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PrECISE

Financiado

PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION

Despite of their great promise, high-throughput technologies in cancer research have often failed to translate to major therapeutic advances in the clinic. One challenge has been tumour heterogeneity, where multiple competing subc... ver más

31/12/2018

TECHNIKON

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto

TECHNIKON FORSCHUNGS UND PLANUNGSGESELLSCHAFT... No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Ver 11 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2018-12-31

Línea de financiación objetivo El proyecto se financió a través de la siguiente ayuda:

PHC-02-2015: Understanding disease: systems medicine

I+D

Cerrada hace 10 años

0% 54% 100%

11 Participantes

TECHNIKON

358.13K€ | Lider

INIA NEURAL

489.61K€ | Participante

UZH

828.95K€ | Participante

TECHNISCHE UNIVERSITAT DARMS...

539.38K€ | Participante

Últimas noticias

30-11-2024: Cataluña Gestión For... Se ha cerrado la línea de ayuda pública: Gestión Forestal Sostenible para Inversiones Forestales Productivas para el organismo:

29-11-2024: IDAE En las últimas 48 horas el Organismo IDAE ha otorgado 4 concesiones

29-11-2024: ECE En las últimas 48 horas el Organismo ECE ha otorgado 2 concesiones

Duración del proyecto: 36 meses Fecha Inicio: 2015-12-09
Fecha Fin: 2018-12-31

Presupuesto del proyecto 6M€

Descripción del proyecto Despite of their great promise, high-throughput technologies in cancer research have often failed to translate to major therapeutic advances in the clinic. One challenge has been tumour heterogeneity, where multiple competing subclones coexist within a single tumour. Genomic heterogeneity renders it difficult to identify all driving molecular alterations, and thus results in therapies that only target subsets of aggressive tumour cells. Another challenge lies in the integration of multiple types of molecular data into mathematical disease models that can make actionable clinical statements. We aim to develop predictive computational technology that can exploit molecular and clinical data to improve our understanding of disease mechanisms and to inform clinicians about optimized strategies for therapeutic intervention. We propose to focus on prostate cancer, a leading cause of cancer death amongst men in Europe, but also prone to over-treatment. Our approach combines the exploitation of genomic, transcriptomic, proteomic, and clinical data in primary and metastatic tumours, prospective cohorts of well characterized prostate cancer patients, drug screenings in cell lines, and the use of the Watson technology, a last generation cognitive computer developed at IBM. The translational objective of this study is to develop technology for identifying disease mechanisms and produce treatment recommendations for individual patients based on a therapeutic biomarker panel. The proposed software framework will be accessible through a graphical interface that will facilitate its dissemination and use by researchers, clinicians, and biomedical industries. The framework will provide intuitive tools to deposit, share, analyze, and visualize molecular and clinical data; as well as to infer prognosis, elucidate implicated mechanisms and recommend therapy accordingly. This software framework will serve as a proof of concept for future development by industrial partners in Europe.

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