LncRNA antigens- a novel resource to improve immunotherapy efficacy predictions in Melanoma

Abstract

AbstractBackgroundICI (immune checkpoint inhibitor) therapy is one of the most promising treatments for melanoma. ICI response however varies among patients, emphasizing the importance of identifying genomic biomarkers to predict likely therapeutic efficacy in advance of treatment. We hypothesised that a lncRNA based immunogencity (lnc-IM) score could be used to predict individual response to ICI treatment, and that this could complement the existing criterion for ICI selection based on tumor mutation burden (TMB).MethodologyThe TCGA-SKCM (n=101) and the ICI treated UCLA (n=25), MSKCC (n=16) and DFCI (n=40) melanoma cohorts were used in this study, involving both clinical and transcriptomic data. Each patient was assigned an lnc-IM score based on the number of lncRNA sORF derived peptides predicted to be presented by their tumor’s MHC-I genotype. For the ICI treated cohorts, a combined antigen score was defined as a sum of neo-antigen load (derived from TMB) and lnc-IM score. A logistic regression-based classifier was used to predict ICI responses based on these combined antigen scores.ResultsSurvival analysis showed improved overall survival among patients with low lnc-IM scores (HR= 0.39, p=0.009) in the TCGA-SKCM cohort. We also observed a negative association between tumor immune cell concentration and lnc-IM scores, with low lnc-IM groups showing higher anti-tumor immune cell concentrations . Using the ICI treated cohorts, we demonstrated that a classifier based on combined antigen scoring improved the prediction of immunotherapy outcomes as compared to using TMB alone, yielding an area under the curve (AUC) of 0.71 with an accuracy of 0.54 and recall of 1. We also demonstrated a reduced rate of false negatives (14%) by using a combined antigen score as compared to the use of TMB alone (33%) in ICI treated cohorts.ConclusionOur findings suggest that the use of combined antigen scores (using lnc-IM scores along with TMB derived neoantigen load) have potential in improving immunotherapy efficacy predictions. Prospective validation in larger cohort sizes is warranted.KEY MESSAGES:What is already known on this topicPrevious studies have established actionable associations between TMB neoantigen load and immunotherapy responses.What this study addsThis study introduces lnc-IM scores as a novel metric that predicts patients antigen load based on translatable lncRNAs expression. These lnc-IM scores when combined with TMB associated neoantigen load indicate an improvement in immunotherapy efficacy predictions.How this study might affect research, practice or policyFuture research is needed to further validate lnc-IM scores as a predictive biomarker for immunotherapy response in various cancer types. The use of lnc-IM scores can empower clinicians to make more informed decisions about administering immunotherapy treatments, improvingpatient outcomes.