Individualized Proteogenomics Reveals the Mutational Landscape of Melanoma Patients in Response to Immunotherapy

Simple Summary: Melanoma is the most aggressive form of skin cancer, with a rapidly increasing incidence rate. Due to ineffective treatment options in the late stage melanoma, patients have an overall poor prognosis. Over the last decades, the role of the immune system in the control of tumor progression has been established and immune checkpoint inhibitors (ICi) have shown remarkable clinical activity. While current trials suggest durable responses in patient under ICi therapy, there is increasing evidence pointing towards existence of innate and acquired resistance to ICi therapy; and it is now clear that personalized medicine will be critical for effective patient therapy. Proteogenomics is a powerful tool to study the mode of action of disease-associated mutations at the genome, transcriptome, proteome and PTM level. Here, we applied a proteogenomic workflow to study melanoma samples from human tumors. Such workflow may be applicable to other patient-derived samples and different cancer types. Immune checkpoint inhibitors are used to restore or augment antitumor immune responses and show great promise in the treatment of melanoma and other types of cancers. However, only a 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 the 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, tumor heterogeneity and resistance mechanisms. Using this approach, we characterized the mutational landscape of four clinical melanoma patients. This enabled the quantification of hundreds of sample-specific amino acid variants, among them many that were previously not reported in melanoma. Changes in abundance at the protein and phosphorylation site levels revealed patient-specific over-represented pathways, notably linked to melanoma development (MAPK1 activation) or immunotherapy (NLRP1 inflammasome). Personalized data integration resulted in the prediction of protein drug targets, such as the drugs vandetanib and bosutinib, which were experimentally validated and led to a reduction in the viability of tumor cells. Our study emphasizes the potential of proteogenomic approaches to study personalized mutational landscapes, signaling networks and therapy options.