Figures
Abstract
Background
The prognostic value of the copy number (GCN) and protein expression of the mesenchymal-epithelial transition (MET) gene for survival of patients with non-small cell lung cancer (NSCLC) remains controversial. This study aims to comprehensively and quantitatively asses the suitability of MET GCN and protein expression to predict patients' survival.
Methods
PubMed, Embase, Web of Science and Google Scholar were searched for articles comparing overall survival in patients with high MET GCN or protein expression with those with low level. Pooled hazard ratio (HR) and 95% confidence intervals (CIs) were calculated using the random and the fixed-effects models. Subgroup and sensitivity analyses were also performed.
Results
Eighteen eligible studies enrolling 5,516 patients were identified. Pooled analyses revealed that high MET GCN or protein expression was associated with poor overall survival (OS) (GCN: HR = 1.90, 95% CI 1.35–2.68, p<0.001; protein expression: HR = 1.52, 95% CI 1.08–2.15, p = 0.017). In Asian populations (GCN: HR = 2.22, 95% CI 1.46–3.38, p<0.001; protein expression: HR = 1.89, 95% CI 1.34–2.68, p<0.001), but not in the non-Asian subset. For adenocarcinoma, high MET GCN or protein expression indicated decreased OS (GCN: HR = 1.49, 95% CI 1.05–2.10, p = 0.025; protein expression: HR = 1.69, 95% CI 1.31–2.19, p<0.001). Results were similar for multivariate analysis (GCN: HR = 1.61, 95% CI 1.15–2.25, p = 0.005; protein expression: HR = 2.18, 95% CI 1.60–2.97, p<0.001). The results of the sensitivity analysis were not materially altered and did not draw different conclusions.
Citation: Guo B, Cen H, Tan X, Liu W, Ke Q (2014) Prognostic Value of MET Gene Copy Number and Protein Expression in Patients with Surgically Resected Non-Small Cell Lung Cancer: A Meta-Analysis of Published Literatures. PLoS ONE 9(6): e99399. https://doi.org/10.1371/journal.pone.0099399
Editor: William B. Coleman, University of North Carolina School of Medicine, United States of America
Received: March 23, 2014; Accepted: May 14, 2014; Published: June 12, 2014
Copyright: © 2014 Guo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All data are included within the paper.
Funding: The authors have no funding or support to report.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Lung cancer continues to be the most common and deadly malignant cancers worldwide [1]. Although important progress in the management of this disease has been observed over the last decade, non-small cell lung cancer (NSCLC) remains a lethal disease, and improving poor prognosis (5-year survival of approximately 15%) remains a challenge [2]. Multiple independent prognostic factors, such as performance status, disease stage, age, sex and amount of weight lost, have previously been identified for predicting survival [3]. Although the use of these factors has been widely accepted, the prognosis of NSCLC is not sufficiently predictable, thus additional prognostic markers are required for more accurate estimation.
The MET gene, located at 7q21-q31, is a potential prognostic genetic marker, which encodes a receptor tyrosine kinase for the HGF/scatter factor (SF) [4]. Met-receptor tyrosine kinase is activated through phosphorylation and the cognate ligand HGF, leading to the activation of a number of downstream pathways, such as the phosphoinositide-3-kinase (PI3K), Ras-Rac/Rho, Ras mitogen-activated protein kinase (MAPK) and phospholipase C-γ signaling pathways, in several types of human cancers, including NSCLC [5]. HGF/Met signaling promotes biological activities, resulting in tumor growth, angiogenesis and the development of invasive phenotypes, making this receptor an attractive target for the potential anti-cancer treatment of NSCLC [6]–[8]. Alterations in the MET gene, including amplification, overexpression and mutations, have been described in a number of solid tumors, including breast and esophageal cancers [9], [10]. The rate of MET amplification in NSCLC remains controversial, ranging from 3% to 10%, depending on the detection method and cut-off criteria [11], [12]. Most studies have indicated a negative prognostic impact of high MET GCN on NSCLC survival [11]–[17], however, other studies have not confirmed this finding [18]-[21]. MET overexpression in NSCLC is variable, ranging from 5% to 75%. Several studies have shown that the overexpression of MET is associated with poor outcome [13], [19], [21]–[26]. However, the prognostic relevance of MET overexpression remains unclear.
