BeiGene (Beijing) Co., Ltd., Beijing, China.
BMC Bioinformatics. 2021 Mar 17;22(1):127. doi: 10.1186/s12859-021-04050-6.
An increasing number of clinical trials require biomarker-driven patient stratification, especially for revolutionary immune checkpoint blockade therapy. Due to the complicated interaction between a tumor and its microenvironment, single biomarkers, such as PDL1 protein level, tumor mutational burden (TMB), single gene mutation and expression, are far from satisfactory for response prediction or patient stratification. Recently, combinatorial biomarkers were reported to be more precise and powerful for predicting therapy response and identifying potential target populations with superior survival. However, there is a lack of dedicated tools for such combinatorial biomarker analysis.
Here, we present dualmarker, an R package designed to facilitate the data exploration for dual biomarker combinations. Given two biomarkers, dualmarker comprehensively visualizes their association with drug response and patient survival through 14 types of plots, such as boxplots, scatterplots, ROCs, and Kaplan-Meier plots. Using logistic regression and Cox regression models, dualmarker evaluated the superiority of dual markers over single markers by comparing the data fitness of dual-marker versus single-marker models, which was utilized for de novo searching for new biomarker pairs. We demonstrated this straightforward workflow and comprehensive capability by using public biomarker data from one bladder cancer patient cohort (IMvigor210 study); we confirmed the previously reported biomarker pair TMB/TGF-beta signature and CXCL13 expression/ARID1A mutation for response and survival analyses, respectively. In addition, dualmarker de novo identified new biomarker partners, for example, in overall survival modelling, the model with combination of HMGB1 expression and ARID1A mutation had statistically better goodness-of-fit than the model with either HMGB1 or ARID1A as single marker.
The dualmarker package is an open-source tool for the visualization and identification of combinatorial dual biomarkers. It streamlines the dual marker analysis flow into user-friendly functions and can be used for data exploration and hypothesis generation. Its code is freely available at GitHub at https://github.com/maxiaopeng/dualmarker under MIT license.
越来越多的临床试验需要基于生物标志物的患者分层，尤其是对于革命性的免疫检查点阻断疗法。由于肿瘤与其微环境之间的复杂相互作用，单一生物标志物（如 PDL1 蛋白水平、肿瘤突变负担（TMB）、单个基因突变和表达）远不能满足预测反应或患者分层的要求。最近，组合生物标志物被报道为更准确和强大的预测治疗反应和识别具有优越生存潜力的潜在目标人群的方法。然而，目前缺乏专门用于此类组合生物标志物分析的工具。
在这里，我们提出了 dualmarker，这是一个专为双生物标志物组合数据探索而设计的 R 包。给定两个生物标志物，dualmarker 通过 14 种类型的图（如箱线图、散点图、ROC 曲线和 Kaplan-Meier 图）全面可视化它们与药物反应和患者生存的关联。dualmarker 使用逻辑回归和 Cox 回归模型，通过比较双标志物与单标志物模型的数据拟合度，评估双标志物相对于单标志物的优越性，用于新的生物标志物对的从头搜索。我们使用来自一个膀胱癌患者队列（IMvigor210 研究）的公共生物标志物数据演示了这个简单易用的工作流程和全面的功能；我们证实了之前报道的生物标志物对 TMB/TGF-beta 特征和 CXCL13 表达/ARID1A 突变用于反应和生存分析的情况，分别。此外，dualmarker 还可以从头识别新的生物标志物伙伴，例如，在总体生存模型中，组合 HMGB1 表达和 ARID1A 突变的模型比单独使用 HMGB1 或 ARID1A 作为单标志物的模型具有更好的拟合优度。
dualmarker 包是一个用于可视化和识别组合双生物标志物的开源工具。它将双标志物分析流程简化为用户友好的功能，并可用于数据探索和假设生成。其代码在 GitHub 上以 MIT 许可证免费提供，网址为 https://github.com/maxiaopeng/dualmarker。
