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Table of Contents
ORIGINAL ARTICLE
Year : 2015  |  Volume : 2  |  Issue : 2  |  Page : 19-27

Association of CYP3A4*1B and CYP3A5*3 genetic polymorphisms with lung cancer and its impact on taxane metabolism in Indian population


1 Genetics Department, Bhagwan Mahavir Medical Research Centre, Mahavir Marg, Masab Tank, Hyderabad- 500004, Telangana, India
2 Medical Oncologist and Hematologist, ClinSyn Clinical Research Pvt. Ltd, 4-1-1, Hyatnagar, Hyderabad, Telangana, India

Date of Web Publication5-Jul-2017

Correspondence Address:
Kaiser Jamil
Head of Genetics Department, Bhagwan Mahavir Medical Research Centre, # 10-1-1, Mahavir Marg, Masab Tank, Hyderabad-500004, Telangana
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.5530/ami.2015.2.6

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  Abstract 


Background: The purpose of this study was to investigate the genotype frequencies of CYP3A4*1B and CYP3A5*3 in lung cancer patients which may be useful in determining the patients' predisposition to platinum based therapies, and may be helpful for individualized drug dosing and improved therapeutics and disease management.
Results: We evaluated these two common polymorphisms in south Indian population, based on case-control study of 126 lung cancer cases and 111 controls using a PCR-RFLP-based assay. The investigation of the CYP3A4*1B gene polymorphism revealed, no significant difference in distribution between the lung cancer patients and the controls (p=0.65) . The distribution of CYP3A5*3 homozygous genotypes and heterozygous plus homozygous genotypes were significantly associated (p=0.0004 & p=0.0001) with lung cancer patients, and the *3/*3 genotype had a 4.38 fold increased risk for lung cancer. In our study *1A/*1B heterozygous genotype patients were found to constitute a major percentage of patients receiving Gemcitabine plus carboplatin therapy.
Conclusions: In conclusion, the results of our study indicated a relationship between CYP 3A4*1B and CYP3A5*3 polymorphisms and genetic predispositions to lung cancer. Thus detection of CYP3A4*1B/CYP3A4 and CYP3A5*3/CYP3A5 genotype frequencies in Indian population may be important in view of interindividualized drug dosing, improved therapeutics and disease management.

Keywords: CYP3A4, CYP3A5, Lung cancer, Polymorphisms, Chemotherapeuticsmetabolism


How to cite this article:
Subhani S, Jamil K, Atilli S. Association of CYP3A4*1B and CYP3A5*3 genetic polymorphisms with lung cancer and its impact on taxane metabolism in Indian population. Acta Med Int 2015;2:19-27

How to cite this URL:
Subhani S, Jamil K, Atilli S. Association of CYP3A4*1B and CYP3A5*3 genetic polymorphisms with lung cancer and its impact on taxane metabolism in Indian population. Acta Med Int [serial online] 2015 [cited 2019 Jul 20];2:19-27. Available from: http://www.actamedicainternational.com/text.asp?2015/2/2/19/209643




