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

Genome Medicine, Journal Year: 2019, Volume and Issue: 11(1)

Published: May 24, 2019

We introduce quanTIseq, a method to quantify the fractions of ten immune cell types from bulk RNA-sequencing data. quanTIseq was extensively validated in blood and tumor samples using simulated, flow cytometry, immunohistochemistry data.quanTIseq analysis 8000 revealed that cytotoxic T infiltration is more strongly associated with activation CXCR3/CXCL9 axis than mutational load deconvolution-based scores have prognostic value several solid cancers. Finally, we used show how kinase inhibitors modulate contexture reveal immune-cell underlie differential patients' responses checkpoint blockers.Availability: available at http://icbi.at/quantiseq .

Language: Английский

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Cell type–specific genetic regulation of gene expression across human tissues

Science, Journal Year: 2020, Volume and Issue: 369(6509)

Published: Sept. 10, 2020

Cell type composition, estimated from bulk tissue, maps the cellular specificity of genetic variants.

Language: Английский

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The Tumor Microenvironment in the Response to Immune Checkpoint Blockade Therapies

Frontiers in Immunology, Journal Year: 2020, Volume and Issue: 11

Published: May 7, 2020

Tumor cells constantly interact with their microenvironment, which comprises a variety of immune together endothelial and fibroblasts. The composition the tumor microenvironment (TME) has been shown to influence response checkpoint blockade (ICB). ICB takes advantage cell infiltration in reinvigorate an efficacious antitumoral response. In addition intrinsic biomarkers, increasing data pinpoint importance TME guiding patient selection combination therapies. Here, we review recent efforts determining how various components can resistance ICB. Although large body evidence points extent functional orientation T infiltrate as important therapy response, studies also confirm role for other TME, such B cells, myeloid lineage cancer-associated fibroblasts vasculature. If ultimate goal curative cancer therapies is induce long-term memory may positively or negatively modulate induction efficient antitumor immunity. emergence novel high-throughput methods analyzing including transcriptomics, allowed tremendous developments field, expansion cohorts identification TME-based markers Together, these open possibility selecting patients that are likely respond specific therapies, pave way personalized medicine oncology.

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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Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery

International Journal of Surgery, Journal Year: 2022, Volume and Issue: 107, P. 106936 - 106936

Published: Sept. 20, 2022

Postoperative progression and chemotherapy resistance is the major cause of treatment failure in patients with triple-negative breast cancer (TNBC). Currently, there a lack an ideal predictive model for drug sensitivity postoperative TNBC patients. Diverse programmed cell death (PCD) patterns play important role tumor progression, which has potential to be prognostic indicator after surgery.Twelve PCD (apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic death, netotic parthanatos, lysosome-dependent autophagy-dependent alkaliptosis, oxeiptosis) were analyzed construction. Bulk transcriptome, single-cell genomics, clinical information collected from TCGA-BRCA, METABRIC, GSE58812, GSE21653, GSE176078, GSE75688, KM-plotter cohorts validate model.The machine learning algorithm established index (CDI) 12-gene signature. Validated five independent datasets, high CDI had worse prognosis surgery. Two molecular subtypes distinct vital biological processes identified by unsupervised clustering model. A nomogram performance was constructed incorporating features. Furthermore, associated immune checkpoint genes key microenvironment components integrated analysis bulk transcriptome. are resistant standard adjuvant regimens (docetaxel, oxaliplatin, etc.); however, they might sensitive palbociclib (an FDA-approved luminal cancer).Generally, we novel comprehensively analyzing diverse patterns, can accurately predict user-friendly website created facilitate application this prediction (https://tnbc.shinyapps.io/CDI_Model/).

Language: Английский

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High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer

Cancer Cell, Journal Year: 2022, Volume and Issue: 40(12), P. 1503 - 1520.e8

Published: Nov. 10, 2022

Non-small cell lung cancer (NSCLC) is characterized by molecular heterogeneity with diverse immune infiltration patterns, which has been linked to therapy sensitivity and resistance. However, full understanding of how phenotypes vary across different patient subgroups lacking. Here, we dissect the NSCLC tumor microenvironment at high resolution integrating 1,283,972 single cells from 556 samples 318 patients 29 datasets, including our dataset capturing low mRNA content. We stratify into immune-deserted, B cell, T myeloid subtypes. Using bulk genomic clinical information, identify cellular components associated histology genotypes. then focus on analysis tissue-resident neutrophils (TRNs) uncover distinct subpopulations that acquire new functional properties in tissue microenvironment, providing evidence for plasticity TRNs. Finally, show a TRN-derived gene signature anti-programmed death ligand 1 (PD-L1) treatment failure.

Language: Английский

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Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Jan. 19, 2022

Mapping cell types across a tissue is central concern of spatial biology, but type abundance difficult to extract from gene expression data. We introduce SpatialDecon, an algorithm for quantifying populations defined by single sequencing within the regions studies. SpatialDecon incorporates several advancements in deconvolution. propose harnessing log-normal regression and modelling background, outperforming classical least-squares methods. compile profile matrices 75 types. identify genes whose minimal cancer cells makes them suitable immune deconvolution tumors. Using lung tumors, we create dataset benchmarking methods against marker proteins. simple flexible tool mapping It obtains estimates that are spatially resolved, granular, paired with highly multiplexed

Language: Английский

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