A pan-cohort transcriptional landscape of breast cancer maps subtype and microenvironmental programs

Abstract

Breast cancer comprises heterogeneous transcriptional states that are incompletely captured by discrete clinical or molecular subtype labels. To visualize this heterogeneity in a unified framework, we integrated bulk RNA-seq data from 2,284 patient samples across 13 studies using 18,089 protein coding genes, a harmonized processing pipeline, batch correction, consensus clustering and PaCMAP dimensionality reduction to construct an interactive breast cancer transcriptional landscape. Consensus clustering identified five major regions, which were annotated using PAM50 scores calculated for each sample: Luminal A, Luminal B, HER2-enriched, and two basal-associated clusters. The basal clusters separated into an immune-rich region marked by T cell–inflamed, tumor-associated macrophages (TAM), and low-purity signatures, and a cell-cycle–driven region enriched for proliferation and DNA replication programs. Overlay of marker genes, pathways, kinases, neuronal-like signaling programs, cancer associated fibroblasts (CAF) states, and TAM programs revealed spatially organized subtype biology and microenvironmental heterogeneity. Finally, projection of therapy-associated resistance signatures identified landscape regions linked to predicted resistance to HER2-targeted therapy and hormone receptor–directed endocrine therapies. By enabling interactive exploration of transcriptional states, marker genes, pathways, and therapeutic response programs, this resource provides a community framework for biomarker discovery in breast cancer. Significance statement We present a unified, multi-cohort transcriptional landscape of breast cancer that organizes canonical subtypes along continuous biological axes and reveals spatially structured regions of therapeutic sensitivity and resistance. By enabling projection of patient samples and patient-derived models into this framework, we provide a practical tool for interpreting tumor biology and guiding translational discovery. Graphical abstract caption We integrate 13 breast cancer transcriptomic datasets using a harmonized processing pipeline with batch correction and dimensionality reduction to construct a unified landscape spanning basal, HER2-enriched, luminal A, luminal B, and normal-like subtypes. Continuous gradients of proliferation, endocrine signaling, metabolic activity, and tissue architecture organize the space and recapitulate subtype biology. Overlay of clinical outcomes reveals high-risk regions beyond discrete subtype boundaries, while integration of gene expression and copy number alterations links genomic events to pathway-level programs. Microenvironmental features, including cancer-associated fibroblast programs, map to distinct regions of the landscape. Projection of drug response signatures identifies zones of sensitivity and intrinsic resistance. The framework supports embedding of patient samples and patient-derived xenograft models and is available as an interactive resource for exploration and projection of new datasets. One Sentence Summary A landscape built using only transcriptomic analysis for breast cancer reveals novel insights about subtype-specific biology.