@article{JTD20509,
author = {Mingzhi Ye and Shiyong Li and Weizhe Huang and Chunli Wang and Liping Liu and Jun Liu and Jilong Liu and Hui Pan and Qiuhua Deng and Hailing Tang and Long Jiang and Weizhe Huang and Xi Chen and Di Shao and Zhiyu Peng and Renhua Wu and Jing Zhong and Zhe Wang and Xiaoping Zhang and Karsten Kristiansen and Jian Wang and Ye Yin and Mao Mao and Jianxing He and Wenhua Liang},
title = {Comprehensive targeted super-deep next generation sequencing enhances differential diagnosis of solitary pulmonary nodules},
journal = {Journal of Thoracic Disease},
volume = {10},
number = {Suppl 7},
year = {2018},
keywords = {},
abstract = {Background: A non-invasive method to predict the malignancy of surgery-candidate solitary pulmonary nodules (SPN) is urgently needed.
Methods: Super-depth next generation sequencing (NGS) of 35 paired tissues and plasma DNA was performed as an attempt to develop an early diagnosis approach.
Results: 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.
Conclusions: 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.},
issn = {2077-6624}, url = {https://jtd.amegroups.org/article/view/20509}
}