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Rare Immune Cells, Cytokine May Predict Response to Cancer Immunotherapy

NEW YORK (GenomeWeb) – The presence of certain rare immune cells and a cytokine one of them produces may affect how well cancer patients respond to immunotherapies, according to a new study.

While immunotherapies to jump-start T-cell responses have been successful in a portion of cancer patients, many don't appear to respond to such treatments. The University of California, San Francisco's Matthew Krummel and his colleagues previously uncovered a type of dendritic cell — which they dubbed stimulatory dendritic cells (SDCs) — in mice that appeared to help T cells respond to immunotherapy. 

In the new study, he and his colleagues found that SDCs were also more common in human melanoma tumors that responded to treatment. As they reported in Nature Medicine yesterday, they traced this effect to a particular cytokine produced by another type of immune cell, natural killer cells, and suggested these cells could be used as biomarkers.

This suggested to the researchers that the activation of natural killer cells in patient tumors might recruit SDCs to tumors to then "prime" them to better respond to immunotherapies. "If you want to stimulate T cells to attack cancer, do you need to recruit any specific allies in the tumor first?" Krummel said in a statement. "We didn't know who were the good and bad partners within the immune system, so we began systematically taking apart tumors and asking of every cell type that was in it, 'Can you activate T cells?'"

In this study, they used an SDC gene signature they previously developed to examine whether the cells behaved in human tumors as they did in mouse tumors. Like in mice, a high expression of the signature was linked to increased overall survival in a cohort of melanoma patients, a finding the researchers confirmed using a melanoma dataset from The Cancer Genome Atlas.

Similarly, with tumor biopsies from two independent cohorts, the researchers found that an increased SDC abundance predicted how responsive patients were to anti-PD1 immunotherapy.

In RNA-seq data from their cohort, the researchers noticed a significant correlation between the expression level of the gene encoding the cytokine FLT3L and SDC levels in tumors. TCGA melanoma samples with high FLT3L gene expression had significantly higher overall survival, they added. This suggested to the researchers that FLT3L influences SDC levels and the immune response to cancer.

In a series of mouse studies, the researchers found that lymphocytes, particularly natural killer cells, produce FLT3L in the tumor microenvironment. In addition, they reported that natural killer cells often interact stably with SDCs in the tumor microenvironment, suggesting that targeting natural killer cell levels could increase FLT3L production to then boost the levels of SDCs in tumors.

When they then looked back at human melanoma data, the researchers again found that the abundance of natural killer cells correlated with both the expression of the gene encoding FLT3L and the level of SDCs. This correlation held in other cancer types, including head and neck squamous cell carcinoma, they reported.

Human melanoma patients with high levels of natural killer cells had increased overall survival. This suggested to the researchers that, as in mice, natural killer cells in people with melanoma produce FLT3L, which controls the number of SCDs in the tumor to lead to better survival.

At the same time, natural killer cell levels also predict whether or not patients with melanoma would respond to anti-PD-1 immunotherapy. This suggests that the presence of SDCs and natural killer cells could be used as biomarkers to guide immunotherapy, the researchers said.

"Ultimately, we would like to know if the presence of these cells in tumors can be detected in the blood," first author Kevin Barry from UCSF said in a statement. "Currently we depend on biopsies or surgically removed tumor samples, but if we could find correlates in the blood, it would make for a really useful clinical tool to identify patients who are likely to have a great response to immunotherapy."