Association analysis of genetic variation of estrogen related candidate genes in breast and endometrial cancers

ASSOCIATION ANALYSIS OF GENETIC VARIATION OF ESTROGEN RELATED CANDIDATE GENES IN BREAST AND ENDOMETRIAL CANCERS LI YUQING NATIONAL UNIVERSITY OF SINGAPORE 2011 -0- DEPARTMENT OF EPIDEMIOLOGY AND PUBLIC HEALTH YONG LOO LIN SCHOOL OF MEDICINE NATIONAL UNVERISTIY OF SINGAPORE Association Analysis of Genetic Variation of Estrogen Related Candidate Genes in Breast and Endometrial Cancers LI YUQING SINGAPORE 2011 Acknowledgements During the journey of my PhD studies, many people contributed either directly or indirectly to my work. They all deserve my gratitude. Specifically, I would like to thank: Jianjun Liu, my mentor and main supervisor. I owe my greatest gratitude to you, for introducing me to the research of cancer genetics. Your enthusiasm, guidance, encouragement and support, as well as expertise in the field of cancer genetics, have been invaluable for the completion of this work. Kee Seng Chia, my co-supervisor and director of Epidemilogy and Public Health Department in National University of Singapore. I express my sincere thanks to you, for your support and guidance and for providing me the opportunity to study for the PhD program. Edison Liu, my co-author and director of Genome Institute of Singapore (GIS). I sincerely thank you for sharing your great knowledge in cancer biology and power of deduction, and for your kind assurance and encouragement. Per Hall, Keith Humphreys, Kamila Czene and Heli Nevanlinna, my project collaborators and co-authors in Sweden and Finland. Many thanks belong to you for your support and awesome knowledge. Jia Nee Foo and Hui Qi Low, my colleagues and friends in GIS. It has been a pleasure working with you. Warm thanks for your generous help, support, polishing my writing and many hours of discussion in genetic epidemiology topics. Kristjana Einarsdottir, Sara Wedren and Yenling Low, my friends and co-authors in Australia, Sweden and Singapore. I am thankful for your patience and willingness to offer help me at any time. Shirlena Soh Wee Ling, Ling Ling, Yao Fei, Xue Ling Sim, Gek Hsiang Lim and Devindri Ioni Perera, my friends in Singapore and Australia. I also owe my gratitude to you for friendly encouragement and all the fun we have shared, which was a resource for brightening many bad days. Thanks also to all colleagues at the Department of Human Genetics in GIS for help and support and for creating a friendly atmosphere. My parents, I owe my deepest gratitude to you for your love, continuous support and for always being there when I needed your help. My most loving thanks belong to my husband, Zhou Xiaowei. I thank you for all the things that you've done for me and the kids. Not only are you a wonderful husband, but also a terrific father, provider and caregiver. I also wish to say “thank you” to my little ones, Runxin and Yuanxin. You have brought so much joy and wonderful things into my life. Abstract Breast cancer is the most common cancer in women worldwide and endometrial cancer is the fourth most common cancer in Western countries. Given the established role of estrogen in the development of breast and endometrial cancer, we surmised that common genetic variation in the pathways of hormonal exposure and response may alter individual responses to endogenous estrogen and consequently modify hormonal related cancer risk. Therefore, I used a candidate gene based approach in three independent