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Carcinogen metabolism genes, meat intake, and colorectal cancer risk

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Carcinogen metabolism genes, meat intake, and colorectal cancer risk

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Content CARCINOGEN METABOLISM GENES,
MEAT INTAKE, AND COLORECTAL CANCER RISK

by

Jun Wang

A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(APPLIED BOISTATISTICS/EPIDEMIOLOGY)

August 2008

Copyright 2008 Jun Wang

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Acknowledgements

I would like to thank all the people who helped, inspired and encouraged me during
my master’s studies.

I am deeply indebted to Dr. Mariana C. Stern, for her kindness, her patience and
most of all -her guidance and encouragement during the preparation of my master’s
thesis. I would also thank my committee members Dr. Sue Ingles and Dr. Kimberly
Siegmund for their suggestions.

I would like to express my gratitude to all the people in Dr. Stern’s lab, who
provided great help to me. I want to thank Joshi Amit for his help with the statistical
programming and thank Roman Corral for providing all the genotypic data.

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Table of Contents

Acknowledgements

List of Tables
Abstract
Introduction
Material and Methods
Study population
Genotype methods
Exposure assessment
Statistical analyses

Results

Discussion

References

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List of Tables

Table 1: Demographic characteristics of probands and
unaffected siblings

Table 2: Carcinogen metabolism SNPs, NAT2 phenotype and colorectal
cancer risk

Table 3: Proband-only analysis of interactions between CYP1A2 -154A>C
and red meat intake variables

Table 4: Proband-sibling analysis of interactions between CYP1A2
-154A>C and red meat intake variables

Table 5: Proband-only analysis of interaction between CYP1B1
Leu432Val, GSTP1 Ile105Val and poultry intake variables

Table 6: Proband-sibling analysis of interactions between CYP1B1
Leu432Val, GSTP1 Ile105Val and poultry intake variables

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Abstract

We conducted a family based case-control study to investigate the association
between xenobiotic metabolism genes (CYP1A2 -154A>C, CYP1B1 Leu432Val,
CYP2E1 -1054C>T, GSTP1 Ile105Val, PTGS2 5UTR -765, EPHX1 Tyr113His,
NAT2 Ile114Thr, NAT2 Arg197Gln and NAT2 Gly286Glu) and colorectal cancer
(CRC) risk. We found GSTP1 Ile105Val is statistically significantly associated with
colorectal cancer risk (OR=1.7, 95%CI=1.05-2.63, p=0.03). We tested the gene-meat
interaction in proband-only sample (N=577) and then further examined in
proband-sibling pairs (307 pairs). We only found CYP1B1 may modulate the effect of
heavily browned poultry on the outside on CRC risk at border line significance
(p=0.05). Our results also suggest that CYP1A2 may exert effects on risk of colon
cancer and rectal cancer through different pathways when associated with cooked red
meat intake or levels of doneness of red meat on the outside. In particular, when
interacting with levels of doneness of red meat on the outside, individuals carrying
AC or CC genotype had lower risk on colon cancer but much higher risk on rectal
cancer (p for heterogeneity=0.05).

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Introduction
Colorectal (CRC) cancer is the second leading cause of cancer death for both men
and women, and the third in each gender in the United States (Landis et al., 1999).
Meat consumption has been reported a “convincing” risk factor for colorectal cancer
in a large review by World Cancer Research Fund (WCRF) (Food, nutrition, physical
activity, and the prevention of cancer: a global perspective, 2007). Conclusions with
regard to fish or poultry are inconsistent: some reported decreased risk (Gaard et al.,
1996; Kato et al., 1997; Tiemersma et al., 2002; Willett et al., 1990), some suggested
increased risk (Hsing et al., 1998; Jarvinen et al., 2001; Singh et al., 1998) while
others reported no association (Flood et al., 2003; Giovannucci et al., 1994; Knekt et
al., 1999; Malila et al., 1998).
However, it is not known what components of meat are the culprits for this
association: carcinogens that form during the cooking of meats might be responsible.
The main carcinogens hypothesized to be related to a diet of much cooked meat are:
heterocyclic amines (HCAs), polycyclic aromatic hydrocarbons (PAHs) and N-nitroso
compounds (NOCs) (Cross et al., 2004). High cooking temperature or prolonged
duration of cooking favors the formation of HCA (Cross et al., 2004; Sugimura, 1985).
Experimental studies reported 10 highly-mutagenic HCAs induced tumors in rodents
at multiple sites (Sugimura, 1997; Wakabayashi et al., 1992); DNA adducts from HCA
exposure have also been reported in humans (Schut et al., 1999). Exposure to NOCs
can occur through two main routes: exogenous exposure and endogenous exposure.
The former refers to exposure to NOCs directly from food preserved using nitrites or
processed by smoking or fire-drying; the latter is thought to be as a result of
endogenous formation of NOCs due to nitrosating agents that react with amines
derived from red meat (Cross et al., 2004). Previous studies suggested a clear

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association between red meat dose-dependent intake and the endogenous formation of
NOCs (Bingham et al., 1996; Hughes et al., 2001; Silvester et al., 1997). In terms of
PAHs, they are formed mainly during char-broiling, grilling and smoking of meats
(van Maanen et al., 1994), as well as formed during curing and processing of food
with smoke (Phillips, 1999). The relative contribution of PAHs to colorectal cancer,
compared to NOCs and HCAs, is still uncertain.
Among the epidemiological studies of colorectal cancer that have taken into
account cooking methods and doneness levels of red meat and poultry, some studies
(Butler et al., 2003; Navarro et al., 2004; Nowell et al., 2002) suggested a modest
positive relationship between diets high in heavily brown red meats and colorectal
cancer; however Marchand et al. found an association only when the relevant
bioactivation phenotypes were considered (Le Marchand et al., 2002). A few
epidemiological studies have considered elevated levels of HCAs (Butler et al., 2003;
Le Marchand et al., 2002; Nowell et al., 2002) related to well-done red meat and
overall support a role for HCAs in colorectal cancer risk, although results are not
conclusive. In addition, a few studies have reported the role of the key HCA or PAH
metabolism enzymes as potential modifiers on the effects of cooked red meat
(Kampman et al., 1999; Roberts-Thomson et al., 1999) (Le Marchand et al., 2001)
(Kampman et al., 1999) (Sweeney et al., 2002) (Rothman et al., 1995). These findings
lend support to the hypothesis that carcinogens that form through the cooking of red
meat might be relevant culprits of the red meat and colorectal cancer risk association.
HCAs, PAHs, and NOCs require metabolic activation before they can be detoxified
in either the liver or the colon. These carcinogens are first absorbed in the intestine
and then transported to the liver. Most absorbed HCAs experience oxidation in the
liver (Boobis et al., 1994) but can also be transported back to the intestines and be

