Baseline DSB repair prediction of chronic rare Grade ≥ 3 toxicities induced by radiotherapy using classification algorithms.

Abstract

Small fractions of patients suffer from radiotherapy late severe adverse events (AEs Grade ≥ 3), which are usually irreversible and badly affect their quality of life. A novel functional DNA repair assay characterizing several steps of double-strand break (DSB) repair mechanisms was used. DNA repair activities of peripheral blood mononuclear cells were monitored for 1 week using NEXT-SPOT assay in 177 breast and prostate cancer patients. Only seven patients had Grade ≥ 3 AEs, 6 months after radiotherapy initiation. The machine learning method established the importance of variables among demographic, clinical and DNA repair data. The most relevant ones, all related to DNA repair, were employed to build a predictor. Predictors constructed with random forest and minimum bounding sphere predicted late Grade ≥ 3 AEs with a sensitivity of 100% and specificity of 77.17 and 86.22%, respectively. This multiplex functional approach strongly supports a dominant role for DSB repair in the development of chronic AEs. It also showed that affected patients share specific features related to functional aspects of DSB repair. This strategy may be suitable for routine clinical analysis and paves the way for modelling DSB repair associated with severe AEs induced by radiotherapy.