Abstract
INTRODUCTION: Disease heterogeneity is the hallmark of breast cancer, which is the most common female malignancy. With a disturbing increase in mortality and disease burden, there remains a need for effective early-stage theragnostic and prognostic biomarkers. In this work, we improved on BrcaDx (https://apalania.shinyapps.io/brcadx/) for cancer vs control screening and examined a cluster of adjoining learning problems in breast cancer heterogeneity: (i) identification of metastatic cancers; (ii) molecular subtyping (TNBC, HER2, or luminal); and (iii) histological subtyping (invasive ductal or invasive lobular). METHODS: We analyzed the transcriptomic profiles of breast cancer patients from public-domain databases such as the TCGA using stage-encoded problem-specific statistical models of gene expression and unveiled stage-salient and progression-significant genes. Using a consensus approach, we identified potential machine learning features, and considered six model classes for each learning problem, with hyperparameter optimization on a training dataset and evaluation on a holdout test dataset. A nested approach enabled us to identify the best model class for each learning problem. RESULTS: External validation of the best models yielded balanced accuracies of 97.42% for cancer vs normal; 88.22% for metastatic v/s non metastatic; 88.79% for ternary molecular subtyping; and ensemble accuracy of 94.23% for histological subtyping. The model for molecular subtyping was validated on a 26-sample TNBC-only out-of-distribution cohort, yielding 25 correct predictions. We performed a late integration of multi-omics datasets by validating the feature space used in each problem with miRNA profiles, methylation profiles, and commercial breast cancer panels. DISCUSSION: Pending prospective studies, we have translated the models into BC-Predict that forks the best models developed for each problem in a unified interface and provides a complete readout for input instances of expression data, including uncertainty estimates. BC-Predict is freely available for non-commercial purposes at: https://apalania.shinyapps.io/BC-Predict.