MelanoDB: MAPK抑制剂治疗晚期黑色素瘤患者的临床和分子特征数据集。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
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
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引用次数: 0

摘要

MAPK抑制剂(MAPKi)已经彻底改变了晚期黑色素瘤患者的治疗。然而,原发和获得性耐药机制限制了它们的疗效。由于患者队列的规模有限,从肿瘤基线特征预测MAPKi反应仍然具有挑战性。因此,我们收集了9个不同患者队列(共n = 417例接受MAPKi治疗的晚期黑色素瘤患者)的数据,以确定临床和分子特征。我们整理的数据集名为MelanoDB,包括191名患者的全部或部分外显子组测序数据,66名患者的拷贝数改变信息和132名患者的基因表达数据。我们提供了一个web应用程序来探索集成的数据集和数据分布,并根据可查找、可访问、可互操作、可重用(FAIR)原则与科学界共享该数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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

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

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

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

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.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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