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Deciphering and predicting the evolution of cancer cell populations

Total Cost €


EC-Contrib. €






Project "CANCEREVO" data sheet

The following table provides information about the project.


Organization address
address: OLD BROMPTON ROAD 123
city: LONDON
postcode: SW7 3RP

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]
 Total cost 2˙000˙000 €
 EC max contribution 2˙000˙000 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-COG
 Funding Scheme ERC-COG
 Starting year 2019
 Duration (year-month-day) from 2019-03-01   to  2024-02-29


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 


 Project objective

The fundamental evolutionary nature of cancer is well recognized but an understanding of the dynamic evolutionary changes occurring throughout a tumour’s lifetime and their clinical implications is in its infancy. Current approaches to reveal cancer evolution by sequencing of multiple biopsies remain of limited use in the clinic due to sample access problems in multi-metastatic disease. Circulating tumour DNA (ctDNA) is thought to comprehensively sample subclones across metastatic sites. However, available technologies either have high sensitivity but are restricted to the analysis of small gene panels or they allow sequencing of large target regions such as exomes but with too limited sensitivity to detect rare subclones. We developed a novel error corrected sequencing technology that will be applied to perform deep exome sequencing on longitudinal ctDNA samples from highly heterogeneous metastatic gastro-oesophageal carcinomas. This will track the evolution of the entire cancer population over the lifetime of these tumours, from metastatic disease over drug therapy to end-stage disease and enable ground breaking insights into cancer population evolution rules and mechanisms. Specifically, we will: 1. Define the genomic landscape and drivers of metastatic and end stage disease. 2. Understand the rules of cancer evolutionary dynamics of entire cancer cell populations. 3. Predict cancer evolution and define the limits of predictability. 4. Rapidly identify drug resistance mechanisms to chemo- and immunotherapy based on signals of Darwinian selection such as parallel and convergent evolution. Our sequencing technology and analysis framework will also transform the way cancer evolution metrics can be accessed and interpreted in the clinic which will have major impacts, ranging from better biomarkers to predict cancer evolution to the identification of drug targets that drive disease progression and therapy resistance.


year authors and title journal last update
List of publications.
2019 Alice Newey, Beatrice Griffiths, Justine Michaux, Hui Song Pak, Brian J. Stevenson, Andrew Woolston, Maria Semiannikova, Georgia Spain, Louise J. Barber, Nik Matthews, Sheela Rao, David Watkins, Ian Chau, George Coukos, Julien Racle, David Gfeller, Naureen Starling, David Cunningham, Michal Bassani-Sternberg, Marco Gerlinger
Immunopeptidomics of colorectal cancer organoids reveals a sparse HLA class I neoantigen landscape and no increase in neoantigens with interferon or MEK-inhibitor treatment
published pages: , ISSN: 2051-1426, DOI: 10.1186/s40425-019-0769-8
Journal for ImmunoTherapy of Cancer 7/1 2019-11-22
2019 Michael Davidson, Louise J. Barber, Andrew Woolston, Catherine Cafferkey, Sonia Mansukhani, Beatrice Griffiths, Sing-Yu Moorcraft, Isma Rana, Ruwaida Begum, Ioannis Assiotis, Nik Matthews, Sheela Rao, David Watkins, Ian Chau, David Cunningham, Naureen Starling, Marco Gerlinger
Detecting and Tracking Circulating Tumour DNA Copy Number Profiles during First Line Chemotherapy in Oesophagogastric Adenocarcinoma
published pages: 736, ISSN: 2072-6694, DOI: 10.3390/cancers11050736
Cancers 11/5 2019-09-16

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The information about "CANCEREVO" are provided by the European Opendata Portal: CORDIS opendata.

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