Understanding neutrophil heterogeneity and its relationship to disease progression has become a recent focus of cancer research.,Indeed, several studies have identified neutrophil subpopulations associated with protumoral or antitumoral functions.,However, this work has been hindered by a lack of widely accepted markers with which to define neutrophil subpopulations.,To identify markers of neutrophil heterogeneity in cancer, we used single-cell cytometry by time-of-flight (CyTOF) coupled with high-dimensional analysis on blood samples from treatment-naïve patients with melanoma.,Our efforts allowed us to identify seven blood neutrophil clusters, including two previously identified individual populations.,Interrogation of these neutrophil subpopulations revealed a positive trend between specific clusters and disease stage.,Finally, we recapitulated these seven blood neutrophil populations via flow cytometry and found that they exhibited diverse capacities for phagocytosis and reactive oxygen species production in vitro.,Our data provide a refined consensus on neutrophil heterogeneity markers, enabling a prospective functional evaluation in patients with solid tumors.
Immune-checkpoint blockade (ICB) has demonstrated efficacy in many tumor types, but predictors of responsiveness to anti-PD1 ICB are incompletely characterized.,In this study, we analyzed a clinically annotated cohort of patients with melanoma (n = 144) treated with anti-PD1 ICB, with whole-exome and whole-transcriptome sequencing of pre-treatment tumors.,We found that tumor mutational burden as a predictor of response was confounded by melanoma subtype, whereas multiple novel genomic and transcriptomic features predicted selective response, including features associated with MHC-I and MHC-II antigen presentation.,Furthermore, previous anti-CTLA4 ICB exposure was associated with different predictors of response compared to tumors that were naive to ICB, suggesting selective immune effects of previous exposure to anti-CTLA4 ICB.,Finally, we developed parsimonious models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 ICB in individual tumors, with validation in smaller independent cohorts limited by the availability of comprehensive data.,Broadly, we present a framework to discover predictive features and build models of ICB therapeutic response.,Analysis of fully clinically annotated and sequenced melanoma tumor samples collected before anti-PD1 treatment suggests that determinants of response differ on the basis of previous anti-CTLA4 therapy, and that tumor mutational burden may not be a strong predictor of response across melanoma subtypes.