Don Mathew

Abstract

While conventional radical radiotherapy is a primary treatment for Head and Neck Squamous Cell Carcinoma (HNSCC), its efficacy is often overshadowed by significant radiation-induced toxicities, such as acute mucositis, which severely impact patients' quality of life. This presentation will explore the emerging field of radio genomics, which seeks to personalize cancer treatment by understanding how individual genetic variations influence a patient's response to radiotherapy.

We will detail a study designed to develop a predictive model for identifying HNSCC patients at high risk for developing severe radiation toxicity. The research focused on 16 specific Single-Nucleotide Polymorphisms (SNPs) within genes critical to DNA repair, xenobiotic metabolism, and radiation fibrogenesis pathways. Genetic data from 222 patients, obtained via RFLP analysis, were integrated with clinical parameters and treatment details.

Our findings successfully yielded several robust risk prediction models, including Multivariable regression, Cox regression, and Decision tree (CART & Random Forest) models. A key discovery was that a specific SNP in the XRCC1 DNA repair gene, when combined with clinical factors like a history of tobacco chewing and a low Karnofsky Performance Status (KPS) score, served as a powerful predictor for higher grades of acute mucositis.

This work underscores the potential of a radio genomics-based approach to stratify patients into high-risk and low-risk categories for toxicity before treatment. By enabling more tailored radiotherapy strategies, such as intensified supportive care for high-risk individuals or treatment de-escalation for low-risk groups, this model promises to significantly reduce treatment-related complications, thereby improving the therapeutic window and overall survivorship experience in precision oncology.