Luxton Jared J, McKenna Miles J, Lewis Aidan M, Taylor Lynn E, Jhavar Sameer G, Swanson Gregory P, Bailey Susan M
Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA.
Cell and Molecular Biology Program, Colorado State University, Fort Collins, CO 80523, USA.
J Pers Med. 2021 Mar 8;11(3):188. doi: 10.3390/jpm11030188.
The ability to predict a cancer patient's response to radiotherapy and risk of developing adverse late health effects would greatly improve personalized treatment regimens and individual outcomes. Telomeres represent a compelling biomarker of individual radiosensitivity and risk, as exposure can result in dysfunctional telomere pathologies that coincidentally overlap with many radiation-induced late effects, ranging from degenerative conditions like fibrosis and cardiovascular disease to proliferative pathologies like cancer. Here, telomere length was longitudinally assessed in a cohort of fifteen prostate cancer patients undergoing Intensity Modulated Radiation Therapy (IMRT) utilizing Telomere Fluorescence in situ Hybridization (Telo-FISH). To evaluate genome instability and enhance predictions for individual patient risk of secondary malignancy, chromosome aberrations were assessed utilizing directional Genomic Hybridization (dGH) for high-resolution inversion detection. We present the first implementation of individual telomere length data in a machine learning model, XGBoost, trained on pre-radiotherapy (baseline) and in vitro exposed (4 Gy γ-rays) telomere length measurements, to predict post radiotherapy telomeric outcomes, which together with chromosomal instability provide insight into individual radiosensitivity and risk for radiation-induced late effects.
预测癌症患者对放疗的反应以及出现晚期不良健康影响的风险，将极大地改善个性化治疗方案和个体治疗效果。端粒是个体放射敏感性和风险的一个引人注目的生物标志物，因为辐射暴露可导致功能失调的端粒病理状态，这些病理状态与许多辐射诱发的晚期效应恰好重叠，从纤维化和心血管疾病等退行性疾病到癌症等增殖性疾病。在此，利用端粒荧光原位杂交（Telo-FISH）对15名接受调强放射治疗（IMRT）的前列腺癌患者队列进行了纵向端粒长度评估。为了评估基因组不稳定性并增强对个体患者继发恶性肿瘤风险的预测，利用定向基因组杂交（dGH）进行高分辨率倒位检测来评估染色体畸变。我们首次将个体端粒长度数据应用于机器学习模型XGBoost中，该模型基于放疗前（基线）和体外照射（4 Gy γ射线）的端粒长度测量进行训练，以预测放疗后的端粒结果，这些结果与染色体不稳定性一起，有助于深入了解个体放射敏感性和辐射诱发晚期效应的风险。
