Pan-cancer profiling of tumor-infiltrating natural killer cells through transcriptional reference mapping

Abstract

AbstractThe functional diversity of natural killer (NK) cell repertoires stems from differentiation, homeostatic receptor-ligand interactions, and adaptive-like responses to viral infections. Here, we generated a single-cell transcriptional reference map of healthy human blood and tissue-derived NK cells, with temporal resolution and fate-specific expression of gene regulator networks defining NK cell differentiation. Using transfer learning, transcriptomes of tumor-infiltrating NK cells from seven solid tumor types (427 patients), combined from 39 datasets, were incorporated into the reference map and interrogated for tumor microenvironment (TME)-induced perturbations. We identified six functionally distinct NK cellular states in healthy and malignant tissues, two of which were commonly enriched for across tumor types: a dysfunctional ‘stressed’ CD56brightstate susceptible to TME-induced immunosuppression and a cytotoxic TME-resistant ‘effector’ CD56dimstate. The ratio of ‘stressed’ CD56brightand ‘effector’ CD56dimwas predictive of patient outcome in malignant melanoma and osteosarcoma. This resource may inform the design of novel NK cell therapies and can be extended endlessly through transfer learning to interrogate new datasets from experimental perturbations or disease conditions.