studies to systematically investigate DNA polymorphisms within 37 genes of the estrogen metabolism pathway and 60 genes encoding ER-cofactors in samples of European ancestry to ascertain whether these genetic variants could modify the risk of breast and/or endometrial cancer. In the first study, polymorphisms within the androgen-to-estrogen conversion subpathway were found to be associated with both breast (pglobal=0.008) and endometrial cancer (pglobal=0.014) in the Swedish population. This was validated in a Finnish sample of breast cancer (pglobal=0.015). Furthermore, it was showed that the sub-pathway association was largely confined to postmenopausal women with sporadic ER positive tumors (pglobal=0.0003), and CYP19A1 and UGT2B4 are the major players within the sub-pathway. In the second study, it was shown that six SNPs located within PPARGC1B, encoding an ER co-activator, showed consistent association with ER-positive breast cancer in Swedish and Finnish samples with the strongest association at rs741581 (OR = 1.41, P = 4.84 × 10-5). Interestingly, a significant synergistic interaction effect between the genetic polymorphisms within PPARGC1B and ESR1 was observed in ER-positive breast cancer (Pinter = 0.008). This genetic interaction is biologically plausible, because PPARGC1B was shown to augment the transcriptional regulation activity of ER, and the expression of PPARGC1B can be directly regulated by ER. In the last study, we found no significant association between individual SNPs or genes and the risk of endometrial cancer. Although the marginal association of the cumulative genetic variation of the NCOA2 complex as a whole (NCOA2, CARM1, CREBBP, PRMT1 and EP300) with endometrial cancer risk was observed (Padjusted=0.033), the association failed to be demonstrated in an independent European dataset. Overall, the findings from the current studies reflect the complex genetic architecture of breast and endometrial cancers where individual variants have very moderate impact on risk that are too weak to be detected by single variant analysis in moderate sample sizes. By targeting the cumulative effect of multiple variants, multi-variant analysis has better power for detecting the overall contribution of these variants to disease risk. The combination of multi-variant analysis with biochemically and genomically informed candidate genes, particularly through pathway-based studies, can enhance the discovery of moderate disease susceptibility alleles and their interactions. The findings in the current studies may help to improve our understanding on the genetic basis of breast cancer risk and facilitate the effort of identifying women with high risk for breast cancer. Further studies will be needed to examine if common variants with weaker effects or rare variants with larger effects within these genes may play a role in influencing breast or endometrial cancer risk. List of Publications This thesis is based on the following three papers: І Low YL*, Li YQ*, Humphreys K*, Thalamuthu A, Li Y, Darabi H, Wedrén S, Bonnard C, Czene K, Iles MM, Heikkinen T, Aittomäki K, Blomqvist C, Nevanlinna H, Hall P, Liu ET, Liu J. Multi-variant pathway association analysis reveals the importance of genetic determinants of estrogen metabolism in breast and endometrial cancer susceptibility. PLoS Genet. 