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.

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BibTeX RIS

BibTeX

@misc{arora2026pancohort,
  title = {{A pan-cohort transcriptional landscape of breast cancer maps subtype and microenvironmental programs}},
  author = {S. Arora and R. Suresh and N. Holland and G. Glatzer and M. Jensen and E. Q. Konnick and C. Pritchard and Y. Li and H. A. Parsons and S. A. Hurvitz and E. C. Holland},
  howpublished = {bioRxiv},
  year = {2026},
  month = aug,
  doi = {10.64898/2026.08.31.748296},
  url = {https://www.biorxiv.org/content/10.64898/2026.08.31.748296v1},
  note = {CC BY-NC-ND},
}

RIS

TY  - UNPB
TI  - A pan-cohort transcriptional landscape of breast cancer maps subtype and microenvironmental programs
AU  - S. Arora
AU  - R. Suresh
AU  - N. Holland
AU  - G. Glatzer
AU  - M. Jensen
AU  - E. Q. Konnick
AU  - C. Pritchard
AU  - Y. Li
AU  - H. A. Parsons
AU  - S. A. Hurvitz
AU  - E. C. Holland
PY  - 2026
DA  - 2026/08/31
PB  - bioRxiv
DO  - 10.64898/2026.08.31.748296
UR  - https://www.biorxiv.org/content/10.64898/2026.08.31.748296v1
AB  - 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.
ER  -

Abstract

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