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locally activated in the colon. In particular, CYP1A2 and NAT2 play important roles
in the metabolism of HCAs. CYP1A2, a phase I metabolizing enzyme, is responsible
for the N-oxidation of HCAs in the liver (Boobis et al., 1994) and can be induced by
dietary factors, such as fried/broiled meat and cruciferous vegetables, and cigarette
smoking. NAT2, a phase II metabolizing enzyme, catalyzes O-acetylation of HCAs
(Hein et al., 1993). In terms of the metabolism of NOCs, N-nitrosamines can be
directly activated in human colon (Autrup et al., 1978). Although NOCs reach the
liver after absorption in intestines, their highly reactive nature makes it unlikely that
liver metabolites might reach the intestines. Thus, metabolic enzymes expressed in the
colon are the most relevant for the local activation of NOCs in the colon, such as
CYP2E1, which plays a critical role in the α-hydroxylation of many N-nitrosamines
(Yamazaki et al., 1992). In addition, NOCs can also be metabolized by CYP1A2.
PAHs from the diet are also first metabolized in the liver and then transported to the
intestines. A proportion of the ingested PAHs can also be locally activated in the colon
by enzymes such as CYP1A1 and CYP1B1. CYP1B1 assumes an important role,
activating PAHs to epoxide intermediates in the liver and colon, which are further
converted to more reactive diol-epoxides by microsomal epoxide hydrolase, EPHX1
(Shimada et al., 1996) (Thomas, 1990). Epoxide intermediates of PAHs can be
detoxified by GSTP1 (Eaton et al., 1999). In the colon, PAH epoxides can also be
further activated by prostaglandin synthase 2 (PTGS2), also known as
cyclooxygenase 2(COX2), which is induced in the inflammatory response and has
been widely studied with respect to the interaction with nonsteroidal
anti-inflammatory drugs on CRC risk. Though many SNPs have been identified in
PTGS2, only one common polymorphism in the regulatory region(-765G>C) has been
shown to reduced gene expression (Papafili et al., 2002).

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Colorectal cancer is a multifactorial disease during which genetic alterations,
dietary and environmental factors interact with each other (Ishibe et al., 2002). The
Phase I and Phase II metabolism enzymes responsible for the activation and
detoxification of carcinogenic compounds vary in their metabolic activity, which is
postulated to contribute to distinct susceptibility to colorectal cancer risk. Hence, it is
biologically plausible to assume that the inheritance of specific allelic variants of
metabolism genes may influence colorectal cancer risk. Several case-controls studies
examined the affecting effect of Phase I and Phase II metabolizing gene
polymorphisms on colorectal cancer risk; however, results from previous studies have
not come to be conclusive (Kury et al., 2007; Landi et al., 2005; Sachse et al., 2002).
Given that diets high in meat are convincing risk factors for colorectal cancer, we
hypothesize that key xenobiotic metabolism genes would modify the risk of colorectal
cancer associated with cooked meats. Therefore, we proposed to study the following
metabolic gene polymorphisms: CYP1A2 (-154A>C), CYP1B1 Leu432Val, CYP2E1
(-1054C>T), GSTP1 Ile105Val, EPHX1 Tyr113His, PTGS2 (-765G>C), NAT2
Ile114Thr, NAT2 Arg197Gln, NAT2 Gly286Glu. All these SNPs were chosen based
on their known impact on protein function and previous studies with respect to
colorectal cancer risk.

Materials and Methods
Study population
We conducted this study using subjects from the USC Consortium of the Colon
Cancer Family Registry, which is formed by the following centers: Arizona Cancer
Center, Dartmouth College, Cleveland Clinic Foundation, University of Colorado,
University of Minnesota, University of North Carolina, and University of Southern

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California. Cases were the probands with the diagnosis of colorectal cancer; controls
were the siblings without history of colorectal cancer and preference was given to
older and same gender sibling and/or cousin. A two-stage sampling method was used:
in the first stage, subjects were contacted and screened by a family history
questionnaire (Haile et al., 1999); in the second stage, probands with a family history
in a parent or sibling all participated in our study and some of the probands without a
family history also participated in the study with the sampling fraction varying from
16% and 75% according to study centers. Dietary information, including meat intake
and cooking methods, was assessed from the Core Questionnaire used in all the
centers. Blood samples and the Core questionnaire along with demographic
information were collected during an in-person interview. This study was approved by
the Institutional Review Board of all the institutes mentioned above. All subjects
signed a written informed consent before participating in the study.

Genotype methods
Genomic DNA was extracted from peripheral blood lymphocytes and Taqman
assays from Applied Biosystems (Foster City, CA) were used to determine the
following SNPs: CYP1A2 -154A>C (rs762551), CYP1B1 Leu432Val (rs1056836),
CYP2E1 -1054C>T (rs2031920), GSTP1 Ile105Val (rs947894), PTGS2 5UTR -765
(rs20417), EPHX1 Tyr113His (rs1051740), NAT2 Ile114Thr (rs1801280), NAT2
Arg197Gln (rs1799930) and NAT2 Gly286Glu (rs1799931). We used an ABI 7900HT
Sequence Detection and Scoring System for allele scoring. 12 blank wells were
included as negative controls for the purpose of quality control. Approximately 6% of
the sample was randomly selected for repeated analysis using a unique identification
numbers and were blinded to the laboratory personnel. 100% concordance between all

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duplicate samples was observed. The calling rates for all assays ranged from 96.5% to
99.4%.

