Identification of immunogenic mutant neoantigens in the genome of murine melanoma

Introduction. One of the promising approaches to cancer immunotherapy is the generation of personal antitumor vaccines that provides immune system recognition of mutant tumor neoantigenes.

The aim of this study was to develop bioinformatic approaches for melanoma NGS-sequencing data analysis. On the basis of data obtained, to predict the peptides capable of inducing an immune response against B16F10 mouse melanoma.

Materials and methods. Exom and transcriptom of B16/F10 melanoma tumor tissue and normal tissue of C57BL/6 mice were sequenced. Library sequencing procedure was performed on Illumina HiSeq 2500platform, quality analysis of all obtained libraries was performed using FastQC and MultiQC. The obtainedfiles were usedfor further bioinformatics analysis. The GATKMuTect2 and Strelka were used for searching the mutations in tumor samples.

Results. Identification of somatic mutations specific for tumor was based on the analysis of few mice tumors. Here we show that mutations in different tumor samples significantly overlap, but are not identical. Prediction of short peptides affinity to the mouse main histocompatibility complex (MHC) H2 was performed using netMHCpan version 3.0. Mutations such as missense and frameshift were used to predict short peptides that could trigger an immune response. Transcriptome data confirm that mutated alleles are expressed in tumors. Vaxrank pipeline predicted immunogenic peptides with a length of25—27. We also present the synthesis and solubility of these peptides.

Conclusion. A bioinformatic approach has been developed to predict peptides capable of increasing immune response of mouse melanoma B16/F10.