Immune checkpoint inhibitors are used to restore or augment antitumor immune response and show great promise in treatment of melanoma and other types of cancers. However, only a relatively small percentage of patients are fully responsive to immune checkpoint inhibition, mostly due to tumor heterogeneity and primary resistance to therapy. Both of these features are largely driven by accumulation of patient-specific mutations, pointing to the need for personalized approaches in diagnostics and immunotherapy. Proteogenomics integrates patient-specific genomic and proteomic data to study cancer development and resistance mechanisms, as well as tumor heterogeneity in individual patients. Here, we use a proteogenomic approach to characterize the mutational landscape of samples derived from four clinical melanoma patients at the genomic, proteomic and phosphoproteomic level. Integration of datasets enabled identification and quantification of an extensive number of sample-specific amino acid variants, among them many were not previously reported in melanoma. We detected a disproportional number of alternate peptides between treated and untreated (naïve) samples with a high potential to influence signal transduction. This is one of the first proteogenomic study designed to study the mutational landscape of patient-derived melanoma tissue samples in response to immunotherapy.