Exposure assessment
For this study we used data from risk factor questionnaire collected at baseline,
which provided us information regarding number of servings of red meat (beef, steak,
hamburger, prime rib, ribs, veal, lamb, bacon, pork, pork in sausages or venison) per
week, number of servings of red meat cooked by high-temperature methods (i.e.,
pan-frying, oven-broiling or grilling) per week, level of doneness of red meat from
outside (lightly browned, medium browned and heavily browned), level of doneness
from inside (red, pink, brown), number of servings of poultry (chicken, turkey, fowl)
cooked by pan-frying, oven-broiling or grilling, and level of doneness of poultry from
outside (lightly browned, medium browned, level of doneness from outside). In our
analyses we defined “cooked” red meat or poultry as those cooked with either
pan-frying, oven broiling or grilling/barbecuing, which serve as surrogates for “high
temperature” methods, which are known sources of HCA and PAH formation.

Statistical analyses
We excluded 123 subjects who were recruited from Cleveland Clinic Foundation
since we only included subjects recruited from population-based registries. We
compared observed and expected genotypic frequencies under Hardy-Weinberg
equilibrium by using chi-square tests among Caucasian siblings. Gene main effects
(odds ratio along with 95%CI) were estimated among proband-sibling pairs using
conditional logistic regression. Gene action mode was coded as additive, dominant or
recessive based on SNP function; however, for genes with very rare allele frequency

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(for instance, CYP2E1, PTGS2 and NAT2 Gly286Glu), we did not consider the
recessive mode due to the instability for fitting models. NAT2 phenotype (slow/fast)
was generated based on the three NAT2 SNPs- NAT2 Ile114Thr, NAT2 Arg197Gln
and NAT2 Gly286Glu. NAT*4, NAT*5, NAT*6 and NAT*7 alleles were defined in
consistent with previous studies (Chan et al., 2005; Hein et al., 2000). For the
statistical analysis, we used haplotype probabilities which were estimated using the
Expectation-Maximization algorithm (Excoffier et al., 1995) to classify slow/fast
NAT2 phenotype.
We checked whether age and gender would confound the association between
metabolism gene and colorectal cancer risk and we found age and gender were not
confounders for the association between studied SNPs and colorectal cancer risk.
We first tested for gene-environment interactions among colorectal cancer probands
(Case-only analyses) in order to gain more statistical power. ORs obtained from
case-only analyses are comparable to interaction ORs obtained from case-control
comparisons, provided that gene and exposure are independent in the base population
(Khoury et al., 1996). Regarding exposure variables, we dichotomized the following
variables using median value based on the distributions among cousins of the
probands, who were also recruited in the study: number of servings of red meat /
pan-fried, oven-broiled or grilled red meat/ pan-fried, oven-broiled or grilled poultry
intake per week, level of doneness of red meat or poultry on the outside
(light-medium brown/heavily browned-blackened) and level of doneness of red meat
in the inside (rare-medium/well-done). The reason we chose the cut-point based on
the distribution among cousins instead of siblings lies in that we assume the
distributions among cousins may be more representative of the general population
than that among siblings. Proband-only odds ratio and 95% CI were estimated by

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using unconditional logistic regression when modeling SNP as dependent variable and
exposure as independent variable without any other covariates. Probands with missing
values for genotypes or exposure variables were excluded from the analysis. If
proband-only analysis indicated significant interaction between exposure and SNP, we
further tested gene-exposure interaction in proband-sibling pairs on a multiplicative
scale by adding a product term of exposure variable and SNP into the conditional
logistic regression model using a likelihood ratio test. We compared the odds ratios
from proband-only analysis and interaction odds ratios from proband-sibling analysis
to decide whether they are consistent; theoretically, they should be compatible.
Furthermore, we investigated disease heterogeneity by tumor site (colon vs. rectal)
in both proband-only and proband-sibling pairs. We collapsed tumor sites into two
major groups: colon cancer (ICD-O-2 C180-C188, n=351) and rectal cancer
(ICD-O-2 C199, C209, n = 151). We did not include probands with ICD code-
ICD-O-2 C189. In proband-only analysis, we modeled SNP as dependent variable and
added the product term of exposure variable and tumor site variable in the logistic
regression model. Likelihood ratio tests were used to determine the heterogeneity of
the effect of meat exposure by tumor site, and if it indicated significantly different
effects across tumor sites, we extended our test to proband-sibling pairs. In
probands-sibling analysis, considering the three-way (gene, exposure and tumor site)
interaction, we added the product term of gene, exposure and tumor site variable in
the conditional logistic regression which also contain the product term of gene or
exposure variable and tumor site variable. Likelihood ratio test was applied to
compare the model with and without the product of gene, exposure and tumor site
variables. We corrected gene-meat analysis among probands-only sample using
Benjamini Hochberg method in order to control the false positive rate. All tests were

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two-sided and all analyses were done using the statistical software STATA version 8
(STATA Corporation, College Station, TX).

Results
In our analyses we included data from 577 probands and 362 siblings. All 577
probands were used for proband-only analyses. For proband-sibling analyses we had a
total of 307 pairs. Table 1 shows the demographic characteristics of subjects. The
mean age of probands and unaffected siblings were 60.0 and 59.3 respectively.
Among probands, 52.3% were males while in unaffected siblings, 46.3% were males.
Caucasian subjects consisted of approximately 74% among probands while 82%
among siblings. Among probands, approximately 70% were diagnosed as colon
cancer and 30% diagnosed as rectal cancer.

Table 1 Demographic characteristics of probands and unaffected siblings
Characteristics
Proband
N=577
Sibling
N=362
Age 60.0(0.5) 59.3(0.6)
Gender
Male 302(52.3) 168(46.3)
Female 275(47.7) 195(53.7)
Race
Caucasian 425(73.7) 299(82.4)
African American 54(9.4) 19(5.2)
Hispanic 53(9.2) 27(7.4)
Asian 19(3.3) 8(2.2)
Others 26(4.5) 10(2.8)
Tumor site
Colon cancer 351(69.9) NA
Rectal cancer 151(30.1) NA
Note: for continuous variable, Mean(SE) is given; for categorical
variables, N(%) is given

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Carcinogen metabolism gene polymorphisms and colorectal cancer risk
We did not find any significant difference between the observed genotypic
frequencies and those expected among Caucasian siblings under Hardy Weinberg
equilibrium. Gene main effect models adjusted for age at diagnosis and gender did not
statistically significantly differ from unadjusted models; therefore, we present
estimates obtained with the latter. We only found a statistically significant association
between GSTP1 Ile105Val and colorectal cancer risk (p = 0.03) (Table 2). Individuals
carrying Ile/Val or Val/Val genotypes had approximately 70% increased CRC risk
compared to individuals carrying Ile/Ile genotype (OR = 1.7, 95%CI = 1.05, 2.63).

