<마더리스크> biomarkers of methylation

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  • 1. Biomarkers of methylation instudies of breast cancer risk Yoon Hee Cho, M.P.H., Ph.D.
  • 2. Contents Epigenetics Breast cancer – study population Study I: methylation as prognostic marker for breast cancer Study II: methylation as diagnostic marker for breast cancer Study III: blood methylation and breast cancer risk
  • 3. EpigeneticsThe study of heritable changes in phenotype (appearance)or gene expression caused by mechanisms other thanchanges in the underlying DNA sequence• DNA methylation• Histone modification
  • 4. Epigenetic MechanismsDNA methylation and histone modificationshttp://www.ncc.go.jp/en/nccri/divisions/14carc/14carc01.html Nature 441, 143-145, 2006
  • 5. Epigenetic Mechanisms http://en.wikipedia.org/wiki/Epigenetics
  • 6. Epigenetic alterations Epigenetic changes, in particular DNA methylation, are emerging as one of the most important in carcinogenesis They are widely accepted as a potential source of early biomarkers for diagnosis/prognosis of cancer Epigenetic alterations Gene specific hypermethylation Genomic DNA hypomethylation
  • 7. Nature Reviews Genetics 8, 286, 2007The number of methyl(CH3) groups The loss of methylation in genomicattached to –C- in CpG island in DNA promote chromosomalspecific gene promoter -> regulate instability and increased cellthe expression of key genes. proliferation through alteration in the expression of proto-oncogenes.
  • 8. Breast cancer The most common cancer and second cause of cancer- death among females in USA. - an overall lifetime risk of >10% of developing breast cancer The etiology of breast cancer is complex and involves genetic and environmental factors. Early detection and novel treatments can improve patient outcome and survival rates in breast cancer. However, disease initiation and progression are still poorly understood.
  • 9. Populations Studied Long Island Breast Cancer Study Project (LIBCSP)  Population-based case-control study  Long Island Follow-up Study (case series) Breast Cancer Family Registry (BCFR)  High risk families Turkish breast cancer patients
  • 10. LIBCSP Study Purpose:Population-based study undertaken to identify environmentalfactors associated with breast cancer among women on LongIsland, NY  Population-based case-control study 1508 cases and 1556 controls, residents of Nassau or Suffolk county were collected from 1996 to 1997  Long Island Follow-up Study (case series)
  • 11. Breast Cancer Family Registry Project An infrastructure for cooperative multinational, interdisciplinary and translational epidemiologic studies of breast cancer Study Purpose: Understanding familial aggregation is a key to understanding the cause of breast cancer and to facilitating the development of effective prevention and therapy. Population-based Clinic-based Melbourne Sydney New York San Francisco Philadelphia Ontario Utah Informatics centers Biospecimens repositories NCI program managementhttp://epi.grants.cancer.gov/CFR/
  • 12. Turkish patients Breast cancer patients undergoing mastectomy in the Oncology Institute, University of Istanbul between 1991 and 1997. All patients were diagnosed with invasive ductal carcinoma with tumors >2 cm. Ethnicity-matched healthy women, mostly employees of the Oncology Institute. Cases Controls Adjacent normal Tumor DNA WBC DNA WBC DNA tissue DNA
  • 13. Study I Are epigenetic changes in tumors prognostic markers for breast cancer?
  • 14. LIBCSP Follow-Up studyBreast Cancer Cases Determine case vital status, change of address Primary exposures of interest are measures:  Assessed at baseline case-control study, and during the follow-up interview Re-interview case participants or proxy at 5-year follow-up Collect medical records and determine outcome status  NYS Tumor Registry, NDI, respondent, medical recordSpecific Aims to:Determine associations of gene specific hypermethylation markersin tumors with prognosis of breast cancer.