With the aim to gain a better insight into the prognostic value of the copy number or protein expression of the MET gene for survival of patients with non-small cell lung cancer, we conducted the first comprehensive meta-analysis of published literature on this topic.
Materials and Methods
Identification and selection of relevant studies
PubMed, Embase, Web of Science and Google Scholar were searched for articles concerning the MET GCN, MET protein expression, disease status and survival in patients with NSCLC. The last search update was December 12, 2013. The search strategy included the following medical subject heading terms and keywords variably combined: “Proto-Oncogene Proteins c-met” [Mesh], “Carcinoma, Non-Small-Cell Lung” [Mesh], “MET”, “c-met”, “met Proto-Oncogene Proteins” “Hepatocyte Growth Factor Receptor”, “Scatter Factor Receptor”, “HGF Receptor”, “met gene copy number” “lung cancer”, “NSCLC”, “prognosis”, “prognostic” and “survival”. We did not apply any language restrictions.
Studies meeting the following inclusion criteria were considered for this meta-analysis: (I) Clinical trials and prospective or retrospective cohort studies investigating the correlation of the MET GCN and protein expression status with the OS of NSCLC patients; (II) Measurement methods, including fluorescent in situ hybridization (FISH), reverse transcription-polymerase chain reaction (RT-PCR), and immunohistochemistry (IHC); and (III) Findings providing sufficient information for the estimation of hazard ratios and 95% confidence intervals. Only studies published in peer-reviewed journals were included, data from letters and meetings abstracts were not eligible. Two researchers (B.P.G and H.C) independently screened and determined the relevant studies. Any discrepancies were settled through discussion until a consensus was reached.
Data extraction
Two reviewers independently (B.P.G and H.C) extracted the relevant data from each study and subsequently assessed the data to estimate reliability. The following information was obtained from the MET GCN studies: the first author, year of publication, country of origin, inclusion period, number of patients (Male/Female), age at time of diagnosis (mean, median, range), tumor stage, method of MET GCN detection, cutoff value of high MET GCN, histology, number of patients of high MET GCN, treatment, time of follow-up (median, mean, range), and OS data. The information obtained from each MET protein expression study included the first author, year of publication, country of origin, inclusion period, number of patients (Male/Female), age at time of diagnosis (mean, median, range), tumor stage, method of MET protein expression detection, specimen, cutoff, antibodies, histology, number of patients of high MET protein expression, treatment, time of follow-up (median, mean, and range), and OS data.
Quality assessment
Two authors (B.P.G and X.H.T) independently assessed the quality of the selected studies using the Newcastle-Ottawa Scale for cohort studies (NOS) [27]. This tool comprises three quality parameters: selection, comparability, and outcome assessment. “Stars” were awarded to demonstrate “high” quality. The stars were subsequently added and used to compare the overall quality in a quantitative manner. A consensus reviewer (H.C) resolved any observed discrepancies.
Statistical analysis
The primary results were stratified according to MET GCN (high vs. low) and protein expression (high vs. low). The HRs and 95% CIs were combined to obtain the effective value. When the HR was not reported in an article, this parameter was calculated using the methods of Parmar et al [28].
A heterogeneity test based on I2 and Q statistics was performed. The heterogeneity of individual HRs was calculated using Χ2 tests according to the method of Peto [29]. Significant heterogeneity was determined at a p value less than 0.10. I2 was used to quantify inconsistencies, where a value of 0% indicates no observed heterogeneity, a value less than 25% denotes low heterogeneity, a value from 25.1–50% indicates moderate heterogeneity, and a value greater than 50% indicates substantial heterogeneity [30]. When heterogeneity was observed between primary studies, the random effects model was used. When no heterogeneity was observed, the fixed effects model was used for analysis [31]. HR>1 implies worse survival for the group with high MET GCN or protein expression. The impact of MET on survival was considered statistically significant when the 95% CI did not overlap with 1. Subgroup analyses were performed using different methods to detect the MET GCN and protein expression, conduct univariate and multivariate analyses, and assess the histological subtypes and ethnic source.
