BreastSubtypeR: Assumption-aware intrinsic molecular subtyping for breast cancer research

Intrinsic molecular subtyping of breast cancer into categories such as Luminal A, Luminal B, HER2-enriched, Basal-like, and Normal-like is fundamental for understanding tumour biology and guiding personalised treatment strategies. In clinical practice, molecular subtyping is standardised and performed with approved diagnostic assays. However, research implementations of these subtyping methods are fragmented across various tools, leading to inconsistencies and reduced reproducibility. We present BreastSubtypeR, an R/Bioconductor package that integrates ten established subtyping approaches into a unified, reproducible framework. Its core innovation, AUTO mode, evaluates cohort characteristics (e.g., ER/HER2 prevalence, subtype purity, subgroup size) and programmatically disables classifiers whose assumptions are violated – reducing misclassification in skewed or small cohorts. Additional features include direct cross-method benchmarking within a single interface, standardised input handling with method-specific normalisation, and Entrez ID–based probe/gene mapping for robust cross-platform fidelity. For accessibility, the companion application iBreastSubtypeR provides a local R Shiny GUI, enabling non-programmers to apply single-method reproducible workflows used by computational researchers. By combining assumption-aware method selection, harmonised preprocessing, and dual accessibility, BreastSubtypeR addresses a critical need for reproducibility and transparency in translational breast cancer research. The package and Shiny app are freely available via Bioconductor and GitHub, with extensive documentation and example datasets. Presented at the European Bioconductor Conference (EuroBioC2025), Barcelona, September 2025.