|Year : 2015 | Volume
| Issue : 2 | Page : 397-402
Association between BIM deletion polymorphism and clinical outcome of EGFR-mutated NSCLC patient with EGFR-TKI therapy: A meta-analysis
Ji-Yong Ma, Hai-Jun Yan, Wei Gu
Department of Respiration, Nanjing First Hospital, Nanjing Medical University, Nanjing, Jiangsu, China
|Date of Web Publication||7-Jul-2015|
Department of Respiration, Nanjing First Hospital, Nanjing Medical University, 68 Changle Road, Nanjing
Source of Support: The study was supported by the fund of health department of Jiangsu province (H201341), Conflict of Interest: None
Aim: BIM deletion polymorphism was deemed to be associated with downregulation of BIM, resulting in a decreased apoptosis induced by epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) in EGFR mutation-positive non-small cell lung cancer (NSCLC). However, accumulating evidences concerning the association between BIM deletion polymorphism and efficacy of EGFR-TKI and survival in EGFR-mutation-driven NSCLC patient reported contradictory results.
Materials and Methods: A meta-analysis was conducted by combing six original eligible studies including 871 NSCLC patients.
Results: Our study showed that BIM deletion polymorphism was significantly associated with poor response to EGFR-TKI therapy in mutant EGFRNSCLC patients (P h = 0.309, P z = 0.001, OR = 0.39, 95% confidence interval (CI) = 0.23-0.67). Disease control rate (DCR) in mutant EGFRNSCLC patient with treatment of EGFR-TKI was significantly decreased in patients with BIM deletion polymorphism comparing to patients harbored BIM wild variant (P h = 0.583, P Z = 0.007, OR = 0.46, 95%CI = 0.25-0.85). EGFR mutation-derived NSCLC patient carrying BIM deletion polymorphism had a shorter progression-free survival (PFS; P h < 0.001, P z < 0.001, hazard ratio (HR) = 1.37, 95%CI = 1.09-1.71) and overall survival (OS; P h = 0.90, P z = 0.003, HR = 1.25, 95%CI = 1.08-1.45), than those harbored BIM wild variant.
Conclusion: These results suggested that BIM deletion polymorphism might be a cause that contributes to primary EGFR-TKI resistance, and it could be used as a genetic predictor for EGFR-TKI outcome and an independent prognostic factor of EGFR mutation-driven NSCLC patient.
Keywords: BIM, EGFR-TKI, non-small cell lung cancer
|How to cite this article:|
Ma JY, Yan HJ, Gu W. Association between BIM deletion polymorphism and clinical outcome of EGFR-mutated NSCLC patient with EGFR-TKI therapy: A meta-analysis. J Can Res Ther 2015;11:397-402
|How to cite this URL:|
Ma JY, Yan HJ, Gu W. Association between BIM deletion polymorphism and clinical outcome of EGFR-mutated NSCLC patient with EGFR-TKI therapy: A meta-analysis. J Can Res Ther [serial online] 2015 [cited 2019 Nov 22];11:397-402. Available from: http://www.cancerjournal.net/text.asp?2015/11/2/397/157308
| > Introduction|| |
Lung cancer is the first most common morbidity and mortality cancer worldwide. According to cancer statistics in 2014, approximately 224,210 individuals will be estimated with new non-small cell lung cancer (NSCLC) cases and 159,260 patients will die of the disease in USA.  In China, a total of 605,946 persons were diagnosed as new NSCLC patients, and 486,555 cases died in 2010.  Among them, 89% patients were diagnosed as NSCLC and the majority of them were advantaged NSCLC patients in their first surgical treatment losing the opportunity for radical resection. 
Although the rapid advancement in NSCLC diagnosis and therapy recently, the 5 years' overall survival (OS) is still unsatisfactory. 
