A novel method to identify and prioritize drugs for an individual (N=1) based on clinical and multi-omics data

Friday, November 9, 2018 - 11:40am - 12:10pm
Krishna Kalari (Mayo Clinic)
We have recently developed a computational genomics method (PANOPLY- Precision cancer genomics report: single sample inventory) that uses an individual’s clinical, germline and tumor multi-omics data to identify potential drug targets and chemotherapy drugs; this enables the personalization of medicine for cancer. The PANOPLY is an open-source computational framework that analyzes complex multidimensional data and summarizes the findings in a clinician-friendly manner; it repurposes the existing FDA-approved drugs and prioritizes the drugs for cancer patients based on their omics profile and reports the results to the oncologist in a summary fashion to guide treatment decisions. In short, PANOPLY applies machine learning and knowledge-driven network analysis to integrate cancer omics profiles and clinical data, to identify patient-specific drugs. We applied PANOPLY to an in-house breast cancer dataset and publically available colon and breast cancer datasets from The Cancer Genome Atlas (TCGA). In addition, we have confirmed PANOPLY findings in a breast cancer patient using patient-derived xenograft (PDX) models. We are now in the process of validating the PANOPLY predictions in additional PDX models from breast and other cancer types.