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Table 2 Carcinogen metabolism SNPs, NAT2 phenotype and colorectal cancer risk
Gene Case Control OR(95%CI) P
CYP1A2 -154A>C
AA 164 184 1
AC 117 144 0.90(0.58, 1.39) 0.62
CC 24 29 0.86(0.37, 1.97) 0.72
AA 164 184 1
AC+CC 141 173 0.89(0.58, 1.38) 0.62
CYP1B1 Leu432Val
Leu/Leu 86 118 1
Leu/Val 139 151 1.43(0.91, 2.26) 0.12
Val/Val 75 81 1.51(0.81, 2.84) 0.2
Leu/Leu 86 118 1
Leu/Val + Val/Val 214 232 1.45(0.92, 2.27) 0.11
CYP2E1 -1054C>T
CC 277 329 1
CT 26 26 1.30(0.62, 2.72) 0.5
TT 0 0
CC 277 329 1
CT+TT 26 26 1.30(0.62, 2.72) 0.5
GSTP1 Ile105Val
Ile/Ile 127 171 1
Ile/Val 137 144 1.67(1.05, 2.66) 0.03
Val/Val 38 43 1.59(0.80, 3.16) 0.18
Ile/Ile 127 171 1
Ile/Val+Val/Val 175 187 1.66(1.05,2.63) 0.03
EPHX1 Try113His
Tyr/Tyr 167 188 1
Tyr/His 108 141 0.92(0.58, 1.48) 0.75
His/His 28 29 1.30(0.61, 2.78) 0.5
PTGS 2 -765G>C
GG 207 238 1
GC 87 111 0.78(0.49, 1.24) 0.3
CC 11 10 1.21(0.39, 3.74) 0.74
NAT2 Ile114Thr
Ile/Ile 102 118 1
Ile/Thr 140 175 0.89(0.57, 1.39) 0.61
Thr/Thr 61 62 1.26(0.66, 2.39) 0.48
NAT2 Arg197Gln
Arg/Arg 159 201 1
Arg/Gln 117 131 1.25(0.82, 1.90) 0.31
Gln/Gln 29 28 1.81(0.84, 3.89) 0.13
NAT2 Gly186Glu
Gly/Gly 283 342 1
Gly/Glu 20 15 1.74(0.71, 4.27) 0.23
Glu/Glu 1 3 0.50(0.05,5.28) 0.56
Gly/Gly 283 342 1
Gly/Glu+Glu/Glu 21 18 1.63(0.67, 3.96) 0.28
NAT2 phenotype
Slow 166 185 1
Fast 134 170 0.87(0.58, 1.30) 0.49

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Carcinogen gene polymorphism, red meat and colorectal cancer risk
We examined possible gene-environment interactions between all nine SNPs, along
with the estimated NAT2 phenotype, and the following meat intake variables: number
of servings of red meat per week, number of servings of pan-fried or oven-broiled or
grilled red meat per week, level of doneness of red meat on the outside (light-medium
brown/heavily browned-blackened), and level of doneness of red meat in the inside
(rare-medium/well-done). In proband-only analyses and when considering all cases
combined we did not find evidence that either of the nine SNPs we investigated or the
NAT2 phenotype modified the effect of total red meat intake or level of doneness on
the outside of red meat on colorectal cancer risk. We observed that the NAT2
phenotype statistically significantly modified the association between pan-fried,
oven-broiled or grilled red meat intake and risk of CRC (p = 0.02). However,
proband-sibling analyses did not confirm this finding, so it is more likely to be a false
positive (data not shown).
Nevertheless, we found consistent and statistically significant evidence that the
effect of doneness in the inside of red meat on risk of CRC was modified by CYP1A2
-154A>C (P<0.01) (Table 3). This finding remained statistically significant after
correcting for multiple testing. The results from proband-sibling analysis of CYP1A2
supported the findings from proband-only analysis with interaction ORs that were
consistent with the ORs from proband-only analysis (Table 4). Proband-sibling
analyses indicated that whereas intake of meat well done in the inside was not
associated with CRC risk among carriers of the AA genotype (OR = 0.9; 95% CI =
0.55, 1.41), among carriers of one or two copies of the C allele it was positively
associated with CRC risk (OR = 1.5; 95% CI = 0.92, 2.30), with an interaction OR of
similar magnitude to that observed among proband-only analyses (interaction OR =

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1.7, 95%CI = 0.89, 3.09)(Table 4).
When considering colon and rectum cases separately, we did not find evidence that
any of the nine SNPs we studied and NAT2 phenotype modified the effect of total red
meat intake. However, we found that CYP1A2 -154A>C exerted different modifying
effects on the risk of colon and rectal cancer associated with pan-fried, oven-broiled
or grilled red meat intake and level of doneness of red meat on the outside. In
proband-only analysis, we found evidence of the effect modification of CYP1A2
-154A>C on the association between colon and rectal cancer risk and level of
doneness of red meat on the outside among rectal cancer cases (OR = 2.8; 95% CI =
1.33, 5.72) but not colon cases (OR = 0.8; 95% CI = 0.50, 1.24). The heterogeneity
test for colon vs rectal cancer was statistically significant (p < 0.01). Similarly, we
also observed different modifying effects of CYP1A2 on the association between
colon and rectal cancer risk and pan-fried, oven-broiled or grilled red meat intake,
with stronger evidence of a CYP1A2 x cooked red meat interaction among rectal
cancer (OR = 1.6, 95%CI = 0.85, 3.13) than among colon cancer (OR = 0.7, 95%CI =
0.43, 1.01) (p for the heterogeneity test: 0.02) (Table 3). However, neither of these
two findings remained statistically significant after correcting for multiple testing.
Analyses among proband-sibling pairs only showed support for heterogeneity
between colon and rectal cancer for the interaction of CYP1A2 -154A>C x red meat
level of doneness on the outside (p for test of heterogeneity = 0.05) (Table 4).
Similarly to what we observed in proband-only analyses, among rectal cancer cases,
compared to individuals who ate lightly-medium browned red meat on the outside,
individuals who eat heavily browned or blackened red meat on the outside and carry
AC or CC genotype had approximately 130% increased risk of rectal cancer (OR =
2.3, 95%CI = 0.73, 7.45) whereas no effect was observed among carriers of the AA

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genotype (OR = 0.9; 95% CI = 0.33, 2.50). Among colon cancer cases no association
between level of doneness and cancer risk was observed among carriers of the AC or
CC genotypes, and a moderate positive association was observed among carriers of
the AA genotype (OR = 1.7; 95% CI = 0.83, 3.31) (Table 4).