  Background Top


The treatment for lung cancer has progressed over the past few decades, with the identification of interindividual variation, which lead to classification as per the subtypes and advent of histology based treatment.[1],[2],[3],[4] In lung cancer, adenocarcinoma is one of the histological subsets accounting for nearly 40%. The CYP3A sub family is involved in oxidative metabolism of 50% of administered drugs.[5] Four CYP3A subfamily members–CYP3A4, CYP3A5, CYP3A7, and CYP3A43 have been identified in humans.[6] of these, in humans, CYP3A4 levels are highest in the liver and intestine, followed closely by CYP3A5.[7] Drug metabolizing enzymes play an important role in absorption, metabolism elimination and detoxification. Cytochrome P450 phase I metabolizing enzymes are critical for bio-transformation of various drugs.[8] The primary metabolism of a variety of xenobiotic carcinogens is mainly mediated by cytochrome P450 (CYP)enzymes belonging to the CYP 1, 2 or 3 families, which together comprise 25 different isoenzymes. The CYP3A family is a well-known phase I metabolism- related gene family and consists of four genes, CYP3A4, CYP3A5, CYP3A7, and CYP3A43, all of which are located in the 231-kb region of chromosome 7q21.1. Among them, the most relevant are CYP3A4 and CYP3A5.[9] CYP3A4 and CYP3A5 are the major enzymes for drug metabolism in adults, both enzymes making up nearly 30% of the total CYP enzymes expressed in the human liver.[10] Paclitaxel and Docetaxel are the most important anticancer drugs for the treatment of non–small cell lung cancer.[11] Cytochrome P450 (CYP) 3A4 and 3A5 enzymes are responsible for metabolism of paclitaxel and docetaxel, anticancer drugs, and most active single agents in the treatment of lung cancer. Paclitaxel and docetaxel have antitumor effect which works against depolymerization through their enhanced tubulin polymerization and stabilization which results in arresting the tumor cells during the mitotic phase of the cell cycle; finally cell death occurs through apoptosis.[12],[13] Microtubule has the ability to polymerize and depolymerize which results in chromosomal segregation and cell division during mitosis.[14] With the inception of mitosis, the microtubules gets depolymerized and replaced by the microtubules which are more dynamic in nature.[15] During mitosis, microtubules form the bipolar mitotic spindle which is essential for the segregation of sister chromatids.[16] Paclitaxel and Docetaxel target β-tubulin of the taxane site.[17] Assembled tubulin contains the taxane-binding site on microtubule.[18] Electron crystallography studies determined that, paclitaxel binds to the intermediate domain on β-tubulin.[17] When the paclitaxel binds β-tubulin, it causes lateral polymerization and microtubule stability.[18] Similar mechanisms are mediated by Docetaxel. During cell death through apoptosis, microtubule-targeting drugs function in suppressing spindle microtubule dynamics, which inhibits the metaphase anaphase transition and blocks mitosis.[19] Microtubule-targeting drugs stabilize microtubules by binding to polymeric tubulin which prevents disassembly.[20] Microtubule associated proteins, consisting of subtypes MAP1A, MAP1B, MAP2, MAP4 and tau proteins, are also involved in paclitaxel mediated cell death. CYP3A4 metabolizes paclitaxel to its inactive hydroxylated forms. Paclitaxel is also metabolized by CYP2C8.[21] Polymorphisms in CYP3A4 may lead to increased enzyme activity which may in turn enhance metabolism of paclitaxel decreasing its therapeutic efficacy. CYP 3A5 enzyme activity is also known to affect drug metabolism. Hence, it is necessary to investigate the CYP3A4 and CYP3A5 polymorphisms in lung cancer patients to determine the dosage regimen for individualized therapy. It may lead to progression in the standards of current chemotherapy. Genetic polymorphisms within the drug-metabolizing enzymes are quite general and may participate in the risk of developing cancers [Figure 1].
Figure 1: Overview of Taxane Metabolism and Mechanism of action

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So far, around eighty SNPs of CYP3A4/5 have been reported to the Human P450 Allele Nomenclature Committee (www.imm.ki.se/CYPalleles) . But few of them occur frequently enough to contribute to variations in CYP3A activity. It was also reported that CYP3A4 and CYP3A5 polymorphisms affected the treatment of various diseases by changing the balance of drug metabolism.[22] The CYP3A4*1B is a common SNP located in the 5'promoter region, which probably has an effect on transcription, so inter individual variability in CYP3A4 activity may be the result of transcriptional regulation.[23] CYP3A4*1B allelic frequency varies among different ethnic groups: 0% in Asian, 5% in Caucasians and 54% in Africans.[24] CYP3A4*1B demonstrates a frequency of 60% & 4% in Africans and Caucasians respectively and is absent in Chinese & Japanese.[25],[26] Functional SNPs are more commonly observed in the CYP3A5 gene. To date 10 CYP3A5 alleles, consisting of 2 SNPs, have been identified (Human Cytochrome P450 (CYP) Allele Nomenclature Committee. CYP3A5 allele nomenclature). Of the variants found, CYP3A5*3 (A6986G) is the only one found in all ethnic groups tested. The frequencies vary from 27% in African-Americans to 95% in Caucasians.[7],[27],[28],[29],[30],[31] The CYP3A5*3 SNP in intron 3 causes alternative splicing and protein truncation.[7] Pharmacogenetic studies with south Indian population are relatively scarce compared to those with other populations.

In this study, we tried to find out the association between CYP3A4*1B, CYP3A5*3 polymorphisms and lung cancer risk and its impact on paclitaxel therapy in south Indian population. This study may help to find different genotypes among this population and may be useful to determine which is considered to be very important for dose selection. This investigation could also be helpful in treatment of patients receiving drugs metabolized by these alleles.


