Abstract

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 tumor. Though it has been shown that genetic features implicate variability, the exact relationship between these features and radiosensitivity is poorly understood. Our work focuses on predicting radiosensitivity of squamous cell carcinoma from molecular features. First, we employ RadioGx, a computational platform for integrative analysis of radiogenomic datasets, to identify a gene expression signature predictive of radiosensitivity. To mitigate the high-dimensionality of gene expression data, we employ pathway-level transcriptomic modeling methods to identify cellular programs associated with radiation sensitivity. Our pancancer radiosensitivity model achieves a Pearson’s correlation of 0.47 with measured radiosensitivity from a radiogenomic dataset using only high level transcriptomic features. Second, we use that gene expression signature to query the Connectivity Map via the PharmacoGx resource and identify candidate radiosensitizers - compounds that may induce the radiosensitivity signature. Further validation is necessary to evaluate the efficacy of these compounds at improving radiosensitivity in both model systems and patient contexts. These results implicate cellular processes in radiosensitivity and advance an approach for identifying radiosensitizing agents through integration of large scale datasets. Citation Format: Ian Smith, Rachel Bell, Meghan Lambie, Benjamin Haibe-Kains, Scott Bratman. Characterizing transcriptomic indicators of radiosensitivity in cancer and identifying sensitizing therapeutic agents [abstract]. In: Proceedings of the AACR Virtual Special Conference on Radiation Science and Medicine; 2021 Mar 2-3. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(8_Suppl):Abstract nr PO-071.

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