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PredAlgoBC

Financiado

Cerrado

Machine learning prediction for breast cancer therapy

Breast cancer is the cancer with the highest incidence in women worldwide, and is the leading cause of cancer-related death, mainly due to treatment resistance. Recently, tumor heterogeneity has been described as one of the key dr... ver más

30/09/2021

ICO

185K€

Presupuesto del proyecto: 185K€

Líder del proyecto

INSTITUT DE CANCEROLOGIE DE LOUEST No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-09-30

MSCA-IF-2018: Individual Fellowships

I+D

Cerrada hace 6 años

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Información adicional privada

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

1 Participantes

ICO

184.71K€ | Lider

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Duración del proyecto: 29 meses Fecha Inicio: 2019-04-11
Fecha Fin: 2021-09-30

Líder del proyecto

INSTITUT DE CANCEROLOGIE DE LOUEST No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 185K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Breast cancer is the cancer with the highest incidence in women worldwide, and is the leading cause of cancer-related death, mainly due to treatment resistance. Recently, tumor heterogeneity has been described as one of the key driver in treatment failure. Indeed, tumor is not a homogeneous entity to treat, but a complex association of subclonal populations driven by their own genetic alterations, and immune and stromal cells from microenvironment. Breast cancer subtypes and tumor heterogeneity advocate for the development of tailored, personalized treatments, but so far, the discovery of efficient predictive markers has been compromised by the lack of adapted biological models and methodological tools. The recent developments of high-throughput methods for bulk and single-cell analyses has generated large ‘omics’ datasets from patients, stored in open access databases (ArrayExpress, GEO). Combining these numerous datasets will grant a sufficient statistical power to reveal a comprehensive overview of tumor complexity. However, this data mining is currently limited by methodological challenges like cross-platform normalization and the difficulty to analyze complex data structure with high dimension observations. To overcome these issues, I propose to implement a multidisciplinary project at the interface between mathematics, biology, and information technologies. With the support of the mathematicians and bioinformaticians from the Bioinfomics unit of the regional comprehensive cancer center (ICO), I will develop and implement machine-learning algorithms in the search of predictive biomarkers for breast cancer treatment. This innovative strategy will lead to personalized medicine in breast cancer by guiding clinicians in the selection of the optimal therapeutic option. Moreover, this generated pipeline for predictive marker discovery could be further adapted for the treatment of other cancer types.

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