Radiotherapy, one of the most fundamental cancer treatments, is confronted with the dilemma of treatment failure due to radioresistance. To predict the radiosensitivity and improve tumor treatment efficiency in pan-cancer, we developed a model called Radiation Intrinsic Sensitivity Evaluation (RISE). The RISE model was built using cell line-based mRNA sequencing data from five tumor types with varying radiation sensitivity. Through four cell-derived datasets, two public tissue-derived cohorts, and one local cohort of 42 nasopharyngeal carcinoma patients, we demonstrated that RISE could effectively predict the level of radiation sensitivity (area under the ROC curve [AUC] from 0.666 to 1 across different datasets). After the verification by the colony formation assay and flow cytometric analysis of apoptosis, our four well-established radioresistant cell models successfully proved higher RISE values in radioresistant cells by RT-qPCR experiments. We also explored the prognostic value of RISE in five independent TCGA cohorts consisting of 1137 patients who received radiation therapy and found that RISE was an independent adverse prognostic factor (pooled multivariate Cox regression hazard ratio [HR]: 1.84, 95% CI 1.39-2.42; p < 0.01). RISE showed a promising ability to evaluate the radiotherapy benefit while predicting the prognosis of cancer patients, enabling clinicians to make individualized radiotherapy strategies in the future and improve the success rate of radiotherapy.
Keywords: pan‐cancer; prediction model; radioresistance; radiosensitivity evaluation; radiotherapy.
© 2024 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.