  Materials & Methods Top


Study Population

Lung cancer patients were assessed on the basis of clinical and pathological examinations. This is a hospital-based case-control study conducted in South Indian population. We collected the blood samples (n=126) to investigate the association between CYP3A4*1B (rs 2740574) and CYP3A5*3 (rs 776746) and with different combinations of chemotherapy. This study was approved by the Ethics Committee, MNJ Hospitals, Hyderabad, India. The procedures followed were in accordance with the ethical standards of responsible committee of the hospital. A face-to-face interview was conducted with each patient using a structured questionnaire.

Selection Criteria

Senior pathologists confirmed all diagnoses. We interviewed and collected the data about the patient's demographic factors. We collected the information on age, smoking and previous cancer diagnoses. Participants were also asked about their family history of cancer, and the clinical information for these cases was obtained from medical records like tumor size, stage, and whether they were receiving chemotherapy, and radiotherapy. Patients were recruited following certain inclusion and exclusion criteria, which were determined before the beginning of the study.

Inclusion & Exclusion Criteria

All cases of clinically confirmed lung cancer were taken for study. Patients of confirmed lung carcinoma who gave their consent were included. All patients who refused to give consent were excluded. The patients under a combined radiation plus chemotherapy treatments were excluded. The demographic details of the study group were collected using a structured questionnaire including age, sex, and personal history having smoking habit. Other details such as clinical history and primary cancer site were retrieved from medical records.

Collection of lung cancer blood samples

Based on the above criteria, about 3 ml of blood samples were collected from a total of 126 lung cancer patients and 111 age-matched controls (voluntarily accepted) by venipuncture, were enrolled for genotyping study. Sampling was done from some of the major hospitals in Hyderabad, Andhra Pradesh, between the period June 2011 to March 2013 (Omega Hospitals and MNJ Institute of Oncology & Regional Cancer Centre), Hyderabad.

DNA Extraction

Genomic DNA was extracted from the blood samples of 237 individuals (cases and controls) by a rapid non-enzymatic method by salting out cellular proteins with saturated solution and precipitation by dehydration. The red blood cells were lysed completely using TKM1 buffer and Triton X 100. Standard TKM1 buffer contains 10 mM Tris HCl, 10 mM KCl, 10 mM MgCl2 and 2 mM EDTA. The lysate were then treated with cell lysis buffer (TKM 2 Buffer) in order to lyse the cell components. TKM 2 Buffer contains 10 mM Tris HCl, 10 mM KCl, 10 mM MgCl2 2 mM EDTA and 0.4 M NaCl. 10% SDS was used to breakdown the cell membrane. The proteins were precipitated by treating with 6M NaCl. 70% ethanol was added inorder to remove any excess salts. The DNA was air-dried. After thorough drying, 50 μl of TE buffer was added to dissolve the DNA and kept under -20°C until further use. Analytical grade chemicals from HI MEDIA were used.

Gel electrophoresis

Gel electrophoresis was performed using 2% agarose to ensure the best quality and yield of the DNA was obtained. These DNA samples were used further for PCR amplification.

Genotyping of the CYP3A4*1B (rs 2740574)Polymorphism

CYP3A4*1B (--392G>A) Polymorphism was analyzed using a set of gene specific primers Forward: 5'-GGAATGAGGACAGCCATAGAGACAAGGGGA-3' and Reverse: 5'-CCTTTCAGCTCTGTGTTGCTCTTTGCTG-3' synthesized at Bioserve Biotechnologies (Hyderabad, India) . A three step PCR amplification was performed. Briefly, a 25 μl reaction was set up containing 0.2 mM of each dNTP, 1X buffer, 1.5 mM MgCl2 and 2U of Taq DNA polymerase (Bioserve, India). 30 cycles of PCR procedure was performed with denaturation at 95°C for 35 seconds, annealing at 55°C for 30 seconds and elongation at 72°C for 30 seconds. Final elongation was performed at 72°C for 5 minutes.

Restriction fragment length polymorphism

After amplification, 5μl of PCR product was subjected to restriction digestion with one unit of Hhf1 (from Fermentas, USA)restriction enzyme. The restriction fragments were then visualized by 2% agarose gel electrophoresis, with ethidium bromide staining. Three types of band patterns were obtained: Wild type homozygote (A/A), one band corresponding to 207bp; Heterozygote (A/G), three bands corresponding to 18, 189 and 207 bp; Polymorphic homozygote (G/G), two bands corresponding to 18 and 189 bp [Figure 2].
Figure 2: Hhf1 –digested RFLP CYP 3A4*1B. Lane-1: 100 bp ladder, Lane-2 & 3: 207bp, Lane 3 & 4: 207bp, 189bp, 18bp not visible

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Genotyping of the CYP3A5*3 (rs 776746)Polymorphism

CYP3A5*3 (6986A>G) polymorphism was analyzed using a set of gene specific primers Forward: 5' - C AT G AC T TAG T AG AC AG ATG AC-3' and Reverse:5'- GGTCCAAACAGGG AAGAAATA-3' synthesized at Bioserve Biotechnologies (Hyderabad, India). 30 cycles of PCR procedure was performed with denaturation at 94°C for 35 seconds, annealing at 55°C for 30 seconds and elongation at 72°C for 30 seconds. Final elongation was performed at 72°C for 5 minutes.

