ArticleTranslational oncology2026
Multi-regional transcriptomics reveals robust consensus subtypes beyond tumor heterogeneity of breast cancer.
Article in Translational oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCommercial molecular classifiers such as Prediction Analysis of Microarray 50 (PAM50), Oncotype DX, and MammaPrint have revolutionized breast cancer management. However, their clinical reliability is undermined by tumor heterogeneity.
methodsWe quantified the discordance in clinical molecular typing across multi-regional samples. Gene set enrichment analysis (GSEA) was employed to explore potential sources of intratumoral heterogeneity (ITH). Gene-level heterogeneity was evaluated by quantifying expression variation within versus between tumors. A robust subtyping framework was constructed via Non-negative Matrix Factorization (NMF) based on a screened low-ITH gene set.
resultsITH was observed in over 50% of tumors, triggering significant classification discordance across commercial tests. GSEA revealed that transcriptomic heterogeneity might be driven by stromal and matrix-related pathways. The low-ITH-based NMF framework established an optimal three-subtype classification that exhibited superior prognostic stratification and maintained high stability across metastatic sites.
conclusionsWe established a low-ITH subtyping framework that overcomes the limitations of single-biopsy-based molecular typing. Our findings offer a novel strategy to enhance the precision of breast cancer management.
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