Reference-free RNA profiling predicts triple negative breast cancer chemoresistance to neoadjuvant treatment

Triple negative breast cancer (TNBC) is the most aggressive breast cancer (BC) and often affects young women. TNBCs are highly heterogeneous and do not benefit from personalized medicine at localized stages. Most TNBC patients undergo neoadjuvant chemotherapy (NAC) before surgery. In case of chemoresistance with residual tumor after NAC, survival is poor despite execution of complete tumor resection. There is currently no clinically useful biomarker to predict TNBC chemoresistance to NAC that would enable targeted therapeutic intensification. We analyzed here a unique cohort of 106 TNBC tumors before NAC, including 58 chemoresistant and 48 chemosensitive cases, from 2 independent hospitals. Using machine learning under a nested cross-validation design, we obtained two transcriptomic signatures respectively generated from standard differential gene expression analysis and reference-free analysis of differential fragments of transcripts, without any annotation bias. This approach resulted in accurate signatures of TNBC chemoresistance to NAC. Gene ontology analyses of reference-free signatures highlighted DNA repair, replication, and metabolism, in agreement with current knowledge of TNBC resistance biology. In summary, these results show the potential of a reference-free generated transcriptomic signature as predictive biomarker of early TNBC chemoresistance.

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Publication Details

Journal
NAR Cancer
Published
2025-10-07
DOI
https://doi.org/10.1093/narcan/zcaf036
Citations
2
Primary Topic
Cancer Genomics and Diagnostics
Type
article
Field-Weighted Citation Impact
1.87

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article

Reference-free RNA profiling predicts triple negative breast cancer chemoresistance to neoadjuvant treatment

Nouritza Torossian, C. Le Tourneau, Marc Gabriel, M. Kamal et al.
2 citations
NAR Cancer
Cancer Genomics and Diagnostics
1.87
article

Reference-free RNA profiling predicts triple negative breast cancer chemoresistance to neoadjuvant treatment

Nouritza Torossian, C. Le Tourneau, Marc Gabriel, M. Kamal, Charlotte Lecerf, Adriana Aguilar‐Mahecha, Josiane Lafleur, Maral Halladjian, Anne Vincent‐Salomon, Camille Brochard, Daniel Gautheret, Panagiotis Papoutsoglou, Mark Basik, Dominika Foretek, Antonin Morillon, Sergio Roman‐Roman, Célia Dupain, Constance Lamy, Linda Ramdani
article en
2 citations

Abstract

Triple negative breast cancer (TNBC) is the most aggressive breast cancer (BC) and often affects young women. TNBCs are highly heterogeneous and do not benefit from personalized medicine at localized stages. Most TNBC patients undergo neoadjuvant chemotherapy (NAC) before surgery. In case of chemoresistance with residual tumor after NAC, survival is poor despite execution of complete tumor resection. There is currently no clinically useful biomarker to predict TNBC chemoresistance to NAC that would enable targeted therapeutic intensification. We analyzed here a unique cohort of 106 TNBC tumors before NAC, including 58 chemoresistant and 48 chemosensitive cases, from 2 independent hospitals. Using machine learning under a nested cross-validation design, we obtained two transcriptomic signatures respectively generated from standard differential gene expression analysis and reference-free analysis of differential fragments of transcripts, without any annotation bias. This approach resulted in accurate signatures of TNBC chemoresistance to NAC. Gene ontology analyses of reference-free signatures highlighted DNA repair, replication, and metabolism, in agreement with current knowledge of TNBC resistance biology. In summary, these results show the potential of a reference-free generated transcriptomic signature as predictive biomarker of early TNBC chemoresistance.

NAR CancerVol. 7(4)
Centre National de la Recherche Scientifique (FR), Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR), Université Paris Sciences et Lettres (FR), Université Paris-Saclay (FR), Jewish General Hospital (CA), Sorbonne Université (FR), Université Sorbonne Paris Nord (FR), Institut de Biologie Intégrative de la Cellule (FR), Dynamique de l'information génétique : bases fondamentales et cancer (FR), CEA Paris-Saclay (FR), International Drug Development (FR), McGill University (CA), Institut Curie (FR)
Fondation du cancer du sein du Québec, Agence Nationale de la Recherche, Fondation pour la Recherche Médicale, Fondation ARC pour la Recherche sur le Cancer, Fonds de Recherche du Québec - Santé
Openalex Percentile: Top 12%
Cancer Genomics and Diagnostics
1.87
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