Restriction fragment length polymorphism

After amplification, 5μl of PCR product was subjected to restriction digestion with one unit of Ssp1 restriction enzyme. The restriction fragments were then visualized by 2% agarose gel electrophoresis, with ethidium bromide staining. The 293 bp amplicon was digested with restriction enzyme and cut the wild genotype into three fragments of 148 bp, 125 bp and 20 bp. However, A to G transition at position 6986 results in loss of restriction site and result into two fragments of 168 bp and 125 bp in case of mutant genotype [Figure 3].
Figure 3: Ssp1 –digested RFLP CYP 3A5*3. AA Genotype: 145, 125, & 20bp (lanes 1, 2, 3 and 6), AG Genotype: 145, 125, & 20bp (lanes 4 and 5), GG Genotype: 20bp (is not visible)

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Statistical Analysis

The clinical parameters such as age, sex, smokers and no-smokers, tumor grade, etc were correlated and calculated for patients with different genotypes. Values were expressed as percentage, mean, and standard deviation. Using the chi- squared test, the distribution of the genotype frequencies for patients and control subjects were compared; and Odds ratio (OR) with respective confidence interval (95% CI) for disease susceptibility was also calculated. The observed frequency of polymorphism between patients and controls were tested for Hardy–Weinberg equilibrium using the v2 method. Fisher's two tailed test and Pearson correlation were used to evaluate statistical significance and calculate p value of the parameters tested, all the statistical analysis was done using Medcal statistical software;. A p-value of <0.05 was considered significant.


  Results Top


Subject Characteristics

A total of 126 lung cancer patients and 111 controls were recruited into the study. The age range of lung cancer patients was 25-78 with a mean age of 57.56. The 111 controls were of similar racial and ethnic background as the lung cancer patients. The age of the controls was 22- 73 with mean age of 57.56. Peripheral blood was taken from the lung cancer patients and controls that consented to molecular analysis of CYP3A4*1B (-392G>A) (rs 2740574)and CYP3A5*3 (6986A>G) (rs 776746) polymorphisms. Among the patients, (n=116)92% had a diagnosis of non small cell carcinoma, (n=6) 5% of large cell cancer, remaining (n=4) 3% are undifferentiated. There were (n=62) 49% smokers and (n=26) 21% non smokers in the lung cancer patient group. Compared with controls, patients with lung cancer had a higher rate of smoking. The grade of cancer is a descriptor (usually numbers I to IV) of how much the cancer has spread. The grade often takes into account the size of a tumor. In the present study Stage III showed the highest frequency n=46 (30%)when compared to Stage II n=31 (25%) and Stage IV n=39 (31%) . In the present study, details of different combination of chemotherapy received by patients were recorded, Carboplatin + Gemcitabine n=52 (42%), Paclitaxel + Cisplatin n=34 (27%) and Docetaxel+ Cisplatin n=6 (7%) [Table 1].
Table 1: Demographic details of lung cancer cases included in the study

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CYP3A4*1B (rs 2740574)Genotype Distribution in lung cancer patients and control subjects

The investigation of the CYP3A4*1B gene polymorphism revealed that in lung cancer patient group 104 (83%)had the *1A/*1A genotype, 16 (13%)had the *1A/*B genotype and 6 (4%)had the *1B/*B genotype. In the control group 97 (87%)had the *1A/*1A genotype, 12 (11%)had the *1A/*B genotype and 2 (2%)had the *1B/*B genotype. With respect to genotype distribution, no significant difference between the lung cancer patients and the controls lung were observed (p=0.65) [Table 2].
Table 2: Distribution of CYP3A4*1B in lung cancer patients and controls

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CYP3A5*3 (rs 776746)Genotype Distribution in lung cancer patients and control subjects

