2026-05-16T22:07:36Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/236242024-11-29T02:21:55Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967
Repisalud author Ensenyat-Mendez, Miquel author Orozco, Javier I J author Llinàs-Arias, Pere author Íñiguez-Muñoz, Sandra author Baker, Jennifer L author Salomon, Matthew P author Martí, Mercè author DiNome, Maggie L author Cortés, Javier author Marzese, Diego M 2024-10-09T06:33:49Z 2024-10-09T06:33:49Z 2023-07-10 Ensenyat-Mendez M, Orozco JIJ, Llinàs-Arias P, ïñiguez-Muñoz S, Baker JL, Salomon MP, et al. Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer. Commun Med. 2023 Jul 10;3(1):93. 10.1038/s43856-023-00311-y 2730-664X Communications medicine https://hdl.handle.net/20.500.13003/19969 37430006 https://hdl.handle.net/20.500.12105/23624 1026199000002 Background: Immune checkpoint inhibitors (ICI) improve clinical outcomes in triple-negative breast cancer (TNBC) patients. However, a subset of patients does not respond to treatment. Biomarkers that show ICI predictive potential in other solid tumors, such as levels of PD-L1 and the tumor mutational burden, among others, show a modest predictive performance in patients with TNBC. Methods: We built machine learning models based on pre-ICI treatment gene expression profiles to construct gene expression classifiers to identify primary TNBC ICI-responder patients. This study involved 188 ICI-naïve and 721 specimens treated with ICI plus chemotherapy, including TNBC tumors, HR+/HER2- breast tumors, and other solid non-breast tumors. Results: The 37-gene TNBC ICI predictive (TNBC-ICI) classifier performs well in predicting pathological complete response (pCR) to ICI plus chemotherapy on an independent TNBC validation cohort (AUC = 0.86). The TNBC-ICI classifier shows better performance than other molecular signatures, including PD-1 (PDCD1) and PD-L1 (CD274) gene expression (AUC = 0.67). Integrating TNBC-ICI with molecular signatures does not improve the efficiency of the classifier (AUC = 0.75). TNBC-ICI displays a modest accuracy in predicting ICI response in two different cohorts of patients with HR + /HER2- breast cancer (AUC = 0.72 to pembrolizumab and AUC = 0.75 to durvalumab). Evaluation of six cohorts of patients with non-breast solid tumors treated with ICI plus chemotherapy shows overall poor performance (median AUC = 0.67). Conclusion: TNBC-ICI predicts pCR to ICI plus chemotherapy in patients with primary TNBC. The study provides a guide to implementing the TNBC-ICI classifier in clinical studies. Further validations will consolidate a novel predictive panel to improve the treatment decision-making for patients with TNBC. eng Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer research article