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Title:
Inferring tumour purity and stromal and immune cell admixture from expression data | Nature Communications
Description:
Infiltrating stromal and immune cells form the major fraction of normal cells in tumour tissue and not only perturb the tumour signal in molecular studies but also have an important role in cancer biology. Here we describe ‘Estimation of STromal and Immune cells in MAlignant Tumours using Expression data’ (ESTIMATE)—a method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples. ESTIMATE scores correlate with DNA copy number-based tumour purity across samples from 11 different tumour types, profiled on Agilent, Affymetrix platforms or based on RNA sequencing and available through The Cancer Genome Atlas. The prediction accuracy is further corroborated using 3,809 transcriptional profiles available elsewhere in the public domain. The ESTIMATE method allows consideration of tumour-associated normal cells in genomic and transcriptomic studies. An R-library is available on https://sourceforge.net/projects/estimateproject/ . Tumour biopsies contain contaminating normal cells and these can influence the analysis of tumour samples. In this study, Yoshihara et al.develop an algorithm based on gene expression profiles from The Cancer Genome Atlas to estimate the number of contaminating normal cells in tumour samples.
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tumour, data, immune, stromal, cancer, cell, cells, article, expression, purity, estimate, scores, google, scholar, samples, gene, cas, genes, types, ovarian, tcga, supplementary, fig, nature, high, analysis, sets, score, signature, set, infiltrating, normal, fraction, carcinoma, tissue, affymetrix, based, breast, lung, mutation, human, number, tumours, signatures, copy, microarray, algorithm, absolute, genome, sample,
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nature portfolio /partners_programs/programs/developer/tools/powertools privacy policy open-source package advertising tcga research network tcga data portal fibroblast/mesenchymal nature gynecology service social media r-library author information authors language 0/ reprints data analysis tools rna-seq-based transcriptome profiles clear-cell renal carcinoma apt-probeset-genotype files cholestasis-induced phenotypic transformation benjamini–hochberg fdr correction resulting apt-probeset-summarize author correspondence affymetrix ht-hg-u133a microbead-based cell sorting cancer-type-specific manner4 genome-wide association scans gene set-enrichment analysis nature 487 nature 462 nature 455 nature 474 nature 490 nature 489 nature 497 nature 483 nature small-cell lung cancer low-purity group compared adipose differentiation-related protein low-purity cases suggests hr + /her2− breast cancer gov/docs/publications/unified_expression/ selected single-nucleotide alterations supplementary figs s2–s6 adoptive t-cell therapies epcam-negative cell fractions cell-type-specific profiles histopathologic-based prognostic factors low-purity groups based laser-capture microdissection compared
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- Stromal cells in the tumor microenvironment: accomplices of tumor progression?
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headline:Inferring tumour purity and stromal and immune cell admixture from expression data
description:Infiltrating stromal and immune cells form the major fraction of normal cells in tumour tissue and not only perturb the tumour signal in molecular studies but also have an important role in cancer biology. Here we describe âEstimation of STromal and Immune cells in MAlignant Tumours using Expression dataâ (ESTIMATE)âa method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples. ESTIMATE scores correlate with DNA copy number-based tumour purity across samples from 11 different tumour types, profiled on Agilent, Affymetrix platforms or based on RNA sequencing and available through The Cancer Genome Atlas. The prediction accuracy is further corroborated using 3,809 transcriptional profiles available elsewhere in the public domain. The ESTIMATE method allows consideration of tumour-associated normal cells in genomic and transcriptomic studies. An R-library is available on
https://sourceforge.net/projects/estimateproject/
. Tumour biopsies contain contaminating normal cells and these can influence the analysis of tumour samples. In this study, Yoshihara et al.develop an algorithm based on gene expression profiles from The Cancer Genome Atlas to estimate the number of contaminating normal cells in tumour samples.
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name:Department of Systems Biology, The University of Texas MD Anderson Cancer Centre, Houston, USA
name:Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Centre, Houston, USA
name:Catedra de Bioinformatica, Tecnologico de Monterrey, Campus Monterrey, Monterrey, Mexico
name:Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Centre, Houston, USA
name:Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Centre, Houston, USA
name:Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Centre, Houston, USA
name:Catedra de Bioinformatica, Tecnologico de Monterrey, Campus Monterrey, Monterrey, Mexico
name:USC Epigenome Centre, University of Southern California,
name:Los Angeles, USA
name:USC Epigenome Centre, University of Southern California,
name:Los Angeles, USA
name:Department of Surgery, Gynecology Service, Memorial Sloan-Kettering Cancer Centre, New York, USA
name:The Broad Institute of Harvard and MIT, Cambridge, USA
name:The Broad Institute of Harvard and MIT, Cambridge, USA
name:Department of Systems Biology, The University of Texas MD Anderson Cancer Centre, Houston, USA
name:Department of Systems Biology, The University of Texas MD Anderson Cancer Centre, Houston, USA
name:Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Centre, Houston, USA
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