Inferring tumour purity and stromal and immune cell admixture from expression data

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Abstract

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/.

Abstract

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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Hallmarks of Cancer: The Next Generation

Douglas Hanahan, Robert A. Weinberg (2011)

The hallmarks of cancer comprise six biological capabilities acquired during the multistep development of human tumors. The hallmarks constitute an organizing principle for rationalizing the complexities of neoplastic disease. They include sustaining proliferative signaling, evading growth suppressors, resisting cell death, enabling replicative immortality, inducing angiogenesis, and activating invasion and metastasis. Underlying these hallmarks are genome instability, which generates the genetic diversity that expedites their acquisition, and inflammation, which fosters multiple hallmark functions. Conceptual progress in the last decade has added two emerging hallmarks of potential generality to this list-reprogramming of energy metabolism and evading immune destruction. In addition to cancer cells, tumors exhibit another dimension of complexity: they contain a repertoire of recruited, ostensibly normal cells that contribute to the acquisition of hallmark traits by creating the "tumor microenvironment." Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer. Copyright © 2011 Elsevier Inc. All rights reserved.

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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Bo Li, Colin Dewey (2011)

Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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pROC: an open-source package for R and S+ to analyze and compare ROC curves

Xavier Robin, Natacha Turck, Alexandre Hainard … (2013)

Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

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Author and article information

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Pub. Group

ISSN (Electronic): 2041-1723

Publication date (Electronic): 11 October 2013

Volume: 4

Electronic Location Identifier: 2612

Affiliations

[1 ]Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Centre , Houston, Texas 77030, USA

[2 ]Department of Obstetrics and Gynecology, Niigata University Graduate School of Medical and Dental Sciences , Niigata 951-8510, Japan

[3 ]Department of Systems Biology, The University of Texas MD Anderson Cancer Centre , Houston, Texas 77030, USA

[4 ]Catedra de Bioinformatica, Tecnologico de Monterrey, Campus Monterrey , Monterrey, Nuevo Leon 64849, Mexico

[5 ]USC Epigenome Centre, University of Southern California , Los Angeles , California 90033, USA

[6 ]Gynecology Service, Department of Surgery, Memorial Sloan-Kettering Cancer Centre , New York, New York 10065, USA

[7 ]The Broad Institute of Harvard and MIT , Cambridge, Massachusetts 02142, USA

Author notes

[a ] rverhaak@ 123456mdanderson.org

Article

Publisher Item ID: ncomms3612

DOI: 10.1038/ncomms3612

PMC ID: 3826632

PubMed ID: 24113773

SO-VID: 6cdf9f09-00e9-4c1a-9b65-62b1b771f9bf

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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/

History

Date received : 12 July 2013

Date accepted : 13 September 2013

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Inferring tumour purity and stromal and immune cell admixture from expression data

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HLA-G and immune tolerance in pregnancy

Most cited references 62

Hallmarks of Cancer: The Next Generation

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

pROC: an open-source package for R and S+ to analyze and compare ROC curves

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