VENUS, a Novel Selection Approach to Improve the Accuracy of Neoantigens’ Prediction

Abstract

Neoantigens are tumor-specific antigens able to induce T-cell responses, generated by mutations in protein-coding regions of expressed genes. Previous studies demonstrated that only a limited subset of mutations generates neoantigens in microsatellite stable tumors. We developed a method, called VENUS (Vaccine-Encoded Neoantigens Unrestricted Selection), to prioritize mutated peptides with high potential to be neoantigens. Our method assigns to each mutation a weighted score that combines the mutation allelic frequency, the abundance of the transcript coding for the mutation, and the likelihood to bind the patient’s class-I major histocompatibility complex alleles. By ranking mutated peptides encoded by mutations detected in nine cancer patients, VENUS was able to select in the top 60 ranked peptides, the 95% of neoantigens experimentally validated including both CD8 and CD4 T cell specificities. VENUS was evaluated in a murine model in the context of vaccination with an adeno vector encoding the top ranked mutations prioritized in the MC38 cell line. Efficacy studies demonstrated anti tumoral activity of the vaccine when used in combination with checkpoint inhibitors. The results obtained highlight the importance of a combined scoring system taking into account multiple features of each tumor mutation to improve the accuracy of neoantigen prediction.

Prime and pull of T cell responses against cancer-exogenous antigens is effective against CPI-resistant tumors

“…We have previously shown that adenovirus vectored neoAg vaccines prevent tumor development in a prophylactic setting and cure large established tumors when used in combination with CPI in a therapeutic setting in different mouse tumor models. 4 , 6 , 11 Here, we confirmed our previous findings by generating a new vaccine targeting 31 neoAgs selected from the CT26 mouse colon cancer cells (Ad-CT26; Table 1 ; materials and methods ) by using the VENUS algorithm, a recently described bioinformatic tool that uses DNA and RNA NGS data to prioritize mutated peptides with high potential to be neoAgs. 11 …”

“… 4 , 6 , 11 Here, we confirmed our previous findings by generating a new vaccine targeting 31 neoAgs selected from the CT26 mouse colon cancer cells (Ad-CT26; Table 1 ; materials and methods ) by using the VENUS algorithm, a recently described bioinformatic tool that uses DNA and RNA NGS data to prioritize mutated peptides with high potential to be neoAgs. 11 …”

Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction

“…Compared to the other pipelines, ease of use was also an advantage of Seq2Neo, since the Seq2Neo model provides a one-stop solution for neoantigen prediction using raw sequencing data. To demonstrate the performance of Seq2Neo, we applied Seq2Neo to samples from five cancer patients with experimentally validated neoantigenic mutations [ 34 , 35 , 36 , 37 ]. Those cancer samples contained WES, RNA-seq data and 16 experimentally validated neoantigenic DNA sites ( Figure S3A,B ).…”

Getting personal in metastatic melanoma: neoantigen-based vaccines as a new therapeutic strategy

“…As tumors can accumulate hundreds of mutations, bioinformatic algorithms have been developed to select the ‘best’ candidates and prioritize those with the highest probability of inducing T-cell responses [ 17 ]. The allelic frequency of the mutation, the abundance of the RNA transcript coding for the mutation and the theoretical MHC-binding affinity of the mutated peptide are the most commonly used parameters for the selection [ 18 ]. Single nucleotide variant mutations (SNVs) represent the major source of neoantigens, followed by nucleotide insertions or deletions (indels).…”