TIMER2.0 for analysis of tumor-infiltrating immune cells

Nucleic Acids Research, Journal Year: 2020, Volume and Issue: 48(W1), P. W509 - W514

Published: May 17, 2020

Abstract Tumor progression and the efficacy of immunotherapy are strongly influenced by composition abundance immune cells in tumor microenvironment. Due to limitations direct measurement methods, computational algorithms often used infer cell from bulk transcriptome profiles. These estimated infiltrate populations have been associated with genomic transcriptomic changes tumors, providing insight into tumor–immune interactions. However, such investigations on large-scale public data remain challenging. To lower barriers for analysis complex interactions, we significantly improved our previous web platform TIMER. Instead just using one algorithm, TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation infiltration levels The Cancer Genome Atlas (TCGA) or user-provided profiles six state-of-the-art algorithms. four modules investigating associations between infiltrates genetic clinical features, exploring cancer-related TCGA cohorts. Each module can generate a functional heatmap table, enabling user easily identify significant multiple cancer types simultaneously. Overall, server comprehensive visualization functions infiltrating cells.

Language: Английский

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Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology

Bioinformatics, Journal Year: 2019, Volume and Issue: 35(14), P. i436 - i445

Published: May 9, 2019

The composition and density of immune cells in the tumor microenvironment (TME) profoundly influence progression success anti-cancer therapies. Flow cytometry, immunohistochemistry staining or single-cell sequencing are often unavailable such that we rely on computational methods to estimate immune-cell from bulk RNA-sequencing (RNA-seq) data. Various have been proposed recently, yet their capabilities limitations not evaluated systematically. A general guideline leading research community through cell type deconvolution is missing.We developed a systematic approach for benchmarking assessed accuracy tools at estimating nine different immune- stromal RNA-seq samples. We used dataset ∼11 000 TME simulate samples known proportions, validated results using independent, publicly available gold-standard estimates. This allowed us analyze condense more than hundred thousand predictions provide an exhaustive evaluation across seven over types ∼1800 five simulated real-world datasets. demonstrate performs high well-defined cell-type signatures propose how fuzzy can be improved. suggest future efforts should dedicated refining population definitions finding reliable signatures.A snakemake pipeline reproduce benchmark https://github.com/grst/immune_deconvolution_benchmark. An R package allows perform integrated (https://grst.github.io/immunedeconv).Supplementary data Bioinformatics online.

Language: Английский

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IOBR: Multi-Omics Immuno-Oncology Biological Research to Decode Tumor Microenvironment and Signatures

Frontiers in Immunology, Journal Year: 2021, Volume and Issue: 12

Published: July 2, 2021

Recent advances in next-generation sequencing (NGS) technologies have triggered the rapid accumulation of publicly available multi-omics datasets. The application integrated omics to explore robust signatures for clinical translation is increasingly emphasized, and this attributed success immune checkpoint blockades diverse malignancies. However, effective tools comprehensively interpreting data are still warranted provide increased granularity into intrinsic mechanism oncogenesis immunotherapeutic sensitivity. Therefore, we developed a computational tool Immuno-Oncology Biological Research (IOBR), providing comprehensive investigation estimation reported or user-built signatures, TME deconvolution, signature construction based on data. Notably, IOBR offers batch analyses these their correlations with phenotypes, long non-coding RNA (lncRNA) profiling, genomic characteristics, generated from single-cell (scRNA-seq) different cancer settings. Additionally, integrates multiple existing microenvironmental deconvolution methodologies convenient comparison selection. Collectively, user-friendly leveraging facilitate immuno-oncology exploration unveil tumor-immune interactions accelerating precision immunotherapy.

Language: Английский

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EPIC: A Tool to Estimate the Proportions of Different Cell Types from Bulk Gene Expression Data

Methods in molecular biology, Journal Year: 2020, Volume and Issue: unknown, P. 233 - 248

Published: Jan. 1, 2020

Language: Английский

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GEPIA2021: integrating multiple deconvolution-based analysis into GEPIA

Nucleic Acids Research, Journal Year: 2021, Volume and Issue: 49(W1), P. W242 - W246

Published: May 3, 2021

In 2017, we released GEPIA (Gene Expression Profiling Interactive Analysis) webserver to facilitate the widely used analyses based on bulk gene expression datasets in TCGA and GTEx projects, providing biologists clinicians with a handy tool perform comprehensive complex data mining tasks. Recently, deconvolution tools have led revolutionary trends resolve RNA at cell type-level resolution, interrogating characteristics of different types cancer controlled cohorts became an important strategy investigate biological questions. Thus, present GEPIA2021, standalone extension GEPIA, allowing users multiple interactive analysis results, including proportion comparison, correlation analysis, differential expression, survival analysis. With experimental could easily explore large validate their hypotheses enhanced resolution. GEPIA2021 is publicly accessible http://gepia2021.cancer-pku.cn/.