2010 Jul 1;6:e1001012. *, co-first author П Li YQ, Li Y, Wedren S, Li G, Charn TH, Vasant DK, Bonnard C, Czene K, Humphreys K, Darabi H, Einarsdttir K, Heikkinen T, Aittomaki K, Blomqvist C, Chia KS, Nevanlinna H, Hall P, Liu ET, Liu J. Genetic variation of ESR1 and its co-activator PPARGC1B is synergistic in augmenting the risk of estrogen receptor positive breast cancer. Breast Cancer Res. 2011 Jan 26;13 (1):R10. Ш Li YQ, Hui Qi Low, Jia Nee Foo, Hatef Darabi, Kristjana Einarsdόttir, Keith Humphreys, Amanda Spurdle, ANECS Group, Douglas F. Easton, Deborah J Thompson, Kamila Czene, Kee Seng Chia, Per Hall and Jianjun Liu Association analysis between genetic variants in ER cofactor genes and endometrial cancer risk. In manuscript Table of Contents Acknowledgements . Abstract . List of Publications . Table of Contents Abbreviations 11 Introduction . 12 Background . 15 7.1 Breast and endometrial cancer and their risk factors .15 7.1.1 Breast cancer incidence 15 7.1.2 Endometrial cancer incidence 17 7.1.3 Risk factors for breast and endometrial cancer 20 7.2 Subtypes of breast and endometrial cancer 23 7.3 Determination of ER phenotype and reliability of testing .25 7.4 Genetic polymorphisms in Estrogen Receptor .29 7.5 Candidate gene based genetic association study 30 7.5.1 Hormonal exposure: Genetic polymorphisms in Estrogen metabolisms pathway 31 7.5.1.1 Estrogen metabolism 33 7.5.1.2 Genetic association study of estrogen metabolism genes 35 7.5.2 Response to hormonal exposure: Genetic polymorphisms in ER cofactors 37 7.5.2.1 Molecular function of ER coactivator and ER corepressor .38 7.5.2.2 The constraints of ER cofactor study .43 7.5.2.3 Genetic association study of ER cofactor genes .44 7.5.3 Estrogen metabolism enzymes and ER cofactor genes are drug targets for breast and endometrial cancer treatments 45 Aims 49 Study Populations . 51 9.1 Swedish sample sets .52 9.1.1 Parent Studies .52 9.1.2 Present Studies .55 9.1.2.1 Selection of present study populations .55 9.1.2.2 Collection of biological samples 56 9.1.2.3 Questionnaire information and risk factors collection .59 9.2 Finnish sample set 59 9.3 ECAC sample set .61 10 Methodologies . 63 10.1 Candidate Gene and Tagging SNP Selection .63 10.2 Genotyping, quality control and other experiments .64 10.2.1 Genotyping and quality control 64 10.2.2 Reverse transcriptase-quantitative PCR analysis .67 10.3 Statistical Analysis .68 10.3.1 Single SNP association analysis 68 10.3.2 Meta-analysis .68 10.3.3 Interaction analysis 69 10.3.4 Admixture maximum likelihood (AML) test .69 10.3.5 Imputation analysis 70 11 Study I . 72 11.1 Results 72 11.2 Findings and implications 81 12 Study II 84 12.1 Results 84 12.2 Findings and implications 95 13 Study III 99 13.1 Results 99 13.1.1 Discovery analysis .99 13.1.2 Validation study in GWAS 104 13.2 14 Findings and implications 106 General Discussion . 108 14.1 Study Design 108 14.2 Precision and Validity 108 14.2.1 Precision and random error 109 14.2.1.1 Genotyping misclassification .109 14.2.1.2 Sample size and statistical power .110 14.2.2 Validity .111 14.2.2.1 Selection bias and information bias .112 14.2.2.2 Confounding .114 14.2.2.3 External validity .116 14.3 Effect modification .120 14.4 Polymorphisms in estrogen related genes and recent findings in GWAS .121 14.5 Rare variants 123 15 Conclusion and Future Study 125 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240. 241. 242. 