Even though most men diagnosed with prostate cancer will not die of the disease, prostate cancer is still the second leading cause of cancer death among men in the United States. While screening for prostate cancer reduces death from disease, this comes at the price of both unnecessary biopsies that reveal no evidence of cancer and treatment of otherwise indolent cancer resulting in unnecessary adverse events. Therefore, there is an unmet need for improved screening tools for prostate cancer. To address this need, we have previously developed a four-kallikrein biomarker panel that is now commercially available as a reflex test for use after an initial PSA screening; found that the four kallikrein model improves the prediction, prior to any diagnosis of prostate cancer, of which men may die of prostate cancer; and identified SNPs associated with survival time after diagnosis, independent of known prognostic factors. Combining these SNPs and the four kallikrein panel improves our ability to identify men at risk of dying from prostate cancer even further. Based on these findings, we propose here a germline genomic approach to identify men at risk of dying from prostate cancer. By leveraging recent computational advances in genomic analysis, we will take a gene-centered approach to identify genes for which genetically controlled transcriptional alterations and/or functional coding mutations influence survival time in prostate cancer. Using these genes, along with known genetic risk factors for prostate cancer and the four kallikrein panel, we will build and test models designed to identify men at risk for clinically significant prostate cancer in order to better stratify men in the screening context prior to biopsy. Specifically, we will: 1) Identify genes for which genetically controlled expression level changes and/or rare coding variants alter the risk of dying from prostate cancer; 2) Determine at what stage(s) of disease progression these genetic changes operate; and 3) Improve our 4-kallikrein biomarker predictor of lethal prostate cancer through incorporation of genetic data. This will be achieved by conducting both a transcriptome-wide association study (TWAS) with prostate specific models and a whole exome sequencing study in a set of well-annotated cohorts with long follow-up time after prostate cancer diagnosis. Successful completion of these aims will enable better risk stratification of men prior to prostate cancer diagnosis. We envision these findings being useful in the screening context, enabling more precise identification of men at high risk of dying from prostate cancer in the next two decades, thereby reducing death from prostate cancer due to the benefits of early detection while avoiding unnecessary biopsies and unneeded treatment of otherwise indolent cancers. Furthermore, these findings will be useful in understanding the biology of lethal prostate cancer as we anticipate these findings will pinpoint new genes and pathways that play important roles in prostate cancer progression.
While prostate cancer screening reduces death from prostate cancer, it does so at the cost of unnecessary treatment of men with nonaggressive disease. To address this, we propose to identify inherited genetic changes that influence survival time after a diagnosis of prostate cancer and incorporating such genetic information into prostate cancer screening tools. If successful, this will enable more precise prostate cancer screening, reducing both death from prostate cancer and unnecessary treatment.
