Natural killer (NK) cells comprise one subset of the innate lymphoid cell (ILC) family. Despite reported antitumor functions of NK cells, their tangible contribution to tumor control in humans remains controversial. This is due to incomplete understanding of the NK cell states within the tumor microenvironment (TME). Here, we demonstrate that peripheral circulating NK cells differentiate down two divergent pathways within the TME, resulting in different end states. One resembles intraepithelial ILC1s (ieILC1) and possesses potent in vivo antitumor activity. The other expresses genes associated with immune hyporesponsiveness and has poor antitumor functional capacity. Interleukin-15 (IL-15) and direct contact between the tumor cells and NK cells are required for the differentiation into CD49a+CD103+ cells, resembling ieILC1s. These data explain the similarity between ieILC1s and tissue-resident NK cells, provide insight into the origin of ieILC1s, and identify the ieILC1-like cell state within the TME to be the NK cell phenotype with the greatest antitumor activity. Because the proportions of the different ILC states vary between tumors, these findings provide a resource for the clinical study of innate immune responses against tumors and the design of novel therapy.

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Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We propose a novel machine learning algorithm, CELESTA, which uses both cell's protein expression and spatial information to identify cell type of individual cells. We demonstrate the performance of CELESTA on multiplexed immunofluorescence in situ images of colorectal cancer and head and neck cancer. Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, which we validate in an independent cohort. By coupling our in situ spatial analysis with single-cell RNA-sequencing data on proximal sections of the same tissue specimens, we identify and validate cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of spatial biology to reveal clinically-relevant cellular interactions.

The presence and function of innate lymphoid cells (ILC), including natural killer (NK) cells, within human tumors has been poorly characterized. Here, we have assessed the heterogeneity of NK and ILC populations by single-cell RNA sequencing (scRNAseq) of hundreds of individual NK cells and ILCs within human head and neck squamous cell carcinoma (HNSCC), matched lymph node metastases, matched peripheral blood, and blood from healthy donors. Fresh tumor specimens and blood were obtained from 8 patients undergoing surgical resection of HNSCC. Unsupervised clustering revealed 8 different clusters of NK cells and ILC subsets. We observed significant heterogeneity, with distinct subsets showing profiles consistent with that of conventional NK cells, ILC1-like cells and ILC3-like cell. Importantly, peripheral NK cells were distinct from intratumoral NK cells. The presence of these different cell subsets within primary HNSCC tumors was confirmed by flow cytometry. Further, plasticity between the NK cell subsets was supported by in vitro and in vivo experimental data. Given the ability of NK and ILCs to polarize the immune responses through the secretion of cytokines and the ability of certain subsets to kill target cells, we hypothesize that the differences observed in NK and ILC populations and their plasticity may result in different immune responses, and may influence clinical outcomes following therapy.