Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer.
Document Type
Article
Publication Date
7-10-2023
Publication Title
Commun Med (Lond)
Keywords
california; santa monica; sjci
Abstract
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.
Clinical Institute
Cancer
Department
Oncology
Recommended Citation
Ensenyat-Mendez, Miquel; Orozco, Javier I J; Llinàs-Arias, Pere; Íñiguez-Muñoz, Sandra; Baker, Jennifer L; Salomon, Matthew P; Martí, Mercè; DiNome, Maggie L; Cortés, Javier; and Marzese, Diego M, "Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer." (2023). Articles, Abstracts, and Reports. 7677.
https://digitalcommons.providence.org/publications/7677
