De Ruysscher Dirk, Defraene Gilles, Ramaekers Bram L T, Lambin Philippe, Briers Erik, Stobart Hilary, Ward Tim, Bentzen Søren M, Van Staa Tjeerd, Azria David, Rosenstein Barry, Kerns Sarah, West Catharine
Maastricht University Medical Center, Department of Radiation Oncology (MAASTRO Clinic), The Netherlands; KU Leuven, Radiation Oncology, Belgium.
KU Leuven, Radiation Oncology, Belgium.
Radiother Oncol. 2016 Dec;121(3):440-446. doi: 10.1016/j.radonc.2016.11.003. Epub 2016 Dec 12.
The optimal design and patient selection for interventional trials in radiogenomics seem trivial at first sight. However, radiogenomics do not give binary information like in e.g. targetable mutation biomarkers. Here, the risk to develop severe side effects is continuous, with increasing incidences of side effects with higher doses and/or volumes. In addition, a multi-SNP assay will produce a predicted probability of developing side effects and will require one or more cut-off thresholds for classifying risk into discrete categories. A classical biomarker trial design is therefore not optimal, whereas a risk factor stratification approach is more appropriate. Patient selection is crucial and this should be based on the dose-response relations for a specific endpoint. Alternatives to standard treatment should be available and this should take into account the preferences of patients. This will be discussed in detail.
放射基因组学介入试验的最佳设计和患者选择乍一看似乎很简单。然而，放射基因组学不像例如可靶向突变生物标志物那样提供二元信息。在这里，发生严重副作用的风险是连续的，随着剂量和/或体积的增加，副作用的发生率也会增加。此外，多单核苷酸多态性分析将产生发生副作用的预测概率，并且需要一个或多个截止阈值来将风险分类为离散类别。因此，经典的生物标志物试验设计并非最佳，而风险因素分层方法更为合适。患者选择至关重要，这应基于特定终点的剂量反应关系。应该有标准治疗的替代方案，并且这应该考虑到患者的偏好。这将进行详细讨论。