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Table 3 Proband-only analysis of interactions between CYP1A2 -154A>C and red meat intake variables
Colorectal Cancer Colon Cancer Rectal Cancer P*
AA/ AC+CC OR (95%CI) P
AA/
AC+CC
OR (95%CI) P
AA/
AC+CC
OR (95%CI) P
Number of servings of cooked§ red meat per week
<=3 168/161 1 97/107 1

52/34 1
>3 128/113 0.9 (0.66, 1.28) 0.63 83/60 0.7 (0.43, 1.01) 0.06 31/33 1.6 (0.85, 3.13) 0.14 0.02
Level of doneness of cooked§ red meat in the inside
rare-medium 176/120 1 102/75 1 56/30 1
well-done 121/155 1.9 (1.35, 2.62) <0.01 78/92 1.6 (1.05, 2.45) 0.03 27/38 2.6 (1.35, 5.10) <0.01 0.22
Level of doneness of cooked§ red meat on the outside
Light-Medium browned 208/186 1

119/119 1 67/41 1
Heavily browned/Blackened 89/89 1.1 (0.78, 1.59) 0.54 61/48 0.8 (0.50, 1.24) 0.30 16/27 2.8 (1.33, 5.72) 0.01 <0.01
* P-value for test of heterogeneity of tumor site: colon vs. rectal. § Pan-fried, oven-broiled or grilled

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Table 4 Proband-sibling analysis of interactions between CYP1A2 -154A>C and red meat intake variables
Colorectal Cancer Colon Cancer Rectal Cancer
AA AC+CC AA AC+CC AA AC+CC
ca/co OR (95%CI) ca/co OR (95%CI) ca/co OR (95%CI) ca/co OR (95%CI) ca/co OR (95%CI) ca/co OR (95%CI)
Number of servings of cooked red meat per week
<=3 92/118 1 78/113 0.8 (0.51, 1.41) 54/75 1 50/65 1.1 (0.58, 2.18) 31/30 1 19/28 0.5 (0.19, 1.32)
>3 71/66 1.4 (0.91, 2.20) 62/59 1.5 (0.86, 2.78) 46/37 1.8 (1.04, 3.27) 34/34 1.7 (0.77, 3.82) 17/22 0.7 (0.28, 1.67) 15/17 0.7 (0.24, 1.99)
Genotype-specific
OR*(95%CI)
1.4 (0.91, 2.20) 1.8 (1.08, 3.08) 1.8 (1.04, 3.27) 1.5 (0.75, 3.08) 0.7 (0.28, 1.67) 1.4 (0.53, 3.53)
Interaction OR (95%CI) 1.3 (0.65, 2.55) 0.8 (0.34, 2.01) 2.0 (0.54, 7.53)
Heterogeneity test of tumor sub-site p-value 0.27
Level of doneness of cooked red meat in the inside
rare-medium 95/103 1 58/86 0.7 (0.41, 1.20) 55/64 1 36/48 0.9 (0.45, 1.84) 33/29 1 15/24 0.4 (0.15, 1.16)
well-done 69/81 0.9 (0.55, 1.41) 83/86 1.0 (0.59, 1.76) 45/48 1.1 (0.60, 2.10) 48/51 1.2 (0.57, 2.37) 15/23 0.5 (0.23, 1.28) 20/21 0.6 (0.20, 1.63)
Genotype-specific
OR*(95%CI)
0.9 (0.55, 1.41) 1.5 (0.92, 2.30) 1.1 (0.60, 2.10) 1.3 (0.71, 2.32) 0.5 (0.23, 1.28) 1.3 (0.55, 3.20)
Interaction OR (95%CI) 1.7 (0.89, 3.09) 1.1 (0.50, 2.59) 2.5 (0.79, 7.75)
Heterogeneity test of tumor sub-site p-value 0.27
Level of doneness of cooked red meat on the outside
Light-Medium
browned
114/140 1 98/113 1.0 (0.61, 1.57) 65/83 1 59/61 1.2 (0.65, 2.27) 39/43 1 24/37 0.6 (0.23, 1.32)
Heavily browned/
Blackened
50/44 1.4(0.79, 2.32) 43/58 0.9 (0.51, 1.63) 35/29 1.7 (0.83, 3.31) 25/37 0.9 (0.41, 1.91) 9/9 0.9 (0.33, 2.50) 11/8 1.3 (0.37, 4.54)
Genotype-specific
OR*(95%CI)
1.4(0.79, 2.32) 0.9 (0.56, 1.54) 1.7 (0.83, 3.31) 0.7 (0.38, 1.42) 0.9 (0.33, 2.50) 2.3 (0.73, 7.45)
Interaction OR (95%CI) 0.7 (0.33, 1.42) 0.4 (0.17, 1.13) 2.6 (0.55, 11.92)
Heterogeneity test of tumor sub-site p-value

0.05
*OR of higher versus lower red meat category within each genotype. § Pan-fried, oven-broiled or grilled