The genotype frequency of the CYP3A5*3 *1/*1, *1/*3 and *3/*3 polymorphisms in lung cancer and healthy control groups is shown in [Table 3]. Frequencies of CYP3A4*3 *1/*1, *1/*3 and *3/*3 genotypes were 71%, 25%and 4 % in lung cancer patients and 92%, 7% and 1% in the controls, respectively. The distribution of CYP3A5*3 homozygous genotypes was significantly associated with lung cancer patients, and the *3/*3 genotype had a 4.38 fold increased risk for lung cancer. (OR = 4.38; 95% CI = 1.92-9.98, p=0.0004) [Table 3].
Table 3: Distribution of CYP3A5*3 in lung cancer patients and controls

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Correlation between CYP3A4*1B (rs 2740574)and CYP3A5*3 (rs 776746)and clinicopathological characteristics

CYP3A4*1B (rs 2740574) and CYP3A5*3 (rs 776746) genotypes were correlated with demographic factors, like gender, age, Histology, staging, and habitual risks of lung cancer patients to see the effect of genetic polymorphism in modulating the risk of developing lung cancer in association with all demographics factors. We analyzed a possible age and gender differences in the prevalence of CYP3A4*1B (rs 2740574)and CYP3A5*3 (rs 776746)polymorphisms.

Frequency of CYP3 A4*1B (rs 2740574)& CYP3A5*3(rs 776746)genotypes were found equally distributed in both age groups i.e., above and below 50 years with gender matched, no significant differences were observed regarding the age and gender. The frequency distribution of CYP3A4*1B (rs 2740574) and CYP3A5*3 (rs 776746) genotypes were equally distributed in stage III and stage IV, we found that tumor stage was not associated with CYP3A4*1B (rs 2740574) and CYP3A5*3 (rs 776746) genotypes.

We also analyzed the drug therapy association in relation to the prevalence of CYP3A4*1B (rs 2740574)and CYP3A5*3 (rs 776746) polymorphisms. Frequency of heterozygous genotypes (*1A/*1B and*3/*3) of CYP3A4*1B (rs 2740574) & CYP3A5*3 (rs 776746) were more in paclitaxel and platinum combination when compared with other Gemcitabine combination, we suggest that there may be association between CYP3A4*1B (rs 2740574) & CYP3A5*3 (rs 776746) genotypes and paclitaxel pharmacokinetics.

We further categorized the genotype data according smokers and non smokers to find out the modifying effect of CYP3A4 (rs 2740574) and CYP3A5 (rs 776746) genotypes on the association of smoking with lung cancer. In the present study, *1A/*1B and *3/*3 genotypes frequencies were equally distributed in smokers and non smokers. Out study indicated that, tobacco use did not influence the distribution of *1A/*1B and *3/*3 genotypes studied among lung cancer patients [Table 4] and [Table 5]. There was no significant association between the genotypes and stage III and IV of lung cancer for CYP3A4*1B and CYP3A5*3 [Figure 4] and [Figure 5].
Figure 4: Frequency of CYP3A4*1B (rs 2740574) in comparison with disease grade

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Figure 5: Frequency of CYP3A5*3 (rs 776746) in comparison with disease grade

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Table 4: Association between CYP3A4*1B and lung cancer clinicopathological characteristics

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Table 5: Association between CYP3A5*3 and Lung cancer clinicopathological characteristics

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  Discussion Top


Pharmacogenetics could represent a useful and innovative tool for optimization of schedule and dosage of anticancer chemotherapy. The individualization of chemotherapy based on pharmacogenetic information could discern among subgroups of patients in which the treatment may provide a real benefit limiting side effects. CYP3A4 and CYP3A5 are the most important among the four CYP3A subfamily members of CYP3A mediated drug metabolizing enzymes (CYP3A4, CYP3A5, CYP3A7 and CYP3A43). CYP3A4 and CYP3A5 isoenzymes are responsible for the metabolism of over 50% of all clinically used drugs.[32] One of the most important oncologic drugs utilized in the clinical practice is paclitaxel. CYP3A isozymes account for 40-60% of paclitaxel metabolism. Paclitaxel is partially metabolized by CYP3A4 & CYP3A5. Paclitaxel is also principally eliminated through multiple hydroxylation reaction mediated by cytochrome isoforms CYP3A4 and CYP3A5. Genetic variations of the drug metabolizing enzymes are reported to contribute to the pharmacokinetic variability of many drugs. Single nucleotide polymorphisms in the CYP family may have the most impact on the fate of paclitaxel drug whose metabolism they regulate. This investigation was based on the most important metabolic enzymes with a putative impact on pharmacokinetics of paclitaxel.

