# Single Nucleotide Polymorphisms in Selected  Apoptotic Genes and BPDE-Induced Apoptotic Capacity  in Apparently Normal Primary Lymphocytes:  A Genotype-Phenotype Correlation Analysis

Authors: Zhibin Hu; Chunying Li; Kexin Chen; Li-E Wang; Erich M. Sturgis; Margaret R. Spitz; Qingyi Wei
Journal: Journal of Cancer Epidemiology
 (2008)

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## Abstract

Apoptotic capacity (AC) in primary lymphocytes may be a marker for cancer susceptibility, and functional single nucleotide polymorphisms (SNPs) in genes involved in apoptotic pathways may modulate cellular AC in response to DNA damage. To further examine the correlation between apoptotic genotypes and phenotype, we genotyped 14 published SNPs in 11 apoptosis-related genes (i.e.,p53, Bcl-2, BAX, CASP9, DR4, Fas, FasL, CASP8, CASP10, CASP3, and CASP7) and assessed the AC in response to benzo[a]pyrene-7,8-9,10-diol epoxide (BPDE) in cultured primary lymphocytes from 172 cancer-free subjects. We found that among these 14 SNPs, R72P, intron 3 16-bp del/ins, and intron 6 G>A in p53, −938C>A in Bcl-2, and I522L in CASP10 were significant predictors of the BPDE-induced lymphocytic AC in single-locus analysis. In the combined analysis of the three p53 variants, we found that the individuals with the diplotypes carrying 0-1 copy of the common p53 R-del-G haplotype had higher AC values compared to other genotypes. Although the study size may not have the statistical power to detect the role of other SNPs in AC, our findings suggest that some SNPs in genes involved in the intrinsic apoptotic pathway may modulate lymphocytic AC in response to BPDE exposure in the general population. Larger studies are needed to validate these findings for further studying individual susceptibility to cancer and other apoptosis-related diseases.


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## Body

## 1. Introduction

Apoptosis, also known as the programmed cell death, is a biological process
that regulates physiological cell death and plays an important role in the
pathogenesis of a variety of human diseases, including cancer
[1]. Resistance to apoptosis or reduced
cellular apoptotic capacity (AC) provides a survival advantage of the cells
that may develop into cancer cells, commonly seen in almost all types of
malignant diseases, and mutations in the genes involved in apoptotic pathways
are one of the molecular mechanisms underlying carcinogenesis
[2, 3] and cancer
therapy [3, 4].Benzo[a]pyrene (B[a]P) is a classic DNA-damaging carcinogen found in both
tobacco smoke and the environment as a result of fuel combustion
[5]. Its bioactivated form, benzo[a]pyrene
diol epoxide (BPDE), can cause irreversible damage to DNA by forming DNA
adducts through covalent binding or oxidation [6, 7]. If these adducts are unrepaired, the cells will
undergo apoptosis through activation of p53, caspase-9 (CASP9), and caspase-3 (CASP3) [8, 9]. As a
pivotal regulator of cellular response to DNA damage, the transcription factor
encoded by the p53 tumor suppressor gene has been clearly implicated in
B[a]P-induced apoptosis, and the levels of p53 protein expression has been
correlated with the levels of B[a]P-DNA adducts [8, 10]. Although details of the signaling pathways that trigger apoptosis in lymphocytes
remain not fully understood, possible mechanisms include transcriptional
activation of the Bcl-2 family members [11] and
transcriptional upregulation of the death receptors (DRs)
[12, 13]. These complex proteins participate in the
activation of a sequential signaling that modulates two main apoptotic pathways
[4]. One is the intrinsic or mitochondrial pathway, in which the stimuli
of p53-Bcl-2 pathway lead to the activation of CASP9 and release of
cytochrome c from the mitochondria [14]. The other, referred to as
the extrinsicor cytoplasmic pathway, involves a group of proteins
such as the DRs, the membrane-bound Fas ligand, the Fas complexes, the Fas-associated death domain, caspase-8 (CASP8), and caspase-10 (CASP10)
[15, 16].
Activation of these two pathways initiates a common downstream proteolytic cascade
that involves CASP3 and caspase-7 (CASP7) [4].It is likely that the efficiency of these apoptotic pathways is genetically
determined. Therefore, we hypothesized that functional polymorphisms in genes involved in these apoptotic pathways may
modulate the AC phenotype, thus contributing to individual variation in response
to DNA damage. To test this hypothesis, we selected 14 potentially functional
polymorphisms in 11 genes, that is,p53, Bcl-2, BAX, and
CASP9 involved in the intrinsic pathway; DR4, Fas, FasL, CASP8, and CASP10 involved in the extrinsic pathway; and CASP3 and CASP7, the effective CASPs. We genotyped for these 14
polymorphisms and assessed in vitro AC with a terminal deoxynucleotidyl
transferase-mediated
dUTP-biotin nick-end labeling (TUNEL) assay using BPDE-treated primary
lymphocytes from 172 subjects without cancer to evaluate associations between
their apoptotic genotypes and the AC phenotype.


