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Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer.

Publication ,  Journal Article
Ensenyat-Mendez, M; Orozco, JIJ; Llinàs-Arias, P; Íñiguez-Muñoz, S; Baker, JL; Salomon, MP; Martí, M; DiNome, ML; Cortés, J; Marzese, DM
Published in: Commun Med (Lond)
July 10, 2023

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.

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Published In

Commun Med (Lond)

DOI

EISSN

2730-664X

Publication Date

July 10, 2023

Volume

3

Issue

1

Start / End Page

93

Location

England
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Ensenyat-Mendez, M., Orozco, J. I. J., Llinàs-Arias, P., Íñiguez-Muñoz, S., Baker, J. L., Salomon, M. P., … Marzese, D. M. (2023). Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer. Commun Med (Lond), 3(1), 93. https://doi.org/10.1038/s43856-023-00311-y
Ensenyat-Mendez, Miquel, Javier I. J. Orozco, Pere Llinàs-Arias, Sandra Íñiguez-Muñoz, Jennifer L. Baker, Matthew P. Salomon, Mercè Martí, Maggie L. DiNome, Javier Cortés, and Diego M. Marzese. “Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer.Commun Med (Lond) 3, no. 1 (July 10, 2023): 93. https://doi.org/10.1038/s43856-023-00311-y.
Ensenyat-Mendez M, Orozco JIJ, Llinàs-Arias P, Íñiguez-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 (Lond). 2023 Jul 10;3(1):93.
Ensenyat-Mendez, Miquel, et al. “Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer.Commun Med (Lond), vol. 3, no. 1, July 2023, p. 93. Pubmed, doi:10.1038/s43856-023-00311-y.
Ensenyat-Mendez M, Orozco JIJ, Llinàs-Arias P, Íñiguez-Muñoz S, Baker JL, Salomon MP, Martí M, DiNome ML, Cortés J, Marzese DM. Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer. Commun Med (Lond). 2023 Jul 10;3(1):93.

Published In

Commun Med (Lond)

DOI

EISSN

2730-664X

Publication Date

July 10, 2023

Volume

3

Issue

1

Start / End Page

93

Location

England