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Abstract
Aim: We tested whether machine-learning algorithm could find biomarkers predicting overall survival in breast cancer patients using blood-based whole-exome sequencing data. Materials & methods: Whole-exome sequencing data derived from 1181 female breast cancer patients within the UK Biobank was collected. We found feature genes (n = 50) regarding total mutation burden using the long short-term memory model. Then, we developed the 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 ≥56 years (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.

Affiliations

Bum-Sup Jang  1   2 , In Ah Kim  1   2   3
1 Department of Radiation Oncology, Seoul National University Bundang Hospital, Seongnam, 13620, Korea.
2 Department of Radiation Oncology, Seoul National University, College of Medicine, Seoul, Korea.
3 Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Korea.