This project will develop a new framework for discovery of genes involved in the breast carcinogenesis process. Among families that have a predisposition to breast cancer, approximately 25% have inherited mutations in either breast cancer ("BRCA") genes BRCA1 or BRCA2, but the predisposing mutated genes in the majority of the families are unknown. BRCA1 and BRCA2 gene products both regulate cell division pathways that involve DNA repair and centrosome duplication, and their expression is correlated in microarray analyses in many cell types. We hypothesize that other unidentified BRCA genes may be involved in the same pathways that BRCA1 and BRCA2 regulate, and thus may be discovered by identifying genes whose expression also is correlated with that of BRCA1 and BRCA2. We will interrogate public-domain gene expression databases using newly developed computational tools that include combinatorial and algebraic clustering methods to identify genes whose expression correlates with these tumor suppressors. RNA interference will be used to disrupt the expression of the candidate BRCA gene products in two cell-based assays that are dependent on BRCA1 and BRCA2 expression. The first assay models the regulation of homology-directed recombination repair of double-strand DNA breaks, and the second assay tests the control of duplication of the centrosome. We will also perform a third test to determine whether the informatics-identified candidate BRCA gene product can form a protein complex with BRCA1 since several of the already identified co-expressed genes do form a complex with BRCA1. Candidate BRCA genes that are positive in the functional cell based assays will then be tested for changes in expression of their gene products in clinical samples, using an antibody-based, high-throughput tissue microarray system. In summary, this proposal outlines a novel experimental framework that will develop new bioinformatic tools for identifying candidate genes whose regulation suggests the potential for involvement in breast carcinogenesis, testing whether depletion of the proteins encoded by these candidate genes results in phenotypes in the laboratory that are consistent with breast cancer, and determining whether the expression of these candidate genes in clinical samples indicates their potential as biomarkers for breast carcinogenesis. This project defines a framework that may also be applicable to the identification of groups of genes involved in common pathways in other disease processes.

Public Health Relevance

Among families that have a predisposition to breast cancer, approximately 25% have inherited mutations in either breast cancer ("BRCA") genes BRCA1 or BRCA2, but the predisposing mutated genes in the majority of the families are unknown. BRCA1 and BRCA2 gene products both regulate cell division pathways that involve DNA repair and centrosome duplication, and their expression is correlated in microarray analyses in many cell types. We hypothesize that other unidentified BRCA genes may be involved in the same pathways that BRCA1 and BRCA2 regulate, and thus may be discovered by identifying genes whose expression also is correlated with that of BRCA1 and BRCA2. These candidate BRCA genes will be identified through computer-program driven analyses of publicly available gene expression data. Candidate BRCA genes identified using these computer-based approaches will then be screened in laboratory tests using RNA interference assays to identify candidates that regulate homology-directed recombination repair of double-strand DNA breaks and/or centrosome duplication. Candidate BRCA genes that are validated in the laboratory will then be tested for changes in expression of their gene product using high-throughput labeled antibody assays in clinical samples. Thus, this proposal outlines a novel experimental framework that starts with a broad computer-based screen of gene expression libraries to identify initial candidates, performs a second screening using in vitro laboratory analyses of function, and then a third screen using expression in clinical samples. Genes that pass all three screens would be excellent candidates for genes that are responsible for breast cancer disposition in families, that may serve as biomarkers for the diagnosis of breast cancer, and that may contribute predictive value for the success of treatment modalities for an individual patient.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA141090-04
Application #
8248594
Study Section
Genomics, Computational Biology and Technology Study Section (GCAT)
Program Officer
Li, Jerry
Project Start
2009-06-01
Project End
2014-04-30
Budget Start
2012-05-01
Budget End
2013-04-30
Support Year
4
Fiscal Year
2012
Total Cost
$450,687
Indirect Cost
$150,229
Name
Ohio State University
Department
Miscellaneous
Type
Schools of Medicine
DUNS #
832127323
City
Columbus
State
OH
Country
United States
Zip Code
43210
Ding, Hao; Wang, Chao; Huang, Kun et al. (2014) iGPSe: a visual analytic system for integrative genomic based cancer patient stratification. BMC Bioinformatics 15:203
Wang, Chao; Machiraju, Raghu; Huang, Kun (2014) Breast cancer patient stratification using a molecular regularized consensus clustering method. Methods 67:304-12
Hu, Yiheng; Wang, Chao; Huang, Kun et al. (2014) Regulation of 53BP1 protein stability by RNF8 and RNF168 is important for efficient DNA double-strand break repair. PLoS One 9:e110522
Kotian, Shweta; Banerjee, Tapahsama; Lockhart, Ainsley et al. (2014) NUSAP1 influences the DNA damage response by controlling BRCA1 protein levels. Cancer Biol Ther 15:533-43
Liebner, David A; Huang, Kun; Parvin, Jeffrey D (2014) MMAD: microarray microdissection with analysis of differences is a computational tool for deconvoluting cell type-specific contributions from tissue samples. Bioinformatics 30:682-9
Hu, Yiheng; Parvin, Jeffrey D (2014) Small ubiquitin-like modifier (SUMO) isoforms and conjugation-independent function in DNA double-strand break repair pathways. J Biol Chem 289:21289-95
Wang, Chao; Pecot, Thierry; Zynger, Debra L et al. (2013) Identifying survival associated morphological features of triple negative breast cancer using multiple datasets. J Am Med Inform Assoc 20:680-7
Zhang, Jie; Ni, Shiwei; Xiang, Yang et al. (2013) Gene Co-expression analysis predicts genetic aberration loci associated with colon cancer metastasis. Int J Comput Biol Drug Des 6:60-71
Eren, Kemal; Deveci, Mehmet; Kucuktunc, Onur et al. (2013) A comparative analysis of biclustering algorithms for gene expression data. Brief Bioinform 14:279-92
Kalluru, Vikram; Machiraju, Raghu; Huang, Kun (2013) Identify condition-specific gene co-expression networks. Int J Comput Biol Drug Des 6:50-9

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