Influence of the ABCB1 Polymorphisms Upon the Response to Taxane-containing Regimen Treatment

3841 words (15 pages) Dissertation

16th Dec 2019 Dissertation Reference this

Tags: Sciences

Disclaimer: This work has been submitted by a student. This is not an example of the work produced by our Dissertation Writing Service. You can view samples of our professional work here.

Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NursingAnswers.net.

Influence of the ABCB1 polymorphisms upon the response to Taxane-containing regimen treatment: A systematic review and meta-analysis

Abstract

Purpose The misregulation of ATP-binding cassette subfamily B member 1 (ABCB1) gene expression and its activity change have been perceived as one major obstacle for cancer chemotherapy. To verify the effect of the ABCB1 rs1045642 and rs1128503 polymorphisms upon the response to Taxane-containing regimen treatment, this meta-analysis was performed.

Methods Pooled odds ratios (ORs) and corresponding 95% confidence intervals (CIs) were employed to evaluate the impact of these two ABCB1 polymorphisms. R scripts were developed to perform the meta-analysis.

Results A total of ten articles (including ten datasets for rs1045642 and six for rs1128503) were collected in our systematic review. However, our meta-analysis yielded no significance for the effect of these two ABCB1 polymorphisms upon the response to Taxane-containing regimen.

Conclusions This highlights possibly the unsuitability of relying on the ABCB1 rs1045642 and rs1128503 polymorphisms as therapeutic response biomarkers of Taxane-containing chemotherapy. Further polycentric studies in larger and multiracial populations are needed to validate the conclusions.

Keywords: ABCB1 · Polymorphism ·Taxane-containing chemotherapy · sensitivity · Meta-analysis

 

Introduction

As one of the cornerstones of systemic treatment, Taxanes are widely used in the chemotherapy for different kinds of cancers. The platinum-based doublet regimen with a taxane is regarded as one of standard combinational therapeutic approaches. However, taxane-containing chemotherapy response varies greatly between individuals. Together with the external environmental influence and clinical factors, inherited genetic variations, such as single nucleotide polymorphism(SNP)s, could lead to the inter-individual variability. For this reason, identifying biomarkers indicating the response to taxane-containing regimen treatment is increasingly understood to be an important means to best optimize survival of cancer patients.

ATP-binding cassette subfamily B member 1 (ABCB1), also known as permeability glycoprotein 1 (P-gp) or multiple drug resistance protein 1 (MDR1), functions as one transmembrane active efflux pump for many kinds of drugs [1]. ABCB1 could mediate a cellular elimination of a vast spectrum of chemotherapeutic drugs, including paclitaxel [2]. Its up-regulation has been regarded as one of the major obstacles for chemotherapy, which correlates with undesirable treatment response and low remission rate [3-5]. The pharmacokinetics changes caused by genetic variations involved in drug transporter proteins, such as SNPs, may directly and adversely impact on the efficacy of many therapeutic agents [6]. Although being synonymous variants, the ABCB1 rs1128503 (C1236T) polymorphism may affect the substrate specificity led by the change of substrate structure and inhibitor interaction sites [7], and the ABCB1 rs1045642 (C3435T) polymorphism could lead to both decreased P-gp expression and diminished activity [8,9]. Thus, given the importance of the ABCB1 gene, it would be necessary to assess the impact of these two polymorphisms on the response to taxane-containing regimen treatment.

Although several studies tried to evaluate the impact of the ABCB1 rs1045642 and rs1128503 polymorphisms on the sensitivity to taxane-containing chemotherapy treatment, those scattered evidences remain inconclusive. Not only the different criteria for sample selection in previous studies, but also some confounding factors, including ethnicity, sample size and chemotherapy strategies, could lead to the incommensurability of their results. Thus, we performed this meta-analysis, which drew more credible evidence by systematically integrating eligible data sets, to clarify the effects of the ABCB1 rs1045642 and rs1128503 polymorphisms on the response to taxane-containing chemotherapy.

Materials and methods

Literature search

We queried Web of Science, PubMed and Cochrane Library databases up to November 28, 2016. Keywords combinations for Taxane drugs (Paclitaxel, Docetaxel, Taxol, Taxane and Cabazitaxel), cancer (epithelioma, adenocarcinoma, osteosarcoma, carcinoma and cancer), polymorphism (polymorphism, SNP and variant), Gene symbol and synonym for the ABCB1 gene (ABCB1, MDR1, CLCS, P-GP, PGY1, ABC20, CD243 and GP170) were used to form a Boolean query formulas. Both the query text and the searching results were reviewed independently by three authors (M.X., Y.L. and Q.J.). Inconsistencies in the numbers of the yielded papers were discussed to reach complete consensus.

