Stand-alone transcriptional immune response prediction in primary triple-negative breast cancer - Roostee, Suze ; 0000-0003-3908-7417; Killander, Fredrika; Saghir, Hani; Nacer, Deborah F ; 0000-0002-7117-1371; Häkkinen, Jari ; 0000-0002-8466-9179; Sasiain, Iñaki ; 0009-0007-6177-0155; Veerla, Srinivas ; 0000-0001-7328-6239; Vallon-Christersson, Johan ; 0000-0002-2195-0385; Loman, Niklas; Ohlsson, Mattias ; 0000-0003-1145-4297; Staaf, Johan ; 0000-0001-5254-5115 (Cancer Research Communications, 2025)
Triple-negative breast cancer (TNBC) accounts for 10-20% of primary breast cancers and often has early relapses and aggressive progression. An activated tumour immune response can be prognostic in treatment-naive and chemotherapy-treated TNBC patients and may be assessed using gene expression data. We derived a stand-alone predictor for a proposed immunomodulatory transcriptional TNBC subtype in a training cohort of 235 patients with primary disease based on random forest modelling of RNA-sequencing data. Validation in independent TNBC cohorts totalling more than 1200 patients demonstrated that the classifier recapitulates the immunomodulatory mRNA subtype classification, is associated with elevated immune expression and diversity of T-cell receptor genes, is associated with response to neoadjuvant chemotherapy, and can separate patients into subgroups with better or worse prognosis after adjuvant chemotherapy. The availability of stand-alone classifiers for mRNA-based prediction may further enhance RNA-sequencing's usability in a more routine clinical context and for translational endpoints in clinical trials.