Ye Mingzhi, Li Shiyong, Huang Weizhe, Wang Chunli, Liu Liping, Liu Jun, Liu Jilong, Pan Hui, Deng Qiuhua, Tang Hailing, Jiang Long, Huang Weizhe, Chen Xi, Shao Di, Peng Zhiyu, Wu Renhua, Zhong Jing, Wang Zhe, Zhang Xiaoping, Kristiansen Karsten, Wang Jian, Yin Ye, Mao Mao, He Jianxing, Liang Wenhua
BGI-Guangzhou Medical Laboratory, BGI-Shenzhen, Guangzhou 510006, China.
The First Affiliated Hospital of Guangzhou Medical University, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou 510120, China.
J Thorac Dis. 2018 Apr;10(Suppl 7):S820-S829. doi: 10.21037/jtd.2018.04.09.
A non-invasive method to predict the malignancy of surgery-candidate solitary pulmonary nodules (SPN) is urgently needed.
Super-depth next generation sequencing (NGS) of 35 paired tissues and plasma DNA was performed as an attempt to develop an early diagnosis approach.
Only ~6% of malignant nodule patients had driver mutations in the circulating tumour DNA (ctDNA) with >10,000-fold sequencing depth, and the concordance of mutation between tDNA and ctDNA was 3.9%. The first innovative whole mutation scored model in this study predicted 33.3% of malignant SPN with 100% specificity.
These results showed that lung cancer gene-targeted deep capture sequencing is not efficient enough to achieve ideal sensitivity by simply increasing the sequencing depth of ctDNA from early candidates. The sequencing could not be evaluated hotspot mutations in the early tumour stage. Nevertheless, a larger cohort is required to optimize this model, and more techniques may be incorporated to benefit the SPN high-risk population.
迫切需要一种非侵入性方法来预测手术候选孤立性肺结节（SPN）的恶性程度。
对35对组织和血浆DNA进行超深度下一代测序（NGS），试图开发一种早期诊断方法。
在测序深度大于10000倍时，只有约6%的恶性结节患者在循环肿瘤DNA（ctDNA）中存在驱动突变，tDNA与ctDNA之间的突变一致性为3.9%。本研究中首个创新的全突变评分模型以100%的特异性预测了33.3%的恶性SPN。
这些结果表明，肺癌基因靶向深度捕获测序通过简单增加早期候选者ctDNA的测序深度来实现理想敏感性的效率不够高。该测序无法评估早期肿瘤阶段的热点突变。然而，需要更大的队列来优化该模型，并且可能需要纳入更多技术以使SPN高危人群受益。
