PI: Jian Ma, Ph.D., Assistant Professor, Bioengineering
Title: Discovering Patient-Specific Driver Mutations in Prostate Cancer
One of the most pressing challenges in cancer genomics now is to distinguish “driver” mutations (i.e. mutations involved in tumorigenesis) and “passenger” mutations (i.e. functionally neutral mutations). DNA sequencing of cancer genomes has shown that many of the mutated genes are found in multiple tumors or are found in genes that have been implicated previously as cancer genes. Looking for these recurrent events has been the major approach so far to identifying driver mutations in cancer. However, it is now acknowledged that individual tumors of the same type are highly heterogeneous and have diverse genomic alterations. Indeed, cancer genome sequencing studies have detected many patient-specific mutations that are found only rarely. It is largely unknown how to discover driver mutations with low mutation frequency. In this pilot project, we will develop a novel method to discover patient-specific driver mutations in prostate cancer patients. Our specific aims are: (1) Develop a probabilistic model to discover patient-specific driver mutations. Our new algorithm will allow us to quantitatively assess mutations in any individual tumor based on mutations’ propensity to become advantageous alterations. (2) Apply our new method to discover driver mutations in prostate cancer samples (one with the TMPRSS2-ERG fusion gene and one with loss of PTEN). We will sequence the exome and transcriptome of these tumor samples and their matched normal, followed by computational analysis. Identified driver genes will be assessed and validated. The method developed in this project will help us predict what mutations are important in tumorigenesis in a patient-specific manner. We believe our method will potentially allow us to determine the optimal treatment strategy for each patient through individualized assessment based on the molecular signatures of their cancer.