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<p>Baseline genomic data have not demonstrated significant value for predicting the response duration to MAPK inhibitors (MAPKi) in patients with advanced BRAF<sup>V600</sup>-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, <i>n</i> = 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, <i>n</i> = 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.</p>

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Improved prediction of MAPKi response duration in melanoma patients using genomic data and machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月9日

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  • Sarah Dandou,
  • Kriti Amin,
  • Véronique D’Hondt,
  • Jérôme Solassol,
  • Olivier Dereure,
  • Peter J. Coopman,
  • Ovidiu Radulescu,
  • Holger Fröhlich,
  • Romain M. Larive

摘要

Baseline genomic data have not demonstrated significant value for predicting the response duration to MAPK inhibitors (MAPKi) in patients with advanced BRAFV600-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.