17
Carcinogen gene polymorphism, poultry and colorectal cancer risk
We also for tested gene-environment interactions between the nine SNPs along with
the estimated NAT2 phenotype and the following poultry variables: servings per week
of pan-fried, oven-broiled, or grilled poultry and level of doneness of poultry on the
outside (light-medium brown/heavily browned-blackened). When considering all
tumors combined and using proband-only analyses, our results suggested GSTP1
Ile105Val may modify the association between pan-fried, oven-broiled or grilled
poultry intake and risk of CRC (interaction p < 0.01) (Table 5). Furthermore, this
interaction seemed slightly stronger among rectal cancer cases, although a test of
heterogeneity did not reach statistical significance (p = 0.08). Similarly, our results
suggest that CYP1B1 Leu432Val may modify the association between doneness of
poultry on the outside and risk of CRC (interaction p = 0.01) (Table 5). When we
corrected our results for multiple comparisons, the modifying effect of GSTP1
Ile105Val on the association between pan-fried, oven-broiled or grilled poultry and
CRC risk remained statistically significant. In contrast, the interaction between
CYP1B1 Leu432Val and level of doneness of poultry on the outside was no longer
statistically significant.
We further examined these two gene-exposure interactions in proband-sibling
analysis (Table 6). These analyses supported a role for CYP1B1 Leu432Val as an
effect modifier of the association between level of doneness of poultry on the outside
and colorectal cancer risk (Interaction p = 0.05, interaction OR = 0.4; 95% CI =
0.19-1.01) (Table 6). Our data suggested that intake of heavily browned/blackened
poultry positively associated with colorectal cancer risk only among carriers of the
Leu/Leu genotype (OR = 1.7; 95% CI = 0.84, 3.42); whereas among carriers of one or
two copies of the Val allele it associated with approximately 20% decreased risk of

18
CRC (OR = 0.8, 95%CI = 0.47, 1.21). We did not find evidence that this effect
modification differed across tumor sub-types (colon vs rectum). In terms of the
GSTP1 Leu432Val SNP, our results weakly supported a role of this SNP as a modifier
of the association between pan-fried, oven-broiled or grilled poultry intake and
colorectal cancer risk (Interaction p = 0.07, interaction OR = 0.5, 95%CI = 0.23, 1.07).
Diets high in cooked poultry were positively associated with colorectal cancer risk
among carriers of the Ile/Ile genotype (OR = 1.3; 95% CI = 0.76-2.26). Conversely,
among carriers of one or two copies of the Val allele diets high in cooked poultry were
associated with approximately 40% decreased risk of CRC (OR = 0.6, 95%CI = 0.37,
1.10). We did not find the effect of this GSTP1 SNP on the association between
cooked poultry intake and colorectal cancer risk differed by tumor site as well (Table
6).

19

Table 5 Proband-only analysis of interaction between CYP1B1 Leu432Val, GSTP1 Ile105Val and poultry intake variables

Colorectal Cancer Colon Cancer Rectal Cancer
CYP1B1
Leu/Leu /
Leu/Val+Val/Val
OR (95%CI) P
Leu/Leu /
Leu/Val+Val/Val
OR (95%CI) P
Leu/Leu /
Leu/Val+Val/Val
OR (95%CI) P
Level of doneness of cooked poultry on the outside
Light-Medium browned 113/53 1 65/31 1 29/75 1
Heavily
browned/Blackened
307/86 0.60 (0.40, 0.90) 0.01 197/47 0.5 (0.29, 0.85) 0.01 17/25 0.6 (0.27, 1.20) 0.14
Heterogeneity test of tumor sub-site p-value 0.79

Colorectal Cancer Colon Cancer Rectal Cancer
GSTP1
Ile/Ile /
Ile/Val+Val/Val
OR (95%CI) P
Ile/Ile /
Ile/Val+Val/Val
OR (95%CI) P
Ile/Ile /
Ile/Val+Val/Val
OR (95%CI) P
Number of servings of cooked poultry per week
<=3 164/254 1 107/155 1 33/69 1
>3 80/69 0.56 (0.38, 0.81) <0.01 42/41 0.7 (0.41, 1.11) 0.12 29/19 0.3 (0.15, 0.64) <0.01
Heterogeneity test of tumor sub-site p-value 0.08
§ Pan-fried, oven-broiled or grilled

20
Table 6 Proband-sibling analysis of interactions between CYP1B1 Leu432Val, GSTP1 Ile105Val and poultry intake variables
Colorectal Cancer Colon Cancer Rectal Cancer
Leu/Leu Leu/Val+Val/Val Leu/Leu Leu/Val+Val/Val Leu/Leu Leu/Val+Val/Val
CYP1B1 ca/co OR (95%CI) ca/co OR (95%CI) ca/co OR (95%CI) ca/co OR (95%CI) ca/co OR (95%CI) ca/co OR(95%CI)
Level of doneness of cooked poultry on the outside
Light-Medium
browned
59/91 1 172/174 1.9 (1.11, 3.13) 35/52 1 103/104 1.5 (0.81, 2.92) 12/26 1 50/47 3.5 (1.24, 10.15)
Heavily browned/
Blackened
26/26 1.7 (0.84, 3.42) 42/58 1.4 (0.74, 2.64) 16/14 1.7 (0.68, 4.19) 26/38 1.2 (0.54, 2.51) 8/10 2.1 (0.62, 7.20) 11/12 2.8 (0.77, 10.09)
Genotype-specific
OR*(95%CI)
1.7 (0.84, 3.42) 0.8 (0.47, 1.21) 1.7 (0.68, 4.19) 0.8 (0.42, 1.35) 2.1 (0.62, 7.20) 0.8 (0.29, 2.12)
Interaction OR (95%CI) 0.4 (0.19, 1.02) 0.4 (0.16, 1.26) 0.4 (0.08, 1.77)
Heterogeneity test of tumor sub-site p-value 0.85
Colorectal Cancer Colon Cancer Rectal Cancer
Ile/Ile Ile/Val+Val/Val Ile/Ile Ile/Val+Val/Val Ile/Ile Ile/Val+Val/Val
GSTP1 ca/co OR(95%CI) ca/co OR(95%CI) ca/co OR(95%CI) ca/co OR(95%CI) ca/co OR(95%CI) ca/co OR(95%CI)
Number of servings of cooked poultry per week
<=3 85/123 1 136/131 2.1 (1.22, 3.53) 57/81 1 77/75 1.9 (0.99, 3.62) 15/31 1 41/36 5.1 (1.51, 17.46)
>3 42/48 1.3 (0.76, 2.26) 38/55 1.3 (0.71, 2.50) 21/25 1.3 (0.61, 2.69) 26/32 1.5 (0.69, 3.17) 18/16 2.6 (0.96, 7.28) 9/14 2.9 (0.64, 12.79)
Genotype-specific
OR*(95%CI)
1.3 (0.76, 2.26) 0.6 (0.37, 1.10) 1.5 (0.77, 3.00) 0.8 (0.40, 1.52) 2.6 (0.96, 7.28) 0.6 (0.17, 1.78)
Interaction OR (95%CI) 0.5 (0.23, 1.07) 0.6 (0.22, 1.69) 0.2 (0.04, 1.06)
Heterogeneity test of tumor sub-site p-value 0.27
*OR of higher versus lower poultry category within each genotype. § Pan-fried, oven-broiled or grilled

