Homologous recombination deficiency reflects the heterogeneity and monitoring treatment response for patients with breast cancer.

ABSTRACT

BACKGROUND: In breast cancer (BC), homologous recombination defect (HRD) is a common carcinogenic mechanism. It is meaningful to classify BC according to HRD biomarkers and to develop a platform for identifying BC molecular features, pathological features and therapeutic responses. METHODS: In total, 109 HRD genes were collected and screened by univariate Cox regression analysis to determine the prognostic genes, which were used to construct a consensus matrix to identify BC subtype. Differentially expressed genes (DEGs) were filtered by the Limma package and screened by random forest analysis to build a model to analyze the immunotherapy response and sensitivity and prognosis of patients suffering from BC to different drugs. RESULTS: Thirteen out of 109 HRD genes were prognostic genes of BC, and BC was classified into two subgroups based on their expression. Cluster 1 had a significantly backward survival outcome and a significantly higher adaptive immunity score relative to cluster 2. Six genes were identified by random forest analysis as factors for developing the model. The model provided a prediction called risk score, which showed a significant stratification effect on BC prognosis, immunotherapy response and IC50 values of 62 drugs. CONCLUSIONS: In the present study, two HRD subtypes of BC were successfully identified, for which mutation and immunological features were determined. A model based on differential genes of HRD subtypes was established, which was a potential predictor of prognosis, immunotherapy response and drug sensitivity of BC.