This group is the Oncostat team of the UMR1018/CESP.
The Oncostat team develops methods for the evaluation of precision medicine in oncology based on the level of evidence.
The recent revolution in –omics technology and the advent of targeted and immune therapies have increased the interest in molecular biomarkers capable of predicting the diagnostic, the clinical outcome of cancer patients or the response to specific therapies. The development of stratified medicine implies the segmentation of common cancers in small groups of tumors with specific abnormalities. A new generation of clinical trial designs requiring repeated biomarker measurements and surrogate clinical endpoints is needed to evaluate treatment effects in trials with limited sample sizes (Theme 1 "Clinical Trial Methodology"). Large-scale collaborative individual patient data (IPD) meta-analyses are useful tools to provide high level of evidence on the efficacy and toxicity of anti-cancer therapies in molecularly defined strata (Theme 2 "Meta-analysis of treatments and biomarkers”). With the increasing number of therapies available for a specific indication in oncology, methods for network meta-analysis are being developed to compare their effectiveness. Because of the high costs associated with the tandem diagnostic and therapeutic medicine, economic analyses will be needed to evaluate the strategy associating the biomarkers with the molecularly targeted treatments, which represents a new field of research (Theme 3 "Economic evaluation of treatments and biomarkers"). Once a potential biomarker has been identified for the prediction of diagnosis or clinical outcome of patients, an evidence-based evaluation implies careful replication in other cohorts. Advances in molecular genetics and in the knowledge of pathology of cancer are re-shaping the traditional methods in clinical epidemiology (Theme 4 "Development and validation of biomarkers ").
The statistical software packages developed in the team are available to the scientific community on the website https://github.com/Oncostat