Inferring tumour purity and stromal and immune cell admixture from expression data
Kosuke Yoshihara,Maria Shahmoradgoli,Emmanuel Martinez,Rahulsimham Vegesna,Hoon Kim,Wandaliz Torres-Garcia,Victor Trevino,Hui Shen,Peter W. Laird,Douglas A. Levine,Scott L. Carter,Gad Getz,Katherine Stemke-Hale,Gordon B. Mills,Roel G.W. Verhaak +14 more
TLDR
A method that uses gene expression signatures to infer the fraction of stromal and immune cells in tumour samples and prediction accuracy is corroborated using 3,809 transcriptional profiles available elsewhere in the public domain.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/.read more
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Robust enumeration of cell subsets from tissue expression profiles
Aaron M. Newman,Chih Long Liu,Michael R. Green,Andrew J. Gentles,Weiguo Feng,Yue Xu,Chuong D. Hoang,Maximilian Diehn,Arash Ash Alizadeh +8 more
TL;DR: CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types when applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors.
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The consensus molecular subtypes of colorectal cancer
Justin Guinney,Rodrigo Dienstmann,Rodrigo Dienstmann,Xingwu Wang,Xingwu Wang,Aurélien de Reyniès,Andreas Schlicker,Charlotte Soneson,Laetitia Marisa,Paul Roepman,Gift Nyamundanda,Paolo Angelino,Brian M. Bot,Jeffrey S. Morris,Iris Simon,Sarah Gerster,Evelyn Fessler,Felipe De Sousa E Melo,Edoardo Missiaglia,Hena R. Ramay,David Barras,Krisztian Homicsko,Dipen M. Maru,Ganiraju C. Manyam,Bradley M. Broom,Valérie Boige,Beatriz Perez-Villamil,Ted Laderas,Ramon Salazar,Joe W. Gray,Douglas Hanahan,Josep Tabernero,René Bernards,Stephen H. Friend,Pierre Laurent-Puig,Jan Paul Medema,Anguraj Sadanandam,Lodewyk F. A. Wessels,Mauro Delorenzi,Mauro Delorenzi,Scott Kopetz,Louis Vermeulen,Sabine Tejpar +42 more
TL;DR: An international consortium dedicated to large-scale data sharing and analytics across expert groups is formed, showing marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features.
Journal ArticleDOI
Genomic analyses identify molecular subtypes of pancreatic cancer
Peter Bailey,David K. Chang,Katia Nones,Katia Nones,Amber L. Johns,Ann-Marie Patch,Ann-Marie Patch,Marie-Claude Gingras,David Miller,David Miller,Angelika N. Christ,Timothy J. C. Bruxner,Michael C.J. Quinn,Michael C.J. Quinn,Craig Nourse,Craig Nourse,Murtaugh Lc,Ivon Harliwong,Senel Idrisoglu,Suzanne Manning,Ehsan Nourbakhsh,Shivangi Wani,Shivangi Wani,J. Lynn Fink,Oliver Holmes,Oliver Holmes,Chin,Matthew J. Anderson,Stephen H. Kazakoff,Stephen H. Kazakoff,Conrad Leonard,Conrad Leonard,Felicity Newell,Nicola Waddell,Scott Wood,Scott Wood,Qinying Xu,Qinying Xu,Peter J. Wilson,Nicole Cloonan,Nicole Cloonan,Karin S. Kassahn,Karin S. Kassahn,Karin S. Kassahn,Darrin Taylor,Kelly Quek,Alan J. Robertson,Lorena Pantano,Laura Mincarelli,Luis Navarro Sanchez,Lisa Evers,Jianmin Wu,Mark Pinese,Mark J. Cowley,Jones,Jones,Emily K. Colvin,Adnan Nagrial,Emily S. Humphrey,Lorraine A. Chantrill,Lorraine A. Chantrill,Amanda Mawson,Jeremy L. Humphris,Angela Chou,Angela Chou,Marina Pajic,Marina Pajic,Christopher J. Scarlett,Christopher J. Scarlett,Andreia