Sensitivity analyses were performed to assess the stability of the results. Egger's test [32] was used to detect potential publication bias. Statistical significance was considered for a p-value of less than 0.05 for summary HR and publication biases. All calculations were performed using STATA version 11.0 (Stata Corporation, College Station, TX, USA).
Results
Eligible studies
A total of 939 records were identified by the primary computerized literature search. After screening the titles and abstracts, thirty-one articles were further reviewed in detail. As indicated in the search flow diagram (Figure 1), 18 studies were finally included in the meta-analysis [11]–[26], [33], [34]. 6 studies provided survival data for both MET GCN and protein expression are listed twice in Table 1 and Table 2 respectively [13], [14], [18]–[20], [21].
MET, mesenchymal-epithelial transition; GCN, gene copy number.
Study characteristics
For MET GCN, most studies were retrospective cohorts and only one study was a prospective cohort. A total of 11 studies [11]–[21], analyzing 2,866 patients for MET GCN and OS in patients with NSCLCs. Six of these studies employed FISH [11], [21], [14]–[16], [19], one study employed SISH [18], one study employed BISH [20] and three studies employed RT-PCR [13], [17], [21]. The median study sample size was 189 (range 61–844). Frequencies of high MET GCN ranged from 4% to 22% in the eligible studies. In this analysis, 9 studies (2230 patients, 74%) were conducted in Asian populations [12]–[17], [19], [20], [21], and 2 studies (636 patients, 26%) were conducted in non-Asian subsets [11], [18]. Seven studies involved NSCLCs of all histological subtypes [11],[13],[16]–[20], three studies involved adenocarcinoma [14], [15], [21], two studies involved NSCLCs of all histological subtypes and adenocarcinoma [19], [20], and two studies involved squamous cell carcinoma [12], [19]. A total of 9 studies contained information about all cancer stages (I–IV) [11]–[13], [15]–[20], and 2 studies contained information about cancer stages I–III [14], [21]. A total of 7 of the 12 studies (58.3%) reported that a high MET GCN was a poor prognostic factor for survival [11]–[17], and the remaining 4 studies (41.7%) concluded that no statistically significant effect of a high MET GCN on survival was observed [18]–[21], irrespective of whether these studies used univariate or multivariate analyses. The main features of the eligible studies are summarized in Table 1.
For MET protein expression, all eligible studies were retrospective cohorts. A total of 2,650 patients were included in 13 studies [13], [14], [18]–[20], [21]–[26], [33], [34], with sample sizes ranging from 61 to 883 patients (median 125). In all 13 studies, immunohistochemistry was used to detect MET expression in paraffin-embedded specimens. Eleven studies (2283 patients, 86%) were conducted in Asian populations [13], [14], [19], [20], [21], [23]–[26], [33], [34], and two studies (367 patients, 14%) were conducted in non-Asian subsets [18], [22]. Overall, eight studies involved NSCLCs of all histological subtypes [13], [18]–[20], [22], [23], [26], [33], and five studies involved adenocarcinoma [14], [21], [24], [25], [34]. Seven studies investigated patients at all cancer stages (I–IV) [13], [18]–[20], [24], [25], [26], whereas six studies concerned patients at stages I–III (include IA-IIIB) [14], [21]–[23], [33], [34]. Eight of 13 studies identified high MET protein expression as an indicator of poor prognosis [13], [19], [21]–[26], and the remaining 5 studies showed no statistically significant effect of high MET expression on survival [14], [18], [20], [33], [34], irrespective of whether these studies used univariate or multivariate analyses. The main features of the 13 eligible studies are summarized in Table 2.
Qualitative assessment
The study quality was assessed using the Newcastle–Ottawa quality assessment scale, generating scores ranging from 4 to 9 (with a mean of 5.85), with a higher value indicating better methodology. The results of quality assessment are shown in supplementary Table S1.
