Computational approaches for characterizing the tumor immune microenvironment

Abstract

Recent advances in high-throughput molecular profiling technologies and multiplexed imaging platforms have revolutionized our ability to characterize the tumor immune microenvironment. As a result, studies of tumor-associated immune cells increasingly involve complex data sets that require sophisticated methods of computational analysis. In this review, we present an overview of key assays and related bioinformatics tools for analyzing the tumor-associated immune system in bulk tissues and at the single-cell level. In parallel, we describe how data science strategies and novel technologies have advanced tumor immunology and opened the door for new opportunities to exploit host immunity to improve cancer clinical outcomes.

Keywords: bioinformatics; cancer genomics; tumor immunology; tumor microenvironment.

Figure 1

Overview of approaches for in silico characterization of the tumor immune microenvironment. Top: Common methods for studying the tumor immune microenvironment at the tissue level rely on the use of fluorescence‐ or epitope‐labeled antibodies, which can be analyzed using dedicated algorithms and/or imaging by microscopy. Middle: Bulk transcriptomics and epigenomics accompanied by deconvolution and gene enrichment analyses can help investigate the cellular heterogeneity of tumor‐infiltrating leukocytes (TILs) at scale. Bottom: Single‐cell transcriptomics and epigenomics can unravel critical molecular heterogeneity in individual immune cells. Immune repertoire profiling, where the goal is to reconstruct the sequences of B‐cell and T‐cell receptors, as well as the prediction of mutated proteins as potential cancer neoantigens, can reveal important insights into the molecular dynamics and antigen‐binding affinities of the adaptive immune system.