Eligibility criteria

Studies were included on the following grounds: 1) manuscripts from peer-reviewed journals; 2) case-control study for assessing the association between the ABCB1 single nucleotide polymorphisms (rs1045642 and rs1128503) and the sensitivity to the Taxane-containing regimen chemotherapy for cancers; 3) studies with all included samples receiving Taxane-containing regimen treatment; 4) no inconsistencies in genotype data for both case and control; 5) studies with enough genotype data to calculate the odds ratio (OR) and the 95% confidence intervals (CI) in at least one genetic comparing model. Three individual authors (M.X., Y.L. and D.L.) performed the literature selecting process. The other author (X.Y.) did an investigation to thrash out an eventually agreement with all authors, when any information on screening results was not exactly the same.

Data extraction

For each relevant study, information on name of first author, year of publication, country, cancer types, chemotherapy strategies, genotype numbers were carefully extracted independently by three authors (M.X., Y.L. and Y.C.), using a unified table with predefined data format. All disagreements were resolved by an internal discussion and deliberation until a consensus was finally reached. A proofreading was drawn by two authors (D.L. and X.Y.) for error reduction.

Statistics analysis

All statistics analyses were conducted under R environment (version 3.3.3) using our in-house R scripts (developed by M.X. and Y.L.), which integrated the built-in functions from the package “meta” (version: 4.7-1, http://cran.r-project.org/web/packages/meta/) [10]. Four authors (M.X., Y.L., D.L. and X.Y.) independently completed the analyzing process, and any disagreement of the results was resolved by collective confirmatory calculation. The aggregated estimate of the OR and corresponding 95% CI were assessed for the Dominant model(Aa+aa versus AA, A stands for the major allele and a for the minor allele), the Recessive model(aa versus Aa+AA), the Heterozygous model(Aa versus AA) and the Homozygous model(aa versus AA). Heterogeneity assumption was conducted by the Cochran’s Chi-square-based Q-test. A P-value  less than 0.10 for Q-test indicated the detection of between-study heterogeneity, the DerSimonian and Laird method (random effect model) would be applied for aggregation of data [11]. Otherwise, when no evidence for high heterogeneity was found (P-value no less than 0.10), pooled ORs and 95% CIs were calculated using a fixed effect model employing Mantel-Haenszel algorithm [12]. The estimate of the OR and 95% CI were graphically presented by forest plots. The implementation of subgroup analysis according to region (Asian or European), cancer types (breast cancer or others), and chemotherapy strategies (Platinum containing: Platinum-based or not; Docetaxel usage: Docetaxel or other), were fulfilled by the particular module in our customized R scripts. The existence of publication bias was detected by funnel plot via visual inspection, and a funnel asymmetry may indicate publication bias in the meta-analysis. The leave-one-out sensitivity analysis was done by iteratively removing one single study from the pooled data set (n, n stands for the number of involved studies) and re-analyzing the remaining ones (n-1), to confirm that our results were not statistically driven by any individual study. At the same time, if the removal of this study could significantly impact the results of heterogeneity evaluation, it would be identified as the source of heterogeneity. Finally, the verifications were done by the Review Manager software (version: 5.3, The Nordic Cochrane Centre, Cochrane; Copenhagen, Denmark).

For this meta-analysis, all investigators adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [13].

Results

Characteristics of eligible studies

The initial literature screen from databases and references searches retrieved a total of 158 articles. Finally, ten articles met the pre-defined eligibility criteria through layers of screening [14-23]. The workflow for literature identification was illustrated in Fig. 1. The characteristics of the involved studies are shown in Table 1. Among the remaining studies eligible for data extraction, ten contained information on the rs1045642 polymorphism and six had data on the rs1128503 polymorphism. These studies covered head and neck cancer, gastric cancer, breast cancer, non-small cell lung cancer, esophagus cancer, and others. A total of 802 individuals (359 responders and 443 non-responders) from 10 studies were involved in our rs1045642 polymorphism study. Three studies reported data on European population, while seven on Asian countries. However, one of these ten studies only provided the data in the recessive model [23], while another two only did in the dominant model [14,15]. As for the rs1128503 polymorphism, 524 samples (249 responders and 275 non-responders) were enrolled. They came from four studies for Asian population and another two for European country. One study only showed the data in the dominant model [15].

