TIminer: NGS data mining pipeline for cancer immunology and immunotherapy

Tappeiner, Elias; Finotello, Francesca; Charoentong, Pornpimol; Mayer, Clemens; Rieder, Dietmar; Trajanoski, Zlatko

doi:10.1093/bioinformatics/btx377

Cited by 76 publications

(43 citation statements)

References 9 publications

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“…Computational methods for quantitative immunophenotyping of tumors from bulk RNA sequencing (RNA-seq) data hold potential for efficient and low-cost profiling of a large number of samples, but currently suffer from several limitations. Bioinformatics methods based on gene signatures of immune cells, like MCP-counter 8 , xCell 9 , and other approaches based on gene set enrichment analysis (GSEA) [10][11][12] , compute only scores that predict the enrichment of specific immune cell types 13 and hence, do not provide quantitative information about cell proportions. Deconvolution algorithms (reviewed in 14 ) enable quantitative estimation of the proportions of the cell types of interest.…”

Section: Introductionmentioning

confidence: 99%

Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

Finotello

Mayer

Plattner

et al. 2017

Preprint

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We introduce quanTIseq, a method to quantify the tumor immune contexture, determined by the type and density of tumor-infiltrating immune cells. quanTIseq is based on a novel deconvolution algorithm for RNA sequencing data that was validated with independent data sets. Complementing the deconvolution output with image data from tissue slides enables in silico multiplexed immunodetection and provides an efficient method for the immunophenotyping of a large number of tumor samples.Cancer immunotherapy with antibodies targeting immune checkpoints has shown durable benefit or even curative potential in various cancers 1,2 . As only a fraction of patients are responsive to immune checkpoint blockers, efforts are underway to identify predictive markers as well as mechanistic rationale for combination therapies with synergistic potential. Thus, comprehensive and quantitative immunological characterization of tumors in a large number of clinical samples is of utmost importance, but it is currently hampered by the lack of simple and efficient methods. Therefore, we developed quanTIseq, a computational pipeline for the quantification of the Tumor Immune contexture using RNA-seq data and images of haematoxylin and eosin (H&E)-stained tissue slides (Fig. 1a). As part of quanTIseq, we first developed a deconvolution algorithm based on constrained least squares regression 12 . We then designed a signature matrix from a compendium of 51 RNA-seq data sets (Supplementary (Fig. 1c).To validate quanTIseq we first used both simulated data and published data. We simulated 1,700RNA-seq data sets from human breast tumors by mixing various numbers of reads from tumor and immune-cell RNA-seq data, considering different immune compositions and sequencing depths.quanTIseq obtained a high correlation between the true and the estimated fractions and accurately quantified tumor content (measured by the fraction of "other" cells) (Supplementary Figure 1). We then validated quanTIseq using experimental data from a previous study 13 , in which peripheral blood mononuclear cell (PBMC) mixtures were subjected to both, RNA-seq and flow cytometry. A high accuracy of quanTIseq estimates was also observed with this data set ( Fig. 1d and Supplementary Figure 2). Additionally, we successfully validated quanTIseq using two previous published data sets (Supplementary Figures 3 and 4).As most of the validation data sets available in the literature are based on microarray data or consider a limited number of phenotypes, we generated RNA-seq and flow cytometry data from mixtures of peripheral-blood immune cells collected from nine healthy donors. Flow cytometry was used to quantify all the immune sub-populations considered by quanTIseq signature matrix except macrophages, which are not present in blood. Comparison between quanTIseq cell estimates and flow cytometry fractions showed a high correlation at a single and multiple cell-type level ( Fig. 1e and Supplementary Figure 5).We then validated quanTIseq using two independent data sets. The first data...

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Section: Introductionmentioning

confidence: 99%

Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

Finotello

Mayer

Plattner

et al. 2017

Preprint

Self Cite

173

196

View full text Add to dashboard Cite

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“…Multiple immunoinformatics studies endeavored to predict mutationderived neoantigens and identify those with clinical relevance from large-scale cancer sequencing data [11,12]. These methods, such as Tumor Immunology miner (TIminer) [13], pVACSeq [14], INTEGRATE-neo [15], TSNAD [16], have contributed to a major breakthrough in the discovery of neoantigens. TIminer integrates bioinformatics tools to predict tumor neoantigens through analyzing single-sample RNA-seq data and somatic DNA mutations.…”

Section: Introductionmentioning

confidence: 99%

ProGeo-neo: a customized proteogenomic workflow for neoantigen prediction and selection

Wang

Tan

et al. 2020

BMC Med Genomics

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Background: Neoantigens can be differentially recognized by T cell receptor (TCR) as these sequences are derived from mutant proteins and are unique to the tumor. The discovery of neoantigens is the first key step for tumor-specific antigen (TSA) based immunotherapy. Based on high-throughput tumor genomic analysis, each missense mutation can potentially give rise to multiple neopeptides, resulting in a vast total number, but only a small percentage of these peptides may achieve immune-dominant status with a given major histocompatibility complex (MHC) class I allele. Specific identification of immunogenic candidate neoantigens is consequently a major challenge. Currently almost all neoantigen prediction tools are based on genomics data. Results: Here we report the construction of proteogenomics prediction of neoantigen (ProGeo-neo) pipeline, which incorporates the following modules: mining tumor specific antigens from next-generation sequencing genomic and mRNA expression data, predicting the binding mutant peptides to class I MHC molecules by latest netMHCpan (v.4.0), verifying MHC-peptides by MaxQuant with mass spectrometry proteomics data searched against customized protein database, and checking potential immunogenicity of T-cell-recognization by additional screening methods. ProGeoneo pipeline achieves proteogenomics strategy and the neopeptides identified were of much higher quality as compared to those identified using genomic data only. Conclusions: The pipeline was constructed based on the genomics and proteomics data of Jurkat leukemia cell line but is generally applicable to other solid cancer research. With massively parallel sequencing and proteomics profiling increasing, this proteogenomics workflow should be useful for neoantigen oriented research and immunotherapy.

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“…In the last few years, advances in next-generation sequencing have provided an accessible way to generate patient-specific data, which allows the prediction of tumor neoantigens in a rapid and comprehensive manner [7]. Several approaches have been developed, such as pVAC-Seq [8], MuPeXI [9], TIminer [10] and TSNAD [11], which predict potential neoantigens produced by non-synonymous mutations. However, none of these proposed tools considers tumor transcriptome sequencing data (RNA-seq) for identifying somatic mutations.…”

Section: Introductionmentioning

confidence: 99%

neoANT-HILL: an integrated tool for identification of potential neoantigens

et al. 2020

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Background: Cancer neoantigens have attracted great interest in immunotherapy due to their capacity to elicit antitumoral responses. These molecules arise from somatic mutations in cancer cells, resulting in alterations on the original protein. Neoantigens identification remains a challenging task due largely to a high rate of false-positives. Results: We have developed an efficient and automated pipeline for the identification of potential neoantigens. neoANT-HILL integrates several immunogenomic analyses to improve neoantigen detection from Next Generation Sequence (NGS) data. The pipeline has been compiled in a pre-built Docker image such that minimal computational background is required for download and setup. NeoANT-HILL was applied in The Cancer Genome Atlas (TCGA) melanoma dataset and found several putative neoantigens including ones derived from the recurrent RAC1:P29S and SERPINB3:E250K mutations. neoANT-HILL was also used to identify potential neoantigens in RNA-Seq data with a high sensitivity and specificity. Conclusion: neoANT-HILL is a user-friendly tool with a graphical interface that performs neoantigens prediction efficiently. neoANT-HILL is able to process multiple samples, provides several binding predictors, enables quantification of tumor-infiltrating immune cells and considers RNA-Seq data for identifying potential neoantigens.

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TIminer: NGS data mining pipeline for cancer immunology and immunotherapy

Cited by 76 publications

References 9 publications

Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data

ProGeo-neo: a customized proteogenomic workflow for neoantigen prediction and selection

neoANT-HILL: an integrated tool for identification of potential neoantigens

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