Computational ONcology TRaining Alliance

Informations projet

CONTRA

N° de convention de subvention: 766030

DOI 10.3030/766030

Projet clôturé

Date de signature de la CE 31 Août 2017

Date de début 1 Janvier 2018

Date de fin 30 Juin 2022

Financé au titre de

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coût total € 3 863 978,28

Contribution de l’UE € 3 863 978,28

Coordonné par

KUNGLIGA TEKNISKA HOEGSKOLAN
Sweden

Livrables

Demonstrators, pilots, prototypes (5)

Software for phylogeny with migration

A software for analysing phylogeny in combination with migration.

Progression inference software

A software for progression inference for multiple tumors.

Resistance onset prediction model

A model for resistance onset prediction.

A first software for tumour phylogeny reconstruction

Software for tumour phylogeny reconstruction from mutations.

Resistance risk evaluation tool

A software tool for resistance risk evaluation.

Other (3)

Project communicating and promoting material, including web site.

Compilation of all project communicating and promoting material, including web site.

Communication and dissemination activities

Completion of all communication and dissemination activities

All recruitment measures

All recruitment measures finalized including advertising on web, Facebook, and standard media.

Documents, reports (6)

Experimentally validated relapse and metastasis drivers

A list of experimentally validated relapse and metastasis drivers

Growth rate and progression type based biomarker

A growth rate and progression type based biomarker for cancer

Experimentally validated cancer drivers

A list of experimentally validated cancer drivers

Time dependent mutation signatures

Time dependent cancer mutation signatures

CONTRA PhD Theses

Completion of all CONTRA PhD Theses

New biomarkers that inform treatment sensitivity

A list of new biomarkers that inform treatment sensitivity

Publications

Non-peer reviewed articles (1)

CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells

Auteurs: Shadi Darvish Shafighi; Szymon M. Kielbasa; Julieta Sepulveda-Yanez; Ramin Monajemi; Davy Cats; Hailiang Mei; Roberta Menafra; Susan L. Kloet; Hendrik Veelken; Cornelis A.M. van Bergen; Ewa Szczurek
Publié dans: Genome Medicine, Numéro 4, 2021, ISSN 1471-2458
Éditeur: BioMed Central
DOI: 10.1101/2020.06.05.134452

Peer reviewed articles (4)

Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer.

Auteurs: Paula Martin-Gonzalez; Mireia Crispin-Ortuzar; Leonardo Rundo; Maria Delgado-Ortet; Marika Reinius; Lucian Beer; Ramona Woitek; Stephan Ursprung; Helen Addley; Helen Addley; James D. Brenton; Florian Markowetz; Evis Sala
Publié dans: Insights into Imaging, Vol 11, Iss 1, Pp 1-10 (2020), Numéro 4, 2020, ISSN 1869-4101
Éditeur: Springer Science and Business Media Deutschland GmbH
DOI: 10.17863/cam.74310

Bayesian non-parametric clustering of single-cell mutation profiles

Auteurs: Nico Borgsmüller; Nico Borgsmüller; Jose Bonet; Francesco Marass; Francesco Marass; Abel Gonzalez-Perez; Nuria Lopez-Bigas; Niko Beerenwinkel; Niko Beerenwinkel
Publié dans: Bioinformatics, Numéro 10, 2020, ISSN 1367-4803
Éditeur: Oxford University Press
DOI: 10.1101/2020.01.15.907345

Pan-cancer detection of driver genes at the single-patient resolution.

Auteurs: Joel Nulsen; Hrvoje Misetic; Christopher Yau; Francesca D. Ciccarelli
Publié dans: Genome Medicine, Numéro 9, 2021, ISSN 1756-994X
Éditeur: BioMed Central
DOI: 10.1186/s13073-021-00830-0

DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data

Auteurs: Jose Bonet; Mandi Chen; Mandi Chen; Marc Dabad; Simon Heath; Abel Gonzalez-Perez; Nuria Lopez-Bigas; Nuria Lopez-Bigas; Jens Lagergren; Jens Lagergren
Publié dans: Bioinformatics, Numéro Volume 38, Numéro 5,, 2021, Page(s) 1235–1243, ISSN 1367-4803
Éditeur: Oxford University Press
DOI: 10.1101/2021.06.28.450135

Conference proceedings (2)

Cell Abundance Aware Deep Learning For Cell Detection On Highly Imbalanced Pathological Data

Auteurs: Yeman Brhane Hagos; Catherine S. Y. Lecat; Dominic Patel; Lydia Lee; Thien-An Tran; Manuel Rodriguez–Justo; Kwee Yong; Yinyin Yuan
Publié dans: ISBI, Numéro 10, 2021
Éditeur: IEEE
DOI: 10.1109/isbi48211.2021.9433994

ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images

Auteurs: Hagos, Yeman Brhane; Narayanan, Priya Lakshmi; Akarca, Ayse U.; Marafioti, Teresa; Yuan, Yinyin
Publié dans: Numéro 7, 2019
Éditeur: Springer

Book chapters (1)

ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images

Auteurs: Yeman Brhane Hagos; Priya Narayanan; Ayse U. Akarca; Teresa Marafioti; Yinyin Yuan
Publié dans: MICCAI (1), Numéro 2, 2019
Éditeur: Springer
DOI: 10.1007/978-3-030-32239-7_74