Quantitative synthesis and Subgroup analysis

Overall, the summary OR and 95% CI of combined analyses for the ABCB1 rs1128503 polymorphism revealed no significantly altered response to taxane-containing chemotherapy (Homozygous model: OR=1.25, 95% CI: 0.78-2.00; Heterozygous model: OR=1.17, 95% CI: 0.76-1.78; Dominant model: OR=1.01, 95% CI: 0.72-1.41, Fig. 2; Recessive model: OR=0.84, 95% CI: 0.43-1.63; Table 2). Meanwhile, the quantitative synthesis of the involved studies provided no evidence of an association between the ABCB1 rs1045642 polymorphism and chemotherapy response (Homozygous model: OR=1.14, 95% CI: 0.39-3.33; Heterozygous model: OR=1.02, 95% CI: 0.60-1.74; Dominant model: OR=1.09, 95% CI: 0.68-1.74, Fig. 3; Recessive model: OR=0.95, 95% CI: 0.62-1.46; Table 3).

For both the ABCB1 rs1045642 and rs1128503 polymorphisms, when the analysis was restricted to studies for breast cancer patients, no evidence of a significant association could be detected. Similarly, the pooled effect estimate remained insignificant for subgroups enrolled subjects with other cancer types (Fig. 2 and Fig. 3). After stratifying the data into subgroups, according to the region, no statistically significant association between the response to taxane-containing chemotherapy and these two ABCB1 polymorphisms could be found. Then, the differences among therapeutic strategies were evaluated based on the group assignment according to the ingredients of chemotherapy. On the one hand, neither the Platinum-based groups, nor the non-Platinum-based groups of these two ABCB1 polymorphisms, showed significantly increased or decreased sensitivity to chemotherapy. On the other hand, the ORs remained statistically insignificant despite the usage of Docetaxel.

Publication bias and Sensitivity analysis

No obvious asymmetric distribution could be observed in the funnel plots of all genetic models for the ABCB1 rs1128503 (Fig. 4) and rs1045642 (Fig. 5) polymorphisms. In the leave-one-out sensitivity analysis for both rs1128503 and, rs1045642 statistically similar results could be obtained, indicating the stability of our meta-analysis (data not shown).

Heterogeneity analysis

As for the ABCB1 rs1128503 polymorphism, significant heterogeneity could be observed in the Homozygous model and the Recessive model. The source of heterogeneity was identified in the leave-one-out sensitivity analysis. When one single study was removed [16], the heterogeneity in both the Homozygous model and the Recessive model was significantly reduced. However, as the result of sensitivity shown, although the removal of this study slightly changed the pooled ORs and 95% CIs, no significantly association between this polymorphism and patient response to Taxane-containing regimen treatment could be observed yet (Homozygous model: OR=0.77, 95% CI: 0.40-1.48; Recessive model: OR=0.81, 95% CI: 0.51-1.27).

The recessive model of the rs1045642 polymorphism showed significant heterogeneity. The sensitivity analysis revealed the one study as the source of heterogeneity [23]. The deletion of this study from the recessive model, not only significantly reduced the heterogeneity, but also kept the stableness of the pooled results (Recessive model: OR=1.04, 95% CI: 0.69-1.55).

Discussion

Individualized chemotherapy for cancers is tailored to enhance its effectiveness, which is frequently compromised by the pharmacoresponse-related genetic variation[24-26]. Therefore, it is important to identify molecular biomarkers of chemotherapy drug sensitivity and resistance, which may facilitate the improvement for rationality of treatment decisions. Given the important biological effects of the variant alleles of the ABCB1 rs1045642 and rs1128503 polymorphisms, the quantitative synthesis based on eligible data was performed. The findings of this meta-analysis suggested that neither the rs1045642 polymorphism, nor the rs1128503 polymorphism, could influence the effectiveness of the Taxane-containing regimen chemotherapy.

Several specific patient characteristics may significantly influence treatment effects, and subgroup analyses could be undertaken to assess these differences [27]. Many confounding factors such as ethnicity, life-styles, medical conditions and medication may contribute to the regional differences of therapeutic effects. In order to elucidate the variation among different regions, we stratified the pooled dataset into two subgroups. Nevertheless, neither Asian population, nor European, showed significantly altered sensitivity to Taxane-containing regimen chemotherapy. The response to chemotherapy may also vary between different cancer types. However, this meta-analysis indicated these two ABCB1 polymorphisms had no obvious impact for both the breast cancer population and the subgroup of other cancers. Chemotherapy strategies were developed depending on the particular circumstances, playing important roles in advancement of treatment efficiency. The pooled effect estimate manifested, whether the chemotherapy were platinum-based, platinum-free, or docetaxel-containing or not, the variant alleles of these two ABCB1 polymorphisms could not significantly affect the sensitivity of the treatment.

