The remarkable success of cancer therapies with immune checkpoint blockers is revolutionizing oncology and has sparked intensive basic and translational research into the mechanisms of cancer-immune cell interactions. In parallel, numerous novel cutting-edge technologies for comprehensive molecular and cellular characterization of cancer immunity have been developed, including single-cell sequencing, mass cytometry and multiplexed spatial cellular phenotyping. In order to process, analyse and visualize multidimensional data sets generated by these technologies, computational methods and software tools are required. Researchers from the Medical University of Innsbruck describe computational methods and software tools developed for the visualization and analysis of data generated by cutting-edge technologies like bulk and single-cell RNA sequencing, as well as mass cytometry and multiplexed spatial cellular phenotyping. They discuss the advantages and limitations of the various approaches applied to the study of cancer immunity, and provide guidelines to assist in method selection.
Overview of computational tools for interrogating cancer immunity
a | Putative neoantigens arising from the expression of somatic mutations can be predicted in silico through three main computational steps: prediction of mutated peptides using whole-exome sequencing (WES) or whole-genome sequencing (WGS) data from paired tumour and normal samples; HLA typing from tumour sequencing data (preferentially RNA sequencing (RNA-seq)); and prediction of the binding affinity between HLA types and mutated peptides. b | The analysis of different types of data can reveal different facets of the tumour immune contexture depending on their pros and cons. Bulk RNA-seq data can be analysed with deconvolution methods to quantify the fractions of different cell subpopulations, but cannot be used to study the phenotypes of single cells. By contrast, single-cell RNA-seq (scRNA-seq) is currently not optimal to quantitatively assess the cellular composition of the tumour, but can be used to portray single-cell types and states. Multiplexed imaging allows the study of cells in a spatial context, but only reconstructs a restricted, 2D portion of the tumour microenvironment and is limited in the number of markers that can be phenotyped. c | A basic scRNA-seq analysis pipeline consists of quality control and removal of low-quality cell profiles; selection of informative genes; normalization of expression profiles; and annotation of cell types. d | Schematic representation of the interaction between a tumour cell and a cytotoxic T cell: the T-cell receptor (TCR), composed of an α-chain and β-chain, interacts with the neoantigen bound on the class I HLA molecule of the tumour cell. In humans, there are more than 16,000 class I HLA alleles and ~1016 αβ TCRs, whereas all possible peptides 8–11 amino acids long (mutated or not) amount to ~1014 8–11mers. Class I HLA typing can be performed in silico using WES or RNA-seq data, whereas the binding between class I HLA molecules and putative neoantigens can be predicted by integrating WES (or WGS) and RNA-seq data (details in part a). αβ TCRs of single cells can be reconstructed from scRNA-seq data, but there are currently no computational methods to predict neoantigen recognition by TCRs. β2M, β2-microglobulin.