<p>Cancer immunotherapy with checkpoint blockade (CPB) leads to improved outcomes in melanoma and other tumor types, but a majority of patients do not respond. High tumor mutation burden (TMB) and high levels of tumor-infiltrating T cells have been associated with response to immunotherapy, and integrative models using DNA and RNA have improved predictions of clinical benefit. However, identification of immune and tumor-intrinsic features in models using DNA or RNA alone have not been comprehensively explored in melanoma. In this study, we sequenced DNA and RNA of tumors from 67 melanoma patients receiving CPB, and aggregated this cohort with previously published data, yielding whole exome sequencing (WES) data for 189 patients and bulk RNA sequencing data for 178 patients. Using these datasets, we derived genomic and transcriptomic factors that predict overall survival (OS) and response to immunotherapy. Using whole exome DNA data alone, we calculated T cell burden (TCB) and B cell burden (BCB) based on rearranged TCR/Ig DNA sequences, and found that melanoma patients with high TMB and either high TCB or high BCB survived longer and had higher response rates, as compared to patients with low TMB and either low TCB or low BCB. Next, using bulk RNA-Seq data, differential expression analysis identified 83 genes which are associated with high or low OS. By combining pairs of immune-expressed genes with tumor-expressed genes, we identified three gene pairs which are associated with response and survival (Bonferroni P&#x3C;0.05). All three gene pair models were validated in an independent cohort (n=180) (Bonferroni P&#x3C;0.05). The best performing gene pair model included the lymphocyte-expressed MAP4K1 (<a href="https://www.ncbi.nlm.nih.gov/gene/?term=MAP4K1">Gene ID: 11184</a>) and the transcription factor TBX3 (<a href="https://www.ncbi.nlm.nih.gov/gene/?term=TBX3">Gene ID: 6926</a>) which is overexpressed in poorly differentiated melanomas. We concluded that RNA-based (MAP4K1 and TBX3) or DNA-based (TCB and TMB) models combining with immune and tumor measures improve predictions of outcome after checkpoint blockade in melanoma.</p>

Combined Tumor and Immune Signals From Genomes or Transcriptomes Predict Outcomes of Checkpoint Inhibition in Melanoma

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