Impact of MET gene copy number on survival
For OS, the estimated pooled HR for increased MET GCN, using univariate and multivariate analyses, was 1.90 (95% CI: 1.35–2.68; p<0.001) in eleven studies [11]–[21] and 1.61 (95% CI: 1.15–2.25; p = 0.005) in nine studies [11]–[14], [16]–[19], [21]. There was heterogeneity between studies for both univariate (I2 = 58.0%, p = 0.008) and multivariate (I2 = 71.5%, p<0.001) analyses (Figure 2). Further analysis showed that the observed heterogeneity reflected the inclusion of the studies by Sun et al [13] and Dziadziuszko et al [18] When these studies were excluded from the meta-analysis, less heterogeneity was observed (I2 = 4.2%, p = 0.400; I2 = 0%, p = 0.488), and the pooled results remained practically unchanged (HR for univariate analysis: 1.74, 95% CI: 1.40–2.15, p<0.001; HR for multivariate analysis: 1.53, 95% CI: 1.26–1.87, p<0.001).
Forest plot showing (A) the combined relative HR for OS by univariate analysis; (B) the combined relative HR for OS by multivariate analysis.
When grouped according to histological subtypes, the combined HR for the NSCLC studies was 1.89 (95% CI: 1.26–2.84), the pooled HR for adenocarcinoma was 1.49 (95% CI: 1.05–2.10) and the combined HR for squamous cell carcinoma was 1.64 (95% CI: 0.54–4.60) (Figure 3) (Table 3). For Asian populations, the increased MET GCN was significantly associated with decreased OS in nine studies (HR = 2.22; 95% CI 1.46–3.38; p<0.001), but these results were not observed for non-Asian populations (HR = 1.21; 95% CI 0.55–2.67; p = 0.630) (Figure 3) (Table 3). When grouped according to the different methods for determining the MET GCN, the combined HRs for the FISH (including SISH and BISH) and RT-PCR studies were 1.66 (95% CI: 1.28–2.16) and 2.95 (95% CI: 0.80–10.91), respectively (Figure 4).
Impact of MET protein expression on survival
The combined HR for the nine studies [13], [14], [18]–[20], [21], [23], [24], [34] (involving 2151 cases) included in the univariate analysis was 1.52 (95% CI: 1.08–2.15, p = 0.0017), indicating that MET overexpression had worse survival impact in patients with NSCLC (Figure 5). Because significant inter-study heterogeneity (I2 = 75.8%, p<0.001) was observed, we applied the random-effects model. One study [20] accounted for this heterogeneity; the exclusion of this study from the meta-analysis resulted in less heterogeneity (I2 = 15.2%, p = 0.314), and the pooled results remained practically unchanged (HR = 1.84, 95% CI: 1.45–2.33, p<0.001).
Forest plot showing (A) the combined relative HR for OS by univariate analysis; (B) the combined relative HR for OS by multivariate analysis.
Eight studies [13], [19], [21], [22], [24], [25], [26], [33] (comprising 1254 cases) were included in the multivariate analysis of MET protein expression for OS. The pooled HR showed a significantly increased risk of mortality in patients with MET positivity (HR = 2.18, 95% CI: 1.60–2.97, p<0.00) (Figure 5). Because significant heterogeneity (I2 = 57.5%, p = 0.021) was observed among these studies, a random-effects model was applied. The observed heterogeneity might reflect the difference in the populations studied and experimental methods used. Onisuka et al [21] and Liu et al [26] accounted for some of the observed heterogeneity; the exclusion of these studies from the meta-analysis resulted in less heterogeneity (I2 = 25.9%, p = 0.24), and the pooled results remained practically unchanged (HR = 2.00, 95% CI: 1.55–2.57, p<0.001).
When grouped according to histological subtypes, the combined HR for the NSCLC studies was 1.72 (95% CI: 1.10–2.69), and the pooled HR for adenocarcinoma was 1.69 (95% CI: 1.31–2.19) (Figure 6) (Table 3). For Asian populations, MET overexpression was significantly associated with decreased OS in nine studies (HR = 1.89; 95% CI 1.34–2.68; p<0.001), but these results were not observed in non-Asian populations (HR = 1.28; 95% CI 0.48–3.43; p = 0.623) (Figure 6).
