of the centrosome in cell cycle progression From http://wikipedia.org

  26. New BRCA functional associations • HMMR-centered interactome map • Yeast two-hybrid screens

  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.

  28. Co-Immunoprecipitation • Traditional IP • Co- IP From http://www.piercenet.com/Proteomics/

  29. HMMR-BRCA1 and centrosome dysfunction • Overexpression of HMMR and/or its biochemical modificationresulting in centrosome amplificationare early somatic molecular events thatcontribute to breast tumorigenesis.

  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.

  31. Outline • Introduction • Methods & Results • Discussion • Conclusion

  32. Discussion • Network modeling points to functionalassociations for genes/proteins. • The observationsupports the idea that the HMMR-centered interactomenetwork described has a role in genomic stability andbreast tumorigenesis. • Genetic analysis supports HMMR as a newly defined breast cancersusceptibility gene, thereby delineating a genetic link between risk ofbreast cancer and centrosome dysfunction.

  33. Conclusion • The network modeling strategy is applicable to other types ofcancer. • It will help to discover more cancer-associated genes andto generate a ‘wiring diagram’ of functional interactions betweentheir products.

  34. Thank You !

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