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Meta-Analysis
. 2016 Jul 12;7(28):44583-44595.
doi: 10.18632/oncotarget.10069.

Circulating cell-free DNA as a prognostic and predictive biomarker in non-small cell lung cancer

Bo Ai  1 , Huiquan Liu  2 , Yu Huang  2 , Ping Peng  2
Affiliations
Meta-Analysis

Circulating cell-free DNA as a prognostic and predictive biomarker in non-small cell lung cancer

Bo Ai et al. Oncotarget. .

Abstract

Circulating cell-free DNA (cfDNA), which can be obtained from plasma or serum by non-invasive procedures, has showed great potential to predict treatment response and survival for cancer patients. Several studies have assessed the prognostic and predictive value of cfDNA in non-small cell lung cancer (NSCLC). However, these studies were often small and reported varying results. To address this issue, a meta-analysis was carried out. A total of 22 studies involving 2518 patients were subjected to the final analysis. Our results indicated that NSCLC patients with higher cfDNA concentration had shorter median progression-free survival (PFS) and overall survival (OS) time. In addition, high levels of cfDNA were significantly associated with poor PFS (hazard ratio or HR, 1.32; 95% CI, 1.02-1.71) and OS (HR, 1.64; 95% CI, 1.26-2.15). With respect to tumor specific mutations, we failed to reveal significant differences for PFS (HR, 1.30; 95% CI, 0.66-2.56) and OS (HR, 1.05; 95% CI, 0.49-2.25) when NSCLC patients were grouped according to KRAS genotype detected in cfDNA. However, NSCLC patients which harbored EGFR activating mutation in cfDNA had a greater chance of response to EGFR-TKIs (odds ratio or OR, 1.96; 95% CI, 1.59-2.42). No significant publication bias was detected in this study. In conclusion, cfDNA could act as a prognostic and predictive biomarker for patients with NSCLC.

Keywords: biomarker; circulating cell-free DNA; meta-analysis; non-small cell lung cancer; prognosis.

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Conflict of interest statement

The authors declared no conflicts of interest.

Figures

Figure 1
Figure 1. Flow diagram of study selection
Figure 2
Figure 2. Progression-free survival (PFS) according to cfDNA concentration in NSCLC patients
A. Median PFS time according to cfDNA concentration. B. Forest plot of hazard ratio (HR) for the impact of cfDNA concentration on PFS.
Figure 3
Figure 3. Overall survival (OS) according to cfDNA concentration in NSCLC patients
A. Median OS time according to cfDNA concentration. B. Forest plot of hazard ratio (HR) for the impact of cfDNA concentration on OS.
Figure 4
Figure 4. Forest plot of hazard ratio (HR) for the impact of cfDNA concentration on progression-free survival (PFS) and overall survival (OS) in NSCLC patients with advanced stages
A. The impact of cfDNA concentration on PFS. B. The impact of cfDNA concentration on OS.
Figure 5
Figure 5. Forest plot of hazard ratio (HR) for the impact of cfDNA concentration on progression-free survival (PFS) and overall survival (OS) in NSCLC patients treated with chemotherapy
A. The impact of cfDNA concentration on PFS. B. The impact of cfDNA concentration on OS.
Figure 6
Figure 6. Forest plot of hazard ratio (HR) for the impact of KRAS genotype detected in cfDNA on progression-free survival (PFS) and overall survival (OS)
A. The impact of KRAS genotype detected in cfDNA on PFS. B. The impact of KRAS genotype detected in cfDNA on OS.
Figure 7
Figure 7. Forest plot of odds ratio (OR) for the impact of EGFR genotype detected in cfDNA on response to EGFR-TKIs
Figure 8
Figure 8. Funnel plot for the assessment of publication bias in this study
A. Funnel plot for 8 studies reporting progression-free survival (PFS). B. Funnel plot for 11 studies reporting overall survival (OS).
See this image and copyright information in PMC

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