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Allele-specific Deconvolution of Tumour DNA Methylation and Expression Data toReveal Underlying Cell Populations

Total Cost €


EC-Contrib. €






 DECODE project word cloud

Explore the words cloud of the DECODE project. It provides you a very rough idea of what is the project "DECODE" about.

genome    massive    innovative    clinical    intrinsic    validation    computationally    treatment    transcriptome    underlying    normal    leverages    cancer    heterogeneity    improvements    recommender    pan    international    turn    deconvolution    basis    epigenomes    seq    samples    single    tumours    separate    owing    translate    ongoing    admixed    molecular    transcriptomic    methodological    pure    datasets    driving    disentangle    prognostic    cancers    collaborative    shift    accurately    clones    omics    sequencing    reveal    algorithms    deconvoluted    genomic    flowing    personalised    rna    mechanisms    epi    issue    beginning    drawing    dna    profiles    careful    alterations    taxonomy    transcriptomes    construct    methylation    paradigm    heterogeneous    gene    evolution    derive    populations    interpretation    interactomic    copy    expression    epigenome    laying    signal    tumour    complete    data    bulk    rates    mixed    apart    amounts    prediction    teasing    guide    cells    understand    bisulphite    confounding    sub    estimates    actionable    cell    wealth    proteomic    containing    coming    allele   

Project "DECODE" data sheet

The following table provides information about the project.


Organization address
address: 1 MIDLAND ROAD
city: LONDON
postcode: NW1 1AT

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website
 Total cost 195˙454 €
 EC max contribution 195˙454 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2015
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2016
 Duration (year-month-day) from 2016-08-01   to  2018-07-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

Owing to advances in sequencing technology, we are now beginning to understand the molecular mechanisms underlying cancer development and evolution. Tumours however are heterogeneous, often containing admixed normal cells and different (sub)clones, confounding interpretation of the massive amounts of data flowing from large initiatives such as the International Cancer Genome Consortium. To address this issue, I will develop methods that disentangle tumour bulk gene expression (RNA-Seq) and DNA methylation (Bisulphite-Seq) data to accurately reveal the states of the distinct underlying cell populations. The innovative algorithms will derive estimates of the allele-specific expression/methylation rates and tumour copy number profiles from the data and use them to separate the signal coming from the tumour from that of the normal cells. In a second step, the method leverages the wealth of available cancer ‘omics data using a recommender-system approach to complete the deconvolution. Careful validation will come from teasing apart computationally mixed pure samples as well as from ongoing and planned collaborative single-cell sequencing projects. A detailed analysis of tumour expression and DNA methylation heterogeneity on these single-cell datasets will guide further methodological advances. As an intrinsic part of the project, massive pan-cancer datasets will be deconvoluted. Drawing on the pure transcriptomes and epigenomes, I will construct a more comprehensive taxonomy of cancers, laying the basis for significant improvements in clinical prognostic prediction and personalised treatment. This project will shift the paradigm of genomic tumour heterogeneity to include the more actionable transcriptome and epigenome. In turn this will lead to a better understanding of how (epi)genomic alterations translate into the transcriptomic (and proteomic, interactomic, …) changes driving cancer evolution.


year authors and title journal last update
List of publications.
2017 Jiqiu Cheng, Jonas Demeulemeester, David C. Wedge, Hans Kristian M. Vollan, Jason J. Pitt, Hege G. Russnes, Bina P. Pandey, Gro Nilsen, Silje Nord, Graham R. Bignell, Kevin P. White, Anne-Lise Børresen-Dale, Peter J. Campbell, Vessela N. Kristensen, Michael R. Stratton, Ole Christian Lingjærde, Yves Moreau, Peter Van Loo
Pan-cancer analysis of homozygous deletions in primary tumours uncovers rare tumour suppressors
published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-017-01355-0
Nature Communications 8/1 2019-06-13
2016 Jonas Demeulemeester, Parveen Kumar, Elen K. Møller, Silje Nord, David C. Wedge, April Peterson, Randi R. Mathiesen, Renathe Fjelldal, Masoud Zamani Esteki, Koen Theunis, Elia Fernandez Gallardo, A. Jason Grundstad, Elin Borgen, Lars O. Baumbusch, Anne-Lise Børresen-Dale, Kevin P. White, Vessela N. Kristensen, Peter Van Loo, Thierry Voet, Bjørn Naume
Tracing the origin of disseminated tumor cells in breast cancer using single-cell sequencing
published pages: , ISSN: 1474-760X, DOI: 10.1186/s13059-016-1109-7
Genome Biology 17/1 2019-06-13
2018 Matthew W. Fittall, William Mifsud, Nischalan Pillay, Hongtao Ye, Anna-Christina Strobl, Annelien Verfaillie, Jonas Demeulemeester, Lei Zhang, Fitim Berisha, Maxime Tarabichi, Matthew D. Young, Elena Miranda, Patrick S. Tarpey, Roberto Tirabosco, Fernanda Amary, Agamemnon E. Grigoriadis, Michael R. Stratton, Peter Van Loo, Cristina R. Antonescu, Peter J. Campbell, Adrienne M. Flanagan, Sam Behjati
Recurrent rearrangements of FOS and FOSB define osteoblastoma
published pages: , ISSN: 2041-1723, DOI: 10.1038/s41467-018-04530-z
Nature Communications 9/1 2019-06-13

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