In this study, we focused on the association between CYP3A4*1B (rs 2740574), CYP3A5*3 (rs 776746) polymorphisms and lung cancer risk and its impact on paclitaxel therapy in south Indian population. To our knowledge this is the first study to evaluate the impact of CYP3A4*1B (rs 2740574) and CYP3A5*3 (rs 776746) genotypes on the clinical outcome in paclitaxel therapy.

CYP3A4*1B (rs 2740574) is an A-392G transition in the 5'-promoter region. Our results revealed that, the distribution of the CYP3A4 genotypes was not significantly different between the cases and controls. It is possible that CYP3A4*1B (rs 2740574)polymorphism may affect enzyme activity. The CYP3A5 gene has a common polymorphism (6986 AG rs776746) in the third intron. The G allele in genetic epidemiology studies, replaces an allele (functional allele)of CYP3A5*1 and this change in CYP3A5*3 leads to nonfunctional allele. In the present study we have investigated the frequency of the A6986G SNP in CYP3A5*3 (rs 776746) gene among lung cancer patients and controls compared well with the results and data. We found that homozygous mutant (GG) and heterozygous plus homozygous (*1/*3 +*3/*3) genotypes were significantly (p = 0.0004 & p= 0.0001)associated with lung cancer patients when compared with controls. This mutation results in a cryptic splice site mutation leading to transcripts with premature stop codons at the junction between exons 3 and 4. The resulting mRNAs rapidly degrade via a nonsense-mediated decay mechanism.[33]

Previous studies reported that, polymorphisms in CYP3A genes, such as CYP3A4 and CYP3A5, have been associated with several disease conditions, including lung cancer.[34],[35] Our studies also indicated a relationship between these polymorphisms and a genetic predisposition to lung cancer risk. The frequency of CYP3A variants, including CYP3A4*1B (rs 2740574 )and CYP3A5*3(rs 776746) polymorphisms, varied substantially in different populations. Further we have demonstrated prevalence of these polymorphisms in South Indian population. Islam et al[36] study found strong relationship between CYP3A4*1B polymorphism and lung cancer in Bangladesh population. In our study the CYP3A5*3 (rs 776746) polymorphism was also found to be significantly associated with lung cancer patients in south Indian population. Islam et al[36] study also found strong relationship between CYP3A5*3 polymorphisms and lung cancer in Bangladesh population. CYP3A5*3 was associated with prostate cancer risk.[37] In our study we found that tobacco use did not influence the distribution of the CYP3A4/A5 genotypes in lung cancer. CYP3A4*1B (rs 2740574) and CYP3A5*3 (rs 776746) polymorphisms were not associated with smokers in Bangladesh population.[36]

The clinical significance of CYP3A4*1B (rs 2740574) and CYP3A5*3 (rs 776746) polymorphisms is still under investigation. The variant frequency of this polymorphism among different ethnic groups may contribute significantly to drug efficacy and toxicity. In our study *1A/*1B heterozygous genotypes were significantly associated with paclitaxel plus cisboplatin therapy. In CYP3A5*3 frequency of homozygous mutant genotype was high, but not significant with Paclitaxel and Cisplatin therapy. Previous studies also reported that, CYP3A4*1B (rs 2740574) and CYP3A5*3 polymorphism might be associated with altered pathway of paclitaxel metabolism.[38] CYP3A5*3 was also associated with adverse reactions to paclitaxel therapy.[39]


  Conclusions Top


It is concluded that detection of CYP3A4*1B/CYP3A4 and CYP3A5*3/CYP3A5 genotype frequencies in Indian population group was important to determine the inter-individualized drug dosing for improved therapeutics. However, the genetic polymorphism of CYP3A4 and CYP3A5 alone cannot explain the inter-individual differences reported in CYP3A-mediated metabolism, may be more numbers have to be included for statistical correlations. However genotyping has an advantage to help and reduce the serious adverse reactions for safe, effective and economic treatment in patients.


  Competing Interests Top


The authors declare that they have no competing interests.


  Author's Contributions Top


SS and KJ designed the study, wrote and finalized the manuscript. SS performed the experiments and analyzed the results SA helped in patient selection and discussions all three authors approved the manuscript.


  Acknowledgements Top


We are thankful to Bhagwan Mahavir Medical Research Centre for the facilities and to the volunteers and patients for their cooperation. We thank, Senior Medical Oncologists, for permitting us to access the patients' records, and other details. We thank Dr. Edwards Sellers (USA) for most useful suggestions.



 
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    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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