Individual Radiosensitivity in Oncological Patients: Linking Adverse Normal Tissue Reactions and Genetic Features - Palumbo, Elisa; Piotto, Celeste; Calura, Enrica; Fasanaro, Elena; Groff, Elena; Busato, Fabio; El Khouzai, Badr; Rigo, Michele; Baggio, Laura; Romualdi, Chiara; Zafiropoulos, Demetre; Russo, Antonella; et al. (Frontiers in Oncology, 2019)
Introduction: Adverse effects of radiotherapy (RT) significantly affect patient's quality of life (QOL). The possibility to identify patient-related factors that are associated with individual radiosensitivity would optimize adjuvant RT treatment, limiting the severity of normal tissue reactions and improving patient’s QOL. In this study, we analyzed the relationships between genetic features and toxicity grading manifested by RT patients looking for possible biomarkers of individual radiosensitivity. Methods: Early radiation toxicity was evaluated on 143 oncological patients according to the Common Terminology Criteria for Adverse Events (CTCAE). An individual radiosensitivity (IRS) index defining four classes of radiosensitivity (highly radiosensitive, radiosensitive, normal and radioresistant) was determined by a G2-chromosomal assay on ex vivo irradiated, patient-derived blood samples. The expression level of 15 radioresponsive genes has been measured by quantitative real-time PCR at 24 h after the first RT fraction, in blood samples of a subset of 57 patients, representing the four IRS classes. Results: By applying univariate and multivariate statistical analyses, we found that fatigue was significantly associated with IRS index. Interestingly, associations were detected between clinical radiation toxicity and gene expression (ATM, CDKN1A, FDXR, SESN1, XPC, ZMAT3, BCL2/BAX ratio) and between IRS index and gene expression (BBC3, FDXR, GADD45A, BCL2/BAX). Conclusions: In this prospective cohort study we found that associations exist between normal tissue reactions and genetic features in RT-treated patients. Overall, our findings can contribute to the identification of biological markers to predict RT toxicity in normal tissues.