Robust prediction of response to immunotherapy in a mixed cohort of previously treated and immunotherapy-naive melanoma patients.

Abstract

e21548 Background: 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 multi-dimensional approach modeling both tumor and immune-related molecular mechanisms would better predict ICB response than simpler mutation-focused biomarkers. Methods: Pre-treatment samples from 51 unresectable, stage III/IV melanoma patients who underwent anti-PD-1 therapy were characterized to assess factors influencing response. RECIST criteria were used to evaluate tumor response to therapy, with a median follow-up of 24 months. For each patient, a single paired FFPE tumor and normal blood sample was collected and profiled using Personalis’ ImmunoID NeXT Platform; an augmented exome/transcriptome platform and analysis pipeline which produces comprehensive tumor mutation information, gene expression profiling, neoantigen characterization, HLA typing and LOH, TCR repertoire profiling, and tumor microenvironment profiling. These data were then integrated to form a composite neoantigen presentation score (NEOPS) for each patient. Results: We demonstrate increasing predictive power with deeper modeling of neoantigens and immune-related resistance mechanisms to ICB. Neoantigen burden score (NBS) was derived from SHERPA, our exome and transcriptome integrative neoantigen prediction model. We found that NBS more significantly stratified responders and non-responders (P = 0.016) than TMB alone (P = 0.049). A broader score, NEOPS, incorporated DASH, to capture HLA allele-specific loss of heterozygosity and additional antigen presenting machinery resistance mechanisms. NEOPS consequently demonstrated a stronger association with response (p = 0.002). We used area under the curve (AUC) to benchmark NEOPS performance against established mutational and transcriptomic predictive models, demonstrating the model was more predictive of response in both the treatment naive setting, as well as in a mixed cohort of treatment-naive and experienced patients. These findings were confirmed in an independent cohort of patients (n = 110), suggesting that NEOPS is a robust, novel biomarker of ICB response in melanoma. Conclusions: Given the complex nature of resistance to immunotherapy, as well as potential toxicities associated with treatment, there is a need for biomarkers that can more accurately predict response. Here we demonstrate that NEOPS can significantly improve stratification of patient response. We also demonstrate that data intensive biomarkers like NEOPS can be clinically practical, with comprehensive tumor profiling in our clinical cohort achieved using very limited tumor tissue.