Purpose: While immune checkpoint blockade (ICB) has become a pillar of cancer treatment, biomarkers that consistently predict patient response remain elusive due to the complex mechanisms driving immune response to tumors. We hypothesized that a multidimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers, such as tumor mutational burden (TMB). Experimental Design: Tumors from a cohort of patients with late-stage melanoma (n ¼ 51) were profiled using an immune-enhanced exome and transcriptome platform. We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB. Results: Our neoantigen burden score, which integrates both exome and transcriptome features, more significantly stratified responders and nonresponders (P ¼ 0.016) than TMB alone (P ¼ 0.049). Extension of this model to include immune-related resistance mechanisms affecting the antigen presentation machinery, such as HLA allele-specific LOH, resulted in a composite neoantigen presentation score (NEOPS) that demonstrated further increased association with therapy response (P ¼ 0.002). Conclusions: NEOPS proved the statistically strongest biomarker compared with all single-gene biomarkers, expression signatures, and TMB biomarkers evaluated in this cohort. Subsequent confirmation of these findings in an independent cohort of patients (n ¼ 110) suggests that NEOPS is a robust, novel biomarker of ICB response in melanoma.
Abbott, C. W., Boyle, S. M., Pyke, R. M., McDaniel, L. D., Levy, E., Navarro, F. C. P., … Chen, R. (2021). Prediction of immunotherapy response in melanoma through combined modeling of neoantigen burden and immune-related resistance mechanisms. Clinical Cancer Research, 27(15), 4265–4276. https://doi.org/10.1158/1078-0432.CCR-20-4314