Ying Shi, Qirui Shen, Aimin Jiang, Hong Yang, Kexin Li, Jian Zhang, Anqi Lin, Peng Luo
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引用次数: 0
摘要
背景：使用预测药物敏感性评分作为生存生物标志物可以改善精准医疗，克服基因组指导方法在临床试验中的局限性。方法：Pan-Cancer Drug - Sensitivity Score Survival Analysis （DrugSurvPlot）是一个交互式的、无需登录的web分析仪，使用R （v4.3.1）构建，利用Shiny包用于接口/服务器逻辑，DT包用于数据表查询/下载，生存包用于生存分析。使用oncopdict进行数据预处理，使用户能够导出处理过的表和结果。结果：DrugSurvPlot整合了189个GEO数据集（包括10个免疫检查点抑制剂治疗数据集）和33个TCGA数据集，共计85,531条记录，涉及52种癌症类型和13种生存状态数据类型，同时纳入了来自GDSC2的198种抗癌药物。该工具支持药物敏感性评分的两种截止策略，提供先进的生存分析方法，并支持可定制的高清晰度结果可视化。讨论：DrugSurvPlot通过建立预测药物敏感性评分作为肿瘤生存分析的新型预后生物标志物，代表了计算肿瘤学的重大进步。这个互动平台集成了涵盖198种抗癌药物和52种癌症类型的综合数据集，同时为研究人员提供了直观的工具来生成准备发表的Kaplan-Meier分析。目前药物库覆盖范围和数据集多样性的限制将通过药理学数据库的持续扩展和包括单细胞转录组学在内的新兴数据模式的结合来解决。综上所述，DrugSurvPlot提供了一个无代码平台，具有全面的数据集、多样化的癌症覆盖范围和可定制的生存分析，解决了关键的研究空白。持续的改进将提高预测的准确性和临床效用，使其成为药物生存调查中不断发展的动力。
DrugSurvPlot: A Novel Web-Based Platform Harnessing Drug Sensitivity Scores as Molecular Biomarkers for Pan-Cancer Survival Prognosis.
Background: Using predicted drug sensitivity scores as survival biomarkers may improve precision medicine and overcome the limitations of genomically guided approaches in clinical trials.
Methods: Pan-Cancer Drug Sensitivity Score Survival Analysis (DrugSurvPlot) is an interactive, login-free web analyzer built with R (v4.3.1), leveraging the Shiny package for interface/server logic, the DT package for data table queries/downloads, and the survival package for survival analysis. Data preprocessing was performed using OncoPredict, enabling users to export processed tables and results.
Results: DrugSurvPlot integrates 189 GEO datasets (including 10 immune checkpoint inhibitor treatment datasets) and 33 TCGA datasets, totaling 85,531 records across 52 cancer types and 13 survival status data types, while incorporating 198 anticancer drugs from GDSC2. This tool supports two cutoff strategies for drug sensitivity scores, offers advanced survival analysis methods, and enables customizable high-definition visualization of results.
Discussion: DrugSurvPlot represents a significant advancement in computational oncology by establishing predicted drug sensitivity scores as novel prognostic biomarkers for tumor survival analysis. This interactive platform integrates comprehensive datasets spanning 198 anticancer drugs and 52 cancer types, while providing researchers with intuitive tools for generating publication-ready Kaplan-Meier analyses. Current limitations in drug repertoire coverage and dataset diversity will be addressed through ongoing expansion of pharmacological databases and incorporation of emerging data modalities, including single-cell transcriptomics.
Conclusions: In summary, DrugSurvPlot offers a no-code platform with comprehensive datasets, diverse cancer coverage, and customizable survival analysis, addressing critical research gaps. Continuous enhancements will improve predictive accuracy and clinical utility, establishing it as an evolving powerhouse in drug-survival investigations.
期刊介绍：
Current Gene Therapy is a bi-monthly peer-reviewed journal aimed at academic and industrial scientists with an interest in major topics concerning basic research and clinical applications of gene and cell therapy of diseases. Cell therapy manuscripts can also include application in diseases when cells have been genetically modified. Current Gene Therapy publishes full-length/mini reviews and original research on the latest developments in gene transfer and gene expression analysis, vector development, cellular genetic engineering, animal models and human clinical applications of gene and cell therapy for the treatment of diseases.
Current Gene Therapy publishes reviews and original research containing experimental data on gene and cell therapy. The journal also includes manuscripts on technological advances, ethical and regulatory considerations of gene and cell therapy. Reviews should provide the reader with a comprehensive assessment of any area of experimental biology applied to molecular medicine that is not only of significance within a particular field of gene therapy and cell therapy but also of interest to investigators in other fields. Authors are encouraged to provide their own assessment and vision for future advances. Reviews are also welcome on late breaking discoveries on which substantial literature has not yet been amassed. Such reviews provide a forum for sharply focused topics of recent experimental investigations in gene therapy primarily to make these results accessible to both clinical and basic researchers. Manuscripts containing experimental data should be original data, not previously published.
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