We bring hope to

children with

cancer

About

Cancer in children is rare but when it happens, clinically prescribed treatment options are not always as efficient as one would hope. Of those that are cured, a substantial proportion suffer long-term serious health consequences from the intensive treatments that are currently required. A major reason for cancer being so difficult to treat effectively is that cancer cells undergo many random changes, which means that each cancer has an essentially unique combination of molecular characteristics. To address this problem, it is important to develop ways of specifically tailoring treatment combinations for the molecular profile of each individual cancer, to maximize cures and to minimize short- and long-term treatment side-effects.

Vision

The project team will focus on identifying effective personalized medicine for paediatric cancers and will address a multitude of challenges. To meet these challenges, a comprehensive computational effort to combine knowledge base, machine-learning, and mechanistic models to predict optimal standard and experimental therapies for each child will be proposed. We will produce, assemble, standardize, and harmonize accessible high-quality multi-disciplinary data and leverage the potential of Big Data and HPC for the personalized treatments of European citizens. While the ever-present complexities of cancer continue to challenge our scientific community, it is reassuring that European projects like iPC are using the latest technology and brightest minds to find solutions which, in turn, usher in better patient care.

Keyfacts

Reference

Reference Number:
826121
Programme type:
Horizon 2020
Programme acronym:
H2020-ICT-2018-2

Duration

Project Start: 01.01.2019
48 Months

Cost and Funding

Costs:
€ 15.066.525,00
Funding:
97,89% EU-funded

Motivation

While the ever-present complexities of cancer continue to challenge our scientific community, it is reassuring that European projects, such as iPC are using the latest technology and brightest minds to find solutions, which, in turn, usher in better patient care. To sum up, iPC will address the critical need for personalized medicine for children with cancer, contribute to the digitalization of clinical workflows, and enable the Digital Single Market of the EU data infrastructure.

Mission and Objectives

The goal of the iPC project is to collect, standardize and harmonize existing clinical knowledge and medical data and, with the help of artificial intelligence, create treatment models for patients. Armed with these treatment models, scientists will then test them on virtual patients to evaluate treatment efficacy and toxicity, thus improving both patient survival and their quality of life. To accomplish our goals, we have assembled an interdisciplinary team consisting of basic, translational, and clinical researchers—all amongst the leaders in their respective fields—and established strong relationships with European Centres of Excellence, patient organizations, and clinical trials focus on personalized medicine for our proposed case studies. In summary, iPC will address the critical need for personalized medicine for children with cancer, contribute to the digitalization of clinical workflows, and enable the Digital Single Market of the EU data infrastructure.

Work Packages

WP1

Data collection and generation
28%
01.01.2019
30.04.2022
Work Package detailed description is coming soon!

WP2

Platform Implementation
23%
01.01.2019
31.12.2022
Work Package detailed description is coming soon!

WP3

Big data analysis of available data sources
26%
01.03.2019
31.12.2021
Work Package detailed description is coming soon!

WP4

Network-based meta-analysis of mulit-omics and text-mined data
19%
01.06.2019
31.12.2021
Work Package detailed description is coming soon!

WP5

Blending machine learning and mechanistic models
23%
01.01.2019
31.12.2022
Work Package detailed description is coming soon!

WP6

Mechanistic and agent-based models
23%
01.01.2019
31.12.2022
Work Package detailed description is coming soon!

WP7

Transcriptomic models for clonal deconvolution, intratumoural heterogeneity and non-coding elements
31%
01.01.2019
31.12.2021
Work Package detailed description is coming soon!

WP8

Mulit-omics and metabolomic models for drug discovery
23%
01.01.2019
31.12.2022
Work Package detailed description is coming soon!

WP9

Use cases and validation
0%
01.01.2020
31.12.2022
Work Package detailed description is coming soon!

WP10

Communication, dissemination, exploitation and training
23%
01.01.2019
31.12.2022
Work Package detailed description is coming soon!

WP11

Ethics and legal aspects, project, risk and innovation management
23%
01.01.2019
31.12.2022
Work Package detailed description is coming soon!

WP12

Ethics requirements
23%
01.01.2019
31.12.2022
Work Package detailed description is coming soon!

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