Dandou Sarah, Amin Kriti, D'Hondt Véronique, Solassol Jérôme, Dereure Olivier, Coopman Peter J, Radulescu Ovidiu, Fröhlich Holger, Larive Romain M
IRCM, Université de Montpellier, ICM, INSERM, Montpellier, France.
LPHI, Université de Montpellier, CNRS, Montpellier, France.
NPJ Precis Oncol. 2025 Jul 9;9(1):231. doi: 10.1038/s41698-025-00814-y.
Baseline genomic data have not demonstrated significant value for predicting the response duration to MAPK inhibitors (MAPKi) in patients with advanced BRAF-mutated melanoma. We used machine learning algorithms and pre-processed genomic data to test whether they could contain useful information to improve the progression-free survival (PFS) prediction. This exploratory analysis compared the predictive performance of a dataset that contained clinical features alone and supplemented with baseline genomic data. In the evaluation set (two cohorts, n = 111), the cross-validated model performance improved when pre-processed genomic data, such as mutation rates, were added to the clinical features. In the validation dataset (two cohorts, n = 73), the best model with genomic data outperformed the best model with clinical features alone. Finally, our best model outperformed with baseline genomic data, increasing the number of patients with a correctly predicted relapse by between +12% and +28%. In our models, baseline genomic data improved the prediction of response duration and could be incorporated into the development of predictive models of MAPKi treatment in melanoma.
基线基因组数据尚未显示出对预测晚期BRAF突变黑色素瘤患者对丝裂原活化蛋白激酶抑制剂（MAPKi）的反应持续时间具有显著价值。我们使用机器学习算法和预处理的基因组数据来测试它们是否包含有用信息以改善无进展生存期（PFS）预测。这项探索性分析比较了仅包含临床特征以及补充了基线基因组数据的数据集的预测性能。在评估集（两个队列，n = 111）中，当将诸如突变率等预处理的基因组数据添加到临床特征中时，交叉验证的模型性能有所提高。在验证数据集（两个队列，n = 73）中，包含基因组数据的最佳模型优于仅具有临床特征的最佳模型。最后，我们的最佳模型在基线基因组数据方面表现出色，将正确预测复发的患者数量增加了12%至28%。在我们的模型中，基线基因组数据改善了反应持续时间的预测，并可纳入黑色素瘤MAPKi治疗预测模型的开发中。