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Antoniou, A.C., Wang, X., Fredericksen, Z.S., et al. (2010) A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet, 42, 885-92. Long, J., Cai, Q., Shu, X.O., et al. (2010) Identification of a functional genetic variant at 16q12.1 for breast cancer risk: results from the Asia Breast Cancer Consortium. PLoS Genet, 6, e1001002. Murabito, J.M., Rosenberg, C.L., Finger, D., et al. (2007) A genome-wide association study of breast and prostate cancer in the NHLBI's Framingham Heart Study. BMC Med Genet, Suppl 1, S6. 142 17 Appendix Appendex Literature about genetic variants within ER cofactor genes in relation to breast cancer and endometrial cancer. Author,yr Cases/controls Population Haiman et al. 2000 464/624 Burwinkel et al. 2005 791/1644 Spurdle et al. 2006 1090 BRCA1 affected; 661 BRCA2 affected AmericanCaucasian Approach Repeating allele Gene NCOA3 German & PolishCaucasian Coding SNPs NCOA3 Australian , Europian & North American Repeating allele NCOA3 SNP repeating glutamine codons (CAG/CA A) OR(95%CI) 1.09 (0.78– 1.52) Other findings Not observed associated with ER or PR strated breast cancer Haplotype analysis of two SNPs showed more significant protective effect for breast cancer, OR=0.79 ( 0.670.93) Q586H (G>C) and 0.78 (0.63T960T 0.98), 0.78 (A>G) (0.61-0.99) 0.96 (0.79-1.16) for BRCA1 mutation carriers and repeating 1.05 (0.83-1.33) glutamine for codons BRCA2 (CAG/CA mutation NA A) carriers 143 Haiman et al. 2009 1612/1961 1218 (familial BRCA1/2mutation negative Ryan et breast cancer al. 2009 cases) /1509 Ceschi et al. 2005 Krippl et al. 2003 AfricanAmericans,Latin os,Native Hawaiians, Japanese and European Americans GermanCaucasian Coding SNPs Novel SNPs identified based on sequencing coding regions EP300, CCND1, NCOA1, 45 coding etc variants NCOR , SMRT, NCOA1, 74 novel NCOA3 SNPs 258/ 670 Singapore Chinese CCND1, Polymorphi GSTP1, sms Taqman GSTT1, polymorp genotyping GSTM1 hisms 500 /500 Australian Single SNP genotyping CCND1 CCND1 870G>A NCOR2: His52Arg, OR = 1.79 (1.05– 3.05); CALCOCO1: Arg12His, OR = 2.29 (1.00– 5.26). NCOA3: rs2230782, OR = 0.45 (0.210.98) CCND1 GA, OR =0.67( 0.45-0.99) when compared with the GG genotype Dominant model, OR =1.04(0.77– 1.40); Recessive model, OR=1.25 (0.94– 1.67) 144 NA NA Protective effect of the heterozygous CCND1 GA genotype on breast cancer risk under oxidative stress. NA 2200 / 2280 British Multiple SNPs in 13 genes CCND1, CCND2, CCND3, etc Onay et al. 2008 1228 /719 (Canadian) 728/687 (Finnish) Canadian_Cauca sian & coding Finnish_Caucasi SNPs' an genotyping CCND1, COMT Yu et al. 2008 992 / 960 Taiwanese_Chin ese Single SNP genotyping CCND1 Korean Single SNP genotyping CCND1 Driver et al. 2008 Kang et al. 2005 77 / 154 Common genetic variation in the cell cycle genes is associated with CCND1, 87 SNPs breast cancer using rs678653, OR in cell (CC/GG) = 1.14 the global AML test cycle (P = 0.010). ( 0.99–1.30) genes The combined higher activity alleles of the CCND1 Pro241Pro COMT and and AA of CCND1, CCND1 is OR=1.3 (1.0– associated with COMT Met108/1 1.69) in increased breast 58Val Canadian; cancer risk in both polymorp OR=1.4(1.01– Ontario and Finland hisms 1.84) in Finnish populations. Genotype AA had a higher risk in premenopausal women than postmenopausal ones. The recurrence-free OR=1.35 (1.06, survival was longer 1.70) in in patients with recessive model GG+AG than that CCND1 (AA or AG vs. in patients with 870G>A GG) AA. OR=2.53 (1.34– 4.80) in recessive model (AA vs. CCND1 870G>A AG+GG) NA 145 Ashton et al. 2008 Cai et a. 2011 191 /291 1028 / 1003 Austrailian,Cauc asian Chinese Single SNP genotyping haplotypetagging SNPs CCND1 CCND1 and other cell cycle control genes CCND1 870G>A OR=1.692 (0.939–3.049) (AA vs.GG) CCND1 rs649392 Heterzygous OR=0.96 (0.77,1.20) Homozyous OR=0.75 (0.35,1.60) 146 AA genotype had a higher frequency with HNPCC compared to those with the GG and GA genotypes. SNP rs34330 in CDKN1B is associated with endometrial cancer risk after multiple corrections. Appendix . Genes and number of SNPs used in analyses in the estrogen metabolism pathway. Number of Start Number of tagSNPs SNPs used used in End in LD association evaluation Coverage Gene name Chr Position* Position* analysis analysis (r2>0.80) AKR1C4 10 5225926 5252412 33 11 83% COMT 22 18307834 18338950 58 10 74% CYP11A1 15 72415693 72448634 31 90% CYP11B1 143949280 143959738 40% CYP11B2 143987483 143997761 78% CYP17A1 10 104578782 104588780 75% CYP19A1 15 49286046 49419599 53 14 93% CYP1A1-2 15 72797437 72837494 12 37% CYP1B1 38146750 38158296 83% CYP21A2 32114061 32117398 - CYP3A4-5 99082259 99221244 18 98% GSTP1 11 67106142 67112207 22 - HSD11B1 207924633 207976418 57 95% HSD11B2 16 66021037 66030453 13 50% HSD17B1 17 37953258 37962250 23 82% HSD17B2 16 80624864 80691138 38 11 91% HSD17B3 98035910 98105755 64 88% HSD17B4 118814603 118907426 38 63% HSD17B7 161027120 161049231 11 82% HSD17B8 33280397 13 33% HSD3B1 119849849 119860704 52 91% 147 33282585 NAT1 18111895 18125099 16 80% NAT2 18293035 18303003 14 100% NQO1 16 68299308 68319534 19 60% SOD2 160018641 160035843 10 83% SRD5A1 6685000 6724173 13 69% SRD5A2 31601660 31660973 30 88% SULT1E1 70740020 70761959 12 88% STS X 7145997 7284180 45 92% SULT1A1-2 16 28509267 28543875 12 47% SULT2A1 19 53064182 53082905 24 95% SULT2B1 19 53745741 53795995 39 12 97% UGT1A1-9 234189593 234348188 84 12 99% UGT2B11 70100636 70115038 93 83% UGT2B4 70378974 70397712 24 89% Total 1007 239 *:Based on the Mar 2006 human reference sequence (NCBI Build 36). 148 Appendex 3.Coverage evaluation of common variant in 60 ER cofactor genes. Capture Mean Successful d SNPs Total genotyped # (r^2 SNPs max r^2 † Gene #Tag tags # >0.8) # Coverage* ARA70 10 11 90% 0.993 Calmodulin1 14 50% 0.995 Calmodulin2 20 24 83% 0.968 Calmodulin3 66% E6-AP 48 48 100% 0.977 SRC-2 24 27 154 154 100% 0.985 L7 100% 0.985 NCoA-62 11 42 48 87% 0.985 RAP46 10 70% 0.985 SPT6 100% 0.985 SNF 18 18 100% 0.985 TIF1α 47 48 97% 0.985 Tip60 54 47 229 248 92% 0.985 TRAP/DRIP 29 31 93% 0.985 ASC-1 25 25 100% 0.985 BAF57_SWI/ 149 ASC-2 49 49 100% 0.985 CARM1 13 16 81% 0.985 PRMT1 80% 0.985 CoCoA 10 26 28 92% 0.985 RPF-1 31 34 128 128 100% 0.985 PGC-1 51 40 131 162 80% 0.985 CAPER-alpha 36 39 92% 0.985 CAPER-beta 20 21 95% 0.985 CoAA 66% 0.985 Cyclin D1 83% 0.985 CEBPB 100% 0.985 CREBBP 21 18 23 23 100% 0.985 NCOA1 23 19 106 106 100% 0.985 NCOA7 38 33 115 115 100% 0.985 PPARG 35 30 112 112 100% 0.985 DDX5 16 16 100% 0.985 EP300 10 10 42 42 100% 0.985 NCOA3 20 18 98 98 100% 0.985 PELP1 18 18 100% 0.985 PPARGC1A 55 46 97 100 97% 0.985 SRA1 11 11 100% 0.985 NCoR1 57 74 77% 0.985 150 SMRT 14 10 12 19 63% 0.985 SHARP 100% 0.985 SAFB1 14 18 77% 0.966 SAFB2 10 15 16 93% 0.985 (RIP140) 17 20 85% 0.967 LCoR 38 38 100% 0.962 COUP-TF 100% DP97 87% NSD1 10 38 43 88% 0.977 MTA1 100% 0.991 REA 100% FKHR 11 32 34 94% 0.962 TR2 13 11 23 27 85% 0.985 NEDD8 75% TAF-1β 18 15 136 148 91% 0.959 Smad4 24 24 100% 0.993 mSiah2 66% 0.957 Erβ 20 20 70 70 100% 0.966 BRCA1 42 46 91% 0.991 NCOR2 93 67 131 144 90% 0.972 NROB2 100% Nrip1 151 RBM9 21 16 21 21 100% 0.982 NR0B1 12 100% 0.993 Total 806 675 Criteria: MAF>=0.05 * Number of captured SNPs divide total SNPs