  • 15. Data Collection from (1) case-control in-home interview, (2)Step 1 follow-up telephone interview, (3) medical record abstraction and (4) the National Death Index (NDI) ** Vital status was followed through the end of 2005 with a mean follow up time of 8.0 years MethyLight assay with Tumor tissue DNA (765 cases)Step 2 10 Breast cancer-related tumor suppressor genes : APC, p16, RASSF1A, GSTP1, CyclinD2, DAPK1, TWIST1, HIN1, CDH1 and RARβStep 3 Analysis of associations between breast cancer-specific/ all-cause mortality and methylation levels ** 172 deaths were observed.
  • 16. Table1. Association between gene promoter methylation & general characteristics in a population-based cohort on Long Island, N.Y. HIN1 RASSF1A DAPK1 GSTP1 CyclinD2 TWIST1 RARβ Variables No. + No. + No. + No. + No. + No. + No. + P P P P P P P (%) (%) (%) (%) (%) (%) (%) 481 652 108 213 150 117 211 Total (62.9) (85.2) (14.1) (27.8) (19.6) (15.3) (27.6) Age at diagnosis (y) 126 165 16 54 27 26 46 < 50 (65.0) (85.1) (8.3) (27.8) (13.9) (13.4) (23.7) 355 487 92 159 123 91 165 > 50 0.49 0.94 0.007 0.99 0.02 0.40 0.16 (62.2 (85.3) (16.1) (27.9) (21.5) (15.9) (28.9) Menopausal status 144 190 24 65 31 22 59 Pre- (66.4) (87.6) (11.1) (30.0) (14.3) (10.1) (27.1) 331 450 83 144 117 91 150 Post- 0.29 0.30 0.11 0.42 0.02 0.02 0.78 (62.2) (84.6) (15.6) (27.1) (22.0) (17.1) (28.2) Cancer type 70 82 11 32 17 13 34 In situ (73.7) (86.3) (11.6) (33.7) (17.9) (13.7) (35.8) 411 0.02 570 0.75 97 0.45 181 0.17 133 0.65 104 0.64 177 Invasive 0.06 (61.3) (85.1 (14.5) (27.0) (19.9) (15.5) (26.4)
  • 17. HIN1 RASSF1A DAPK1 GSTP1 CyclinD2 TWIST1 RARβVariables No. + No. + No. + No. + No. + No. + No. + P P P P P P P (%) (%) (%) (%) (%) (%) (%)BMI 202 293 42 92 63 50 96< 25 (58.6) (84.9) (12.2) (26.7) (18.3) (14.5) (27.8) 279 0.07 359 0.83 66 0.16 121 0.51 87 0.40 67 0.58 115 0.89 ≥ 25 (66.4) (85.5) (15.7) (28.8) (20.7) (16.0) (27.4)Family history of breast cancer 375 511 83 171 123 94 165No (62.1) (84.6) (13.7) (28.3) (20.4) (15.6) (27.3) 93 119 21 35 22 21 36Yes 0.17 0.39 0.61 0.54 0.27 0.97 0.84 (68.4) (87.5) (15.4) (25.7) (16.2) (15.4) (26.5)ER status 61 101 14 37 21 22 41ER positive (44.9) (74.3) (10.3) (27.2) (15.4) (16.2) (30.1) 282 383 73 122 96 70 117ER negative 0.01 0.01 0.06 0,77 0.08 0.96 0.52 (65.9) (89.5) (17.1) (28.5) (22.4) (16.4) (27.3)PR status 106 167 28 65 47 41 69PR positive (51.5) (81.1) (13.6) (31.6) (22.8) (19.9) (33.5) 237 317 59 95 70 51 89 0.03PR negative 0.01 0.01 0.36 0,18 0.36 0.08 (66.2) (88.5) (16.5) (26.3) (19.6) (14.3) (24.9)When < 765 data unknown or missing
  • 18. Table 2. Age-adjusted hazard ratios (HRs) and 95% confidence intervals (CI) formethylation status of selected tumor markers and mortality after 8 years of follow up. All-cause mortality Breast cancer-specific mortality No. of cases No. of Age-adjusted HR No. of Age-adjusted HR death (95% CI) death (95% CI)HIN1 Unmethylated 284 62 1.00 (Ref.) 31 1.00 (Ref.) 481 Methylated 110 1.05 (0.77-1.44) 59 1.12 (0.72-1.73)RASSF1A Unmethylated 113 21 1.00 (Ref.) 9 1.00 (Ref.) 652 Methylated 151 1.24 (0.78-1.95) 81 1.61 (0.81-3.21)DAPK1 Unmethylated 657 143 1.00 (Ref.) 74 1.00 (Ref.) 108 Methylated 29 1.12 (0.75-1.67) 16 1.33 (0.77-2.29)GSTP1 Unmethylated 552 113 1.00 (Ref.) 56 1.00 (Ref.) 213 Methylated 59 1.43 (1.05-1.97) 34 1.66 (1.09-2.54)CyclinD2 Unmethylated 615 128 1.00 (Ref.) 69 1.00 (Ref.) 150 Methylated 44 1.23 (0.87-1.74) 21 1.27 (0.77-2.08)TWIST1 Unmethylated 648 138 1.00 (Ref.) 70 1.00 (Ref.) 117 Methylated 34 1.28 (0.88-1.87) 20 1.69 (1.02-2.78)RARβ Unmethylated 554 114 1.00 (Ref.) 56 1.00 (Ref.) 211 Methylated 58 1.37 (1.00-1.89) 34 1.69 (1.10-2.59)
