Diagnostic and prognostic value of blood samples for KRAS mutation identification in lung cancer: a meta-analysis

Hongchang Shen, Keying Che, Lei Cong, Wei Dong, Tiehong Zhang, Qi Liu, Jiajun Du, Hongchang Shen, Keying Che, Lei Cong, Wei Dong, Tiehong Zhang, Qi Liu, Jiajun Du

Abstract

Circulating tumor DNA (ctDNA) and tumor cells (CTC) are novel approaches for identifying genomic alterations. Thus, we designed a meta-analysis to evaluate the diagnostic value and prognostic significance of a KRAS proto-oncogene, GTPase (KRAS) mutation for lung cancer patients. All included articles were from PubMed, EMBASE, Web of Science and Cochrane Library. Twelve articles that described 1,131 patients were reviewed. True positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) were used to calculate pooled sensitivity, specificity, the positive likelihood ratio (PLR), the negative likelihood ratio (NLR), a diagnostic odds ratio (DOR), the area under the curve (AUC) and corresponding 95% confidence intervals (95% CI). PLR is calculated as sensitivity/(1-specificity) and NLR is (1- sensitivity)/specificity. DOR is a measured of diagnostic effectiveness (PLR/NLR). A survival analysis subgroup was also designed to evaluate prognostic significance. Pooled sensitivity, specificity, PLR, NLR, DOR and AUC were 0.79 (95% CI, 0.63-0.89), 0.93 (95% CI, 0.89-0.96), 12.13 (92% CI, 7.11-20.67), 0.22 (95% CI, 0.12-0.41), 54.82 (95% CI, 23.11-130.09), and 0.95 (95% CI, 0.93-0.96), respectively. KRAS mutation and wild-type hazard ratios for overall survival and progression-free survival were 1.37 (95% CI, 1.08-1.66), 1.46 (95% CI, 1.15-1.77) in blood samples, and 1.16 (95% CI, 1.03-1.28), 1.28 (95% CI, 1.09-1.46) in tumor tissue.

Keywords: KRAS; blood; diagnostic test; lung cancer; predictive factor.

Conflict of interest statement

CONFLICTS OF INTEREST

The authors declare no competing financial interests.

Figures

Figure 1. Flow diagram of study inclusion…
Figure 1. Flow diagram of study inclusion and exclusion for meta-analysis
Figure 2
Figure 2
A. Sensitivity analysis plot of meta-analysis. Every row represents an included study. The width of the horizontal line represents the 95% CI for each study. The vertical bar on both sides represents the lowest and highest values of 95% CI. B. SROC curve: each X mark represents a study and AUC is the area under the curve.
Figure 3
Figure 3
Forest plots of sensitivity (A) and specificity (B) for blood samples (ctDNA and CTC). The width of the horizontal line represents the 95% CI of each study, square proportional means the weight of every study. The weight is evaluated by the sample size and is presented as percent of total. The diamond represents pooled sensitivity, specificity and 95% CI.
Figure 4
Figure 4
A. Deek's funnel plot indicates no significant publication bias (p = 0.218 > 0.05). B. Fagan's Nomogram of blood samples for KRAS mutation identification. C. ROC plane for threshold effect. Each black spot represents an included study and does not constitute a “shoulder shape” graph, which represents no significant threshold effect.
Figure 5
Figure 5
Forest plots of sensitivity and specificity for ctDNA (sensitivity, A; specificity, B) and CTC (sensitivity, C; specificity, D). The width of the horizontal line represents the 95% CI of each study, square proportional means the weight of every study. Weight is evaluated by sample size and presented as percent of total. Diamond represents pooled sensitivity, specificity and 95% CI.
Figure 6
Figure 6
Forest plots of pooled HR for OS (tumor tissue: A, blood sample: B) and PFS (tumor tissue: C, blood sample: D) comparing patients of KRAS mutations with wild-type KRAS. The width of horizontal line represents the 95% CI of each study and square proportional means the weight of every study. Weight is evaluated by sample size and is presented as percent of total. Diamond represents pooled HR and 95% CI.

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