It has demonstrated that epidermal growth factor receptor (EGFR)-mediated signaling pathway plays an important role in the pathogenesis and progression of many kinds of cancer, , thus EGFR is one of ideal therapeutic targets for cancer. EGFR-tyrosine kinase inhibitors (TKIs), such as gefitinib, erlotinib, and afatinib, are recommended for treating advanced NSCLC harboring mutant EGFR.  However, the efficacy of EGFR-TKI is not consistent for each patient. Approximately 30% of EGFR-mutation NSCLC patients displayed primary resistance to EGFR-TKI,  and some patients who responded at first eventually would develop secondary resistance to EGFR-TKI in approximately 1 year. , EGFR T790M mutation, mesenchymal-epithelial transition (MET) amplification and phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) mutation have been demonstrated to be associated with acquired resistance to EGFR-TKI treatment in NSCLC patients. ,, Several studies revealed that coexistent genetic variation within NSCLC-related gene would lead to primary resistance of EGFR-TKI in EGFR-mutated NSCLC patients, such as KRAS mutation, PTEN loss, and over expression of CRIPTO1. , These causes could account for a small proportion of cases that are primarily resistant to EGFR-TKI. However, the reason of a large proportion of primary resistance cases remains unknown.
B-cell lymphoma-2-like 11 gene (BIM), which is located at 2q12-q13, is a member of the B-cell lymphoma-2(Bcl-2) family genes that encodes the BCL-2 interaction mediator of cell death protein (BIM). It is a key modulator in promoting cell apoptosis. A 2,903-bp germline deletion polymorphism of BIM could result in missing expression of BIM isoform BH3, which may deregulate the proapoptotic function of BIM, leading to the intrinsic resistance and inferior responses to EGFR-TKIs.  Thus, BIM deletion polymorphism is considered as a candidate factor contributing to EGFR-TKI resistance in EGFR-mutated NSCLC patients. Recently, BIM deletion polymorphism was reported to be significantly associated with primary resistance to TKIs and a poor clinical outcome in NSCLS patients. ,, However, studies conducted by respective Isobe et al., and Lee et al., reported that the polymorphism could not account for intrinsic EGFR-TKI resistance in patients with EGFR-mutated NSCLC. ,
On the basis of accumulating evidence, a comprehensive meta-analysis of eligible studies was conducted to evaluate the association between BIM deletion polymorphism and clinical outcome of EGFR-mutated NSCLC patients treated with EGFR-TKI.
| > Materials and methods|| |
In present study, relevant studies were retrieved in PubMed, Web of Science, and the China National Knowledge Infrastructure (CNKI) databases dating up to November, 2014 according to the proposal of Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines.  Manual retrieval was performed to gain substantial relative articles. The following search terms: "BIM and lung cancer", "EGFR-TKI and BIM or BCL2L11", as well as "EGFR-TKI and polymorphism" were used to search articles in the abovementioned databases. Relevant study was obtained through identification of each article's title and abstract. Eligible study were identified from relevant full-text study in accordance with the following inclusion criteria: (i) Prospective study concerning BIM deletion polymorphism and EGFR-TKI treatment; (ii) cases were advanced lung cancer patients that harbored EGFR-mutation; 3) study provided detail BIM deletion polymorphism and response data, survival information, hazard ratio (HR) and 95% confidence interval (CI), or Kaplan-Meier's curve. However; review, comment, communication, letter, meta-analysis, and prospective or retrospective study with duplicated data or without sufficient data were excluded from this study.
The baseline characteristics data, the first author's name and published year, country, ethnicity, cases, treatment of EGFR-TKI, detection, genotype data, number of complete or partial response (CR or PR), stable or progressive disease (SD or PD), objective response rate (ORR) and disease control rate (DCR), and HR and 95%CI of progression-free survival (PFS) or OS were extracted from each eligible study in accordance with criteria of inclusion and exclusion by two independent investigators (Ji-Yong Ma and Hai-Jun Yan). If survival HR and 95%CIwere not provided in the eligible study, the data were extracted from Kaplan-Meier's curve by the method of Tierney et al.  Any disagreement was discussed with the third investigator (Wei Gu) to arrive at a consensus.
