RadioGx: a Package for Integrative Analysis of Cellular Features and Radiosensitivity in Cancer
Ian Smith,Petr Smirnov,Benjamin Haibe-Kains University Health Network, University of Toronto
Abstract
While radiation therapy is an integral method for cancer treatment, clinical choices are not currently informed by the genetic and molecular profile of a patient’s tumour. Though it has been shown that genetic features implicate variability, the exact relationship between these features and radiosensitivity is poorly understood. We introduce RadioGx, a package to unify radiosensitivity data and molecular profiles of cancer cell lines to identify features predictive of sensitivity to radiation. RadioGx models dose-response data using the linear-quadratic model for radiotherapy, and integrates transcriptomic data to determine pathway- and tissue-specific determinants of radioresponse. To demonstrate the utility of RadioGx, we explore a case study on a dataset from the Cleveland Clinic and use pathway-level transcriptomic modeling methods to identify transcriptional programs associated with radiation sensitivity. Our pancancer model achieves a Pearson’s correlation of 0.47 (p<0.001) with measured radiosensitivity using only high level transcriptomic features. These results implicate cellular processes in radiosensitivity and demonstrate a method to identify features associated with radiosensitivity through integration of large scale datasets. RadioGx is a computational tool for hypothesis generation to advance preclinical research for radiation oncology and precision medicine.
Keywords: Radiosensitivity,Pharmacogenomics,Genomics,Biomarker