  • 19. Table 3. Number of methylated genes in relation to all-cause or breast cancer-specific mortality after 8 years of follow-up among a population-based cohortof women diagnosed with breast cancer in 1996-1997, Long Island BreastCancer Study Project All-cause mortality Breast cancer-specific mortality No of genes No. of Methylated* cases No. of No. of HR** (95% CI) HR** (95% CI) death death 0-1 149 32 1.00 14 1.00 2-3 329 59 0.76 (0.49-1.16) 30 0.95 (0.50-1.79) 4-5 215 57 1.24 (0.80-1.91) 31 1.61 (0.85-3.02) 6-10 72 24 1.41 (0.83-2.40) 15 2.38 (1.14-4.96)* Data were combined with previously published data (20, 21) on APC, p16 and CDH1.** Adjusted for age at diagnosis as continuous, P trend = 0.03, HR = 1.21 (95%CI: 1.02-1.43) for all-cause mortality; P trend = 0.004, HR = 1.41 (95%CI: 1.12-1.78) for breast cancer-specific mortality
  • 20. Breast cancer specific survival probability Figure 1. Kaplan-Meier breast cancer survival curves for number of carrying methylated genes in tumor tissue among a population-based cohort of women diagnosed with a first primary breast cancer in 1996-1997 and followed 0-1 methylated gene (14 events/ 149 cases) for 8 years. Black: carrying 2-3 genes are methylated (30 events/ 329 cases) 0-1 methylated gene. Red: 4-5 genes are methylated (31 events/ 215 cases) carrying 2-3 methylated genes. Blue: carrying 4-5 6-10 genes are methylated (15 events/ 72 cases) thylated genes. Yellow: carrying 6-10 methylated genes. Follow-up years after breast cancer diagnosis
  • 21. Conclusions Age-adjusted cox-proportional hazards models revealed that methylation in GSTP1, TWIST and RARβ was significantly associated with higher breast cancer-specific mortality and methylation of GSTP1 and RARβ was associated with higher all-cause mortality. Breast cancer-specific mortality increased in a dose-dependent manner with increasing number of methylated genes. Our results suggest that promoter methylation in gene penal has the potential to be used as a biomarker for predicting prognosis in breast cancer.
  • 22. On going StudiesTumor DNA methylation and environmental factors Understand lifestyle factors and environmental exposures that impact on methylation and breast cancer risk dietary factors (vitamin B, betaine, Choline, folate etc) vs. methylation
  • 23. Study IIIs methylation in plasma DNA adiagnostic marker for breast cancer?
  • 24. Plasma DNA methylation and breast cancer risk Bloods collected prior to diagnosis from the NY and Ontario site of the BCFR NY site : 28 cases and 10 unaffected sibling controls Ontario site : 33 cases and 29 population controls Meant to demonstrate that methylation is a robust marker that can diagnose breast cancer at an early stage and offer an additional approach to screen women with breast cancer.Specific aimTo determine the promoter methylation in plasma DNA as an earlybiomarker for breast cancer diagnosis by comparing methylationfrequencies in cases and unaffected sisters and population-basedcontrols.
