液体活检
一致性
循环肿瘤DNA
精密医学
个性化医疗
微小残留病
数字聚合酶链反应
疾病
疾病监测
医学
癌症研究
计算生物学
聚合酶链反应
生物
肿瘤科
癌症
内科学
生物信息学
病理
基因
白血病
遗传学
作者
R Chin,Kevin Chen,Abul Usmani,Chanelle Chua,Peter K. Harris,Michael S. Binkley,Tej D. Azad,Jonathan C. Dudley,Aadel A. Chaudhuri
标识
DOI:10.1007/s40291-019-00390-5
摘要
Circulating tumor DNA (ctDNA) is a component of cell-free DNA that is shed by malignant tumors into the bloodstream and other bodily fluids. Levels of ctDNA are typically low, particularly in patients with localized disease, requiring highly sophisticated methods for detection and quantification. Multiple liquid biopsy methods have been developed for ctDNA analysis in solid tumor malignancies and are now enabling detection and assessment of earlier stages of disease, post-treatment molecular residual disease (MRD), resistance to targeted systemic therapy, and tumor mutational burden. Understanding ctDNA biology, mechanisms of release, and clearance and size characteristics, in conjunction with the application of molecular barcoding and targeted error correction, have increased the sensitivity and specificity of ctDNA detection techniques. Combinatorial approaches including integration of ctDNA data with circulating protein biomarkers may further improve assay sensitivity and broaden the scope of ctDNA applications. Circulating viral DNA may be utilized to monitor disease in some virally induced malignancies. In spite of increasingly accurate methods of ctDNA detection, results need to be interpreted with caution given that somatic mosaicisms such as clonal hematopoiesis of indeterminate potential (CHIP) may give rise to genetic variants in the bloodstream unrelated to solid tumors, and the limited concordance observed between different commercial platforms. Overall, highly precise ctDNA detection and quantification methods have the potential to transform clinical practice via non-invasive monitoring of solid tumor malignancies, residual disease detection at earlier timepoints than standard clinical and/or imaging surveillance, and treatment personalization based on real-time assessment of the tumor genomic landscape.
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