21
Discussion
In contrast with other epidemiological studies, our case-control study is a family
based design which takes the advantage of reducing the probability of population
stratification. Among the nine SNPs and NAT2 phenotype, we only found GSTP1
Ile105Val was significantly associated with colorectal cancer risk.
The existing epidemiological studies have not indicated a conclusive result
concerning the independent effect of GSTP Ile105Val polymorphism on susceptibility
to colorectal cancer, with most studies reporting no statistically significant association
(Ates et al., 2005; Landi et al., 2005; Moore et al., 2005; Sachse et al., 2002) whereas
one study suggested an inverse associations (Vlaykova et al., 2007). Nevertheless,
two studies reported combined effects of GSTM1, GSTT1 and GSTP1 on risk of CRC
(Ates et al., 2005; Welfare et al., 1999). The GSTP1 enzyme has been found to be
overexpressed in many preneoplastic and neoplastic lesions, including colorectal,
stomach and oral tumors (Hirata et al., 1992; Niitsu et al., 1989; Peters et al., 1992).
Experimental studies suggest that proteins coded by the Ile and Val alleles differ
significantly in their catalytic activity towards different substrates: proteins coded by
the Val allele are associated with reduced enzyme activity towards
1-chloro-2,4-dinitrobenzene in lung tissues (Zimniak et al., 1994) while proteins
coded by the Val allele demonstrate approximately up to 3-fold activity towards PAH
bay-region diol epoxides (Sundberg et al., 1998). Our results showed a significantly
positive association between the Ile/Val or Val/Val genotypes and colorectal cancer
risk. This finding suggests that the Val allele, which is reported to have increased
activity in the metabolism of PAHs, increases risk of colorectal cancer. This finding
seems counter-intuitive, given that GSTP1 is known to detoxify chemical carcinogens;
therefore, it would be speculated that a higher-activity allele would protect against

22
colorectal cancer. It has been documented that glucuronide conjugates formed in the
liver through the action of UGT1 enzymes on PAH epoxides and dihyrodiols can be
excreted to the intestines (Ramesh et al., 2004) where they are able to undergo
enzymatic hydrolysis by bacterial β-glucuronidase and then be further activated or
enter portal circulation again (Kinoshita et al., 1978). This process contributes to an
extended life of reactive PAHs. Thus, we may hypothesize that detoxification
products generated by GSTP1 action in the liver might undergo a similar route and
perhaps could also contribute to more activated PAHs in the colon. Under this
scenario, a more active GSTP1 enzyme in the liver could indeed contribute to higher
levels of PAHs in the colon and thus contribute to increased colorectal cancer risk.
We found a statistically significant interaction between GSTP1 Leu432Val SNP and
consumption of pan-fried, oven-broiled or grilled poultry in proband-only analyses,
with some support for a similar finding among proband-sibling analyses. Altogether,
our results suggest that diets high in poultry cooked using high temperature methods
associated with increased colorectal cancer risk among carriers of the Ile/Ile genotype,
whereas among individuals carrying one or two copies of the Val allele they
associated with decreased risk. The direction of effects when considering poultry
intake are opposite to what we found when considering red meat intake. This might
reflect different contribution of carcinogens (e.g. NOCs more prevalent in red meat
than I in poultry, etc), or might suggest that one of these two findings is more likely to
be a false positive. GSTP1 has been widely studied for its role as a modifier mostly
for the association between colorectal adenoma/colorectal cancer and smoking,
though no statistically significant interaction was found (Ates et al., 2005; Moore et
al., 2005). Previous studies have investigated the modifying effect of GSTP1
Ile105Val on the association between total meat or red meat intake and colorectal

23
cancer risk: a matched case-control study in UK reported no modifying effect of this
GSTP1 SNP (Turner et al., 2004); another case-control study conducted in Norway
did not suggest this GSTP1 SNP was associated with colorectal carcinoma and
adenoma when exposed to red meat or processed meat as well (Skjelbred et al., 2007).
To our knowledge, no study has investigated the potential modulator role of GSTP1 in
the relationship between poultry intake and CRC risk.
We found a significant interaction between CYP1B1 Leu432Val and level of
doneness of poultry on the outside in probands-only analyses, which was supported
by the proband-sibling analyses. Our results suggested that intake of poultry heavily
browned or blackened on the outside associated with increased risk of colorectal
cancer only among carriers of the Leu/Leu genotype; whereas among carriers of one
or two copies of the Val allele associated with a slight decreased risk. To our
knowledge, no study has investigated the role of CYP1B1 as a modifier of the
association between meat and colorectal cancer risk. In vivo study using human lung
microsomal samples taken from individuals (Caucasians, African-Americans and
Chinese populations) with Val/Val and Leu/Leu genotypes suggested that Val may
code for a protein with higher activity and hence this SNP may contribute to the
inter-individual differences in CYP1B1 activity (Tang et al., 2000). Our findings are
not in agreement with the known functional effects of this SNP as they suggest that
the effects of well-done poultry would be most relevant among carriers of the protein
with reduced activity. CYP1B1 is capable of activating most PAHs to epoxide
intermediates in liver, which are further converted to more reactive diol-epoxides by
epoxide hydrolase (Shimada et al., 1996). PAHs can also be locally activated in the
colon by CYP1A1 and CYP1B1, and CYP1B1 is known to be overexpressed in colon
adenocarcinoma compared to normal colon tissues (Gibson et al., 2003). In addition,