Sensitivity analyses and publication bias test
The sensitivity analysis indicated that omitting any single study did not influence the pooled HRs. For MET GCN, A more formal evaluation using Egger's test showed no evidence of significant publication bias (p = 0.352 for univariate analysis and p = 0.063 for multivariate analysis). For the MET protein expression, there was no evidence for significant publication bias (Egger's test: p = 0.076 for univariate analysis and p = 0.116 for multivariate analysis).
Discussion
MET has recently received attention as a molecular target for the treatment of NSCLC. Understanding the mechanisms underlying anti-MET therapy requires the correct evaluation of the impact of MET GCN and protein expression on patient survival.
The summary statistics obtained from 18 published studies, including 5,516 patients with NSCLC, showed that high MET GCN or protein expression significantly predicted the poor OS of NSCLC patients (gene copy: HR 1.90, 95% CI 1.35–2.68; protein expression: HR 1.52, 95% CI 1.08–2.15). The subgroup analysis revealed that high MET GCN or protein expression was also significantly associated with poor prognosis in Asian countries (gene copy: HR 2.22, 95% CI 1.46–3.38; protein expression: HR 1.89, 95% CI 1.34–2.68), but the same tendency was not observed in the non-Asian subset (gene copy: HR 1.21, 95% CI 0.55–2.67; protein expression: HR 1.28, 95% CI 0.48–3.43). The present study was performed using univariate analysis, followed by further multivariate analysis. The results of the meta-analysis showed that high MET GCN or protein expression in NSCLC patients was associated with poor OS (univariate analysis). This effect was also significant according to multivariate analysis, showing that the MET GCN or protein expression might be an independent prognostic factor for OS in NSCLC.
The methods used to detect the MET GCN impacted the significance of these results. The combined HRs of 8 FISH (included SISH and BISH) and 3 RT-PCR studies were 1.66 (95% CI: 1.28–2.16) and 2.95 (95% CI: 0.80–10.91), respectively. We observed that FISH, instead of RT-PCR, was the most widely used technology for determining the gene copy number. In clinical practice, although real-time PCR is a simple and quick method, the results do not directly reflect cancer cells because DNA is typically isolated from whole tissue specimens that also contain normal epithelial cells, inflammatory cells, and fibroblasts. FISH is generally accepted as a better technique than RT-PCR for evaluating gene copy number because FISH can be applied to formalin-fixed paraffin-embedded tumor tissues archived for routine pathological diagnosis, thus facilitating the exclusive estimation of tumor cells. Therefore, FISH is the most widely used technique in clinical practice for the detection of gene amplification to determine therapeutic strategies, such as HER2 FISH in breast cancer. The results obtained in the present study showed that increased MET GCN, evaluated using FISH, was a predictor of worse survival in NSCLC. Due to the small number of primary studies using RT-PCR for analysis, the detection of potentially important differences was limited. Moreover, IHC was the method typically used to detect MET protein expression. IHC is the standard method for the evaluation of proteins (e.g., HER2 and EGFR), and there was consistency in the evaluation process among studies. The results of the present meta-analysis showed that MET overexpression was associated with worse survival.
Moreover, the results of the present study demonstrated that high MET GCN or protein expression was an independent negative prognostic factor in NSCLC. However, the prognostic significance of MET GCN according to the histology of NSCLC remains unclear. Go et al [12] reported that SCC patients with MET amplification showed markedly shorter OS than those without MET amplification. In contrast to these results, the systematic review showed that high MET GCN or protein expression is a worse marker of death risk in lung adenocarcinoma than in squamous carcinoma. These results indicated that MET amplification might be involved in the oncogenesis of SCC and ADC. The differences in the two contrasting results were influenced by two SCC studies reporting a correlation between the MET GCN and survival, and these data were not sufficient to determine the prognostic value of MET expression in SCC.