V. Pinho,Marc Giry-Laterriere,Ilse Rooman,Jaswinder S. Samra,James G. Kench,James G. Kench,James G. Kench,Jessica A. Lovell,Neil D. Merrett,Christopher W. Toon,Krishna Epari,Nam Q. Nguyen,Andrew Barbour,Nikolajs Zeps,Kim Moran-Jones,Nigel B. Jamieson,Janet Graham,Janet Graham,Fraser Duthie,Karin A. Oien,Karin A. Oien,Hair J,Robert Grützmann,Anirban Maitra,Christine A. Iacobuzio-Donahue,Christopher L. Wolfgang,Richard A. Morgan,Rita T. Lawlor,Corbo,Claudio Bassi,Borislav Rusev,Paola Capelli,Roberto Salvia,Giampaolo Tortora,Debabrata Mukhopadhyay,Gloria M. Petersen,Munzy Dm,William E. Fisher,Saadia A. Karim,Eshleman,Ralph H. Hruban,Christian Pilarsky,Jennifer P. Morton,Owen J. Sansom,Aldo Scarpa,Elizabeth A. Musgrove,Ulla-Maja Bailey,Oliver Hofmann,Oliver Hofmann,R. L. Sutherland,David A. Wheeler,Anthony J. Gill,Anthony J. Gill,Richard A. Gibbs,John V. Pearson,John V. Pearson,Andrew V. Biankin,Sean M. Grimmond,Sean M. Grimmond,Sean M. Grimmond +128 more
TL;DR: Detailed genomic analysis of 456 pancreatic ductal adenocarcinomas identified 32 recurrently mutated genes that aggregate into 10 pathways: KRAS, TGF-β, WNT, NOTCH, ROBO/SLIT signalling, G1/S transition, SWI-SNF, chromatin modification, DNA repair and RNA processing.
Journal ArticleDOI
Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression
Etienne Becht,Nicolas A. Giraldo,Nicolas A. Giraldo,Nicolas A. Giraldo,Laetitia Lacroix,Laetitia Lacroix,Laetitia Lacroix,Bénédicte Buttard,Bénédicte Buttard,Bénédicte Buttard,Nabila Elarouci,Florent Petitprez,Janick Selves,Pierre Laurent-Puig,Catherine Sautès-Fridman,Catherine Sautès-Fridman,Catherine Sautès-Fridman,Wolf H. Fridman,Wolf H. Fridman,Wolf H. Fridman,Aurélien de Reyniès +20 more
TL;DR: The Microenvironment Cell Populations-counter method is introduced, which allows the robust quantification of the absolute abundance of eight immune and two stromal cell populations in heterogeneous tissues from transcriptomic data and demonstrates that MCP-counter overcomes several limitations or weaknesses of previously proposed computational approaches.
Journal ArticleDOI
Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.
Michele Ceccarelli,Floris P. Barthel,Tathiane M. Malta,Thais S. Sabedot,Sofie R. Salama,Bradley A. Murray,Olena Morozova,Yulia Newton,Amie Radenbaugh,Stefano Maria Pagnotta,Samreen Anjum,Jiguang Wang,Ganiraju C. Manyam,Pietro Zoppoli,Shiyun Ling,Arjun A. Rao,Mia Grifford,Andrew D. Cherniack,Hailei Zhang,Laila M. Poisson,Carlos Gilberto Carlotti,Daniela Pretti da Cunha Tirapelli,Arvind Rao,Tom Mikkelsen,Ching C. Lau,W. K. Alfred Yung,Raul Rabadan,Jason T. Huse,Daniel J. Brat,Norman L. Lehman,Jill S. Barnholtz-Sloan,Siyuan Zheng,Kenneth R. Hess,Ganesh Rao,Matthew Meyerson,Rameen Beroukhim,Lee Cooper,Rehan Akbani,Margaret Wrensch,David Haussler,Kenneth Aldape,Peter W. Laird,David H. Gutmann,Houtan Noushmehr,Antonio Iavarone,Roel G.W. Verhaak +45 more
TL;DR: The complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas were defined from The Cancer Genome Atlas and molecular profiles were used to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease.
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