An immunological signature to predict outcome in patients with triple-negative breast cancer with residual disease after neoadjuvant chemotherapy
Céline Blaye, Élodie Darbo, Marc Debled, Véronique Brouste, Valérie Velasco, Clémence Pinard, Nicolas Larmonier, I. Pellegrin, A. Tarricone, Mónica Arnedos, Julie Commeny, Hervé Bonnefoi, Claire B. Larmonier, Gaëtan MacGrogan
Abstract
•Infiltrate of cytotoxic cells is higher in the residual disease of TNBC patients who will not experience metastatic relapse.•Underexpression of immune-related pathways is associated with metastatic relapse in residual disease of TNBC patients.•An immune gene-based signature can predict metastatic relapse in TNBC patients after NACT. BackgroundWhen triple-negative breast cancer (TNBC) patients have residual disease after neoadjuvant chemotherapy (NACT), they have a high risk of metastatic relapse. With immune infiltrate in TNBC being prognostic and predictive of response to treatment, our aim was to develop an immunologic transcriptomic signature using post-NACT samples to predict relapse.Materials and methodsWe identified 115 samples of residual tumors from post-NACT TNBC patients. We profiled the expression of 770 genes related to cancer microenvironment using the NanoString PanCancer IO360 panel to develop a prognostic transcriptomic signature, and we describe the immune microenvironments of the residual tumors.ResultsThirty-eight (33%) patients experienced metastatic relapse. Hierarchical clustering separated patients into five clusters with distinct prognosis based on pathways linked to immune activation, epithelial-to-mesenchymal transition and cell cycle. The immune microenvironment of the residual disease was significantly different between patients who experienced relapse compared to those who did not, the latter having significantly more effector antitumoral immune cells, with significant differences in lymphoid subpopulations. We selected eight genes linked to immunity (BLK, GZMM, CXCR6, LILRA1, SPIB, CCL4, CXCR4, SLAMF7) to develop a transcriptomic signature which could predict relapse in our cohort. This signature was validated in two external cohorts (KMplot and METABRIC).ConclusionsLack of immune activation after NACT is associated with a high risk of distant relapse. We propose a prognostic signature based on immune infiltrate that could lead to targeted therapeutic strategies to improve patient prognosis. When triple-negative breast cancer (TNBC) patients have residual disease after neoadjuvant chemotherapy (NACT), they have a high risk of metastatic relapse. With immune infiltrate in TNBC being prognostic and predictive of response to treatment, our aim was to develop an immunologic transcriptomic signature using post-NACT samples to predict relapse. We identified 115 samples of residual tumors from post-NACT TNBC patients. We profiled the expression of 770 genes related to cancer microenvironment using the NanoString PanCancer IO360 panel to develop a prognostic transcriptomic signature, and we describe the immune microenvironments of the residual tumors. Thirty-eight (33%) patients experienced metastatic relapse. Hierarchical clustering separated patients into five clusters with distinct prognosis based on pathways linked to immune activation, epithelial-to-mesenchymal transition and cell cycle. The immune microenvironment of the residual disease was significantly different between patients who experienced relapse compared to those who did not, the latter having significantly more effector antitumoral immune cells, with significant differences in lymphoid subpopulations. We selected eight genes linked to immunity (BLK, GZMM, CXCR6, LILRA1, SPIB, CCL4, CXCR4, SLAMF7) to develop a transcriptomic signature which could predict relapse in our cohort. This signature was validated in two external cohorts (KMplot and METABRIC). Lack of immune activation after NACT is associated with a high risk of distant relapse. We propose a prognostic signature based on immune infiltrate that could lead to targeted therapeutic strategies to improve patient prognosis.