Odds ratio (OR), HR, and 95%CI were used as common measurements for evaluating the strength between BIM deletion polymorphism and survival as well as response to EGFR-TKI therapy in lung cancer patient. Cochran's Q test and I 2 were used to evaluate heterogeneity, and a significant heterogeneity was identified when PH < 0.10.  The fixed model was chosen to evaluate the combined data when PH > 0.10; otherwise, the random model was selected to estimate the overall effect. Sensitivity analysis was used to estimate stability of overall effect by omitting each eligible study or changing combination model. Possible publication bias was evaluated by Begg's funnel plot and asymmetry of funnel plot was considered as an existence of publication bias. , All calculations were performed using Stata 11.0 software. (Stata Corporation, College Station, TX) and Revman 5.3 (The Nordic Cochrane Centre, the Cochrane Collaboration, Copenhagen, Denmark).
| > Results|| |
A total of 159 relevant studies were retrieved and searched in the databases and manual retrieval. A total of 41 duplicated articles; 103 unrelated articles; and nine review, letter, comment, or correspondence were excluded from the present study in accordance with inclusion and exclusion criteria. As a result, six eligible studies concerning BIM polymorphism and clinical response and outcome of EGFR-mutated NSCLC with EGFR-TKI treatment were selected in the meta-analysis. ,,,,, The flow chart of eligible study search was shown in [Figure 1] and the baseline characteristics of eligible studies were listed in [Table 1] and [Table 2].
|Figure 1: Flow chart for retrieval and identification of eligible studies|
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|Table 1: The baseline characteristics of eligible study enrolled in meta - analysis|
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The results of heterogeneity test and Z test have been described in [Table 3]. The association between BIM polymorphism and response to EGFR-TKI in EGFR-mutated NSCLC patients were estimated by combining with four original studies. Significant association was observed between BIM deletion polymorphism and efficacy of EGFR-TKI treatment in patients (Ph = 0.309, Pz = 0.001, OR = 0.39, 95%CI = 0.23-0.67 for BIM deletion vs BIM wild variant). In addition, DCR in patient with treatment of EGFR-TKI was significantly decreased in patients with BIM deletion polymorphism as compared to patients with BIM wild variant (Ph = 0.583, PZ = 0.007, OR = 0.46, 95%CI = 0.25-0.85) [Figure 2].
|Figure 2: Results of meta-analysis of BIM deletion polymorphism and clinical outcome of NSCLC patients with EGFR-TKI therapy. (a) BIM deletion vs BIM wild variant for ORR, (b) BIM deletion vs BIM wild variant for DCR, and (c) BIM deletion vs. BIM wild variant for PFS and OS. EGFR-TKI = Epidermal growth factor receptor-tyrosine kinase inhibitor, NSCLC = Non-small cell lung cancer, ORR = Objective response rate, DCR = Disease control rate, PFS = Progression-free survival, OS = Overall survival|
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The influence of BIM polymorphism on survival of EGFR-mutated NSCLC patients with EGFR-TKI treatment were evaluated in six eligible studies. As shown from [Table 3] and [Figure 2], patients with BIM deletion polymorphism had a shorter PFS than those with BIM wild variant (Ph < 0.001, Pz < 0.001, HR = 1.37, 95% CI = 1.09-1.71). Furthermore, significant association was also observed between BIM deletion polymorphism and OS in EGFR-mutated NSCLC patients with EGFR-TKI treatment
(Ph = 0.90, Pz = 0.003, HR = 1.25, 95%CI = 1.08-1.45).