Language: Английский

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Benchmarking of cell type deconvolution pipelines for transcriptomics data

Nature Communications, Journal Year: 2020, Volume and Issue: 11(1)

Published: Nov. 6, 2020

Many computational methods have been developed to infer cell type proportions from bulk transcriptomics data. However, an evaluation of the impact data transformation, pre-processing, marker selection, composition and choice methodology on deconvolution results is still lacking. Using five single-cell RNA-sequencing (scRNA-seq) datasets, we generate pseudo-bulk mixtures evaluate combined these factors. Both methodologies those that use scRNA-seq as reference perform best when applied in linear scale normalization has a dramatic some, but not all methods. Overall, comparable performance performing whereas semi-supervised approaches show higher error values. Moreover, failure include types are present mixture leads substantially worse results, regardless previous choices. Altogether, factors affecting task across different datasets propose general guidelines maximize its performance.

Language: Английский

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Siglec15 shapes a non-inflamed tumor microenvironment and predicts the molecular subtype in bladder cancer

Theranostics, Journal Year: 2021, Volume and Issue: 11(7), P. 3089 - 3108

Published: Jan. 1, 2021

Rationale: Siglec15 is an emerging target for normalization cancer immunotherapy. However, pan-cancer anti-Siglec15 treatment not yet validated and the potential role of in bladder (BLCA) remains elusive. Methods: We comprehensively evaluated expression pattern immunological using analysis based on RNA sequencing data obtained from The Cancer Genome Atlas. then systematically correlated with characteristics BLCA tumor microenvironment (TME), including immunomodulators, immunity cycles, tumor-infiltrating immune cells (TIICs), checkpoints, T cell inflamed score. also analyzed predicting molecular subtype response to several options BLCA. Our results were public cohorts as well our microarray cohort, Xiangya cohort. developed risk score (IRS), it, tested its ability predict prognosis Results: found that was specifically overexpressed TME various cancers. hypothesize designs a non-inflamed evidence negatively TIICs, Bladder high sensitive immunotherapy, but exhibited higher incidence hyperprogression. High levels indicated luminal characterized by lower infiltration, immunotherapy neoadjuvant chemotherapy, anti-angiogenic therapy targeted therapies such blocking Siglec15, β-catenin, PPAR-γ, FGFR3 pathways. Notably, combination may be more effective strategy than monotherapy. IRS can accurately Conclusions: Anti-Siglec15 might suitable correlates could options.

Language: Английский

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Tumor microenvironment: barrier or opportunity towards effective cancer therapy

Journal of Biomedical Science, Journal Year: 2022, Volume and Issue: 29(1)

Published: Oct. 17, 2022

Abstract Tumor microenvironment (TME) is a specialized ecosystem of host components, designed by tumor cells for successful development and metastasis tumor. With the advent 3D culture advanced bioinformatic methodologies, it now possible to study TME’s individual components their interplay at higher resolution. Deeper understanding immune cell’s diversity, stromal constituents, repertoire profiling, neoantigen prediction TMEs has provided opportunity explore spatial temporal regulation therapeutic interventions. The variation TME composition among patients plays an important role in determining responders non-responders towards cancer immunotherapy. Therefore, there could be possibility reprogramming overcome widely prevailing issue immunotherapeutic resistance. focus present review understand complexity comprehending future perspective its as potential targets. later part describes sophisticated models emerging valuable means extensive account tools profile predict neoantigens. Overall, this provides comprehensive current knowledge available target TME.

Language: Английский

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Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA-Sequencing Data

Methods in molecular biology, Journal Year: 2020, Volume and Issue: unknown, P. 223 - 232

Published: Jan. 1, 2020

Language: Английский

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Immune-Related lncRNA to Construct Novel Signature and Predict the Immune Landscape of Human Hepatocellular Carcinoma

Molecular Therapy — Nucleic Acids, Journal Year: 2020, Volume and Issue: 22, P. 937 - 947

Published: Oct. 10, 2020

The signature composed of immune-related long noncoding ribonucleic acids (irlncRNAs) with no requirement specific expression level seems to be valuable in predicting the survival patients hepatocellular carcinoma (HCC). Here, we retrieved raw transcriptome data from Cancer Genome Atlas (TCGA), identified irlncRNAs by co-expression analysis, and recognized differently expressed irlncRNA (DEirlncRNA) pairs using univariate analysis. In addition, modified Lasso penalized regression. Then, compared areas under curve, counted Akaike information criterion (AIC) values 5-year receiver operating characteristic cut-off point set up an optimal model for distinguishing high- or low-disease-risk groups among HCC. We then reevaluated them viewpoints survival, clinic-pathological characteristics, tumor-infiltrating immune cells, chemotherapeutics efficacy, immunosuppressed biomarkers. 36 DEirlncRNA were identified, 12 which included a Cox regression model. After regrouping point, could more effectively differentiate between based on unfavorable outcome, aggressive tumor infiltration status, low sensitivity, highly established paring regardless levels showed promising clinical prediction value.

Language: Английский

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