Wang Li-Ju, Ning Michael, Nayak Tapsya, Kasper Michael J, Monga Satdarshan P, Huang Yufei, Chen Yidong, Chiu Yu-Chiao
Cancer Therapeutics Program, University of Pittsburgh Medical Center Hillman Cancer Center, Pittsburgh, PA 15232, USA.
Greehey Children's Cancer Research Institute, University of Texas Health San Antonio, San Antonio, TX 78229, USA.
Patterns (N Y). 2024 Jan 12;5(2):100894. doi: 10.1016/j.patter.2023.100894. eCollection 2024 Feb 9.
Advancing precision oncology requires accurate prediction of treatment response and accessible prediction models. To this end, we present shinyDeepDR, a user-friendly implementation of our innovative deep learning model, DeepDR, for predicting anti-cancer drug sensitivity. The web tool makes DeepDR more accessible to researchers without extensive programming experience. Using shinyDeepDR, users can upload mutation and/or gene expression data from a cancer sample (cell line or tumor) and perform two main functions: "Find Drug," which predicts the sample's response to 265 approved and investigational anti-cancer compounds, and "Find Sample," which searches for cell lines in the Cancer Cell Line Encyclopedia (CCLE) and tumors in The Cancer Genome Atlas (TCGA) with genomics profiles similar to those of the query sample to study potential effective treatments. shinyDeepDR provides an interactive interface to interpret prediction results and to investigate individual compounds. In conclusion, shinyDeepDR is an intuitive and free-to-use web tool for anti-cancer drug screening.
推进精准肿瘤学需要准确预测治疗反应以及可获取的预测模型。为此，我们展示了shinyDeepDR，这是我们用于预测抗癌药物敏感性的创新深度学习模型DeepDR的用户友好型实现。该网络工具使没有丰富编程经验的研究人员更容易使用DeepDR。使用shinyDeepDR，用户可以上传癌症样本（细胞系或肿瘤）的突变和/或基因表达数据，并执行两个主要功能：“查找药物”，可预测样本对265种已批准和正在研究的抗癌化合物的反应；以及“查找样本”，可在癌症细胞系百科全书（CCLE）中搜索细胞系，并在癌症基因组图谱（TCGA）中搜索与查询样本基因组特征相似的肿瘤，以研究潜在的有效治疗方法。shinyDeepDR提供了一个交互式界面来解释预测结果并研究单个化合物。总之，shinyDeepDR是一个直观且免费使用的用于抗癌药物筛选的网络工具。
