Multi-omic machine learning predictor of breast cancer therapy response.

ABSTRACT: Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment¹. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy². Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery³ were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and

SUBMITTER: Sammut SJ 

PROVIDER: S-EPMC8791834 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature