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Molecular Epidemiology of Lung Cancer in Chinese Population

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Alper-JCCC Molecular Epidemiology Symposium Molecular Epidemiology of Lung Cancer in Chinese Population Hongbing Shen, M.D., Ph.D Professor of Epidemiology – PowerPoint PPT presentation

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Title: Molecular Epidemiology of Lung Cancer in Chinese Population


1
Alper-JCCC Molecular Epidemiology Symposium
Molecular Epidemiology of Lung Cancer in Chinese
Population
Hongbing Shen, M.D., Ph.D Professor of
Epidemiology Department of Epidemiology
Biostatistics Nanjing Medical University School
of Public Health Tel 86-25-86862756 Email
hbshen_at_njmu.edu.cn
2
Age-standardized Incidence Rates for Lung Cancer
2002 (per 100,000) Parkin et al. CA Cancer J
Clin. 2005
3
Rural
Time trends for lung cancer mortality rates
during 19871999 in China, using age-standardized
rate (ASR) by sex and area
Men
Woman
Urban
Yang et al. Int J Cancer 2003
4
Sales Volume of Cigarettes in China, 1981-1995
100
Yang et al. JAMA 1999
5
Age-Specific Prevalence Rates of Current and
Former Smoking in Men and Women in China
2001
35-74
6.9
10.6
60.2
Yang et al. JAMA 1999 Gu et al. Am J
Public Health 2004
6
Prevalence Rates of Smoking in a Rural Area of
South Jiangsu Province, China (2004-2005)
Age Male Female Total Total
Age No Smk Rate() No Smk Rate() No Smk Rate() No Smk Rate()
lt 30 606 45.7 856 0.5 1462 19.4
30 1530 70.8 2388 0.4 3918 28.4
40 2176 75.4 2865 0.7 5041 33.4
50 2405 73.6 3050 1.0 5455 33.5
60 1302 64.9 1611 0.7 2913 29.8
70 762 51.8 1183 2.5 1945 22.7
Total 8781 68.4 11953 1.7 20734 30.0
Including former smokers
Unpublished data
7
Men
Prevalence of environmental tobacco smoke (ETS)
exposure among nonsmokers by gender and age group
in China, 2000-2001
Woman
Gu et al. Am J Public Health. 2004
8
Background
  • Lung cancer incidence rate has been increasing
    significantly in the last two decades in China.
  • More than 80 lung cancer can be attributed to
    smoking, yet only lt10 smokers develop lung
    cancer.
  • Lung cancer is an excellent model for the
    research of gene-environment interactions.

9
Multi-step Carcinogenic Process of Lung Cancer
Exposure
Nicotine Dependent
10
Smoking Caused DNA Damage and Related Repair
Pathways
11
Hypothesis
DNA repair gene SNPs (genotypes)
DRC (Phenotype)
Lung Cancer Risk
Gene-environment interaction?
The genetic polymorphisms of the DNA
repair genes are associated, independently or
coordinately, with increased risk of lung cancer
12
Subjects Methods
  • Hospital-based case-control studies of lung
    cancer
  • Inclusion Criteria
  • Cases Incident lung cancer patients
  • Newly diagnosed, histopathologically
    confirmed
  • No previous radiotherapy or chemotherapy
  • Controls Cancer-free subjects
  • Recruited at the same time period as
    cases and
  • frequented matched to the cases on age,
    sex
  • All the subjects are Han Chinese (Nanjing).

13
Sequence-based candidate SNPs
approach Potentially functional SNPs of DNA
repair gene XRCC1 and risk of lung cancer
A case-control study of 710 lung cancer cases and
710 cancer-free controls
Hu Z, Shen H. Pharmacogenetics Genomics 2005
15(7) 457-64.
14
Functional polymorphisms of DNA repair gene
XRCC1 Promoter T-77C (2004) Coding
region Arg194Trp (CgtT)
(DNA pol?, PARP domain )
Arg399Gln (GgtA)
(PARP
domain) Case-control study of 710 lung cancer
cases and 710 cancer-free controls
15
1246-bp
5764
16
Distribution of Select Variables and XRCC1
Variant Alleles in Lung Cancer Cases and
Cancer-free Controls
Variable Cases (n710) Cases (n710) Controls (n710) Controls (n710) P value
Variable No. No. P value
Age (years) 0.7100
60 374 52.7 52.7 366 366 51.5
gt 60 336 47.3 47.3 344 344 48.5
Sex 0.7207
Male 520 73.2 73.2 513 513 72.3
Female 190 26.8 26.8 197 197 27.7
Smoking status lt0.0001
Non-smokers 231 32.5 32.5 370 370 52.1
Ever-Smokers 479 67.5 67.5 340 340 47.9
XRCC1 T-77C 0.0003
T allele 1198 84.4 84.4 1264 1264 89.0
C allele 222 15.6 15.6 156 156 11.0
XRCC1 C194T 0.8389
C allele 981 69.1 69.1 986 986 69.4
T allele 439 30.9 30.9 434 434 30.6
XRCC1 G399A 0.4488
G allele 1040 73.2 73.2 1022 1022 72.0
A allele 380 26.8 26.8 398 398 28.0
17
XRCC1 T-77C Polymorphism and Lung Cancer Risk
XRCC1 Genotype Cases(710) Controls(710) Adjusted OR (95CI)
XRCC1 Genotype No No Adjusted OR (95CI)
XRCC1 T-77C
TT (ref.) 500 70.4 558 78.6 1.00
CT 198 27.9 148 20.9 1.51 (1.17-1.94)
CC 12 1.7 4 0.6 2.98 (0.93-9.59)
CT/CC 210 29.6 152 21.4 1.55 (1.21-1.98)
C allele 222 15.6 156 11.0 P 0.0003
Hu Z, Shen H. Pharmacogenetics Genomics 2005
15(7) 457-64.
18
XRCC1 T-77C and Cumulative Smoking
-77TT -77CT/CC
Adjusted OR
Pack-years of smoking
P value for the test of additive gene-smoking
interaction 0.027
Hu Z, Shen H. Pharmacogenetics genomics 2005
15(7) 457-64.
19
This significant association with lung cancer was
validated in another independent case-control
study in a Chinese population
Hao et al. 2006 Oncogene
20
block-free linkage disequilibrium-based SNPs
approach Polymorphisms of NER pathway core
genes (XP) and susceptibility of lung cancer
A case-control study of 1010 lung cancer cases
and 1011 cancer-free controls
Hu Z, Shen H. Carcinogenesis 2006
Jul27(7)1475-1480
Hu Z, Shen H. CEBP 2006 15(7)1336-40.
21
NER Core Genes
1 From NIEHS SNPs database
Modified from James E. Cleaver, Nature Review
Cancer 2005
22
Linkage disequilibrium-based tagSNPs selection
8 candidate core genes in NER pathway 41 Tagging
SNPs were selected / 32 were used in the analyses
Based on the resequencing data of 90
individuals in the Environmental Genome Project
(EGP) database (designed before the release of
HapMap data) Based on the calculation of
pairwise linkage disequilibrium (LD). LD
parameter r2 threshold 0.5 (Carlson et al.).
23
Laboratory Assays
5-nuclease (TaqMan) assay ABI PRISM 7900HT
Sequence Detection System 384-well
format Chinese National Human Genome Center at
Shanghai, Quality control Two blank control
(water) and two duplicated samples in each
384-well format The intensity of each SNP should
meet the criteria of three clear clusters in two
scales generated by SDS software (ABI).
24
TaqMan assay results output
5-nuclease (TaqMan) assay in 384-well format
25
TaqMan Assay Results Output
26
Statistical analyses
Chi-square test, Logistic regression Single locus
analysis (ORs and 95 CIs) Multivariate logistic
regression model Haplotype inference We used the
PHASE 2.0 program to infer haplotype frequencies
based on the observed genotypes of each gene.
27
Single SNPs Analyses in the Dominant (Blue Line)
and Recessive Model (Green Line)
28
Analysis of association between the DDB2(XPE)
polymorphisms and risk of lung cancer
DDB2 Genotype Cases Cases Cases Controls Controls Crude OR (95CI) Adjusted OR (95CI) a
DDB2 Genotype No. () () No. () Crude OR (95CI) Adjusted OR (95CI) a
rs830083 961 961 961 960 960
CC 372 372 (38.7) 429 (44.7) 1.00 1.00
CG 483 483 (50.3) 426 (44.4) 1.31 (1.08-1.58) 1.31 (1.08-1.60)
GG 106 106 (11.0) 105 (10.9) 1.16 (0.86-1.58) 1.22 (0.89-1.67)
CG/GG 589 589 (61.3) 531 (55.3) 1.28 (1.07-1.53) 1.30 (1.08-1.56)

rs37816203 967 967 967 978 978
CC 374 374 (38.7) 429 (43.9) 1.00 1.00
CG 491 491 (50.8) 437 (44.7) 1.29 (1.07-1.56) 1.28 (1.06-1.56)
GG 102 102 (10.6) 112 (11.5) 1.04 (0.77-1.41) 1.11 (0.81-1.51)
CG/GG 593 593 (61.4) 549 (56.2) 1.24 (1.03-1.48) 1.25 (1.04-1.50)
Hu Shen et al, Carcinogenesis, 2006
Jul27(7)1475-1480
29
Stratified analyses between the combined DDB2
rs830083 genotypes and lung cancer risk
DDB2 rs830083 DDB2 rs830083 DDB2 rs830083 DDB2 rs830083 DDB2 rs830083 DDB2 rs830083 DDB2 rs830083 DDB2 rs830083
Cases (n961) Cases (n961) Controls (n960) Controls (n960) Adjusted OR (95 CI) Adjusted OR (95 CI)
CC CG/GG CC CG/GG CC CG/GG
N () N () N () N () CC CG/GG
Age (years) Age (years)
60 175 (37.2) 296 (62.9) 206 (44.4) 258 (55.6) 1.00 1.40 (1.07-1.83)
gt 60 197 (40.2) 293 (59.8) 223 (45.0) 273 (55.0) 1.00 1.20 (0.92-1.56)
Pack-years of smoking
0 112 (38.0) 183 (62.0) 207 (45.1) 252 (54.9) 1.00 1.29 (0.96-1.75)
1-29 95 (39.8) 144 (60.3) 105 (40.4) 155 (59.6) 1.00 1.01 (0.70-1.45)
gt29 165 (38.6) 262 (61.4) 117 (48.5) 124 (51.5) 1.00 1.48 (1.07-2.04)
Family history of cancer
No 313 (39.5) 480 (60.5) 371 (44.3) 467 (55.7) 1.00 1.24 (1.01-1.51)
Yes 59 (35.1) 109 (64.9) 58 (47.5) 64 (52.5) 1.00 1.70 (1.03-2.80)
30
ERCC2 and ERCC3 polymorphisms and lung cancer risk
Genotype Cases Cases Controls Controls Adjusted OR (95CI)
Genotype No. () No. () Adjusted OR (95CI)
ERCC2 rs1618536 975 975 985 985
AA 177 18.2 199 20.2 1.00
AG 492 50.5 488 49.5 1.10 (0.86-1.40)
GG 306 31.4 298 30.3 1.15 (0.89-1.51)
AG/AA 798 81.9 786 79.8 1.12 (0.89-1.41)
ERCC2 rs1799786 965 965 986 986
CC 844 87.5 877 89.0 1.00
CT 117 12.1 108 11.0 1.12 (0.84-1.49)
TT 4 0.4 1 0.1 6.18 (0.68-56.52)
CT/TT 121 12.5 109 11.1 1.16 (0.87-1.54)
ERCC2 rs1799793 970 970 986 986
GG 850 87.6 874 88.6 1.00
AG 116 12.0 111 11.3 1.06 (0.80-1.41)
AA 4 0.4 1 0.1 6.13 (0.67-56.11)
AG/AA 120 12.4 112 11.4 1.10 (0.83-1.46)
ERCC2 rs3916823 986 986 987 987
AAAA/AAAA 744 75.5 750 76.0 1.00
AAAA/-- 225 22.8 227 23.0 1.01 (0.81-1.25)
--/-- 17 1.7 10 1.0 1.88 (0.84-4.21)
AAAA/-- --/-- 242 24.5 237 24.0 1.04 (0.84-1.29)
Hu Shen et al, Cancer Epidemiology Biomarkers
Prevention 2006,15(7)1336-40.
31
ERCC2 rs13181 975 975 997 997
AA 827 84.8 865 86.8 1.00
AC 141 14.5 127 12.7 1.16 (0.89-1.51)
CC 7 0.7 5 0.5 1.64 (0.50-5.36)
AC/CC 148 15.2 132 13.2 1.18 (0.91-1.53)
ERCC3 rs2271026 1002 1002 1000 1000
TT 774 77.3 787 78.7 1.00
CT 214 21.4 200 20.0 1.07 (0.86-1.34)
CC 14 1.4 13 1.3 1.02 (0.46-2.24)
CT/CC 228 22.8 213 21.3 1.07 (0.86-1.33)
ERCC3 rs4150441 952 952 975 975
AA 149 15.7 178 18.2 1.00
AG 474 49.8 475 48.8 1.20 (0.93-1.56)
GG 329 34.6 322 33.1 1.28 (0.97-1.69)
AG/GG 803 84.4 797 81.9 1.24 (0.97-1.58)
Combined genotypes 847 847 899 899
0-1 at risk locus 136 16.1 186 20.7 1.00
2 at risk loci 331 39.1 349 38.8 1.29 (0.98-1.70)
3 at risk loci 231 27.3 230 25.6 1.38 (1.02-1.85)
gt4 at risk loci 149 17.6 134 14.9 1.51 (1.09-2.10)
P for trend 0.02
32
ERCC1 genotypes and lung cancer risk
Genotyope Cases (n1010) Cases (n1010) Controls (n 1011) Controls (n 1011) P Adjusted OR (95CI)
Genotyope No. No. P Adjusted OR (95CI)
rs3212948 992 992 986 986
GG 594 59.9 521 52.8 1.00
CG 338 34.1 403 40.9 0.005 0.73 (0.60-0.88)
CC 60 6.1 62 6.3 0.96 (0.65-1.41)
CG/CC 398 40.2 465 47.2 0.003 0.76 (0.63-0.91)
rs1007616 835 835 908 908
CC 513 61.4 493 54.3 1.00
CT 270 32.3 354 39.0 0.009 0.72(0.59-0.89)
TT 52 6.2 61 6.7 0.90(0.61-1.35)
CT/TT 322 38.5 415 45.7 0.004 0.75(0.62-0.91)
Ma et al, Pharmacogenetics and Genomics, 2007. in
press
33
Combined Effects of NER Core Genes and Lung
Cancer
Six gene in dominant model and two in recessive
model
Unpublished data
34
Stratified Analysis of Dichotomized NER Combined
Diplotypes by Cumulative Smoking
Adjusted OR
Pack-years of smoking
P value for the test of gene-smoking interaction
(multiplicative) 0.004
Unpublished data
35
Two Factor Gene-gene (Diplotype) Interaction and
Gene-environment Interaction
Unpublished data
36
block-based linkage disequilibrium mapping
Haplotyope-Tagging SNPs in MGMT
(O6-alkylguanine-DNA alkyltransferase) and lung
cancer susceptibility
Case-control study of 500 lung cancer cases and
517 cancer-free controls
Hu Z, Shen H. Human Mutation 2007 published
online.
37
MGMT Gene Structure and Haplotype Block for
Beijing Han Chinese From HapMap
No. of SNPs
38
Illumina High Throughout Genotyping Platform
(CHGC-Shanghai)
39
MGMT Tagging SNPs Based on the Gene Blocks (by
Haploview program )
Gabriel et al.
htSNPs
1 1 2
2 2 2
10 htSNPs from the 6 blocks
Stram et al . Rh2 gt 0.80
25 informative SNPs (MAFgt0.05) in our controls
40
Summary of the findings
  • Single variant analysis no significant main
    effects was observed for each single genetic
    variant in MGMT on lung cancer risk.
  • Haplotype analysis no significant difference was
    observed for frequencies of haplotypes in
    different blocks between cases and controls.
  • Diplotype analysis only the diplotype carrying 1
    variant copy of the block 3 was associated with a
    significantly decreased lung cancer risk.
  • Stratification The frequencies of haplotypes in
    blocks 1 and 2, block 4, 5 were significantly
    different between cases and controls in different
    stratum of smoking dose.

Hu Z, Shen H. Human Mutation 2007
41
MDR Models of Selected Gene Regions and
Co-variables
Best models CVC Avg. Testing Accuracy Sign Test P Value
One Factor Pack-years of smoking 100/100 0.5827 0.0009 0.0330
Two Factors Pack-years of smoking block 3 99/100 0.5992 0.0018 0.0344
Three Factors Pack-years of smoking block 5 rs1625649CgtA 100/100 0.6145 0.0000 0.0453
Four Factors Pack-years of smoking block 3 block 5 rs1625649CgtA (pre-block SNP) 100/100 0.6374 0.0000 0.0146
Five Factors Pack-years of smoking block 3 block 5 block 6 rs1625649CgtA 68/100 0.5349 0.5398 0.3194
CVC cross-validation consistency
42
Conclusions
  • DNA repair gene polymorphisms do contribute to
    lung cancer risk and large prospective studies
    are needed to validate the findings.
  • The candidate gene and sequence-based SNPs
    selection approach have a high priority to find a
    biological relevant association but our knowledge
    on the function of different kind of SNPs are
    still limited.
  • The candidate gene and Mapping-based SNPs
    selection approach may have an increased power to
    detect a haplotype-based disease risk, however,
    multiple testing is a big issue when analyzing
    many SNPs and interactions using different
    combinations.

43
The resources for molecular epidemiological
studies in Nanjing, China
  • Lung Cancer Case-control Study
  • gt1200 lung cancer cases and matched controls
  • gt500 lung cancer cases with survival
    information
  • Breast Cancer Case-control Study
  • gt1000 breast cancer cases and matched
    controls
  • Gastric Cancer Case-control Study
  • gt800 gastric cancer cases and matched
    controls
  • Changzhou population-based cohort study
  • Established baseline information and blood
    samples for 21,000 participants (30-75yrs) in the
    suburb of Changzhou city, Jiangsu province.
    Expect to recruit more than 40,000 in the region.

44
Acknowledgement
Dr. Qinyi Wei U T M.D. Anderson
Cancer Center Dr. Dongxin Lin Chinese
Academy of Medical Science Drs. Daru Lu Li Jin
Fudan University Dr. Wei Huang
CHGC, Shanghai Dr. Tangchun Wu Huazhong
University of Science Technology
  • Nanjing Medical University
  • Jiangsu Cancer Hospital
  • First Affiliated Hospital of Nanjing Medical
    University
  • Shanghai Cancer Hospital
  • Wuhan Zhongnan Hospital

The Molecular Epi Lab in NJMU
45
Thank you!
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