24
CYP1B1 can not only participate in the activation of PAHs but also be able to activate
HCAs in the colon. Working together with CYP1A1, CYP1B1 can catalyze
N-hydroxylation of PhIP (Crofts et al., 1998; Turesky, 2002). Previous studies did not
find any significant association between CYP1B1 Leu432Val and CRC risk (Bethke
et al., 2007; Sachse et al., 2002), neither did we.
Our results suggests that the modification effect of CYP1A2 (-154A>C) SNP on
the association between meat and risk of CRC might depends on the tumor sub-site
(colon or rectum). Our data indicated that individuals carrying the AC or CC genotype
have lower risk of colon cancer but higher risk of rectal cancer when diets are high in
red meat heavily browned on the outside or eat more than 3 servings of pan-fried,
oven-broiled or grilled red meat per week. Furthermore, regardless of tumor sub-site
location, subjects with the AC or CC genotype might be at higher risk of developing
colorectal cancer when diets are high in red meat heavily browned on the outside. The
CYP1A2(-154A>C) polymorphism is common among Caucasians (Sachse et al.,
2003) and it may be related to the reported variation in CYP1A2 inducibility (Sachse
et al., 1999). In an in vivo study, statistically significant higher activity was observed
in individuals with AA genotype among smokers while no differences were observed
among non-smokers when using a caffeine-based phenotype study (Sachse et al.,
1999). This suggests that CYP1A2 may modify the effect of its substrates on CRC
risk. We did not find evidence of a main effect of this polymorphism on colorectal
cancer risk. Two previous studies found a significant increased risk of CRC associated
with allele C (Landi et al., 2005; Moonen et al., 2005), but these two studies were
limited in size; other more comprehensive studies suggested no significant association
(Kury et al., 2007; Sachse et al., 2002). Our results suggest that the carcinogenic
effects of diets high in cooked red meat or heavily browned or blackened in the inside

25
would be greater in individuals carrying one or two copies of the C (slower) allele
than individuals carrying two copies of the A (faster) allele. One explanation for this
finding might be that the faster HCAs undergo activation by CYP1A2 in the liver,
more will be detoxified in the liver and thus the availability of HCAs in the colon will
be reduced. The explanation of the disease heterogeneity of tumor site might be
related to the fact that CYP1A2 can not only metabolize HCAs in liver but also be
able to locally metabolize NOCs in colon (Ding et al., 2003). N-nitrosamines such as
NDMA or NDEA can be directly activated in human colon (Autrup et al., 1978),
primarily hydroxylated by CYP2E1 but can also metabolized by CYP1A2. CYP1A2
can generate α-hydroxy-NOCs that can react directly with DNA (Wong et al., 2005)
or decompose into carbocations which finally induce DNA damage (Verna et al.,
1996). Thus, it is possibly that CYP1A2 could induce more carcinogenic effects in
colon than in rectal, which agrees with our results that in proband-only higher meat
intake and heavily browned/blackend meat intake appeared to be related to AA among
colon cancer but related to AC or CC among rectal cancer. The proband-sibling
analysis further confirmed this. From the results of our study, it appears that the
methods of cooking or processing meat and the duration of cooking meat play an
important role in colon carcinogenesis.
There are several limitations of our study. Although our study took advantage of the
family based case-control design, our sample size was still not large enough for
detecting gene-environment interactions of small effects. Nonetheless, given that each
of the SNPs investigated was chosen given the known role each of these enzymes play
in the metabolism of cooked meats-derived carcinogens, we chose not to correct our
findings for multiple testing. However, we cannot discard the potential for false
positives. Finally, we did not consider direct measures of carcinogens but instead we

26
considered information from the questionnaire with respect to the frequency of meat
intake and the meat-cooking methods which indirectly captures the formation of the
carcinogens.
There are also some strengths of our study: the family based case-control design
that reduces the effects of population stratification; the use of proband-only analysis,
which allowed us to take advantage of all available probands regardless of the
availability of siblings, thus increasing statistical power; the analyses of different red
meat and poultry cooking methods.
In conclusion, our results indicate that GSTP1 Ile105Val might be a relevant
susceptibility gene for colorectal cancer. Furthermore, our results suggest that among
subjects who carry certain carcinogen metabolism polymorphisms diets high in
heavily browned/blackened red meat or poultry might be relevant risk factors for
colorectal cancer perhaps through the formation of PAHs, HCAs or NOCs.

27
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Abstract (if available)

Abstract We conducted a family based case-control study to investigate the association between xenobiotic metabolism genes (CYP1A2 -154A>C, CYP1B1 Leu432Val, CYP2E1 -1054C>T, GSTP1 Ile105Val, PTGS2 5UTR -765, EPHX1 Tyr113His, NAT2 Ile114Thr, NAT2 Arg197Gln and NAT2 Gly286Glu) and colorectal cancer (CRC) risk. We found GSTP1 Ile105Val is statistically significantly associated with colorectal cancer risk (OR=1.7, 95%CI=1.05-2.63, p=0.03). We tested the gene-meat interaction in proband-only sample (N=577) and then further examined in proband-sibling pairs (307 pairs). We only found CYP1B1 may modulate the effect of heavily browned poultry on the outside on CRC risk at border line significance (p=0.05). Our results also suggest that CYP1A2 may exert effects on risk of colon cancer and rectal cancer through different pathways when associated with cooked red meat intake or levels of doneness of red meat on the outside. In particular, when interacting with levels of doneness of red meat on the outside, individuals carrying AC or CC genotype had lower risk on colon cancer but much higher risk on rectal cancer (p for heterogeneity=0.05).

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Asset Metadata

Creator Wang, Jun (author)

Core Title Carcinogen metabolism genes, meat intake, and colorectal cancer risk

School Keck School of Medicine

Degree Master of Science

Degree Program Applied Biostatistics

Publication Date 08/01/2008

Defense Date 06/20/2008

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag carcinogen metabolism,colorectal cancer,Meat,OAI-PMH Harvest

Language English

Advisor Stern, Mariana C. (committee chair), Ingles, Sue A. (committee member), Siegmund, Kimberly D. (committee member)

Creator Email jun.wang73@gmail.com,junw@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m1501

Unique identifier UC1280266

Identifier etd-WANG-2297 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-89214 (legacy record id),usctheses-m1501 (legacy record id)

Legacy Identifier etd-WANG-2297.pdf

Dmrecord 89214

Document Type Thesis

Rights Wang, Jun

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu