Machine-learning algorithms predict breast cancer patient survival from UK Biobank whole-exome sequencing data.

Abstract

Aim: We tested whether machine-learning algorithm couldfind biomarkers predicting overall survival in breast cancer patients using blood-based whole-exome sequencingdata. Materials & methods: Whole-exome sequencing data derivedfrom 1181 female breast cancer patients within the UK Biobank wascollected. We found feature genes (n=50) regarding total mutation burdenusing the long short-term memorymodel. Then, we developedthe XGBoost survival model with selected feature genes. Results: The XGBoost survival model performed acceptably, with a concordance index of 0.75 and a scaled Brier score of 0.146 in terms of overall survival prediction. The high-mutation group exhibited inferior overall survival compared with the low-mutation group in patients ≥56years (log-rank test, p=0.042). Conclusion: We showed that machine-learning algorithms can be used to predict overall survival in breast cancer patients from blood-based whole-exome sequencing data.