Udalov Aleksandr, Kumar Lexman, Gaudette Anna N, Zhang Ran, Salomao Joao, Saigal Sanjay, Nosrati Mehdi, McAllister Sean D, Desprez Pierre-Yves
Graduate School of Management, UC Davis, 1 Shields Ave., Davis, CA 95616, USA.
California Pacific Medical Center, Research Institute, 475 Brannan St., San Francisco, CA 94107, USA.
Methods Protoc. 2023 May 1;6(3):46. doi: 10.3390/mps6030046.
The longitudinal monitoring of patient circulating tumor DNA (ctDNA) provides a powerful method for tracking the progression, remission, and recurrence of several types of cancer. Often, clinical and research approaches involve the manual review of individual liquid biopsy reports after sampling and genomic testing. Here, we describe a process developed to integrate techniques utilized in data science within a cancer research framework. Using data collection, an analysis that classifies genetic cancer mutations as pathogenic, and a patient matching methodology that identifies the same donor within all liquid biopsy reports, the manual work for research personnel is drastically reduced. Automated dashboards provide longitudinal views of patient data for research studies to investigate tumor progression and treatment efficacy via the identification of ctDNA variant allele frequencies over time.
对患者循环肿瘤DNA（ctDNA）进行纵向监测，为追踪多种癌症的进展、缓解和复发提供了一种强大的方法。通常，临床和研究方法涉及在采样和基因组检测后人工查看个体液体活检报告。在此，我们描述了一个开发过程，该过程将数据科学中使用的技术整合到癌症研究框架内。通过数据收集、将遗传性癌症突变分类为致病性突变的分析以及在所有液体活检报告中识别同一供体的患者匹配方法，大幅减少了研究人员的人工工作。自动化仪表板为研究提供患者数据的纵向视图，以便通过识别ctDNA变异等位基因频率随时间的变化来研究肿瘤进展和治疗效果。