The sensitivity analysis results showed that the corresponding pooled ORs and HRs were not materially changed in comparison of BIM deletion polymorphism and BIM wild variant by omitting a single study successively each time or alteration of evaluation model. Results of Begg's funnel plot showed that no significant publication bias was found between ORR, DCR, and BIM polymorphism, respectively. However, there was a significant publication bias in comparison of BIM deletion and BIM wild variant in PFS [Figure 3].
|Figure 3: Begg's funnel plots of BIM deletion polymorphism and clinical outcome of NSCLC patients with EGFR-TKI therapy. (a) BIM deletion vs BIM wild variant for ORR, (b) BIM deletion vs BIM wild variant for DCR, and (c) BIM deletion vs. BIM wild variant for survival|
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| > Discussion|| |
Functional dysregulation of EGFR-mediated signaling pathway genes was a critical cause leading to NSCLC carcinogenesis and progression and the use of EGFR-TKIs had elicited remarkable therapeutic responses in individuals with NSCLC driven by EGFR-tyrosine kinases. , However, a large number of NSCLC patients harbored EGFR mutation displayed primary resistance to EGFR-TKI. Germline polymorphisms in related genes were thought to be the reason leading the primary resistance to EGFR-TKI therapy in NSCLC patients.  BIM is one such candidate gene that only encodes a BH3 which belongs to BCL2 family. It acts as an apoptosis facilitator and the function cannot be replaced in the apoptosis pathway. , BIM deletion polymorphism might lead to less or no BH3 expression, leading to the intrinsic resistance and inferior efficacy of EGFR-TKI therapy in NSCLC patients. ,
In order to evaluate the precise role of BIM polymorphism in clinical outcome and prognosis in EGFR-mutated NSCLC patients with treatment of EGFR-TKI, a comprehensive meta-analysis was conducted by combing the data of eligible studies. Our results showed that significant negative associations were found between BIM deletion polymorphism and CR/PR and DCR of EGFR-TKI therapy in EGFR mutation-driven NSCLC patients, indicating that BIM deletion polymorphism was associated with poor response and low DCR in EGFR-mutated NSCLC patients with EGFR-TKI treatment. In addition, EGFR-mutated NSCLC patients who harbored BIM deletion polymorphism had a short PFS and OS as compared to patients with BIM wild variant, suggesting that BIM deletion polymorphism was a poor prognostic factor for EGFR-mutated NSCLC patient with EGFR-TKI therapy. Due to 2,903-bp germline deletion of BIM, it would deregulate the expression of BH3. , Therefore, BH3 could activate cell apoptosis by both opposing the prosurvival BCL2, BCL2L1, MCL1, and BC2A1 and by binding to BAX and BAK1and directly activating their proapoptotic functions. , BIM upregulation was required for TKIs to induce apoptosis in NSCLC patients, , and BIM suppressed expression or targetedBIM protein for proteasomal degradation was sufficient to confer TKI resistance and maintain a survival advantage for EGFR-mutated NSCLC cell line. , Thus, BIM deletion polymorphism would not be responsible for inducing apoptosis in EGFR-TKI-targeted therapies for NSCLC patients, contributing to poor response and worse survival in EGFR-mutated NSCLC.
This meta-analysis, to the best of our knowledge, is the first to report the association between BIM deletion polymorphismand EGFR-TKI efficacy and survival in EGFR mutation-derived NSCLC patient. The most advantage of our meta-analysis is that all the participants in the eligible studies are harbored with EGFR mutation, for it could keep individual homogeneity to get an accurate conclusion. However, several shortages should be addressed to understand our study. First of all, the relevant articles were obtained by retrieving in a few databases and manual search in Chinese and English languages, it may lead to a choice bias for eligible study. Secondly, the sample size in our meta-analysis is small, which may not reach a precise conclusion between BIM deletion polymorphism and response or survival in EGFR-mutated NSCLC. Thirdly, a significant publication bias was found in the association of BIM deletion polymorphism with PFS in the meta-analysis.
In summary, the findings of this meta-analysis showed that BIM deletion polymorphism was significantly associated with poor RR, DCR, PFS, and OS in EGFR-mutated NSCLC with EGFR-TKI therapy, suggesting that BIM deletion polymorphism might be a cause that leads to primary EGFR-TKI resistance, and it could be used as a genetic predictor for clinical outcome of EGFR-TKI therapy and an independent prognostic factor for EGFR-mutated NSCLC patient. However, a larger sample size and multiple-central prospective study should be performed to verify our results.
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[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3]