Impact of bioinformatic procedures in the development and translation of high-throughput molecular classifiers in Oncology
A partir de bases de données d'expression de gènes dans des tumeurs de patients atteints d'un cancer du poumon non à petites cellules, cet article analyse les défis posés par une analyse statistique multi-dimensionnelle pour traduire en pratique clinique les travaux de recherche sur la stratification des tumeurs
The progressive introduction of high-throughput molecular techniques in the clinic allows for the extensive and systematic exploration of multiple biological layers of tumors. Molecular profiles and classifiers generated from these assays represent the foundation of what the National Academy describes as the future of 'precision medicine.' However, the analysis of such complex data requires the implementation of sophisticated bioinformatic and statistical procedures. It is critical that oncology practitioners be aware of the advantages and limitations of the methods used to generate classifiers in order to usher them into the clinic. This article uses publicly available expression data from NSCLC patients to first illustrate the challenges of experimental design and pre-processing of data prior to clinical application highlights the challenges of high-dimensional statistical analysis. It provides a roadmap for the translation of such classifiers to clinical practice and make key recommendations for good practice.
Clinical Cancer Research , résumé, 2013