Early diagnosis of brain metastases using cerebrospinal fluid cell‐free DNA‐based breakpoint motif and mutational features in lung cancer

• Published in: Clinical and translational medicine 2023-03, Vol.13 (3), p.e1221-n/a
• Main Authors: Qin, Xueting, Bai, Yujun, Zhou, Shizhen, Shi, Hongjin, Liu, Xiaoli, Wang, Song, Wu, Xiaoying, Pang, Jiaohui, Song, Xi, Fan, Xiaojun, Ou, Qiuxiang, Xu, Yang, Bao, Hua, Li, Li, Li, Jun, Shao, Yang, Yuan, Shuanghu
• Format: Article
• Language: English
• Subjects: Brain Neoplasms - diagnosis; Brain Neoplasms - genetics; Brain Neoplasms - secondary; Cell-Free Nucleic Acids - genetics; Cellular biology; Cerebrospinal fluid; Conflicts of interest; Disease; Early Detection of Cancer; Humans; Letter to the Editor; Lung cancer; Lung Neoplasms - diagnosis; Lung Neoplasms - genetics; Magnetic resonance imaging; Medical prognosis; Metastasis; Mutation - genetics; Patients; Regression analysis; Sample size
• ISSN: 2001-1326; 2001-1326
• DOI: 10.1002/ctm2.1221

Cancer treatment may also obscure contrast enhancement, making the BM diagnosis more challenging.2 Meanwhile, CSF cytology provides valuable information about the pathologic conditions of cells involved in the central nervous system (CNS) and its coverings but is not sensitive enough for definitive diagnosis and highly relies on the pathologist's experience. [...]exploring sensitive and accurate methods is essential for promoting the early detection of LCBM. Plasma cell-free DNA (cfDNA) analysis has been widely adopted for assessing genomic features of cancer patients, monitoring response to treatment, quantifying minimal residual disease, and examining therapy resistance.3–7 Particularly, Guo et al. have leveraged the elastic-net logistic regression algorithm to integrate the 6 bp BPM feature in plasma cfDNA and successfully built a sensitive model for stage I lung adenocarcinoma detection.8 As CSF ctDNA has been gaining credibility for its high capability of detecting somatic genetic alterations in patients with CNS malignancies,9 this study aims to develop a robust model for the sensitive detection of LCBM using genetic features derived from CSF ctDNA. According to the BM status and the relationship with follow-up time, the 81 patients were classified into three subgroups, including 62 POS patients (patients whose BM status was already positive at CSF sampling), 10 NEG patients (patients whose BM status was negative at CSF sampling and remained unchanged during the follow-up) and nine NTP patients (patients whose BM status turned from negative at CSF sampling to positive during the follow-up). [...]although most patients in our study developed parenchymal BM during progression, the study cohort comprises various BM types due to sample availability. [...]we plan to conduct a more extensive study and develop a BPM model capable of identifying patients with different BM types, which may add significant value to the current model for its clinical utility.