| Literature DB >> 31423285 |
Yong Tang1, Xianling Liu2, Zhu'An Ou1, Zhe He1, Qihang Zhu1, Ye Wang3, Mei Yang4, Junyi Ye5, Han Han-Zhang5, Guibin Qiao6.
Abstract
Personalized medicine is revolutionizing the diagnosis and treatment of cancer; however, for personalized medicine to be used accurately, patient information is essential to determine the appropriate diagnosis, prognosis and treatment. The detection of genomic mutations in liquid biopsy samples is a non-invasive method of characterizing the genotype of a tumor. However, next generation sequencing-based plasma genotyping only has a sensitivity of ~70%. Identifying potential indicators that may reflect the sensitivity of a liquid biopsy analysis could offer important information for its clinical application. In the present study, 47 pairs of patient-matched plasma and tumor tissue samples obtained from patients with advanced lung cancer were sequenced using a panel of 56 cancer-associated genes. The plasma maximum allele frequency (Max AF) was identified as a novel biomarker to indicate the sensitivity of plasma genotyping. Using the identified somatic mutations in patient tissue biopsy samples as a reference, the sensitivity of the corresponding patient plasma test was investigated. The by-variant sensitivity of the plasma test was 68.1%, with 79 matched and 37 missed genetic aberrances. The by-patient sensitivity was calculated as 83%. Patients with a high plasma Max AF value (>2.2%) demonstrated a higher concordance with the range of mutations identified in the patient-matched tissue samples. The Max AF observed in patient plasma samples was positively correlated with liquid biopsy sensitivity and could be used as a potential indicator of liquid biopsy sensitivity. Therefore, patients with a low plasma Max AF (≤2.2%) may need to undergo further tissue biopsy to allow personalized oncology treatment. In summary, the present study may offer a non-invasive testing method for a sub-group of patients with advanced lung cancer.Entities:
Keywords: liquid biopsy; maximum allelic fraction; non-small cell lung cancer; personalized medicine
Year: 2019 PMID: 31423285 PMCID: PMC6607097 DOI: 10.3892/ol.2019.10490
Source DB: PubMed Journal: Oncol Lett ISSN: 1792-1074 Impact factor: 2.967