Abstract
The tumor microenvironment (TME) contributes to breast cancer heterogeneity and outcome but is rarely considered in clinical decision-making. We address this gap by systematically characterizing the TME’s cellular composition to establish its independent clinical utility across intrinsic and genomic subtypes. We first compare 15 TME profiling methods in 693 samples and then apply the deconvolution algorithm InstaPrism to a meta-dataset of 14,837 expression profiles. We identify seven distinct TME patterns that associate with disease-free survival independently of intrinsic subtype. We also identify TME features that modulate chemotherapy response, relapse, and metastatic risk, with divergent patterns observed across estrogen receptor subtypes. Notably, long-term recurrence was regulated by vascular stromal cells and the innate immune response. Furthermore, the depletion of B cell lineage derivatives in metastatic lesions suggests an opportunity for therapeutic intervention. These results provide evidence for using TME characterization as a prognostic and predictive biomarker and identify potential targets for TME-based intervention.
| Original language | English |
|---|---|
| Article number | 102450 |
| Journal | Cell Reports Medicine |
| Volume | 6 |
| Issue number | 11 |
| DOIs | |
| State | Published - 18 Nov 2025 |
Bibliographical note
Publisher Copyright:© 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- B cells
- InstaPrism
- TME types
- breast cancer
- chemotherapy response
- deconvolution
- metastasis
- prognosis
- relapse
- tumor microenvironment
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