Nature CommunicationsCancer BiologyPeer-reviewed journal article
Spatiotemporal organisation of residual disease in mouse and human BRCA1-deficient mammary tumours and breast cancer
Demeter TúrósiD, Morgane DecollognyiD, Anna Moyseos, Astrid Chanfon, Myriam Siffert, Joanne Bousmar, Lou RomanensiD, Jean‐Christophe TilleiD, Olivier TrédaniD, Sana Intidhar Labidi‐GalyiD, Alberto ValdeolivasiD, Sven RottenbergiD
Abstract
Breast cancer remains one of the prominent causes of death worldwide. Although chemotherapeutic agents often result in substantial reduction of primary or metastatic tumours, remaining drug-tolerant tumour cell populations, known as minimal residual disease (MRD), pose a significant risk of recurrence and therapy resistance. In this study, we describe the spatiotemporal organisation of therapy response and MRD in BRCA1;p53-deficient mouse mammary tumours and human clinical samples using a multimodal approach. By integrating single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), and imaging mass cytometry (IMC) across multiple treatment timepoints, we characterise dynamic interactions between tumour cell subpopulations and their surrounding microenvironment. Our analysis identifies a distinct, drug-tolerant epithelial-mesenchymal transition (EMT) cancer cell population, which exhibits a conserved expression program in human BRCA1-deficient tumours and significantly correlates with adverse clinical outcomes. We further reveal the spatial distribution of residual EMT-like tumour cells within specific anatomical niches, providing a framework for understanding the persistence of MRD and potential therapeutic vulnerabilities. These findings yield a comprehensive molecular roadmap of MRD, opening new avenues for therapeutic strategies targeting EMT-driven drug tolerance and tumour relapse.
- organism: mouse
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- assay: imaging
- research focus: cell type annotation
- research focus: spatial domain detection
- research focus: differential expression
- approach: deep learning
- approach: statistical model
- Citations
- 1
- FWCI
- 2.94
- Field percentile
- top 15%
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