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HJM Cancer Research Foundation Corporation, 10606 Candlewick Road, Lutherville, MD 21093, USA.
Int J Mol Sci. 2023 Dec 29;25(1):472. doi: 10.3390/ijms25010472.
Several studies have shown that microsatellite changes can be profiled in urine for the detection of bladder cancer. The use of microsatellite analysis (MSA) for bladder cancer detection requires a comprehensive analysis of as many as 15 to 20 markers, based on the amplification and interpretations of many individual MSA markers, and it can be technically challenging. Here, to develop fast, more efficient, standardized, and less costly MSA for the detection of bladder cancer, we developed three multiplex-polymerase-chain-reaction-(PCR)-based MSA assays, all of which were analyzed via a genetic analyzer. First, we selected 16 MSA markers based on 9 selected publications. Based on samples from Johns Hopkins University (the JHU sample, the first set sample), we developed an MSA based on triplet, three-tube-based multiplex PCR (a Triplet MSA assay). The discovery, validation, and translation of biomarkers for the early detection of cancer are the primary focuses of the Early Detection Research Network (EDRN), an initiative of the National Cancer Institute (NCI). A prospective study sponsored by the EDRN was undertaken to determine the efficacy of a novel set of MSA markers for the early detection of bladder cancer. This work and data analysis were performed through a collaboration between academics and industry partners. In the current study, we undertook a re-analysis of the primary data from the Compass study to enhance the predictive power of the dataset in bladder cancer diagnosis. Using a four-stage pipeline of modern machine learning techniques, including outlier removal with a nonlinear model, correcting for majority/minority class imbalance, feature engineering, and the use of a model-derived variable importance measure to select predictors, we were able to increase the utility of the original dataset to predict the occurrence of bladder cancer. The results of this analysis showed an increase in accuracy (85%), sensitivity (82%), and specificity (83%) compared to the original analysis. The re-analysis of the EDRN study results using machine learning statistical analysis proved to achieve an appropriate level of accuracy, sensitivity, and specificity to support the use of the MSA for bladder cancer detection and monitoring. This assay can be a significant addition to the tools urologists use to both detect primary bladder cancers and monitor recurrent bladder cancer.
多项研究表明，微卫星改变可通过尿液分析进行分析，用于膀胱癌的检测。使用微卫星分析 (MSA) 进行膀胱癌检测需要全面分析多达 15 到 20 个标志物，这基于对许多单个 MSA 标志物的扩增和解释，而且技术上具有挑战性。在这里，为了开发用于膀胱癌检测的快速、更有效、标准化和成本更低的 MSA，我们开发了三种基于多重聚合酶链反应 (PCR) 的 MSA 检测方法，均通过遗传分析仪进行分析。首先，我们根据 9 项选定的出版物选择了 16 个 MSA 标志物。基于约翰霍普金斯大学 (JHU) 样本 (第一组样本) 的样本，我们开发了基于三核苷酸、三管的多重 PCR 的 MSA 基于三重 MSA 检测法。癌症早期检测的生物标志物的发现、验证和转化是国家癌症研究所 (NCI) 早期检测研究网络 (EDRN) 的主要重点。EDRN 赞助了一项前瞻性研究，以确定一组用于膀胱癌早期检测的新型 MSA 标志物的疗效。这项工作和数据分析是通过学术界和行业合作伙伴之间的合作进行的。在当前的研究中，我们对 Compass 研究的主要数据进行了重新分析，以增强数据集在膀胱癌诊断中的预测能力。我们使用现代机器学习技术的四阶段管道，包括使用非线性模型去除异常值、纠正多数/少数类不平衡、特征工程以及使用模型衍生的变量重要性度量来选择预测因子，从而能够增加原始数据集在预测膀胱癌发生方面的效用。与原始分析相比，这种分析的结果显示准确性 (85%)、敏感性 (82%) 和特异性 (83%) 都有所提高。使用机器学习统计分析对 EDRN 研究结果的重新分析证明能够达到适当的准确性、敏感性和特异性水平，支持使用 MSA 进行膀胱癌检测和监测。这种检测方法可以成为泌尿科医生用于检测原发性膀胱癌和监测复发性膀胱癌的工具的重要补充。