## 2. Materials and Methods

### 2.1. Study Population

Subjects in the current
study were the control subjects in a molecular epidemiology study of lung
cancer previously described [17]. Briefly, 172 subjects in this study were
randomly selected from a pool of cancer-free control subjects recruited from
the Kelsey-Seybold Clinics, a large multispecialty physician organization with
several clinics throughout the Houston
metropolitan area. Each subject was scheduled to be interviewed after a written
informed consent was obtained. After
the interview, a venous blood sample of about 20 mL was collected from each
subject. The research protocol was approved by The University of Texas M. D.
Anderson Cancer Center and the Kelsey-Seybold Foundation institutional review
boards.


### 2.2. SNP Selection

We used the National
Center for Biotechnology Information (NCBI) dbSNP database (http://www.ncbi.nlm.nih.gov),
the National Institute of Environmental Health Sciences (NIEHS) Environment
Genome Project SNP databases (http://egp.gs.washington.edu/directory.html and http://www.genome.utah.edu/genesnps/)
and literature search to identify potentially functional variants in genes
involved in both intrinsic and extrinsic apoptotic pathways. Polymorphisms with
a minor allele frequency (MAF) of ≥0.05
were included, if they may theoretically result in amino acid changes
(nonsynonymous SNP, nsSNP), located at regulating regions such as promoters, or are
reportedly associated with known phenotypic effects. Three reported
p53 SNPs were selected, including the well-known codon 72 SNP
(R72P, G > C) and two intronic variants (a 16 bp-del/ins in intron 3 and a
G-to-A transition in intron 6 because their haplotypes were
found to be  functional [18]. Two
previously reported regulating SNPs in the promoters of the Bcl-2 family
members, Bcl-2 (−938C > A) and BAX (−248G > A) [19, 20], were included. For the CASPs, we identified one nsSNP
from each CASP8 (D302H, G > C), CASP10
(I522L, A > T) [21], and CASP7
(D255E, C > G, http://www.ncbi.nlm.nih.gov) and one of the two
CASP9 nsSNPs in tight linkage disequilibrium (LD)
(Q221R, G > A, http://egp.gs.washington.edu/directory.html and
[22]. Because no
nsSNP was found in the coding region of CASP3,
we selected one common variant in its promoter region: −1337C > G
(http://www.genome.utah.edu/genesnps/). For the death receptor genes, one nsSNP
in DR4 and three promoter SNPs in Fas and FasL were selected: T209R (C > G) in
DR4, –1377G > A and –670A > G in Fas, and –844T  >  C in
FasL [23–26].