number † Average r^2 of those captured SNPs 152 Appendix 4. List of statistically significant breast cancer susceptibility loci in genome-wide association studies till April 2012 (Adapted from the National Human Genome Research Institute (NHGRI) GWAS catalog (website http://www.genome.gov)(222) First Author Kim HC(236) Long J(227) Ghoussaini M(223) Initial Sample Size 2,273 Korean ancestry cases, 2,052 Korean ancestry controls 2,918 Chinese ancestry cases, 2,324 Chinese ancestry controls Nine breast cancer GWAS in populations of European ancestry Replication Sample Size 4,049 Korean ancestry cases, 3,845 Korean ancestry controls Up to 6,838 Chinese ancestry cases, up to 6,888 Chinese ancestry controls, up to 1,297 Han Chinese ancestry cases, up to 1,585 Han Chinese ancestry controls, up to 1,066 Taiwan Chinese cases, up to 1,065 Taiwan Chinese controls, up to 5,038 Korean ancestry cases, up to 6,869 Korean ancestry controls, up to 1,934 Japanese ancestry cases, up to 1,875 Japanese ancestry controls 54588 cases of invasive breast cancer, 2401 cases of ductal carcinoma in situ and 58098 controls from 41 case-control studies Region Reported Gene(s) Strongest SNPRisk Allele Context Risk Allele Frequency p-Value OR 95% CI Platform 2q34 ERBB4 rs13393577-T intron 0.051 9.00E14 1.53 [1.371.70] Affymetrix 6q25.1 TAB2 rs9485372-G Intergenic 0.55 4.00E12 1.11 [1.091.15] Affymetrix 12p11 12q24 PTHLH MAPKAPK 5,TBX3 rs10771399-G 0.12 rs1292011-G 0.41 153 2.70E35 4.30E19 0.85 0.92 (0.830.88) (0.910.94) TaqMan, Fluidigm and iPlex Cai Q(237) Fletcher O(238) Gaudet MM(239) 2,062 East Asian cases, 2,066 East Asian controls 1,694 British cases, 2,365 British controls, 1,145 European ancestry cases, 1,142 European ancestry controls 899 European ancestry affected BRCA2 carriers, 804 European ancestry unaffected though the BCAC; additional breast cancer GWAS data obtained from imputation to the HapMap Utah residents of Northern and Western European ancestry population. 21q21 NRIP1 rs2823093-A 15,091 East Asian cases, 14,877 East Asian controls 10q21.2 ZNF365 rs10822013-T 10q26.13 FGFR2 2q35 0.27 1.1E-12 0.94 (0.920.96) intron 0.47 6.00E09 1.12 [1.061.18] rs1219648-? intron 0.42 Intergenic rs13387042-A Intergenic 0.52 3p24.1 SLC4A7 rs4973768-C UTR-3 0.49 8q24.21 Intergenic rs1562430-A Intergenic 0.6 16q12.2 TOX3 rs3112612-T Intergenic 0.43 1.00E30 2.00E10 2.00E08 3.00E11 4.00E10 5p12 rs4415084-T Intergenic 0.42 rs865686-T Intergenic rs2981575-? intron 7,317 British cases, 8,124 British controls 9q31.2 Intergenic KLF4,RAD 23B,ACTL7 A 1,264 cases, 1,222 controls 10q26.13 FGFR2 154 1.15 [1.251.37] [1.111.22] [1.091.19] [1.111.22] [1.101.21] 8.00E11 1.17 [1.111.22] 0.61 2.00E10 1.12 [1.091.18] 0.42 1.00E08 1.28 [1.181.39] 1.31 1.16 1.14 1.16 Affymetrix Illumina Affymetrix Li J(216) Antoniou AC(240) Long J(241) Turnbull C(26) BRCA2 carriers 2,702 European ancestry women, 5,726 European ancestry controls 1,193 white cases, 1,190 white controls 2,073 Chinese ancestry cases, 2,084 Chinese ancestry controls 3,659 UK cases, 4,897 UK controls Up to 7,386 European ancestry cases, 7,576 European ancestry controls 3,012 white cases, 2,974 white controls 8,986 Chinese ancestry cases, 6,653 Chinese ancestry controls, 1,612 Japanese ancestry cases, 1,602 Japanese ancestry controls, 2,797 European ancestry cases, 2,662 European ancestry controls 12,576 European cases, 12,223 European controls 10q26.13 FGFR2 rs1219648-G intron 0.42 2.00E13 1.32 [1.221.42] Illumina 19p13.11 ABHD8,AN KLE1,C19or f62 rs8170-A Coding region 0.17 16q12.1 TOX3 rs4784227-T Intergenic 0.24 10q26.13 FGFR2 rs2981579-A intron 0.42 5q11.2 MAP3K1 MYEOV,C rs889312-C Intergenic 0.28 2.00E09 1.00E28 4.00E31 5.00E09 1.26 1.24 1.43 1.22 [1.171.35] [1.201.29] [1.351.53] [1.141.30] CND1,ORA OV1,FGF19 3.00E- 11q13.3 ,FGF4,FGF3 rs614367-T Intergenic 0.15 10q21.2 ZNF365 CDKN2A,C DKN2B rs10995190-G intron 0.85 9p21.3 rs1011970-T 0.17 155 15 5.00E15 3.00E08 [1.101.15 1.16 1.09 1.20] [1.101.22] [1.041.14] Illumina Affymetrix Illumina Gold B(23) 1,145 cases, 1,142 controls 1,505 Chinese cases, 1,522 Chinese controls 249 cases, 299 controls(Ash kenazi Jewish, nonBRCA1/2 carriers) Murabito JM(242) 1,345 individuals( Framingham ) Thomas G(25) Zheng W(27) 16q12.1 TOX3 rs3803662-A Intergenic 0.26 2q35 Intergenic rs13387042-A Intergenic 0.49 10q22.3 ZMIZ1 rs704010-A intron 0.39 2q35 Intergenic rs13387042-A Intergenic 0.51 10q26.13 FGFR2 rs2981579-T intron 0.41 1p11.2 Intergenic rs11249433-C 16q12.1 TOX3 rs3803662-T Intergenic 1,554 Chinese cases, 1,576 Chinese controls 6q25.1 ESR1, C6orf97 rs2046210-A 1,193 cases,1,166 controls(Ashkenazi Jewish, nonBRCA1/2 carriers) 6q22.33 ECHDC1,R NF146 NR 17q21.33 COL1A1 8,625 cases, 9,657 controls 3.00E15 2.00E10 4.00E09 1.3 1.21 1.07 [1.221.39] [1.141.29] [1.031.11] 0.27 2.00E08 2.00E10 7.00E10 1.00E09 1.16 [1.151.37] [1.071.27] [1.091.24] [1.071.27] Intergenic 0.37 2.00E15 1.29 [1.211.37] Affymetrix rs2180341-G intron 0.21 3.00E08 1.41 [1.251.59] Affymetrix rs2075555-? intron NR 8.00E08 NR NR Affymetrix 0.39 156 1.25 1.17 1.16 Illumina Hunter DJ(24) 390 cases,364 controls 1,145 cases,1,142 controls Stacey SN(28) 1,599 cases,11,546 controls Easton DF(22) 16q12.1 TNRC9, LOC643714 rs3803662-T Intergenic 0.25 11p15.5 LSP1 rs3817198-C intron 0.3 5q11.2 MAP3K1 rs889312-C Intergenic 0.28 10q26.13 FGFR2 rs2981582-A intron 0.38 26,646 cases,24,889 controls 8q24.21 Intergenic rs13281615-C Intergenic 0.4 1,176 cases,2,072 controls 10q26.13 FGFR2 rs1219648-G intron 0.4 2q35 Intergenic rs13387042-A Intergenic 0.5 16q12.1 TNRC9 rs3803662-T Intergenic 0.27 2,934 cases,5,967 controls 157 1.00E36 3.00E09 7.00E20 2.00E76 5.00E12 1.00E10 1.00E13 6.00E19 1.2 1.07 1.13 1.26 1.08 1.2 1.2 1.28 [1.161.24] Perlegen [1.041.11] [1.101.16] [1.231.30] [1.051.11] [1.071.42] [1.141.26] [1.211.35] Illumina Illumina [...]... explore the association between genetic variants in ESR2 and breast cancer risk 7.5 Candidate gene based genetic association study Candidate genes are chosen in genetic association studies based on previous knowledge of mechanisms of diseases In breast and endometrial cancer, sexual homrone related genes are obvious targets In this review, I will focus on two major groups of genes: genes involved in hormonal... SULTs and UGTs are involved in the inactivation and elimination of catechol estrogens, which are in turn responsible for detoxification, oxidation, methylation, sulfonation and glucuronidation (88).Following the metabolic activation of estrogens (2- and 4-hydroxyestrogens), the catechol estrogens are inactivated by COMT (2- and 4-methoxyestrogens) or they are oxidized into quinones and semi-quinones... comprised of high penetrance genes and low penetrance genes Mutations in high penetrance genes, like BRCA1,BRCA2 and TP53 etc account for 15%–25% of the familial component of breast cancer risk (14,42) Much of the genetic component of risk of breast cancer is thought to arise from the combined effect of multiple low penetrant variants and