The Evidence Base for Circulating Tumor DNA-Methylation in Non-Small Cell Lung Cancer: A Systematic Review and Meta-Analysis

Abstract

Background: Non-Small Cell Lung Cancer (NSCLC) remains a challenging disease to manage with effectiveness. Early detection and precise monitoring are crucial for improving patient outcomes. Circulating tumor DNA (ctDNA) offers a non-invasive cancer detection and monitoring method. Emerging biomarkers, such as ctDNA methylation, have shown promise in enhancing diagnostic accuracy and prognostic assessment in NSCLC. In this review, we examined the current evidence regarding ctDNA methylation's role in NSCLC detection through a systematic review of the existing literature and meta-analysis. Methods: We systematically searched PubMed, Medline, Embase, and Web of Science databases up to 26 June 2024 for studies on the role of ctDNA methylation analysis in NSCLC patients. We included studies from 2010 to 2024 on NSCLC patients. We excluded case reports, non-English articles, studies on cell lines or artificial samples, those without cfDNA detection, prognostic studies, and studies with non-extractable data or mixed cancer types. Funnel plots were visually examined for potential publication bias, with a p value < 0.05 indicating bias. Meta-analysis was conducted using R packages (meta, forestplot, and mada). Combined sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive and negative predictive values, diagnostic odds ratio (DOR), and 95% confidence intervals (95% CI) were calculated. A summary receiver operating characteristic curve (SROC) and area under the curve (AUC) with related Standard Error (SE) were used to evaluate the overall diagnostic performance. Additionally, RASSF1A, APC, SOX17, SEPT9, and RARβ2 were analyzed, since their methylation was assessed in two or more studies. Results: From 38 candidate papers, we finally identified 12 studies, including 472 NSCLC patients. The pooled sensitivity was 0.62 (0.47-0.77) and the specificity was 0.90 (0.85-0.94). The diagnostic odds ratio was 15.6 (95% CI 9.36-26.09) and the area under the curve was 0.249 (SE = 0.138). The positive and negative predictive values were 5.38 (95% CI 3.89-7.44) and 0.34 (95% CI 0.22-0.54), respectively. For single genes, the specificity reached 0.83~0.96, except for RARβ2, but the sensitivity was relatively low for each gene. Significant heterogeneity across the included studies, the potential publication bias for specificity (p = 0.0231), and the need to validate the clinical utility of ctDNA methylation for monitoring treatment response and predicting outcomes in NSCLC patients represent the main limitations of this study. Conclusions: These results provide evidence of the significant potential of ctDNA methylation as a valuable biomarker for improving the diagnosis of NSCLC, advocating for its integration into clinical practice to enhance patient management.

Keywords: circulating free DNA; early diagnosis; methylation; non-small cell lung cancer.

Figure 1

PRISMA 2020 flow diagram of literature screening and study selection.

Figure 2

A paired forest plot illustrating the sensitivity and specificity of cfDNA methylation in NSCLC across 11 studies included in the meta–analysis [13,14,15,16,17,18,20,21,22,23,24]. A random–effects (RE) model was employed for the analysis. Each square and horizontal bar denotes the sensitivity and specificity for individual studies, accompanied by a 95% confidence interval (CI). The diamonds represent the overall findings, with the pooled sensitivity calculated as 0.62 (ranging from 0.47 to 0.77) and the pooled specificity at 0.90 (ranging from 0.85 to 0.94).

Figure 3

Summary receiver operating characteristic (SROC) plot. The curve illustrates the diagnostic accuracy of the cfDNA test across multiple studies. The red points represent individual study results, while the black curve summarizes the overall diagnostic performance. Q* is the point on the curve where sensitivity equals specificity, providing a measure of the test’s balance between detecting true positives and minimizing false positives. The SE(Q*) is the standard error of Q*.

Figure 4

Paired Forest Plot of sensitivity and specificity of genes assessed for methylation in two or more studies included in meta-analysis: RASSF1A, APC, SOX17, SEPT9, and RARβ2 [13,14,17,18,20,21,23].

Figure 4

Paired Forest Plot of sensitivity and specificity of genes assessed for methylation in two or more studies included in meta-analysis: RASSF1A, APC, SOX17, SEPT9, and RARβ2 [13,14,17,18,20,21,23].

Figure 4

Paired Forest Plot of sensitivity and specificity of genes assessed for methylation in two or more studies included in meta-analysis: RASSF1A, APC, SOX17, SEPT9, and RARβ2 [13,14,17,18,20,21,23].

Figure 4

Paired Forest Plot of sensitivity and specificity of genes assessed for methylation in two or more studies included in meta-analysis: RASSF1A, APC, SOX17, SEPT9, and RARβ2 [13,14,17,18,20,21,23].

Figure 4

Paired Forest Plot of sensitivity and specificity of genes assessed for methylation in two or more studies included in meta-analysis: RASSF1A, APC, SOX17, SEPT9, and RARβ2 [13,14,17,18,20,21,23].

Figure 5

ROC plane of sensitivity and specificity of cfDNA methylation in NSCLC for 11 studies included in meta–analysis [13,14,15,16,17,18,20,21,22,23,24].

Figure 6

A funnel plot of the sensitivity and specificity of cfDNA methylation in NSCLC for the 11 studies included in the meta-analysis [13,14,15,16,17,18,20,21,22,23,24]. Deek’s test showed a p-value of 0.0231 for specificity and a p-value of 0.6809 for sensitivity.