My dissertation paper, A Network-Based Model of Oncogenic Collaboration for Prediction of Drug Sensitivity is now published! Here’s a lay summary:
One outstanding issue in analyzing genomics in the context of personalized medicine is the incorporation of rare or infrequent genetic alterations (copy number alterations and somatic mutations) that are observed in individual > patients. We hypothesize that these mutations may actually ‘collaborate’ with known oncogenes in the genesis of tumors through their interactions. In order to show this effect, we assess whether these interacting rare mutations cluster around known oncogenes and assess these mutational clusters, which we term surrogate oncogenes. We assess their statistical significance using a simple model of mutation. We show that surrogate oncogenes are predictive of drug sensitivity in breast cancer cell lines. Additionally, they are prevalent in three different cancer cohorts (Breast, Glioblastoma, and Bladder Cancer) from The Cancer Genome Atlas. Within the Breast Cancer and Bladder Cancer populations, surrogate oncogenes are predictive of overall patient survival. The chief strength of the surrogate oncogene approach is that it can be run at a single-patient level in comparison to other methods of assessing mutational significance.
If you’re interested in learning more, you can check out the Surrogate Oncogene Explorer in order to understand the nature of surrogate oncogenes, and my R/Bioconductor Package on GitHub if you’d like to try out the analysis.
There’s a follow-up paper that I’m working on that I’m very excited about. More news soon.