remains uncharacterized (43) Estrogen is the centralized interactor of. .. ligand binding, ERs undergo a conformational change that facilitates receptor dimerization, DNA binding, recruitment of ER cofactors, and modulation of target gene expression(9) Therefore, targeting estrogen signaling at the level of estrogen production and ER function are primary strategies for therapeutic intervention in hormone-dependent cancers Also, components of enzymes and genes that regulate estrogen. .. Project) has been making the data of genetic variation freely available(21) Moreover, the bank of biological material in Swedish and Finnish provided us a great platform to explore the genetic landscape for studying common and rare variants In the projects underlying this thesis, I have studied two groups of genes encoding components of the estrogen metabolism pathway and estrogen receptor cofactors with... National Institute of Standards and Technology (NIST) or College of American Pathologists (CAP) Post-analytic factors: Interpretation of IHC testing results involves the quantitative system (based on proportion of cells stained), the scoring systems (based on proportion of cells stained and the intensity of the staining), and the dichotomous system (established based on a cutoff value to distinguish a positive... be estrogenic and are believed to be carcinogenic (96) Of these compounds, 2-methoxyestrogens do not induce DNA damaging events, but 4-methoxyestrogens form depurinating DNA adducts which can occur in vital genes that control metabolism of estrogens (88,97,98) Therefore, COMT is a key enzyme for preventing quinone and semiquinone formation via the methylation of hydroxyestrogens (99) GSTs, SULTs and. .. in clinical practice (78) and is calibrated according to clinical outcome External quality assurance, such as the guidelines from CAP may help to monitor the 28 quality of the laboratory method and results and to accurately determine the ER status of breast cancer tumors 7.4 Genetic polymorphisms in Estrogen Receptor In view of the estrogen receptor (ER) being important transcription factor belonging... have shown strong associations between endometrial cancer and serum levels of estradiol and estrone, even after controlling for body mass index and other factors Exogenous estrogen levels increase with menopausal estrogen therapy (without use of progestin) and tamoxifen use(50) Pregnancy and the use of combined oral contraceptives (COCs) (51) provide protection against endometrial cancer In addition, women... processing and testing conditions should be used for each test sample In addition, different sensitivities of the assay system may affect the proportion and intensity of the stained cell detection The use of a standardized assay system and automated image analysis may help to accurately and precisely assess staining intensity To perform technically valid IHC assay, both external validation and internal . OF EPIDEMIOLOGY AND PUBLIC HEALTH YONG LOO LIN SCHOOL OF MEDICINE NATIONAL UNVERISTIY OF SINGAPORE Association Analysis of Genetic Variation of Estrogen Related Candidate Genes in Breast. - 0 - ASSOCIATION ANALYSIS OF GENETIC VARIATION OF ESTROGEN RELATED CANDIDATE GENES IN BREAST AND ENDOMETRIAL CANCERS LI YUQING NATIONAL UNIVERSITY OF SINGAPORE 2011. metabolism pathway and 60 genes encoding ER-cofactors in samples of European ancestry to ascertain whether these genetic variants could modify the risk of breast and/ or endometrial cancer. In the first