Heterogeneity may mislead the interpretation of the results. One of the effective methods available to address this challenge for meta-analysis is the leave-one-out sensitivity analysis, which could systematically evaluate not only the impact of each study on the pooled estimates, but also heterogeneity improvement after the removal of the very study. After filtering out the identified sources of heterogeneity, no significant estimate of the pooled ORs and 95% CIs were noted, indicating the stableness of our conclusions. Combining with evidences from publication bias and sensitivity analysis, the robustness in this study could be ensured.

This study was conducted to get comprehensive conclusions about the ABCB1 polymorphisms’ impact on Taxane-containing regimen chemotherapy response. Not only the whole precision, but also the confidence level of the estimation was improved through enlarging the sample size. On the other hand, the stability revealed by not only the subgroup analysis, but also the sensitivity analysis, reinforced our confidence in the cogency of our meta-analysis. However, several possible limitations should be considered. First of all, the ethnical impact in this study was not fully discussed because all involved studies originated from Asia and Europe. Furthermore, the publication bias was only evaluated by funnel plot, neither Egger’s test, nor Begg’s test could be applied because of the small number of studies. In addition, the composite effect with other clinical factors and gene variants was not evaluated due to present data status. Moreover, the sample sizes in the meta-analysis for these two ABCB1 polymorphisms were still small. Finally, the ABCB1 rs2032582 polymorphism (2677G > T/A) was not included in this meta-analysis because of incomplete genotype frequency information and lack of comparability. Despite all these limitations above, our meta-analysis could still demonstrate its advantageous role.

Conclusions

In conclusion, based on the published literature, this meta-analysis did not provide convincing evidence for a significant association between the ABCB1 rs1045642 and rs1128503 polymorphisms and the response to taxane-containing regimen treatment. Future research in larger populations with explicit corresponding information is required to evaluate the discrepancies among different taxane drugs and chemotherapy strategies, and to elucidate the potential synergistic effect of the polymorphisms in the ABCB1 gene and the possible impact of ethnicity, gender and environmental exposures.

Fig. 1 Summary diagram of data sets acquisition.

Fig. 2 Dominant model analysis of effect of the ABCB1 rs1128503 polymorphism over Taxane-containing regimen treatment response. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated under both the fixed and random-effects models. Stratified analysis according to the cancer types was performed at the same time. Each study involved was marked by the first author’s family name and year of publication. The area of the gray square centred on the estimated OR for individual study is proportional to its corresponding weight under fixed-effect model, and the horizontal line represents the matching 95% CI. The columns labelled Weight (fixed) and Weight (random) stood for the percentage weight given to an individual study under the fixed and random effects models. The meta-analyzed measures for both the whole and subgroups were plotted as the gray diamonds, while the lateral points indicated the 95% CI for this estimate. A vertical dotted line was used to represent the pooled OR from the random-effect model for the whole, while a dashed one flagged the pooled OR from the fixed-effect model. A vertical solid line representing no effect (OR = 1) was also plotted.

Fig. 3 Dominant model analysis of effect of the ABCB1 rs1045642 polymorphism over Taxane-containing regimen treatment response. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated under both the fixed and random-effects models. Stratified analysis according to the cancer types was performed at the same time. Each study involved was marked by the first author’s family name and year of publication. The area of the gray square centred on the estimated OR for individual study is proportional to its corresponding weight under fixed-effect model, and the horizontal line represents the matching 95% CI. The columns labelled Weight (fixed) and Weight (random) stood for the percentage weight given to an individual study under the fixed and random effects models. The meta-analyzed measures for both the whole and subgroups were plotted as the gray diamonds, while the lateral points indicated the 95% CI for this estimate. A vertical dotted line was used to represent the pooled OR from the random-effect model for the whole, while a dashed one flagged the pooled OR from the fixed-effect model. A vertical solid line representing no effect (OR = 1) was also plotted.

Fig. 4 Publication analysis for the meta-analysis of the ABCB1 rs1128503 polymorphism. Begg’s funnel plot with pseudo 95% confidence limits was drawn, showing the odds ratio (OR) versus the standard error (SE) for the natural logarithm of OR. ORs were calculated under the dominant model. Each gray square represented a single study. A symmetric funnel shape indicated lower possibility of publication bias.

Fig. 5 Publication analysis for the meta-analysis of the ABCB1 rs1045642 polymorphism. Begg’s funnel plot with pseudo 95% confidence limits was drawn, showing the odds ratio (OR) versus the standard error (SE) for the natural logarithm of OR. ORs were calculated under the dominant model. Each gray square represented a single study. A symmetric funnel shape indicated lower possibility of publication bias.

Cite This Work

To export a reference to this article please select a referencing stye below:

Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.

Related Services

View all

DMCA / Removal Request

If you are the original writer of this dissertation and no longer wish to have your work published on the UKDiss.com website then please: