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<p>MAPK inhibitors (MAPKi) have revolutionized the treatment of patients with advanced melanoma. However, primary and acquired resistance mechanisms limit their efficacy. Predicting MAPKi response from the tumor baseline features remains challenging due to the limited size of patient cohorts. Therefore, we collected data from nine different patient cohorts (total n = 417 patients with advanced melanoma treated with MAPKi) to identify clinical and molecular features. Our curated dataset, named MelanoDB, includes whole or partial exome sequencing data for 191 patients, copy number alteration information for 66 patients, and gene expression data for 132 patients. We provide a web application to explore the integrated dataset and data distribution across the collected studies, and we share this dataset with the scientific community according to the Findable, Accessible, Interoperable, Reusable (FAIR) principles.</p>

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MelanoDB: A dataset of clinical and molecular features of patients with advanced melanoma treated with MAPK inhibitors

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月4日

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  • Sarah Dandou,
  • Julie A. Vendrell,
  • Jérôme Solassol,
  • Baptiste Louveau,
  • Céleste Lebbé,
  • Samia Mourah,
  • Florian Rambow,
  • Eric Richard,
  • Stanislas Du Manoir,
  • Alain Mangé,
  • Peter J. Coopman,
  • Ovidiu Radulescu,
  • Romain M. Larive

摘要

MAPK inhibitors (MAPKi) have revolutionized the treatment of patients with advanced melanoma. However, primary and acquired resistance mechanisms limit their efficacy. Predicting MAPKi response from the tumor baseline features remains challenging due to the limited size of patient cohorts. Therefore, we collected data from nine different patient cohorts (total n = 417 patients with advanced melanoma treated with MAPKi) to identify clinical and molecular features. Our curated dataset, named MelanoDB, includes whole or partial exome sequencing data for 191 patients, copy number alteration information for 66 patients, and gene expression data for 132 patients. We provide a web application to explore the integrated dataset and data distribution across the collected studies, and we share this dataset with the scientific community according to the Findable, Accessible, Interoperable, Reusable (FAIR) principles.