Park et al [19]. demonstrated that MET FISH-positive and MET IHC-positive patients had significantly shorter survival. The results obtained in the present study also provide similar evidence that MET is a negative prognostic factor, further supporting anti-MET strategies, irrespective of MET CGN or MET overexpression. Thus, when patients were divided according to EGFR FISH results, MET positivity had prognostic implications only among EGFR FISH-negative patients. This finding has been consistently reported in recent studies [11], [12], suggesting that anti-MET drugs might be beneficial for EGFR FISH-negative NSCLC patients who are not initially selected for EGFR TKI treatment.
We observed a considerable degree of interstudy heterogeneity. Differences in the detection methods, types and numbers of target genes or antigens, sampling sites and times, and demographic or clinicopathologic data from the included patients, should be considered as potential sources of heterogeneity. In this study, significant heterogeneity was observed among the included studies. Although we used random- and fixed-effects models for pooling data, the source of heterogeneity remained unknown. Moreover, the sensitivity analysis did not clarify the source of the heterogeneity observed in this study. The studies by Sun et al [13] and Dziadziuszko et al [18] primarily accounted for the heterogeneity observed in the MET GCN. Although Sun et al. used RT-PCR, it was not possible to address this technical issue, as these studies used the same primers and other PCR conditions. Dziadziuszko et al [18] used silver in situ hybridization (SISH). Silver in situ hybridization (SISH) is a new technology for gene copy assessment, with some clinical advantages compared with FISH. First, the samples are analyzed using conventional light microscopy with preserved cell morphology based on automation. The new technology facilitates the evaluation of slides through light microscopy for the simultaneous visualization of amplified signals and cell morphology, overcoming the disadvantage of FISH where the fluorescent signals gradually fade over time. This difference might explain the observed heterogeneity.
Factors associated with immunostaining can also contribute to the observed heterogeneity. Onisuka et al [21] and Liu et al [26] used the same antibodies, but differences in the staining techniques and evaluation criteria for MET positivity might contribute to heterogeneity between studies. The exclusion of this study from the analysis only partially reduced the heterogeneity, potentially reflecting immunohistochemistry techniques (various definitions of threshold positivity, use of the mAb at different concentrations and dissimilar staining protocols) or patient characteristics (type of patients, disease characteristics). These factors might not only contribute to the observed statistical heterogeneity but also the clinical heterogeneity. Clinical heterogeneity might result from the different patients (with different age, tumor size, clinical stage, ethnicity, physical condition, etc.), diverse treatment types, various treatment protocols, different dosages and drug types, etc. Moreover, differences in primary antibodies, IHC staining protocols, evaluation standards, and cut-off values for high MET expression might also contribute to heterogeneity. Thus, additional multicenter studies using standardized methods are encouraged.
Some limitations of this meta-analysis need to be discussed. First, our meta-analysis is based on data from trials whose results have been published, and we did not obtain individual patient data. Use of individual patient data may further enhance the accuracy and reduce the uncertainty of the estimates. Second, significant heterogeneity was observed among the included studies. Factors associated with variability in definitions of end point, measurements, and experimental design may contribute to the heterogeneity. Therefore, validation of the prognostic power of MET GCN or protein expression should be conducted through large multicenter prospective studies based on homogeneous populations. Third, the number of studies concerning MET and the effectiveness of therapy (such as chemotherapy or EGFR TKI treatment) was too small to perform a pooled analysis. In the present study, due to the incompleteness of clinicopathological parameters, we did not perform subgroup analyses between MET GCN and clinicopathological parameters or between protein expression and clinicopathological parameters. Fourth, negative studies are less frequently published or published with less detailed results, making these studies less assessable, potentially leading to some bias.
Despite these limitations, this meta-analysis had some advantages. First, the results obtained from the random-effects model were similar to those obtained from the fixed-effects model, indicating that the statistical results were robust. Second, the results of the sensitivity analysis were not materially altered and did not draw different conclusions, indicating that the initial results were strong. Third, Egger's test did not detect publication bias, indicating that the obtained results were not biased. Moreover, the study quality scores, assessed using the Newcastle–Ottawa quality assessment scale, were >5, suggesting that the results of the present meta-analysis were convincing.
In conclusion, this meta-analysis indicated that increased MET GCN and protein expression was significantly associated with poorer survival in patients with NSCLC; this information could potentially further stratify patients in clinical treatment.
