Predicting Hearing Loss in Testicular Cancer Patients after Cisplatin-Based Chemotherapy

Simple Summary To our knowledge, this is the first study that presents a machine learning setup incorporating genetics and clinical factors to predict hearing loss in a large and fairly unique cohort of testicular cancer patients, with follow-up data examining long-range side effects of chemotherapy. Genetic variants in SOD2 and MGST3 are proposed as mechanistically associated with cisplatin-induced hearing loss. Further, the models in this study focus on individual patient benefit and incorporation of quality of life measures to identify hearing loss impact. To study short- and long-term effects of chemotherapy, testicular cancer is ideal as a model disease for other cancers, as patients are young with long life-expectancy and without significant comorbidity. With small adjustments, the model can likely be applied in the treatment of other cancers where cisplatin is used, thus helping with choice of treatment without risking a trade-off in efficacy, standing to influence clinical practice. Testicular cancer is predominantly curable, but the long-term side effects of chemotherapy have a severe impact on life quality. In this research study, we focus on hearing loss as a part of overall chemotherapy-induced ototoxicity. This is a unique approach where we combine clinical data from the acclaimed nationwide Danish Testicular Cancer (DaTeCa)-Late database. Clinical and genetic data on 433 patients were collected from hospital files in October 2014. Hearing loss was classified according to the FACT/GOG-Ntx-11 version 4 self-reported Ntx6. Machine learning models combining a genome-wide association study within a nested cross-validated logistic regression were applied to identify patients at high risk of hearing loss. The model comprising clinical and genetic data identified 67% of the patients with hearing loss; however, this was with a false discovery rate of 49%. For the non-affected patients, the model identified 66% of the patients with a false omission rate of 19%. An area under the receiver operating characteristic (ROC-AUC) curve of 0.73 (95% CI, 0.71-0.74) was obtained, and the model suggests genes SOD2 and MGST3 as important in improving prediction over the clinical-only model with a ROC-AUC of 0.66 (95% CI, 0.65-0.66). Such prediction models may be used to allow earlier detection and prevention of hearing loss. We suggest a possible biological mechanism for cisplatin-induced hearing loss development. On confirmation in larger studies, such models can help balance treatment in clinical practice.