Proteogenomics meets cancer immunology: mass spectrometric discovery and analysis of neoantigens

Abstract

Cancer proteogenomics is an emerging field that aims to identify and quantify protein sequence changes associated with the cancer genome. Besides being involved in cancer development and progression, such protein variants may serve as neoantigens, which provide the T-cell response against tumors. Mass spectrometry-based proteogenomics may be a promising tool for finding neoantigens in individual specimens. It is partly based on a technical background accumulated from mass spectrometric studies of peptide ligands of major histocompatibility complex proteins. Examples of the use of mass spectrometry in neoantigen identification are reviewed in this article. Some experimental workflows are discussed, which may use shotgun and targeted proteomics for translational human studies of neoepitopes, such as cancer vaccine development and checkpoint therapy response prediction.

The Prediction and Function of Neoantigen in Oncobiology

“…The major histocompatibility complex (MHC) molecules that bind antigenic peptides can be isolated, and peptides can be eluted and purified for identification by liquid chromatography and tandem mass spectrometry (LC-MS/MS) based method, which is a very promising way directly identify tumor neoantigens from cancer biopsies now [4].…”

“…So identification of neoantigen is a pivotal work in both fundamental studies and clinical immunotherapies. With the fast development of next generation sequencing (NGS) and mass spectrometry technology, neoantigens can be indirectly predicted from genome research by generating high-throughput sequencing data or directly identified by LC-MS/MS in an unbiased manner [3,4].…”

Targeting Neoantigens in Glioblastoma

“…In silico prediction methods are inherently plagued by large numbers of false positives (peptides predicted to be MHC-binders but ultimately are not immunogenic) while limitations in MS peptide-detection sensitivity predispose to high rates of false negatives. 63 A number of studies have recently begun to use an approach that combines these 2 complementary techniques for neoantigen predictions. Yadav and colleagues 64 demonstrated that vaccination with 2 neoantigens predicted by both in silico binding-affinity algorithms and MS structural analysis resulted in therapeutic T cell responses in tumor-bearing mice.…”

Towards a better cancer precision medicine: Systems biology meets immunotherapy

“…Despite advances in neoantigen characterization, it has been difficult to use neoantigens as predictive biomarkers of immunotherapy response due to several reasons: a) Neoantigen profiles are mostly unique to each patient possibly because the majority of neoantigens originate from passenger mutations [14,16,17,34], b) Neoantigen repertoires might evolve dynamically over time due to genomic instability of the tumors and selection pressures exerted by the immune system; Verdegaal et al for example have shown loss of immunogenic neoantigens in sequential lesions obtained from two melanoma patients [34], c), The accuracy of computational models utilized to construct neoantigen profiles have not been broadly tested; these models may need to be specifically trained on large datasets of tumor isolated neoantigens that are experimentally determined by techniques such as mass spectrometry [35], and d) The neoantigens, albeit predicted in high numbers, might not be visible to the immune system due to disruptions in the antigen processing/presentation machinery. Giannakis et al , for example have shown that colorectal tumors with high neoantigen burden and high immune infiltration (hence favorable anti-tumor immune environment) also harbored immune-evasive mutations, such as those at certain HLA allelic loci and B2M [36].…”