Supporting Information
Table S1.
Assessment of Newcastle-Ottawa Scale methodological quality of cohort studies.
https://doi.org/10.1371/journal.pone.0099399.s001
(DOC)
Acknowledgments
The authors would like to thank the patients and clinical investigators involved in the studies included in this meta-analysis.
Author Contributions
Conceived and designed the experiments: HC BPG. Performed the experiments: BPG XHT QK. Analyzed the data: BPG XHT WJL. Contributed reagents/materials/analysis tools: BPG HC QK. Contributed to the writing of the manuscript: BPG.
References
- 1. Jemal A, Bray F, Center MM, Ferlay J, Ward E, et al. (2011) Global cancer statistics. CA Cancer J Clin 61: 69–90.
- 2. Wistuba, II, Gelovani JG, Jacoby JJ, Davis SE, Herbst RS (2011) Methodological and practical challenges for personalized cancer therapies. Nat Rev Clin Oncol 8: 135–141.
- 3. Paesmans M, Sculier JP, Libert P, Bureau G, Dabouis G, et al. (1995) Prognostic factors for survival in advanced non-small-cell lung cancer: univariate and multivariate analyses including recursive partitioning and amalgamation algorithms in 1,052 patients. The European Lung Cancer Working Party. J Clin Oncol 13: 1221–1230.
- 4. Bottaro DP, Rubin JS, Faletto DL, Chan AM, Kmiecik TE, et al. (1991) Identification of the hepatocyte growth factor receptor as the c-met proto-oncogene product. Science 251: 802–804.
- 5. Birchmeier C, Birchmeier W, Gherardi E, Vande Woude GF (2003) Met, metastasis, motility and more. Nat Rev Mol Cell Biol 4: 915–925.
- 6. Ma PC, Maulik G, Christensen J, Salgia R (2003) c-Met: structure, functions and potential for therapeutic inhibition. Cancer Metastasis Rev 22: 309–325.
- 7. Cipriani NA, Abidoye OO, Vokes E, Salgia R (2009) MET as a target for treatment of chest tumors. Lung Cancer 63: 169–179.
- 8. Trusolino L, Comoglio PM (2002) Scatter-factor and semaphorin receptors: cell signalling for invasive growth. Nat Rev Cancer 2: 289–300.
- 9. Bonine-Summers AR, Aakre ME, Brown KA, Arteaga CL, Pietenpol JA, et al. (2007) Epidermal growth factor receptor plays a significant role in hepatocyte growth factor mediated biological responses in mammary epithelial cells. Cancer Biol Ther 6: 561–570.
- 10. Miller CT, Lin L, Casper AM, Lim J, Thomas DG, et al. (2006) Genomic amplification of MET with boundaries within fragile site FRA7G and upregulation of MET pathways in esophageal adenocarcinoma. Oncogene 25: 409–418.
- 11. Cappuzzo F, Marchetti A, Skokan M, Rossi E, Gajapathy S, et al. (2009) Increased MET gene copy number negatively affects survival of surgically resected non-small-cell lung cancer patients. J Clin Oncol 27: 1667–1674.
- 12. Go H, Jeon YK, Park HJ, Sung SW, Seo JW, et al. (2010) High MET gene copy number leads to shorter survival in patients with non-small cell lung cancer. J Thorac Oncol 5: 305–313.
- 13. Sun W, Song L, Ai T, Zhang Y, Gao Y, et al. (2013) Prognostic value of MET, cyclin D1 and MET gene copy number in non-small cell lung cancer. J Biomed Res 27: 220–230.
- 14. Tachibana K, Minami Y, Shiba-Ishii A, Kano J, Nakazato Y, et al. (2012) Abnormality of the hepatocyte growth factor/MET pathway in pulmonary adenocarcinogenesis. Lung Cancer 75: 181–188.
- 15. Tanaka A, Sueoka-Aragane N, Nakamura T, Takeda Y, Mitsuoka M, et al. (2012) Co-existence of positive MET FISH status with EGFR mutations signifies poor prognosis in lung adenocarcinoma patients. Lung Cancer 75: 89–94.
- 16. Chen YT, Chang JW, Liu HP, Yu TF, Chiu YT, et al. (2011) Clinical implications of high MET gene dosage in non-small cell lung cancer patients without previous tyrosine kinase inhibitor treatment. J Thorac Oncol 6: 2027–2035.
- 17. Okuda K, Sasaki H, Yukiue H, Yano M, Fujii Y (2008) Met gene copy number predicts the prognosis for completely resected non-small cell lung cancer. Cancer Sci 99: 2280–2285.
- 18. Dziadziuszko R, Wynes MW, Singh S, Asuncion BR, Ranger-Moore J, et al. (2012) Correlation between MET gene copy number by silver in situ hybridization and protein expression by immunohistochemistry in non-small cell lung cancer. J Thorac Oncol 7: 340–347.
- 19. Park S, Choi YL, Sung CO, An J, Seo J, et al. (2012) High MET copy number and MET overexpression: poor outcome in non-small cell lung cancer patients. Histol Histopathol 27: 197–207.
- 20. Tsuta K, Kozu Y, Mimae T, Yoshida A, Kohno T, et al. (2012) c-MET/phospho-MET protein expression and MET gene copy number in non-small cell lung carcinomas. J Thorac Oncol 7: 331–339.
- 21. Onitsuka T, Uramoto H, Ono K, Takenoyama M, Hanagiri T, et al. (2010) Comprehensive molecular analyses of lung adenocarcinoma with regard to the epidermal growth factor receptor, K-ras, MET, and hepatocyte growth factor status. J Thorac Oncol 5: 591–596.
- 22. Galleges Ruiz MI, Floor K, Steinberg SM, Grunberg K, Thunnissen FB, et al. (2009) Combined assessment of EGFR pathway-related molecular markers and prognosis of NSCLC patients. Br J Cancer 100: 145–152.
- 23. Masuya D, Huang C, Liu D, Nakashima T, Kameyama K, et al. (2004) The tumour-stromal interaction between intratumoral c-Met and stromal hepatocyte growth factor associated with tumour growth and prognosis in non-small-cell lung cancer patients. Br J Cancer 90: 1555–1562.
- 24. Tokunou M, Niki T, Eguchi K, Iba S, Tsuda H, et al. (2001) c-MET expression in myofibroblasts: role in autocrine activation and prognostic significance in lung adenocarcinoma. Am J Pathol 158: 1451–1463.
- 25. Takanami I, Tanana F, Hashizume T, Kikuchi K, Yamamoto Y, et al. (1996) Hepatocyte growth factor and c-Met/hepatocyte growth factor receptor in pulmonary adenocarcinomas: an evaluation of their expression as prognostic markers. Oncology 53: 392–397.
- 26. Liu XH, Yang JQ, Wang HB, Sun YM (2010) Expression of c-Met and the prognosis in non-small cell lung cancer. Chinese Journal of cellular and molecular immunology 26: 1027–1029.
- 27.
Wells G, Shea B, O'Connell D, Peterson J, Welch V, et al. (2014) The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses.
- 28. Parmar MK, Torri V, Stewart L (1998) Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat Med 17: 2815–2834.
- 29. Yusuf S, Peto R, Lewis J, Collins R, Sleight P (1985) Beta blockade during and after myocardial infarction: an overview of the randomized trials. Prog Cardiovasc Dis 27: 335–371.
- 30. Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327: 557–560.
- 31. DerSimonian R, Laird N (1986) Meta-analysis in clinical trials. Control Clin Trials 7: 177–188.
- 32. Egger M, Davey Smith G, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. BMJ 315: 629–634.
- 33. Hu X, Fu X, Wen S, Zou X, Liu Y (2012) [Prognostic value of MACC1 and c-met expressions in non-small cell lung cancer]. Zhongguo Fei Ai Za Zhi 15: 399–403.
- 34. Nakamura Y, Niki T, Goto A, Morikawa T, Miyazawa K, et al. (2007) c-Met activation in lung adenocarcinoma tissues: an immunohistochemical analysis. Cancer Sci 98: 1006–1013.