Grant Number:	5R21CA220078-02 Interpret this number
Primary Investigator:	Lawrenson, Kate
Organization:	Cedars-Sinai Medical Center
Project Title:	The Role of Splice Quantitative Traits in Ovarian Cancer Pathogenesis
Fiscal Year:	2018

Abstract Genome wide association studies (GWAS) have so far identified more than 30 common low penetrance variants for ovarian cancer; but it is estimated that thousands more risk variants await discovery. In the post-GWAS era a complex set of challenges for the identification, functional characterization and utility of susceptibility alleles have emerged including: (i) Identifying the causal genetic variants and regulatory targets driving cancer development at risk loci; (ii) Identifying the susceptibility genes associated with risk variants; (iii) Establishing if there are common biological networks that explain the functional mechanisms underlying multiple risk loci. Clinically, identifying the genetic risk component of ovarian cancer will likely lead to improved disease prevention through population screening and disease prevention strategies; and understanding the function of risk loci may lead to the discovery of clinical biomarkers and novel targeted therapies, analogous to the paradigm of PARP therapy for BRCA1 or BRCA2 mutation carriers. The current proposal is designed to address many of these challenges for ovarian cancer in the post- GWAS era with respect to genetic risk variants that target splicing mechanisms of target susceptibility genes. Specifically we aim to: (1) Integrate the largest GWAS datasets for ovarian cancer with RNA sequencing data for hundreds of ovarian normal and cancer tissues to identify genetic risk variants associated specifically with differential splicing; (2) Establish the functional mechanisms associated splice associated risk variants for ovarian cancer risk loci using functional assays to analyse the effects of splicing on their target genes on tumor development in novel experimental models of ovarian cancer precursor tissues. These studies will establish a statistical and functional pipeline to evaluate the contribution of risk associated splice variants in other complex traits and disorders





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