Computational genomics tools for dissecting tumour–immune cell interactions

Hackl, Hubert; Charoentong, Pornpimol; Finotello, Francesca; Trajanoski, Zlatko

doi:10.1038/nrg.2016.67

Cited by 231 publications

(205 citation statements)

References 168 publications

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“…Processing can be predicted by several tools available online 20 which model the specificity of the antigen processing machinery such as proteasomal cleavage and TAP transport. TCR recognition is dependent on availability of a suitable TCR repertoire, which is in turn influenced by both positive and negative selection, and peripheral tolerance.…”

Section: Introductionmentioning

confidence: 99%

“…5,20,22 However, most of these studies used different combinations of tools and applied different criteria to prioritize their set of mutated epitopes. 6,23–29 This lack of uniformity in defining ideal criteria for neoepitope prioritization shows the need for a study like ours to determine an optimal approach that combines the main factors influencing immunogenicity within the specific context of cancer.…”

Section: Introductionmentioning

confidence: 99%

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Predicting T cell recognition of MHC class I restricted neoepitopes

et al. 2018

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Epitopes that arise from a somatic mutation, also called neoepitopes, are now known to play a key role in cancer immunology and immunotherapy. Recent advances in high-throughput sequencing have made it possible to identify all mutations and thereby all potential neoepitope candidates in an individual cancer. However, most of these neoepitope candidates are not recognized by T cells of cancer patients when tested in vivo or in vitro, meaning they are not immunogenic. Especially in patients with a high mutational load, usually hundreds of potential neoepitopes are detected, highlighting the need to further narrow down this candidate list. In our study, we assembled a dataset of known, naturally processed, immunogenic neoepitopes to dissect the properties that make these neoepitopes immunogenic. The tools to use and thresholds to apply for prioritizing neoepitopes have so far been largely based on experience with epitope identification in other settings such as infectious disease and allergy. Here, we performed a detailed analysis on our dataset of curated immunogenic neoepitopes to establish the appropriate tools and thresholds in the cancer setting. To this end, we evaluated different predictors for parameters that play a role in a neoepitope’s immunogenicity and suggest that using binding predictions and length-rescaling yields the best performance in discriminating immunogenic neoepitopes from a background set of mutated peptides. We furthermore show that almost all neoepitopes had strong predicted binding affinities (as expected), but more surprisingly, the corresponding non-mutated peptides had nearly as high affinities. Our results provide a rational basis for parameters in neoepitope filtering approaches that are being commonly used.Abbreviations: SNV: single nucleotide variant; nsSNV: nonsynonymous single nucleotide variant; ROC: receiver operating characteristic; AUC: area under ROC curve; HLA: human leukocyte antigen; MHC: major histocompatibility complex; PD-1: Programmed cell death protein 1; PD-L1 or CTLA-4: cytotoxic T-lymphocyte associated protein 4

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting T cell recognition of MHC class I restricted neoepitopes

et al. 2018

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“…26 Methods for estimating the abundance of leukocyte subsets in tumor, based on gene expression data (microarray or RNAseq), have been developed. 27–30 …”

Section: Introductionmentioning

confidence: 99%

Impact of chemotactic factors and receptors on the cancer immune infiltrate: a bioinformatics study revealing homogeneity and heterogeneity among patient cohorts

et al. 2018

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Multiple soluble factors including proteins (in particular chemokines), non-proteinaceous factors released by dead cells, as well as receptors for such factors (in particular chemokine receptors, formyl peptide receptors and purinergic receptors), influence the recruitment of distinct cell subsets into the tumor microenvironment. We performed an extensive bioinformatic analysis on tumor specimens from 5953 cancer patients to correlate the mRNA expression levels of chemotactic factors/receptors with the density of immune cell types infiltrating the malignant lesions. This meta-analysis, which included specimens from breast, colorectal, lung, ovary and head and neck carcinomas as well as melanomas, revealed that a subset of chemotactic factors/receptors exhibited a positive and reproducible correlation with several infiltrating cell types across various solid cancers, revealing a universal pattern of association. Hence, this meta-analysis distinguishes between homogeneous associations that occur across different cancer types and heterogeneous correlations, that are specific of one organ. Importantly, in four out of five breast cancer cohorts for which clinical data were available, the levels of expression of chemotactic factors/receptors that exhibited universal (rather than organ-specific) positive correlations with the immune infiltrate had a positive impact on the response to neoadjuvant chemotherapy. These results support the notion that general (rather than organ-specific) rules governing the recruitment of immune cells into the tumor bed are particularly important in determining local immunosurveillance and response to therapy.

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“…When the learned sample relationships reflect known biology, the corresponding gene signatures can be projected onto new datasets to learn similar sample co-relationships within new datasets. A common example of such an analysis is "computational microdissection" techniques, which often learn the cell type composition of the sample based upon learned gene signatures for each cell type (Hackl et al, 2016) .…”

Section: Section Figure: Definition Of Gene Signature From a Matrix Amentioning

confidence: 99%

Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations

Stein-O’Brien¹,

Arora

Culhane

et al. 2017

Preprint

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SummaryHigh-dimensional data is currently standard for biological inquiry. Biological systems are comprised of interrelated gene regulatory mechanisms, gene-gene interactions, and cellular interactions. These interactions induce low-dimensional structure within the high-dimensional data. Matrix factorization, also known as compressed sensing, learns low-dimensional mathematical representations from high-dimensional data. These factorization techniques can embed assumptions about pleiotropy, epistasis, inter-relationships between complex traits, and context-dependent interactions. They have been applied to uncover new biological knowledge in a breadth of topics ranging from pathway discovery to time course analysis. These techniques have been applied to data from diverse high-throughput omics technologies, including bulk and single-cell data. There are numerous computational techniques within the class of matrix factorization, each of which provides a unique interpretation of the processes in high-dimensional data. We review the visualization and applications of matrix factorization to systems-level analyses, which are diverse and require standardization to enable biological interpretation. Codifying the techniques to decipher biologically relevant features with matrix factorization enables their broad application to discovery beyond the limits of current biological knowledge-answering questions from high-dimensional data that we have not yet thought to ask.

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Computational genomics tools for dissecting tumour–immune cell interactions

Cited by 231 publications

References 168 publications

Predicting T cell recognition of MHC class I restricted neoepitopes

Predicting T cell recognition of MHC class I restricted neoepitopes

Impact of chemotactic factors and receptors on the cancer immune infiltrate: a bioinformatics study revealing homogeneity and heterogeneity among patient cohorts

Enter the matrix: factorization uncovers knowledge from omics Names/Affiliations

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