Proteomics to study cancer immunity and improve treatment

Abstract

Cancer survival and progression depend on the ability of tumor cells to avoid immune recognition. Advances in the understanding of cancer immunity and tumor immune escape mechanisms enabled the development of immunotherapeutic approaches. In patients with otherwise incurable metastatic cancers, immunotherapy resulted in unprecedented response rates with the potential for durable complete responses. However, primary and acquired resistance mechanisms limit the efficacy of immunotherapy. Further therapeutic advances require a deeper understanding of the interplay between immune cells and tumors. Most high-throughput studies within the past decade focused on an omics characterization at DNA and RNA level. However, proteins are the molecular effectors of genomic information; therefore, the study of proteins provides deeper understanding of cellular functions. Recent advances in mass spectrometry (MS)-based proteomics at a system-wide scale may allow translational and clinical discoveries by enabling the analysis of understudied post-translational modifications, subcellular protein localization, cell signaling, and protein-protein interactions. In this review, we discuss the potential contribution of MS-based proteomics to preclinical and clinical research findings in the context of tumor immunity and cancer immunotherapies.

Keywords: Cancer immunotherapy; Mass-spectrometry; Proteomics; Tumor immunity.

Fig. 1

Relative comparison of three MS-based proteomics strategies that have been or can potentially be applied to the study of tumor immunity. Three different MS-based proteomics strategies are compared: single-cell and deep visual proteomics; tissue and cell culture proteomics; plasma proteomics. The comparison is based on the need of specialized instrumentation, the potential to resolve single-cell proteomes, the possibility to discover new disease biomarkers, the preservation of spatial tissue information, and the compatibility with post-translational modification (PTM) analysis. The outer the point, the better that method scores for a given trait

Fig. 2

LC–MS/MS workflow for proteomics in tumor immunity studies. Proteins are extracted from snap-frozen or formalin-fixed paraffin-embedded (FFPE) tumor biopsies, patient-derived cell lines in culture, peripheral blood mononuclear cells (PBMCs), or plasma isolated from peripheral blood. For snap-frozen tissues, a preliminary pulverization step is necessary. After denaturation, proteins are digested into shorter peptides, further transferred on C18 evotips, and analyzed by liquid chromatography-tandem mass spectrometry (LC–MS/MS)

Fig. 3

Strategies to increase sensitivity in plasma proteome profiling by MS-based proteomics. After separation of plasma from peripheral blood, several strategies can be used to increase plasma proteome depth: enrichment of extra-cellular vesicles (EVs), depletion of the most abundant proteins, or the addition of nanoparticles. Proteins are then denatured and digested into shorter peptides, which are analyzed through liquid chromatography-tandem mass spectrometry (LC–MS/MS). Data independent acquisition or BoxCar are advanced MS acquisition methods that can be used to overcome the high dynamic range of plasma samples and therefore increase plasma proteome depth. Off-line peptide fractionation can be exploited to generate deep spectral libraries to deconvolute complex DIA spectra. Alternatively, precursor information in the library can be compared to unidentified peaks in individual MS runs by using the match between runs (MBR) algorithm