### 2.3. Genotyping

The genotyping methods
used to distinguish the 14 selected polymorphisms in 11 apoptosis-related genes
are presented in Table1.
Genotyping methods for seven of the polymorphisms were previously described:
p53 R72P [27]), p53 intron 3
16-bpdel/ins and intron 6 G > A [18], DR4 T209R [28],
Fas –1377G > A and –670A > G [29], and FasL –844T > C
[30]. The remaining seven polymorphisms
(i.e., Bcl-2−938C > A, BAX−248G > A,
CASP9 Q221R, CASP8 D302H,
CASP10 I522L, CASP3 –1337C > G, and
CASP7 D255E) were detected by using a primer-introduced restriction
analysis (PIRA)—polymerase chain reaction (PCR) assay
[31] and summarized in Table
1. Genotyping was performed without
knowledge of the subjects’ phenotype; more than 10% of the samples were randomly
selected for confirmation, and the results were 100% concordant. For the seven self-designed
genotyping assays, PCR products containing each target genotype were purified
and the sequences were confirmed by direct sequencing.Table 1
Conditions of genotyping assays for the selected polymorphisms of some apoptotic genes.
GenePosition, base change, and rs#Genotypingmethod(a)PrimerPCR productEnzymeGel band patternp53R72P G>C rs1042522PCR-RFLP5′-ATCTACAGTCCCCCTTGCCG-3′ (sense)
5′-GCAACTGACCGTGCAAGTCA-3′ (antisense)296 bpBstUIG allele: 169 bp, 127 bpC allele: 296 bpp53Intron 3 16-bpins/del rs17878362PCR5′-TGGGACTGACTTTCTGCTCTT-3′ (sense)
5′-TCAAATCATCCATTGCTTGG-3′ (antisense)Del:
180 bpIns:
196 bpp53Intron 6 G>A
rs1625895PCR-RFLP5′-TGGCCATCTACAAGCAGTCA-3′ (sense) 5′-TTGCACATCTCATGGGGTTA-3′ (antisense)404 bpMspIG allele: 336 bp, 68 bpA allele: 404 bpBcl-2−938C>A
rs2279115PIRA-PCR: mismatch, antisense
primer +2 C-to-G5′-TCCTGCCTTCATTTATCCAGCA-3′ (sense)
5′-CCAGGAGAGAGACAGGGGACA-3′ (antisense)125 bpNlaIIIC allele: 106 bp, 19 bpA allele: 125 bpBAX−248G>A
rs4645878PIRA-PCR: mismatch, sense primer –2 G-to-C5′-CATTAGAGCTGCGATTGGACCG-3′
(sense)
5′-GCTCCCTCGGGAGGTTTGGT-3′ (antisense)109 bpMspIG allele: 89 bp, 20 bpA allele: 109 bpCASP9Q221R G>A rs1052576PIRA-PCR: mismatch, sense primer –2 G-to-C5′-GGCTTTGCTGGAGCTGGCCC-3′ (sense)
5′-AGTACCCAATGCCTGCCCAGGG-3′
(antisense)121 bpMspIG allele: 102 bp, 19 bpA allele: 121 bpDR4T209R C>G
rs4871857PCR-RFLP5′-ATCCTCTGGGAACTCTGTGG-3′ (sense) 5′-GGGGACAGGCAGATGGAC-3′ (antisense)300 bpDraIIIC allele: 200 bp, 100 bpG allele: 300 bpFas−1377G>A
rs2234767PIRA-PCR: mismatch, sense primer –2 A-to-G5′-TGTGTGCACAAGGCTGGCGC-3′ (sense) 5′-TGCATCTGTCACTGCACTTACCACCA-3′ (antisense)122 bpBstUIG allele: 104 bp, 18 bpA allele: 122 bpFas–670A>G
rs1800682PCR-RFLP5′-ATAGCTGGGGCTATGCGATT-3′ (sense) 5′-CATTTGACTGGGCTGTCCAT-3′ (antisense)193 bpScrFIA allele: 193 bpG allele: 136 bp, 57 bpFasL−844T>C
rs763110PIRA-PCR: mismatch, sense primer –7 A-to-C5′-CAATGAAAATGAACACATTG-3′ (sense) 5′-CCCACTTTAGAAATTAGATC-3′ (antisense)85 bpDraIIIT allele: 66 bp, 19 bpG allele: 85 bpCASP8D302H G>C
rs1045485PIRA-PCR: mismatch, sense primer –2 A-to-G5′-CATTTTGAGATCAAGCCCCGC-3′ (sense) 5′-CCCTTGTCTCCATGGGAGAGGA-3′ (antisense)132 bpBstUIG allele: 112 bp, 20 bpC allele: 132 bpCASP10I522L A>T
rs13006529PIRA-PCR: mismatch,
antisense primer +4 A-to-T5′-GAGTGGACAAACAGGGAACAAA-3′ (sense)
5′-AGAACCAACAAAAACTCTCTGCAATA-3′ (antisense)122 bpSspIT allele: 97 bp, 25 bpA allele: 122 bpCASP3−1337C>G
rs1405937PIRA-PCR: mismatch, sense primer –2 C-to-A5′-ATAGGCGCAAGTGTTAGAAACAGGAT-3′ (sense)
5′-CACCAACACATGTGAGCACGAC-3′ (antisense)112 bpFokIC allele: 112 bpG
allele: 76 bp, 36 bpCASP7D255E C>G
rs2227310PIRA-PCR: mismatch,
sense primer –2 G-to-C5′-CCTGGAGGAGCACGGAAAACA-3′ (sense) 5′-TGGAGACCACACAGGGGATCTG-3′ (antisense)137 bpPvuIIG allele: 115 bp, 22 bpC allele: 137 bp(a)RFLP = restriction fragment length
polymorphisme; PIRA = primer-introduced restriction
analysis.


### 2.4. Apoptosis Assay

The apoptosis phenotype (i.e., apoptotic capacity [AC]) was detected with the
TUNEL assay previously described [32]. Briefly,
two parallel short-term cultures from each blood sample were incubated at 37°C
without CO2 for 67 hours before BPDE
treatment. At the end of the incubation, one of the two parallel cultures was
treated with BPDE (98% pure; Midwest Research Institute, Kansas City, Mo, USA) at
a final concentration of 4 μM.
After an additional 5-hour incubation, all cells were pelleted by
centrifugation, resuspended with lysis buffer (Human Erythrocyte Lysing Kit,
R&D Systems, Minneapolis, Minn, USA), fixed for 1 hour, rinsed with
phosphate-buffered saline, and finally stored in 70% ethanol at –20°C until used
for the TUNEL assay.For the TUNEL assay, we used the APO-BRDU kit (Phoenix Flow Systems, San Diego,
Calif, USA) and followed the manufacturer's recommended protocol. The ratio of the
difference in the percentages of apoptotic cells in a subject's BPDE-treated
and untreated cultures to the percentage of apoptotic cells in the untreated
culture was recorded as the AC (AC% = [ACtreated-ACbaseline)/ACbaseline] × 100) [32].


