Best practices for bioinformatic characterization of neoantigens for clinical utility

Abstract

Neoantigens are newly formed peptides created from somatic mutations that are capable of inducing tumor-specific T cell recognition. Recently, researchers and clinicians have leveraged next generation sequencing technologies to identify neoantigens and to create personalized immunotherapies for cancer treatment. To create a personalized cancer vaccine, neoantigens must be computationally predicted from matched tumor–normal sequencing data, and then ranked according to their predicted capability in stimulating a T cell response. This candidate neoantigen prediction process involves multiple steps, including somatic mutation identification, HLA typing, peptide processing, and peptide-MHC binding prediction. The general workflow has been utilized for many preclinical and clinical trials, but there is no current consensus approach and few established best practices. In this article, we review recent discoveries, summarize the available computational tools, and provide analysis considerations for each step, including neoantigen prediction, prioritization, delivery, and validation methods. In addition to reviewing the current state of neoantigen analysis, we provide practical guidance, specific recommendations, and extensive discussion of critical concepts and points of confusion in the practice of neoantigen characterization for clinical use. Finally, we outline necessary areas of development, including the need to improve HLA class II typing accuracy, to expand software support for diverse neoantigen sources, and to incorporate clinical response data to improve neoantigen prediction algorithms. The ultimate goal of neoantigen characterization workflows is to create personalized vaccines that improve patient outcomes in diverse cancer types.

Dendritic Cells in Anticancer Vaccination: Rationale for Ex Vivo Loading or In Vivo Targeting

“…Several highly immunogenic TSAs have already been described [260][261][262][263]. However, modern methods such as high-throughput sequencing technologies and bioinformatics analysis of big data allow the identification of new tumor antigenic determinants, such as neoantigens [264,265]. This makes it possible to analyze and determine the TSAs specifically expressed by each cancer patient's tumor, with further TSA or recombinant production and utilization of its peptide in the vaccines that target DCs in vivo.…”

Optimized Whole Genome Association Scanning for Discovery of HLA Class I-Restricted Minor Histocompatibility Antigens

“…Due to high throughput sequencing techniques which enable genome wide detection of genetic variants, bioinformatic pipelines have been developed to predict neoantigens (32)(33)(34)(35) and minor histocompatibility antigens (31,(36)(37)(38)(39)(40). Based on whole exome sequence data, Koparde et al (39) found an average of 2463 non-synonymous SNP disparities in the Graft-versus-Host direction in patients transplanted with related donors, and an average of 4287 SNP disparities in patients transplanted with unrelated donors (39).…”

Adoptive Cell Therapy—Harnessing Antigen-Specific T Cells to Target Solid Tumours

“…Currently, peptide prediction algorithms use either solely the data generated by mass spectrometry (MS) analysis of eluted MHC-binding peptides or both in vitro MHC-peptide binding affinity and MS peptidome data [109][110][111].…”