Development of an integrated prognostic score using common risk variants and RNA-expression data from primary breast cancers for improved prognostication of outcomes across populations

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PROJECT SUMMARY/ABSTRACT My short term goal is to integrate training in bioinformatics with a postdoctoral fellowship in clinical oncology and prior training in statistical genetics. Relevance of germline data to somatic tumor development and clinical outcomes remains poorly understood due to clinical separation between germline and somatic testing. Somatic expression from breast tumors offers clinical prognostication and prediction only in hormone-receptor positive patients. This limitation particularly affects women of African ancestry, who experience higher rates of hormone- receptor negative and HER2-positive disease. Somatic expression assays also do not accurately reflect outcomes in hormone-receptor positive women of African ancestry. Primary tumors from this population demonstrate more aggressive molecular features (such as more homologous recombination deficiency, greater chromosomal instability, and more TP53 mutations) relative to those from European-ancestry patients, suggesting that common germline variation may influence the trajectory of somatic cancer development and clinical outcomes. We hypothesize that integration of common germline variation and somatic tumor expression data will yield prognostic information for women across breast cancer subtypes and populations. To test this, we will: (1) Develop an integrated prognostic score incorporating common germline breast cancer risk variants and RNA-expression data from breast cancer patients and test for prediction of clinical outcomes. Summary-PrediXcan is a computational method that takes summary-level GWAS and phenotype of interest as inputs, tests how expression changes in each gene affect phenotype, and outputs gene-level association results. We will use summary-level GWAS data from the largest meta-analysis of breast cancer risk with more than 225,000 European-ancestry cases and controls. We will refine/train this score using RNA- expression data from 1,096 patients in The Cancer Genome Atlas (TCGA) and 932 patients from METABRIC. We will test the predictive power of clinical outcomes tracked in TCGA, including overall survival, disease-specific survival, and progression-free survival using Cox regression analysis; (2) Test translation across populations of an integrated germline/somatic approach to breast cancer outcome prediction using patients of African ancestry. We will derive an independent score using summary-PrediXcan with summary-level GWAS data from a 3-consortia meta-analysis of breast cancer risk that includes 6,522 African-American breast cancer patients and 7,643 controls. We will test outcome prediction using 183 African-American TCGA patients with RNA-expression and survival data. We will also pilot validation of this score from two unique cohorts of 78 Nigerian patients and 35 African-American patients with RNA-seq and survival data. As we enter a new era in precision oncology, patients with breast cancer do not have access to prognostic stratification that performs well in diverse populations. My goal is to learn how to use modern tools of bioinformatics to develop a prognostic stratification model usable across subtypes and populations for improved patient care.