ABSTRACT
We report the results of 24 women, 50% (N = 12) with hormone receptor-positive breast cancer and 50% (N = 12) with advanced triple-negative breast cancer, treated with entinostat + nivolumab + ipilimumab from the dose escalation (N = 6) and expansion cohort (N = 18) of ETCTN-9844 ( NCT02453620 ). The primary endpoint was safety. Secondary endpoints were overall response rate, clinical benefit rate, progression-free survival and change in tumor CD8:FoxP3 ratio. There were no dose-limiting toxicities. Among evaluable participants (N = 20), the overall response rate was 25% (N = 5), with 40% (N = 4) in triple-negative breast cancer and 10% (N = 1) in hormone receptor-positive breast cancer. The clinical benefit rate was 40% (N = 8), and progression-free survival at 6 months was 50%. Exploratory analyses revealed that changes in myeloid cells may contribute to responses; however, no correlation was noted between changes in CD8:FoxP3 ratio, PD-L1 status and tumor mutational burden and response. These findings support further investigation of this treatment in a phase II trial.
Subject(s)
Antineoplastic Combined Chemotherapy Protocols , Benzamides , Ipilimumab , Nivolumab , Pyridines , Receptor, ErbB-2 , Humans , Female , Middle Aged , Pyridines/administration & dosage , Pyridines/therapeutic use , Antineoplastic Combined Chemotherapy Protocols/therapeutic use , Nivolumab/therapeutic use , Nivolumab/administration & dosage , Adult , Receptor, ErbB-2/metabolism , Benzamides/therapeutic use , Benzamides/administration & dosage , Aged , Ipilimumab/therapeutic use , Ipilimumab/administration & dosage , Triple Negative Breast Neoplasms/drug therapy , Breast Neoplasms/drug therapy , Breast Neoplasms/mortality , Progression-Free SurvivalABSTRACT
Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.