  • 25. Table 1. Frequency of RASSF1A methylation in breast cancer cases andcontrols Sites Subjects No. of No. of subjects positive (%) All All cases 61 11 (18) New York Cases 28 7 (25) Sibling controls* 10 2 (20) Ontario Cases 33 4 (12) Population based controls ** 29 0 (0)*Unaffected siblings from high risk families.**Population based healthy controls (age and race-matched). H. Yazici et al., Cancer Epidemiol Biomarkers Prev 2009;18:2723-2725
  • 26. Table 2. Distribution of methylated RASSF1A according to years beforediagnosis, age, hormonal status among breast cancer cases Characteristics No. of subjects No. of positive (%) Years prior to diagnosis <1 15 2 (13) 1-2 17 3 (18) >2 29 6 (21) p=0.91 Age at blood collection <40 7 1 (14) 40-49 16 1 (6) 50-59 22 6 (27) >=60 16 3 (19) p=0.42 Age at diagnosis <40 6 1 (17) 40-49 16 1 (6) 50-59 16 4 (25) >=60 23 5 (22) p=0.55 ER Status Positive 14 2 (14) Negative 4 2 (50) p=0.20 PR Status Positive 8 1 (13) Negative 11 3 (27) p=0.60 H. Yazici et al., Cancer Epidemiol Biomarkers Prev 2009;18:2723-2725
  • 27. Table 3. Frequency of RASSF1A methylation according to menopausaland smoking habits among cases and controls Subjects No. subjects No. of positive (%) All Controls 39 2 (5) Ever 21 2 (10) Never 18 0 Premenopausal 16 2 (13) Postmenopausal 20 0 All Cases 61 11 (18) Ever 32 7 (22) Never 26 8 (31) Premenopausal 21 3 (14) Postmenopausal 35 6 (17) H. Yazici et al., Cancer Epidemiol Biomarkers Prev 2009;18:2723-2725
  • 28. Conclusions Two of 10 healthy high risk sibling controls (20%) had plasma DNA positive for RASSF1A methylation in their plasma DNA compared to 0/29 (0%) population-based controls. Tumor tissue was available for 12 cases and all were positive for RASSF1A methylation. These results, if replicated, suggest that aberrant promoter hypermethylation in serum/plasma DNA may be common among high- risk women and may be present years before cancer diagnosis.
  • 29. On going StudiesPlasma DNA methylation and breast cancer risk (BCFR) Samples from all 6 BCFR sites Approximately 400 cases and 400 controls 3 sites (NY, Utah, Philadelphia) : study with sibling controls 3 sites (Melbourne, Ontario, CA) : study with sibling and population- based controls MethyLight for a panel of genes  RASSF1A, APC, BRCA1, RARB, HIN1,DAPK1, CDH1
  • 30. Study IIIIs methylation in blood DNAassociated with breast cancer risk?
  • 31.  There is preliminary evidence that circulating blood DNA contains epigenetic information, which is found in tumors The possibility that methylation in WBC DNA may be a predictor of breast cancer risk Analyze methylation in WBC DNA from cases and controls to determine associations between methylation in blood DNA and breast cancer risk
  • 32. Turkish breast cancer patients• Examined the methylation status of 8 tumor suppressor genes and 3 repetitive DNA elements in breast tumors, paired adjacent normal tissues and WBC using the MethyLight assaySpecific aims are to;1. determine aberrant hyper- and hypo-methylation of selected genes/repetitive DNA elements in invasive ductal carcinoma of the breast, and paired adjacent normal tissue and WBC.2. determine the correlation between methylation status in tumor and non-tumor tissues.3. compare methylation levels in WBC DNA between cases and unaffected controls.
