A mixed clinicopathological and molecular proxy of homologous recombination deficiency in triple negative breast cancer

Abstract: Clinical models are increasingly employed in medical science as either diagnostic or prognostic aids. Machine-learning methods are able to draw links in large data that can be used to predict patient risk and allow more informed decisions regarding treatment and medication intervention. An advanced clinical predictor, HRDetect, can determine loss of homologous recombination-based repair pathways in patients with triple negative breast cancer, other breast cancers, and other cancers with high accuracy through the use of mutational signatures determined through whole genome sequencing. These patients respond well to treatment with targeted therapies, and the predictor is able to identify a far larger number of patients than would be identified using current clinical methods. The aim of this thesis was to predict the results of patients previously classified by HRDetect using only more clinically available data alone. The predictor was developed in SCAN-B data of triple negative breast cancer patients. The process utilised multiple imputation to handle missing values, modelling of continuous variables using restricted cubic splines, and sparse principal component analysis for dimensionality reduction. The model was internally validated using bootstrapping, adjusted to improve calibration and applied to an external breast cancer dataset for external validation. Interpretation of results was made difficult by large differences between the development and validation datasets, and the final model showed seemingly good discrimination but poor calibration. Further investigation of clinical relevance may be required.