Key Points
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Cancer immunotherapies with antibodies that target immune checkpoint molecules show durable clinical effects and hold promise to transform cancer care. As only a fraction of patients are responding, the identification of predictive markers is of utmost importance.
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Next-generation sequencing of cancer genomes, exomes and transcriptomes has provided a wealth of data that can be mined to decipher tumour–immune cell interactions.
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Analytical pipelines and computational tools are necessary for extracting immunologically relevant information from cancer genomics data, including: estimation of the cellular composition of the immune infiltrates into the tumour; characterization of different classes of tumour antigens, including neoantigens and cancer germline antigens; and T cell repertoires.
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Algorithms for de-convolving immune signatures from expression data from bulk tissue and for predicting binding of mutated peptides to major histocompatibility complex (MHC) molecules have been recently developed and applied to cancer genomic data. The results of these analyses provided new insights into cancer immunobiology.
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Analytical pipelines and databases for cancer immunogenomics have been developed and are continuously improved.
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Improvement of existing and development of novel analytical tools is required in order to identify predictive markers for cancer immunotherapy, to select neoantigens for therapeutic vaccination and to develop treatment based on adoptive cell transfer with engineered T cells.
Abstract
Recent breakthroughs in cancer immunotherapy and decreasing costs of high-throughput technologies have sparked intensive research into tumour–immune cell interactions using genomic tools. The wealth of the generated data and the added complexity pose considerable challenges and require computational tools to process, to analyse and to visualize the data. Recently, various tools have been developed and used to mine tumour immunologic and genomic data effectively and to provide novel mechanistic insights. Here, we review computational genomics tools for cancer immunology and provide information on the requirements and functionality in order to assist in the selection of tools and assembly of analytical pipelines.
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Acknowledgements
The authors apologize to fellow researchers not cited owing to restrictions. This work was supported by the Tyrolean Standortagentur (Project Bioinformatics Tyrol), Jubiläumsfonds der Österreichischen Nationalbank (Project 16534) and the Horizon2020 project No 633592 APERIM (Advanced Bioinformatics Platform for Personalized Cancer Immunotherapy).
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Glossary
- Precision oncology
-
The use of systematic assessment of cancer genomic information for personalized diagnosis and therapy.
- Cancer immunotherapy
-
Activation of the immune system to specifically target and kill cancer cells using checkpoint blockers, therapeutic vaccines or engineered T cells.
- Antigens
-
Short peptides that are produced from digested proteins and presented on the surface on the cell by the major histocompatibility complex or the human leukocyte antigen.
- Immune checkpoint
-
An inhibitory pathway of the immune system, commonly a ligand–receptor pair, that maintains self-tolerance and modulates immune responses in peripheral tissues in order to minimize collateral tissue damage.
- Checkpoint blockers
-
Antibodies that target immune checkpoint molecules to activate the immune system.
- Therapeutic vaccines
-
Cancer treatment or therapeutic vaccines use specific antigens to boost the immune system's ability to recognize and to destroy cancer cells.
- Engineered T cells
-
Genetically modified T cells (for example, by expressing a chimeric antigen receptor) that are designed to recognize particular tumour antigens as non-self and lead to tumour destruction.
- Tumour-infiltrating lymphocytes
-
(TILs). Subpopulations of the immune system infiltrating the tumour.
- T cell receptors
-
(TCRs). Proteins that consist of an α-chain and a β-chain on T lymphocytes (T cells), which recognize fragments of antigens bound to the major histocompatibility complex.
- Neoantigens
-
Acquired somatic mutations in the cancer genome that lead to new antigens recognized by the immune system.
- Major histocompatibility complex
-
(MHC). Protein complex that presents antigens on the cell surface. In humans, the MHC is encoded by the human leukocyte antigen (HLA) gene locus.
- Human leukocyte antigens
-
(HLAs). Loci of genes that encode for proteins on the surface of cells and present antigens from inside (class I) and outside (class II) of the cell to T lymphocytes. HLA is the human form of the major histocompatibility complex.
- Microsatellite unstable
-
Pertains to microsatellite instability, which involves changes in the number of repeats of microsatellites (that is, short, repeated DNA sequences) in certain cell types, such as cancer cells, relative to that of inherited DNA. Microsatellite instability is often produced by an impaired DNA mismatch repair system.
- Cancer immunoediting
-
A concept that describes the complex interactions that occur between a developing tumour and the immune system, in which immune cells not only protect the host, but also sculpt or edit the immunogenicity of the tumour.
- Gene set enrichment analysis
-
(GSEA). A computational method to identify whether a predefined set of genes shows statistically significant concordant differences between two biological states (for example, phenotypes) based on gene expression profiling.
- Deconvolution
-
A computational method to discern and quantitate individual components based on bulk measurements of a mixture (for example, gene expression measurement of a complex tumour sample).
- Chemokine
-
A family of small secreted cytokines, the gradient of which causes immune cells with the respective receptors to migrate. This process is known as chemotaxis and is important for guiding the activated immune cells to the tumour site.
- Inverse problem
-
A mathematical problem in which the cause is deduced based on the observed effects of a system.
- High-dimensional data
-
Data with a few dozen to thousands of dimensions that are typically generated when each sample of an experiment or a large cohort is studied by high-throughput genomics or proteomics technologies or when many cells are studied in parallel: that is, using single-cell technologies.
- Cancer germline antigens
-
(CGAs). Proteins that are normally expressed only by trophoblasts and germline cells but that are aberrantly expressed in cancer and recognized by the immune system. Formerly, they were often termed cancer testis antigens.
- Allele frequency
-
Measure of the relative frequency of an allele at a particular genetic locus in a population.
- Phased genotypes
-
Sets of alleles that are co-located on the same chromosome.
- T cell propensity
-
Measure of how much T cell receptors are prone to interact with specific major-histocompatibility-complex-binding peptides.
- Epitope
-
Part of an antigen that is recognized by the immune system.
- Spectratyping
-
A method to study the T cell receptor repertoire. It is based on polymerase chain reaction amplification of rearranged genes of the T cell receptor beta variable gene family. The density of heterogeneous complementarity-determining region 3 (CDR3) lengths, which are separated by electrophoresis, results in a specific spectrum that is then further analysed.
- TCR repertoires
-
(T cell receptor repertoires). Diversity of TCRs that allows the T cells of the immune system to specifically recognize the huge number of various antigens.
- Repertoire sequencing
-
(Rep-seq). Targeted sequencing of the genome loci encoding the T cell (or B cell) receptor taking the complexity of different arrangements into account.
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Hackl, H., Charoentong, P., Finotello, F. et al. Computational genomics tools for dissecting tumour–immune cell interactions. Nat Rev Genet 17, 441–458 (2016). https://doi.org/10.1038/nrg.2016.67
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DOI: https://doi.org/10.1038/nrg.2016.67
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