Stand-alone transcriptional immune response prediction in primary triple-negative breast cancer
(2025) In Cancer Research Communications- Abstract
Triple-negative breast cancer (TNBC) accounts for 10-20% of primary breast cancers and often has early relapses and aggressive progression. An activated tumour immune response can be prognostic in treatment-naive and chemotherapy-treated TNBC patients and may be assessed using gene expression data. We derived a stand-alone predictor for a proposed immunomodulatory transcriptional TNBC subtype in a training cohort of 235 patients with primary disease based on random forest modelling of RNA-sequencing data. Validation in independent TNBC cohorts totalling more than 1200 patients demonstrated that the classifier recapitulates the immunomodulatory mRNA subtype classification, is associated with elevated immune expression and diversity of... (More)
Triple-negative breast cancer (TNBC) accounts for 10-20% of primary breast cancers and often has early relapses and aggressive progression. An activated tumour immune response can be prognostic in treatment-naive and chemotherapy-treated TNBC patients and may be assessed using gene expression data. We derived a stand-alone predictor for a proposed immunomodulatory transcriptional TNBC subtype in a training cohort of 235 patients with primary disease based on random forest modelling of RNA-sequencing data. Validation in independent TNBC cohorts totalling more than 1200 patients demonstrated that the classifier recapitulates the immunomodulatory mRNA subtype classification, is associated with elevated immune expression and diversity of T-cell receptor genes, is associated with response to neoadjuvant chemotherapy, and can separate patients into subgroups with better or worse prognosis after adjuvant chemotherapy. The availability of stand-alone classifiers for mRNA-based prediction may further enhance RNA-sequencing's usability in a more routine clinical context and for translational endpoints in clinical trials.
(Less)
- author
- organization
-
- Computational Science for Health and Environment (research group)
- Research Group Lung Cancer (research group)
- Breast/lung cancer (research group)
- LUCC: Lund University Cancer Centre
- Breast cancer treatment
- Breast/ovarian cancer
- Breast and Ovarian Cancer Genomics (research group)
- Breastcancer-genetics
- LU Profile Area: Natural and Artificial Cognition
- eSSENCE: The e-Science Collaboration
- Artificial Intelligence in CardioThoracic Sciences (AICTS) (research group)
- publishing date
- 2025-11-10
- type
- Contribution to journal
- publication status
- epub
- subject
- in
- Cancer Research Communications
- external identifiers
-
- pmid:41212151
- ISSN
- 2767-9764
- DOI
- 10.1158/2767-9764.CRC-25-0453
- language
- English
- LU publication?
- yes
- id
- 558c11cc-9ad4-4c8e-9937-567eb5389418
- date added to LUP
- 2025-11-19 13:38:46
- date last changed
- 2025-11-19 14:48:44
@article{558c11cc-9ad4-4c8e-9937-567eb5389418,
abstract = {{<p>Triple-negative breast cancer (TNBC) accounts for 10-20% of primary breast cancers and often has early relapses and aggressive progression. An activated tumour immune response can be prognostic in treatment-naive and chemotherapy-treated TNBC patients and may be assessed using gene expression data. We derived a stand-alone predictor for a proposed immunomodulatory transcriptional TNBC subtype in a training cohort of 235 patients with primary disease based on random forest modelling of RNA-sequencing data. Validation in independent TNBC cohorts totalling more than 1200 patients demonstrated that the classifier recapitulates the immunomodulatory mRNA subtype classification, is associated with elevated immune expression and diversity of T-cell receptor genes, is associated with response to neoadjuvant chemotherapy, and can separate patients into subgroups with better or worse prognosis after adjuvant chemotherapy. The availability of stand-alone classifiers for mRNA-based prediction may further enhance RNA-sequencing's usability in a more routine clinical context and for translational endpoints in clinical trials.</p>}},
author = {{Roostee, Suze and Killander, Fredrika and Saghir, Hani and Nacer, Deborah F and Häkkinen, Jari and Sasiain, Iñaki and Veerla, Srinivas and Vallon-Christersson, Johan and Loman, Niklas and Ohlsson, Mattias and Staaf, Johan}},
issn = {{2767-9764}},
language = {{eng}},
month = {{11}},
series = {{Cancer Research Communications}},
title = {{Stand-alone transcriptional immune response prediction in primary triple-negative breast cancer}},
url = {{http://dx.doi.org/10.1158/2767-9764.CRC-25-0453}},
doi = {{10.1158/2767-9764.CRC-25-0453}},
year = {{2025}},
}
