Utilizing phenotypic characteristics of metastatic brain tumors to improve the probability of detecting circulating tumor DNA from cerebrospinal fluid in non-small-cell lung cancer patients: development and validation of a prediction model in a prospective cohort study

Abstract

Background: Circulating tumor DNA (ctDNA) in cerebrospinal fluid (CSF) has become a promising surrogate for genomic profiling of central nervous system tumors. However, suboptimal ctDNA detection rates from CSF limit its clinical utility. Thus precise screening of suitable patients is needed to maximize the clinical benefit.

Patients and methods: Between February 2017 and December 2020, 66 newly diagnosed non-small-cell lung cancer (NSCLC) patients with brain parenchymal metastases were prospectively enrolled as a training cohort and 30 additional patients were enrolled as an external validation cohort. CSF samples and matched primary tumor tissues were collected before treatment and subjected to next-generation sequencing (NGS). The imageological characteristics of patients' brain tumors were evaluated by radiologists using enhanced magnetic resonance imaging images. The clinical and imageological characteristics were evaluated by complete subsets regression, Akaike information criteria, and Bayesian information criteria methods to establish the prediction model. A nomogram was then built for CSF ctDNA detection prediction.

Results: The somatic mutation detection rate of genes covered by our targeted NGS panel was significantly lower in CSF ctDNA (59.09%) than tumor tissue (91.84%). The T_size (diameter of the largest intracranial lesion) and LVD_min (minimum lesion-ventricle distance for all intracranial lesions) were significantly associated with positive CSF ctDNA detection, and thus, were selected to establish the prediction model, which achieved an area under the ROC curve (AUC) of 0.819 and an accuracy of 0.800. The model's predictive ability was further validated in the independent external cohort (AUC of 0.772, accuracy of 0.767) and by internal cross-validation. The CSF ctDNA detection rate was significantly improved from 58.18% (32/55) to 81.81% (27/33) in patients after model selection (P = 0.022).

Conclusions: This study developed a regression model to predict the probability of detecting CSF ctDNA using the phenotypic characteristics of metastatic brain lesions in NSCLC patients, thus, maximizing the benefits of CSF liquid biopsies.

Keywords: CSF ctDNA; brain metastases; prediction model.