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Next-generation computational tools for interrogating cancer immunity

Nature Reviews Genetics volume 20, pages 724–746 (2019)Cite this article

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Abstract

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. Here, we review computational tools for interrogating cancer immunity, discuss advantages and limitations of the various methods and provide guidelines to assist in method selection.

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Fig. 1: Distinct hallmarks of cancer immunity.
Fig. 2: Overview of technologies and analyses for interrogating cancer immunity.
Fig. 3: Overview of computational tools for interrogating cancer immunity.
Fig. 4: Single-cell analysis and visualization of tumour-related T cells.

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Acknowledgements

The authors thank S. Boegel for fruitful discussions on state-of-the-art computational methods. This work was supported by the European Research Council (grant agreement No. 786295 to Z.T.), the Austrian Cancer Aid/Tyrol (project No. 17003 to F.F.), the Austrian Science Fund (FWF) (project No. T 974-B30 to F.F. and projects I3291 and I3978 to Z.T.) and the Vienna Science and Technology Fund (Project LS16–025 to Z.T.). Z.T. is a member of the German Research Foundation (DFG) project TRR 241(INF).

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  1. Biocenter, Institute of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria

    Francesca Finotello, Dietmar Rieder, Hubert Hackl & Zlatko Trajanoski

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Glossary

Immune checkpoint blockers

Monoclonal antibodies that target immune checkpoints to elicit or boost anticancer immune responses. Immune checkpoints are receptors or their ligands expressed on either tumour cells or immune cells that modulate immune cell responses to self-proteins, chronic infections and tumour antigens.

Neoantigens

Short peptides generated from the expression of mutated or rearranged genes in cancer cells, but not in normal cells. Bound to HLA molecules on the surface of cancer cells, neoantigens are recognized by T cells through the interaction of the T cell receptor with the peptide–HLA complex.

Dendritic cells

Professional antigen-presenting cells that act as messengers between the innate and the adaptive immune system. Dendritic cells capture antigens, transport them into lymphoid organs and present them to naive T cells together with co-stimulatory signals to induce T cell priming and activation.

Hot tumours

Immunogenic tumours with high infiltration of T cells and high likelihood of response to immune checkpoint inhibitor therapy (as opposed to cold tumours).

Cold tumours

Poorly immunogenic tumours with low or no infiltration of T cells and low likelihood of response to immune checkpoint inhibitor therapy (as opposed to hot tumours).

Microbiota

The community of microorganisms, including bacteria, viruses and fungi, which are found within a specific environment (for example, the human gut).

Microbiome

The collection of all genomes from all of the microorganisms composing the microbiota.

4-digit HLA typing

The standard nomenclature of HLA alleles is composed of the gene name, an asterisk and eight digits separated by a colon, for example, HLA-A*02:01:01:05. HLA alleles that differ at 4-digit resolution (for example, HLA-A*02:02 and HLA-A*02:01) have similar serological specificity for a peptide, but have different protein sequences that can result in different T cell recognition of the peptide–HLA complex.

Avidity

When pertaining to T cells, a biological measure that describes how well a T cell responds to a given antigen. T cells with high functional avidity respond to low antigen amounts, whereas T cells with low functional avidity require higher antigen amounts to mount an immune response comparable with that of high-avidity T cells.

Data dimensionality

The high dimensionality of single-cell RNA sequencing data is due to the high number of genes measured (20,000–30,000 genes), although for many of those the expression in a certain cell would be zero due to dropouts. Due to the high dimensionality, cells become very similar and difficult to assign to different groups (for example, cell subpopulations). Dimensionality reduction techniques can ameliorate this issue, known as the curse of dimensionality, and decrease the computational time.

Data sparsity

A data set is sparse when it is mainly composed of zeros and the actual information is rare. In single-cell RNA sequencing data sets, data sparsity is mainly due to dropouts.

Dropouts

In single-cell RNA sequencing (scRNA-seq) data, when expressed genes result in null expression values due to the inefficiency of mRNA capture and/or to the stochasticity of mRNA expression. They are the main cause of data sparsity in scRNA-seq data sets.

Doublets

Pairs of cells that are captured and sequenced together in single-cell RNA sequencing experiments. As doublets have hybrid transcriptomes that might be falsely interpreted as intermediate cell phenotypes, they have to be identified and removed before running downstream analysis and data interpretation.

Unsupervised clustering

The objective of clustering is to find different groups within the elements in the data (usually samples or cells in transcriptomic data sets), assigning to the same cluster the elements that are more similar to each other. This process is called unsupervised because the real groups are not known a priori. By contrast, supervised clustering or classification is based on pre-labelled groups of samples, which are used to classify a new sample considering its similarity to the elements of each group.

Cell ontologies

Structured vocabularies of cell types.

Clonotypes

Populations of T cells that carry identical T cell receptors.

CDR3 sequences

Complementarity-determining region 3 (CDR3) is the region of the variable chain in B cell receptors and T cell receptors that binds to the cognate antigen, thus accounting for most of the variation of immune repertoires.

Secretome

All of the factors secreted by a cell into the extracellular space.

Dimensionality reduction

The process of reducing the number of features composing a data set (for example, genes in single-cell RNA sequencing data) to obtain a set of principal features. The original features can be filtered (a process called feature selection) or projected from the high-dimensional space of the original data set into a space composed of fewer dimensions, like in the case of t-distributed stochastic neighbour embedding.

Pseudotime

Single-cell RNA sequencing (scRNA-seq) can capture different cell types and, when the throughput is sufficient, cell transitions from one functional state to another. Algorithms for pseudotime ordering can extract from scRNA-seq data the transcriptional profiles underling dynamic changes of cells moving throughout subsequent states, thereby reconstructing their overall trajectories in time. This estimated time reference is referred to as pseudotime.

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Finotello, F., Rieder, D., Hackl, H. et al. Next-generation computational tools for interrogating cancer immunity. Nat Rev Genet 20, 724–746 (2019). https://doi.org/10.1038/s41576-019-0166-7

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