Médecine de précision et Evidence-Based Medicine : quelle articulation ?

Élodie Giroux

Résumé


L’Evidence-Based Medicine (EBM) et la médecine personnalisée (MP) ont une ambition commune : réduire l’écart entre les résultats de la recherche biomédicale et leur application en clinique. Toutefois la MP est souvent présentée comme un « nouveau paradigme » pour la médecine, comme l’avait d’ailleurs été l’EBM dans les années 1990. Elle recouvre beaucoup de projets différents mais au cœur de la plupart d’entre eux, on retrouve la prétention d’une meilleure prise en compte des spécificités individuelles que ne le permettrait l’EBM avec son approche populationnelle et statistique. Dans cet article, nous concentrons notre attention sur la MP en cancérologie : elle concerne essentiellement un ciblage des traitements à partir de caractéristiques moléculaires des patients. Ce ciblage est rendu notamment possible par la meilleure connaissance des mécanismes moléculaires des cancers. Il conduit à une stratification en sous-groupes plus restreints de patients et met en difficulté l’évaluation classique des thérapeutiques promue par l’EBM, en particulier, les essais cliniques randomisés qui reposent sur des cohortes de grande taille. Mais la meilleure connaissance des mécanismes et la plus grande précision des thérapies pourraient rendre ces essais moins nécessaires. Assistons-nous avec la MP à une revanche de la culture physiopathologique et mécaniste sur celle statistique et empiriste au sein de la recherche clinique ? Notre objectif est de mettre en évidence ce qui, pour cette MP déjà effective, conduit à des changements épistémologiques dans la manière de considérer ce qui compte comme type d’information et de preuve en médecine, en particulier dans le champ de l’évaluation thérapeutique. Nous défendons l’idée que la MP, loin de pouvoir se passer des approches statistiques et place en réalité l’EBM face à de nouveaux défis. Elle la conduit à renforcer l’articulation et l’intégration des données statistiques et mécanistes pour une meilleure prise en charge de chaque patient.

Evidence-Based Medicine (EBM) and Personalized Medicine (PM) share a common goal: reducing the gap between the results of biomedical research and their clinical application. PM is, however, often presented as a “new paradigm” for medicine, just as EBM was in the 1990s. It covers a wide variety of projects but the core idea that generally unites them is the ambition of better taking account of individual specificities than did EBM with its statistical and population-centred approach. In this article, I concentrate on PM in cancerology, the essence of which is to target treatments based on the molecular profile of the patient. This targeting is made possible by gaining better knowledge about the molecular mechanisms of cancers. The classification of patients as a function of their molecular profile entails the creation of patient sub-groups. This creates a problem for the traditional evaluation of therapeutic treatment promoted by EBM, in particular the use of randomized trials using sizeable cohorts. But a better understanding of the mechanisms and the greater precision of treatments could reduce the need for these trials. Does PM thus represent the revenge of a physio-pathological and mechanistic culture in clinical research against the statistical and empirical one of EBM? My objective is to show how current practices of PM leads to epistemological changes in our estimation of what count as relevant types of information and proof in medicine, in particular in the field of therapeutic evaluation. I defend the idea that PM, far from obviating the need for statistical approaches and the search for correlations, ultimately poses new challenges for EBM. PM drives EBM to strengthen the articulation and the integration of statistical and mechanistic data with a view to providing a better service for each patient.

Mots-clés


médecine personnalisée; médecine systémique des 4P; médecine de précision; cancérologie; mécanisme; statistique; Evidence-based Medicine; personalised medicine; P4 systems medicine; precision medicine; oncology; mechanism; statistic

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DOI: http://dx.doi.org/10.20416/lsrsps.v4i2.683

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