### 2.5. Statistical Analysis

DNA quality or quantity was insufficient for genotyping in 2 subjects; thus,
the final analysis included 170 persons. Differences of the continuous AC
measurements between genotypes/diplotypes of apoptotic genes were evaluated by
using Student’st-test. Trend test was performed by
using the general linear regression model with adjustment for age and sex. We
dichotomized the continuous phenotype measurements by using the median (150%)
as the cutoff value to obtain an almost equal low-AC subgroup (84 subjects) and
high-AC subgroup (86 subjects). Logistic regression analyses were used to
estimate theodds ratios (ORs) and 95% confidence intervals
(CIs) between combined genetic variants and dichotomized AC phenotype with adjustment
for age and sex. Alleles/haplotypes associated with the lower AC phenotype in
individual polymorphism analysis were termed as “at-risk” alleles hereinafter.
We used the PHASE 2.0 program [33] to infer haplotype
frequencies based on the observed genotypes for each gene. Diplotype was the most
probable haplotype pair for each individual. The potential gene-gene interaction was
evaluated by logistic regression analysis and tested by comparing the changes in
deviance (–2 log likelihood) between the models of main effects with or without the
interaction term. All of the statistical analyses were performed with statistical
analysis system software (v.9.1.3; SAS Institute, Inc., Cary, NC, USA).


## 2.1. Study Population

Subjects in the current
study were the control subjects in a molecular epidemiology study of lung
cancer previously described [17]. Briefly, 172 subjects in this study were
randomly selected from a pool of cancer-free control subjects recruited from
the Kelsey-Seybold Clinics, a large multispecialty physician organization with
several clinics throughout the Houston
metropolitan area. Each subject was scheduled to be interviewed after a written
informed consent was obtained. After
the interview, a venous blood sample of about 20 mL was collected from each
subject. The research protocol was approved by The University of Texas M. D.
Anderson Cancer Center and the Kelsey-Seybold Foundation institutional review
boards.


## 2.2. SNP Selection

We used the National
Center for Biotechnology Information (NCBI) dbSNP database (http://www.ncbi.nlm.nih.gov),
the National Institute of Environmental Health Sciences (NIEHS) Environment
Genome Project SNP databases (http://egp.gs.washington.edu/directory.html and http://www.genome.utah.edu/genesnps/)
and literature search to identify potentially functional variants in genes
involved in both intrinsic and extrinsic apoptotic pathways. Polymorphisms with
a minor allele frequency (MAF) of ≥0.05
were included, if they may theoretically result in amino acid changes
(nonsynonymous SNP, nsSNP), located at regulating regions such as promoters, or are
reportedly associated with known phenotypic effects. Three reported
p53 SNPs were selected, including the well-known codon 72 SNP
(R72P, G > C) and two intronic variants (a 16 bp-del/ins in intron 3 and a
G-to-A transition in intron 6 because their haplotypes were
found to be  functional [18]. Two
previously reported regulating SNPs in the promoters of the Bcl-2 family
members, Bcl-2 (−938C > A) and BAX (−248G > A) [19, 20], were included. For the CASPs, we identified one nsSNP
from each CASP8 (D302H, G > C), CASP10
(I522L, A > T) [21], and CASP7
(D255E, C > G, http://www.ncbi.nlm.nih.gov) and one of the two
CASP9 nsSNPs in tight linkage disequilibrium (LD)
(Q221R, G > A, http://egp.gs.washington.edu/directory.html and
[22]. Because no
nsSNP was found in the coding region of CASP3,
we selected one common variant in its promoter region: −1337C > G
(http://www.genome.utah.edu/genesnps/). For the death receptor genes, one nsSNP
in DR4 and three promoter SNPs in Fas and FasL were selected: T209R (C > G) in
DR4, –1377G > A and –670A > G in Fas, and –844T  >  C in
FasL [23–26].


## 2.3. Genotyping

The genotyping methods
used to distinguish the 14 selected polymorphisms in 11 apoptosis-related genes
are presented in Table1.
Genotyping methods for seven of the polymorphisms were previously described:
p53 R72P [27]), p53 intron 3
16-bpdel/ins and intron 6 G > A [18], DR4 T209R [28],
Fas –1377G > A and –670A > G [29], and FasL –844T > C
[30]. The remaining seven polymorphisms
(i.e., Bcl-2−938C > A, BAX−248G > A,
CASP9 Q221R, CASP8 D302H,
CASP10 I522L, CASP3 –1337C > G, and
CASP7 D255E) were detected by using a primer-introduced restriction
analysis (PIRA)—polymerase chain reaction (PCR) assay
[31] and summarized in Table
1. Genotyping was performed without
knowledge of the subjects’ phenotype; more than 10% of the samples were randomly
selected for confirmation, and the results were 100% concordant. For the seven self-designed
genotyping assays, PCR products containing each target genotype were purified
and the sequences were confirmed by direct sequencing.Table 1
Conditions of genotyping assays for the selected polymorphisms of some apoptotic genes.
GenePosition, base change, and rs#Genotypingmethod(a)PrimerPCR productEnzymeGel band patternp53R72P G>C rs1042522PCR-RFLP5′-ATCTACAGTCCCCCTTGCCG-3′ (sense)
5′-GCAACTGACCGTGCAAGTCA-3′ (antisense)296 bpBstUIG allele: 169 bp, 127 bpC allele: 296 bpp53Intron 3 16-bpins/del rs17878362PCR5′-TGGGACTGACTTTCTGCTCTT-3′ (sense)
5′-TCAAATCATCCATTGCTTGG-3′ (antisense)Del:
180 bpIns:
196 bpp53Intron 6 G>A
rs1625895PCR-RFLP5′-TGGCCATCTACAAGCAGTCA-3′ (sense) 5′-TTGCACATCTCATGGGGTTA-3′ (antisense)404 bpMspIG allele: 336 bp, 68 bpA allele: 404 bpBcl-2−938C>A
rs2279115PIRA-PCR: mismatch, antisense
primer +2 C-to-G5′-TCCTGCCTTCATTTATCCAGCA-3′ (sense)
5′-CCAGGAGAGAGACAGGGGACA-3′ (antisense)125 bpNlaIIIC allele: 106 bp, 19 bpA allele: 125 bpBAX−248G>A
rs4645878PIRA-PCR: mismatch, sense primer –2 G-to-C5′-CATTAGAGCTGCGATTGGACCG-3′
(sense)
5′-GCTCCCTCGGGAGGTTTGGT-3′ (antisense)109 bpMspIG allele: 89 bp, 20 bpA allele: 109 bpCASP9Q221R G>A rs1052576PIRA-PCR: mismatch, sense primer –2 G-to-C5′-GGCTTTGCTGGAGCTGGCCC-3′ (sense)
5′-AGTACCCAATGCCTGCCCAGGG-3′
(antisense)121 bpMspIG allele: 102 bp, 19 bpA allele: 121 bpDR4T209R C>G
rs4871857PCR-RFLP5′-ATCCTCTGGGAACTCTGTGG-3′ (sense) 5′-GGGGACAGGCAGATGGAC-3′ (antisense)300 bpDraIIIC allele: 200 bp, 100 bpG allele: 300 bpFas−1377G>A
rs2234767PIRA-PCR: mismatch, sense primer –2 A-to-G5′-TGTGTGCACAAGGCTGGCGC-3′ (sense) 5′-TGCATCTGTCACTGCACTTACCACCA-3′ (antisense)122 bpBstUIG allele: 104 bp, 18 bpA allele: 122 bpFas–670A>G
rs1800682PCR-RFLP5′-ATAGCTGGGGCTATGCGATT-3′ (sense) 5′-CATTTGACTGGGCTGTCCAT-3′ (antisense)193 bpScrFIA allele: 193 bpG allele: 136 bp, 57 bpFasL−844T>C
rs763110PIRA-PCR: mismatch, sense primer –7 A-to-C5′-CAATGAAAATGAACACATTG-3′ (sense) 5′-CCCACTTTAGAAATTAGATC-3′ (antisense)85 bpDraIIIT allele: 66 bp, 19 bpG allele: 85 bpCASP8D302H G>C
rs1045485PIRA-PCR: mismatch, sense primer –2 A-to-G5′-CATTTTGAGATCAAGCCCCGC-3′ (sense) 5′-CCCTTGTCTCCATGGGAGAGGA-3′ (antisense)132 bpBstUIG allele: 112 bp, 20 bpC allele: 132 bpCASP10I522L A>T
rs13006529PIRA-PCR: mismatch,
antisense primer +4 A-to-T5′-GAGTGGACAAACAGGGAACAAA-3′ (sense)
5′-AGAACCAACAAAAACTCTCTGCAATA-3′ (antisense)122 bpSspIT allele: 97 bp, 25 bpA allele: 122 bpCASP3−1337C>G
rs1405937PIRA-PCR: mismatch, sense primer –2 C-to-A5′-ATAGGCGCAAGTGTTAGAAACAGGAT-3′ (sense)
5′-CACCAACACATGTGAGCACGAC-3′ (antisense)112 bpFokIC allele: 112 bpG
allele: 76 bp, 36 bpCASP7D255E C>G
rs2227310PIRA-PCR: mismatch,
sense primer –2 G-to-C5′-CCTGGAGGAGCACGGAAAACA-3′ (sense) 5′-TGGAGACCACACAGGGGATCTG-3′ (antisense)137 bpPvuIIG allele: 115 bp, 22 bpC allele: 137 bp(a)RFLP = restriction fragment length
polymorphisme; PIRA = primer-introduced restriction
analysis.


## 2.4. Apoptosis Assay

The apoptosis phenotype (i.e., apoptotic capacity [AC]) was detected with the
TUNEL assay previously described [32]. Briefly,
two parallel short-term cultures from each blood sample were incubated at 37°C
without CO2 for 67 hours before BPDE
treatment. At the end of the incubation, one of the two parallel cultures was
treated with BPDE (98% pure; Midwest Research Institute, Kansas City, Mo, USA) at
a final concentration of 4 μM.
After an additional 5-hour incubation, all cells were pelleted by
centrifugation, resuspended with lysis buffer (Human Erythrocyte Lysing Kit,
R&D Systems, Minneapolis, Minn, USA), fixed for 1 hour, rinsed with
phosphate-buffered saline, and finally stored in 70% ethanol at –20°C until used
for the TUNEL assay.For the TUNEL assay, we used the APO-BRDU kit (Phoenix Flow Systems, San Diego,
Calif, USA) and followed the manufacturer's recommended protocol. The ratio of the
difference in the percentages of apoptotic cells in a subject's BPDE-treated
and untreated cultures to the percentage of apoptotic cells in the untreated
culture was recorded as the AC (AC% = [ACtreated-ACbaseline)/ACbaseline] × 100) [32].


## 2.5. Statistical Analysis

DNA quality or quantity was insufficient for genotyping in 2 subjects; thus,
the final analysis included 170 persons. Differences of the continuous AC
measurements between genotypes/diplotypes of apoptotic genes were evaluated by
using Student’st-test. Trend test was performed by
using the general linear regression model with adjustment for age and sex. We
dichotomized the continuous phenotype measurements by using the median (150%)
as the cutoff value to obtain an almost equal low-AC subgroup (84 subjects) and
high-AC subgroup (86 subjects). Logistic regression analyses were used to
estimate theodds ratios (ORs) and 95% confidence intervals
(CIs) between combined genetic variants and dichotomized AC phenotype with adjustment
for age and sex. Alleles/haplotypes associated with the lower AC phenotype in
individual polymorphism analysis were termed as “at-risk” alleles hereinafter.
We used the PHASE 2.0 program [33] to infer haplotype
frequencies based on the observed genotypes for each gene. Diplotype was the most
probable haplotype pair for each individual. The potential gene-gene interaction was
evaluated by logistic regression analysis and tested by comparing the changes in
deviance (–2 log likelihood) between the models of main effects with or without the
interaction term. All of the statistical analyses were performed with statistical
analysis system software (v.9.1.3; SAS Institute, Inc., Cary, NC, USA).


