henhaobio
Sign in

bioRxivCancer BiologyPreprint

A natural killer cell gene signature predicts melanoma patient survival

Joseph CursonsiD, Fernando Souza-Fonseca-Guimaraes, Ashley Anderson, Momeneh ForoutaniD, Soroor Hediyeh-ZadehiD, Andreas Behren, Nicholas D. HuntingtoniD, Melissa J Davis

PDFDOI

Abstract

Animal models have demonstrated that natural killer (NK) cells can limit the metastatic dissemination of tumors, however their ability to combat established human tumors has been difficult to investigate.\n\nA number of computational methods have been developed for the deconvolution of immune cell types within solid tumors. We have taken the NK cell gene signatures from several tools, then curated and expanded this list using recent reports from the literature. Using a gene set scoring method to investigate RNA-seq data from The Cancer Genome Atlas (TCGA) we show that patients with metastatic cutaneous melanoma have an improved survival rate if their tumor shows evidence of greater NK cell infiltration. Furthermore, these survival effects are enhanced in tumors which have a higher expression of NK cell stimuli such as IL-15, suggesting NK cells are part of a coordinated immune response within these patients. Using this signature we then examine transcriptomic data to identify tumor and stromal components which may influence the penetrance of NK cells into solid tumors.\n\nThese data support a role for NK cells in the regulation of human tumors and highlight potential survival effects associated with increased NK cell activity. Furthermore, our computational analysis identifies a number of potential targets which may help to unleash the anti-tumor potential of NK cells as we enter the age of immunotherapy.

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISShembrey '22Chakraborty '25Friedmann '20Ichise '21Dean '23Liu '23
70% semantic matchCancer Biology
C. Shembrey, C. Behrenbruch, B. P. Loveday+3 more·bioRxiv·Apr 29, 2022