Descripción del proyecto
Algoritmos predictivos para combatir el cáncer de mama
El cáncer de mama es la enfermedad que más muertes relacionadas con el cáncer provoca en mujeres en todo el mundo. La implicación de la heterogeneidad de los tumores en la evolución y recidiva del cáncer de mama está bien documentada y aboga por el desarrollo de tratamientos personalizados. La aplicación de la medicina personalizada requiere el descubrimiento de nuevos biomarcadores, basados en el análisis de datos ómicos junto con información clínica. Se trata de un ámbito difícil que requiere métodos matemáticos sofisticados combinados con un análisis biológico profundo. El proyecto PredAlgoBC, financiado con fondos europeos, ha reunido a médicos, matemáticos y bioinformáticos para el desarrollo de algoritmos de aprendizaje automático que busquen biomarcadores predictivos para el tratamiento del cáncer de mama. La identificación de nuevos biomarcadores y su aplicación en la práctica clínica orientará a los médicos a la hora de elegir la mejor opción de tratamiento.
Objetivo
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.
Ámbito científico
- natural sciencescomputer and information sciencesdata sciencedata mining
- medical and health sciencesclinical medicineoncologybreast cancer
- medical and health scienceshealth sciencespersonalized medicine
- natural sciencesmathematics
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Programa(s)
Régimen de financiación
MSCA-IF-EF-ST - Standard EFCoordinador
49100 Angers
Francia