## 3. Results

The mean age (±SD, years) of the 170 study subjects (119 males and 51 females)
was57.99±12.10,
and we dichotomized age by using the cutoff value of 60 years to facilitate
comparisons between age groups. No statistical difference was found in the
continuous AC measurements between the subgroups according to age and sex (data
not shown). Table 2 shows the
continuous AC measurements by genotypes of the selected apoptotic genes. The
observed genotype frequencies were all consistent with those expected from the
Hardy-Weinberg equilibrium (data not shown). For SNPs in genes involved in the
intrinsic apoptotic pathway, variant homozygotes of p53
intron 3 16-bpins/ins, p53 intron 6 AA, and Bcl-2
−938AA all had significantly higher AC than their corresponding wild-type homozygotes
(496.07±121.26 versus
204.22±183.21 for p53 intron 3 16-bpdel/ins, P = .027; 496.07±121.26 versus 199.44±179.10 for p53 intron 6 G>A, P = .021, and 247.62±225.67 versus
164.06±154.89 for Bcl-2−938C>A, P = .046). However, the significant P values for the trend of higher AC with increasing number
of the variant alleles were observed only for p53 R72P (.016) and
Bcl-2 −938C>A (.037) as assessed in the general linear
regression model with adjustment for age and sex
(Table 2). In contrast, only the
variant homozygotes of CASP10 I522L out of all SNPs in genes involved in the extrinsic apoptotic
pathway had significantly lower AC (159.49±171.44)
than the II homozygote (239.07±205.18, P = .046) as well as a significant trend of lower AC with increasing
number of the variant alleles (P = .046).Table 2
Comparisons of mean BPDE-induced apoptosis capacity in
apparently normal primary lymphocytes by the genotypes of selected apoptotic genes.
VariableNo. (%)AC (mean ± SD)Pvalue(a)Pvalue(b)Intrinsic pathwayp53 R72PRR91 (53.5)180.47±164.13Ref.RP65 (38.2)223.07±196.22.143PP14 (8.2)313.40±243.25.067.016p53 intron 316-bpdel/del134 (78.8)204.22±183.21Ref.16-bpdel/ins34 (20.0)204.51±194.62.99316-bpins/ins2 (1.2)496.07±121.26.027.274p53 intron 6GG135 (79.4)199.44±179.10Ref.GA33 (19.4)224.07±209.170.495AA2 (1.2)496.07±121.26.021.099Bcl-2 −938C>ACC53 (31.2)164.06±154.89Ref.CA76 (44.7)216.62±180.38.087AA41 (24.1)247.62±225.67.046.037BAX –248G>AGG144 (84.7)205.39±189.73Ref.GA25 (14.7)225.99±173.85.613AA1 (0.6)84.85—CASP9 Q221RQQ53 (31.2)194.75±169.29Ref.QR75 (44.1)213.91±199.95.571RR42 (24.7)213.00±187.11.620.704Extrinsic pathwayDR4 T209RTT52 (30.6)179.61±179.86Ref.TR75 (44.1)229.67±206.45.160RR43 (25.3)203.39±155.49.497.485Fas –1377G>AGG126 (74.1)218.66±195.82Ref.GA42 (24.7)179.99±158.13.248AA2 (1.2)100.14±92.38.396.182Fas –670A>GAA43 (25.3)210.82±173.91Ref.GA86 (50.6)236.97±203.18.472GG41 (24.1)143.07±147.70.058.085FasL –844T>CCC79 (46.5)225.68±195.41Ref.CT74 (43.5)190.11±180.80.245TT17 (10.0)200.82±173.81.629.464CASP8 D302HDD127 (74.7)202.30±184.45Ref.DH38 (22.4)210.97±195.10.802HH5 (2.9)320.41±183.38.162.389CASP10 I522LII54 (31.8)239.07±205.18Ref.IL74 (43.5)212.19±177.84.430LL42 (24.7)159.49±171.44.046.046EffectiveCASPsCASP3 –1337C>GCC107 (62.9)210.98±202.46Ref.CG51 (30.0)212.23±166.75.970GG12 (7.1)159.34±109.53.388.516CASP7 D255EDD95 (55.9)214.49±190.29Ref.DE68 (40.0)195.79±166.92.516EE7 (4.1)231.51±318.48.829.801(a)Two-sided
Student t-test.
(b)Trend test
obtained from general linear regress model with adjustment for age and sex.Linkage disequilibrium (LD) analysis showed that the three loci inp53 were in LD (r2 = 0.14, D′ = 0.64 for
R72P and intron 3 16-bpdel/ins; r2 = 0.24, D′ = 0.85 for
R72P and intron 6 G>A; and r2 = 0.60, D′ = 0.79 for
intron 3 16-bpdel/ins and intron 6 G>A). Therefore, we performed haplotype/diplotype
inference using the PHASE 2.0 program based on the observed genotypes.
Overall, three common hapolotypes were derived (Table
3). The diplotype carrying zero copy of the
p53 R-del-G haplotype and the diplotype
carrying two copies of the p53 P-ins-A haplotype all had significantly higher AC (termed as “protective” hereinafter) and the effect
of the R-del-G haplotype was in a dose-response manner (P for trend:
.016; Table 3).Table 3
Comparisons of mean BPDE-induced apoptosis capacity in apparently normal primary
lymphocytes byp53 diplotypes.
p53 diplotypesNo. (%)AC (mean ± SD)Pvalue(a)Pvalue(b)R-del-G2 copies81 (47.6)177.57±159.94Ref.1 copy74 (43.5)220.39±197.21.1380 copy15 (8.8)307.90±235.37.009.016R-ins-G0 copy116 (68.2)190.05±174.57Ref.1 copy49 (28.8)238.99±197.24.1162 copies5 (2.9)310.86±311.47.146.094P-ins-A0 copy143 (84.1)202.33±179.92Ref.1 copy25 (14.7)215.43±215.45.7452 copies2 (1.2)496.07±121.26.023.154(a)Two-sided
Student t-test.
(b)Trend test
obtained from general linear regress model with adjustment for age and sex.


