The molecular complexity of triple-negative breast cancers (TNBCs) provides a challenge for patient management. We set out to characterise this heterogeneous disease by combining transcriptomics and genomics data, with the aim of revealing convergent pathway dependencies with the potential for treatment intervention. A Bayesian algorithm was used to integrate molecular profiles in two TNBC cohorts, followed by validation using five independent cohorts (n = 1,168), including three clinical trials. A four-gene decision tree signature was identified which robustly classified TNBCs into six subtypes. All four genes in the signature (EXO1, TP53BP2, FOXM1 and RSU1) are associated with either genomic instability, malignant growth, or treatment response. One of the six subtypes, MC6, encompassed the largest proportion of tumours (~50%) in early diagnosed TNBCs. In TNBC patients with metastatic disease, the MC6 proportion was reduced to 25%, and was independently associated with a higher response rate to platinum-based chemotherapy. In TNBC cell line data, platinum-sensitivity was recapitulated, and a sensitivity to the inhibition of the phosphatase PPM1D was revealed. Molecularly, MC6-TNBCs displayed high levels of telomeric allelic imbalances, enrichment of CD4+ and CD8+ immune signatures, and reduced expression of genes negatively regulating the mitogen-activated protein kinase (MAPK) signalling pathway. These observations suggest that our integrative classification approach may identify TNBC patients with discernible and theoretically pharmacologically tractable features that merit further studies in prospective trials.
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