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MicroC

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Agent Based Modelling of Gene Networks to model clonal selection in the tumour m...

Agent Based Modelling of Gene Networks to model clonal selection in the tumour microenvironment and predict therapeutic resistance The occurrence of therapeutic resistance is a major cause for the small effect on overall survival showed by targeted cancer therapies. Whilst experimental strategies to evaluate available treatments have been faced by an ever inc... ver más

30/11/2024

2M€

Presupuesto del proyecto: 2M€

Líder del proyecto

UNIVERSITA COMMERCIALE LUIGI BOCCONI No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 3 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2024-11-30

ERC-2017-COG: ERC Consolidator Grant Scope:Objectives

I+D

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Características del participante

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

3 Participantes

455.98K€ | Lider

ALBATROS, SL

657.47K€ | Participante

UOXF

1.54M€ | Participante

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Duración del proyecto: 80 meses Fecha Inicio: 2018-03-07
Fecha Fin: 2024-11-30

Líder del proyecto

UNIVERSITA COMMERCIALE LUIGI BOCCONI No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 2M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto The occurrence of therapeutic resistance is a major cause for the small effect on overall survival showed by targeted cancer therapies. Whilst experimental strategies to evaluate available treatments have been faced by an ever increasing number of possible combinations, computational approaches have been challenged by the lack of a framework able to model the multiple interactions encompassed by the three major factors affecting therapeutic resistance: selection of resistant clones, adaptability of gene signalling networks, and a protective and hypoxic tumour microenvironment. Here I propose a novel modelling framework, Agent-Based Modelling of Gene Networks, which brings together powerful computational modelling techniques and gene networks. This combination allows biological hypotheses to be tested in a controlled stepwise fashion, and it lends itself naturally to model a heterogeneous population of cells acting and evolving in a dynamic microenvironment, which is needed to predict therapeutic resistance and guide effective treatment selection. Using triple negative breast cancer (TNBC) as a testing case (15% of breast cancers, lacks validated), I propose to: 1. Develop a computational model of the TNBC tumour microenvironment using in-vitro and in-vivo, including patient-derived, models and data from clinical samples. 2. Validate the ability of the model to predict driver genes conferring a survival advantage to cancer cells in a hypoxic microenvironment. 3. Predict combinations of druggable targets to tackle TNBC therapeutic resistance. 4. Select most effective drug combinations and validate pre-clinically. This project will deliver pre-clinically validated drug combinations, new therapeutic targets and a virtual environment to study individual tumours and predict therapeutic resistance. Complementing and empowering experimental models and assays, microC will offer a new powerful tool for diagnosis and therapy.

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