Completion of the human genome project has resulted in rapid advances in technology as well as a deluge of genomic data. This tremendous accomplishment has sparked an incredible international community effort to catalog human genetic variation and to relate this variation to human phenotypes with the ultimate payoff of more personalized medicine. While the latest technologies provide unprecedented ability to conduct genome- wide association studies (GWAS) to identify individual susceptibility loci for a given human disease, GWAS are underpowered (due to the multiple hypotheses testing problem) to screen for the multiple gene-gene interactions conferring susceptibility in humans. To overcome this limitation, in this application, we propose a novel, cross-species (yeast-to-human) comparative systems genetics strategy to identify gene-gene and pathway-pathway interactions underlying human breast cancer susceptibility. Specifically, we hypothesize that cellular sensitivity to DNA damage can be used as an intermediate phenotype for breast cancer susceptibility, and that genes and pathways that synergize with defects in the DNA damage response pathway will also synergize to produce breast cancer susceptibility in humans.
In Aim 1, we will leverage existing and emerging data from our genome-wide screens in yeast (R01 CA 129604-01A1 Phenotype-based approach to find gene interactions underlying breast cancer risk;PI: Paulovich) to identify gene-gene interactions likely to underlie susceptibility for breast cancer in humans. Putative human orthologs of interacting yeast genes will be identified using sophisticated data analysis tools. An integrative genomics analysis will then be used to further prioritize gene pairs with high probability of contributing to breast cancer susceptibility based on genomics datasets and networks derived from human breast cancers. Although model organisms can be genetically altered and their environments manipulated to test predictions about contributions of specific gene variants to risk, there are limitations of single or multiple gene knockout or mutant approaches as the sole means to test predictions;hence in Aim 2 we will test the functional significance in human mammary epithelial cells (HMEC) of synthetic or synergistic gene-gene interactions discovered in yeast and prioritized in Aim 1.
In Aim 3, gene- gene interactions functionally verified in HMEC will be tested for association with breast cancer susceptibility using existing and emerging GWAS datasets on human breast cancer. The premise of using sensitivity to DNA damage as an intermediate phenotype is reasonable given the abundance of evidence that defects in this pathway cause germline predisposition to breast cancer. This work will complement and extend the current GWAS studies since significant increases in risk may only be apparent when variant alleles are considered in combination (epistasis), and hence these risk alleles will be frequently missed in GWA studies.
This proposal is relevant to public health for two reasons. First, it will test a novel paradigm for discovering gene-gene and pathway-pathway interactions underlying breast cancer susceptibility in humans. This is significant because our current inability to detect gene-gene interactions is a major impediment to the identification of susceptibility loci. Second, understanding breast cancer risk has important public health implications. Although early detection has reduced mortality from the disease, the cost of screening the population at large is a tremendous public health burden;a recent analysis focusing on the direct medical costs associated with breast cancer screening, follow-up, and treatment found that U.S. screening patterns from 1990 to 2000 resulted in a gain of 1.7 million QALYs for an additional cost of $62.5 billion compared with no screening. Determination of an individual's susceptibility would facilitate cost-effective cancer screening programs tailored to an individual's risk profile, identify women who will most benefit from prevention strategies, and elucidate underlying disease mechanisms, potentially leading to targeted therapeutics or chemoprevention agents. Ultimately, this information may aid the design of targeted epidemiology studies in human populations.
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