## 4. Discussion

In the genotype-phenotype
analysis, we examined the role of potentially functional variants in selected
apoptotic genes in the AC phenotype induced by BPDE treatment in primary lymphocytes.
We found that R72P, intron 3 16-bpdel/ins, intron 6 G>A inp53, –938C>A in Bcl-2, and I522L in CASP10 may be predictors of AC, but the
effects of the p53 variants might also be modulated by its downstream genes involved in the intrinsic pathway. To the best of our
knowledge, this is the first multigene genotype-phenotype correlation analysis
in relation to the apoptotic pathways in primary lymphocytes at a population
level.Because there is tissue specificity in response to carcinogen exposure, it
would be ideal to compare the BPDE-induced AC measurements among different tissues
of the same person. However, few reported studies have addressed this tissue
specificity, nor did our study have such an opportunity. It was reported that B[a]P-induced apoptosis of murine Hepa1c1c7 cells was through
CASP-9 activation related with p53 accumulation and activation
[8] and that a decrease in the expression of Bcl-2
to Bax ratio was another hallmark of the process [8, 34]. Although obtained from different cell types, these findings
are consistent with our current observations that genetic variants in the genes
involved in the intrinsic apoptotic pathway may play an important role in the
prediction of AC phenotype. The Bcl-2 family is a group of
evolutionarily conserved pro- and antiapoptotic proteins that play a
pivotal role in the regulation of the mitochondrial-mediated
(intrinsic) apoptotic pathway [35]. Bcl-2 inhibits apoptosis through
heterodimerization with proapoptotic members of the Bcl-2 family, such
as Bax and also through formation of channels that stabilize the
mitochondrial membrane [36].
Bcl-2 expression has also been implicated in the
pathogenesis of cancers [37, 38], and the expression of Bcl-2 to Bax ratio
seems to be important in determining both in vitro and in vivo response
to chemotherapeutic drugs [39]. Recently, variant allele of Bcl-2 −938C>A was found to be
associated with reduced prostate cancer risk in Caucasians in a small
case-control study, possibly due to the elimination of an Sp1 binding site, a
downregulation of Bcl-2 mRNA transcript levels, and unregulated programmed cell
death [40], which is consistent with what we found in the current study (the
variant A allele carriers were associated with high-AC phenotype that may help
eliminating possible malignant cells).Our results on thep53 polymorphisms were not
consistent with published data. For example, the wild-type 72R allele
was found to be associated with an increased ability to induce apoptosis in
response to radiation or cytotoxic drugs [18, 41, 42].
However, other data suggested that the 72P allele had a stronger transcription
effect in response to DNA damage, leading to enhanced apoptotic phenotype
[43]. These previous studies were mainly based on
assays with cell lines [18, 41] and suffered with a limited sample
size [18, 41, 42]. Most importantly, no previous study took into
account other coexisting polymorphisms in the genes involved in the intrinsic
pathway, which may play an important role in the B[a]P-induced apoptosis
[8]. In the present study with apparently normal primary lymphocytes, we found that all
minor variant alleles of the three p53 polymorphisms
were associated with a higher AC phenotype and that the overall effects on the
AC phenotype appeared to be affected by the CASP9 Q221R
polymorphism, but the interaction between polymorphisms of p53 and
CASP9 was only borderline significant due to a limited study power.
However, our findings of this intrinsic apoptotic pathway in lymphocytes may be
relevant to other types of tissue as well because this kind of induced apoptosis in
lymphocytes may be inheritable [44]. Thus
these results, although preliminary, need to be substantiated in larger studies.Previous finding on theCASP10 I522L
polymorphism from a large breast cancer study showed that the variant LL
genotype was associated with a borderline significant 1.30-fold increased
cancer risk compared with the wild-type II homozygote
[21], which is consistent with the notion that the
LL homozygote contributes to a diminished AC. However,
the role of the extrinsic pathway (or only CASP10) in B[a]P-induced apoptosis was not
obvious in the present study, which needs further evaluation.In conclusion, this proof-of-principle study of genotype-phenotype correlation
provides evidence that potentially functional polymorphisms in the core genes of
the apoptotic pathways may have a role in regulating the apoptotic response to
carcinogen exposure, at least in primary lymphocytes, although there is tissue
specificity in response to exposure to carcinogens. Such a modification of host
carcinogen-induced AC in primary lymphocytes may contribute to variation in
individual susceptibility to cancer in the general population. Although this
study may be limited due to small sample size, multiple tests, and lack of
repeated AC measurements for the same individuals, the findings, if validated
in more rigorously designed and larger studies, should facilitate the design of
future studies aimed at identifying subpopulations at risk of cancer and other
apoptosis-related diseases.



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*Source: 147905-2008-10-29.xml*