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

Abstract

Dear Editor, In this study, we developed a sensitive machine learning model with a remarkable capacity to predict brain metastases (BM) in lung cancer patients using the break-point motif (BPM) features in cerebrospinal fluid (CSF) circulating tumour DNA (ctDNA). We have also assessed the mutational profile in CSF ctDNA, revealing promising BM-related prognostic biomarkers in lung cancer patients. BM is frequently associated with a short life expectancy and a high mortality rate in lung cancer patients. 1 Early detection and timely treatment help to ameliorate the disease severity for lung cancer BM (LCBM). Brain magnetic resonance imaging (MRI) is the preferred method to evaluate the number, size and location of BM, but it lacks clear guidance to indicate the appropriate timing for screening. 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. Therefore, 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. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. In this study, 76.6% of lung cancer patients (62/81) were diagnosed with parenchymal BM with or without other types of CNS diseases by enhanced brain MRI and/or computerized tomography (CT) scan (Table S1). CSF cytology was performed for 71 patients initially admitted to our hospital as a complementary approach for diagnosing leptomeningeal metastasis. All 81 patients underwent lumbar puncture to collect CSF for targeted next-generation sequencing (NGS), followed by extraction of BPM and mutational features for modelling (Supplementary Material). 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). As NTP patients were generally located between POS and NEG patients in the principal component analysis (Figure S1), we, therefore, assigned 70 patients with definitive BM status at CSF sampling (62 POS and eight randomly selected NEG) to the training cohort to develop the BM detection model and 11 patients (nine NTP and two randomly selected NEG) to the testing cohort for independent evaluation of the model performance (Figure 1A). Since the predictive model built solely on CSF ctDNA status showed a relatively high false-positive rate in detecting LCBM (Figure S2), we wondered if incorporating the ctDNA status feature into the model based on BPM features of CSF ctDNA using elastic-net logistic regression, hereafter referred to as "integrated model", could help improve the model performance (Figure 1B). In the training cohort, the integrated model achieved an area under the curve (AUC) of 0.940 (95% confidence Clin. Transl. Med. 2023;13:e1221. wileyonlinelibrary.com/journal/ctm2 1 of 6 https://doi.