  • 33. 40 tumor tissue, adjacent normal tissue and blood pairs fromStep 1 breast carcinoma patients (aged 34-73) and 40 ethnicity matched controls from the Oncology Institute, University of Istanbul between 1991 and 1997. MethyLight assayStep 2 1. 8 Breast cancer-related tumor suppressor genes : APC, RASSF1A, GSTP1, CyclinD2, TWIST1, HIN1, CDH1 and RARβ 2. Repetitive DNA elements (LINE-1, AluM2 and Sat2M1) Analysis of associations between methylation status and breastStep 3 cancer risk
  • 34. Table 1. General and clinicopathologic characteristics in breast cancerpatients and controls Number of subjects (%) P-value Cases a (n = 40) Controls a (n = 40) Age (mean ± S.D, yr) 50.8 ± 10.8 48.3 ± 8.6 0.26† ≤ 40 8 (22.2) 10 (25.0) 41-60 22 (61.1) 27 (67.5) 0.63* > 60 6 (16.7) 3 (7.5) Menopausal status Premenopausal 19 (52.8) 22 (55.0) 0.18* Postmenopausal 17 (47.2) 18 (45.0) Histological stage I and II 11 (42.3) - - III and IV 15 (57.7) - - Family history of cancer No 11 (44.0) - Yes 14 (56.0) - -a When <40, data unknown. * P for the difference between cases and control (Fisher exact test).† P for the differencebetween cases and control (t-test). YH Cho et al., Anticancer Res. 2010; 30(7):2489-2496
  • 35. Figure 1. Map of gene promotermethylation in blood, normal adjacent-and tumor tissues.Box color represents the degree ofmethylation (light gray, 1≤ % methylation<4; dark gray, 4 ≤ % methylation <10;black, 10 ≥ % methylation).YH Cho et al., Anticancer Res. 2010; 30(7):2489-2496
  • 36. Table II. Promoter hypermethylation in breast tumor, paired normal adjacenttissue and WBC DNAs from breast cancer cases Number of positive hypermethylation (%)Source of DNA BRCA1 HIN1 RASSF1A CDH1 RARβ APC TWIST1 CyclinD2 Ta (n = 40) 7 (17.5) 30 (75.0) 33 (82.5) 9 (22.5) 10 (25.0) 21 (52.5) 7 (17.5) 12 (30.0) Ab (n = 27) 2 (7.4) 19 (70.4) 23 (85.2) 5 (18.5) 7 (25.9) 12 (44.4) 3 (11.1) 5 (18.5) Bc (n = 40) 3 (7.5) 4 (10.0) 3 (7.5) 3 (7.5) 4 (10.0) 0 (0.0) 0 (0.0) 0 (0.0)Methylation status BRCA1 HIN1 RASSF1A CDH1 RARβ APC TWIST1 CyclinD2 Tumor Md / Adjacent M 0 (0.0) 17 (89.5) 20 (87.0) 2 (40.0) 4 (57.1) 11 (91.7) 3 (100.0) 2 (40.0) Tumor UMe / Adjacent M 2 (100.0) 2 (10.5) 3 (13.0) 3 (60.0) 3 (42.9) 1 (8.3) 0 (0.0) 3 (60.0) Tumor M / Blood M 2 (66.7) 4 (100.0) 2 (66.7) 2 (66.7) 2 (50.0) 0 (0.0) 0 (0.0) 0 (0.0) Tumor UM / Blood M 1 (33.3) 0 (0.0) 1 (33.3) 1 (33.3) 2 (50.0) 0 (0.0) 0 (0.0) 0 (0.0) Adjacent M/ Blood M 1 (33.3) 4 (100.0) 2 (100.0)† 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) 0 (0.0) Adjacent UM / Blood M 2 (66.7) 0 (0.0) 0 (0.0) 3 (100.0) 3 (100.0)† 0 (0.0) 0 (0.0) 0 (0.0)a T= Tumor tissue; b A= Adjacent normal tissue; c B= Blood; d M= Methylated; e UM= Unmethylated ; † Adjacent tissue wasnot available in subject who was positive for blood. YH Cho et al., Anticancer Res. 2010; 30(7):2489-2496
  • 37. A 140 * † 120 % Methylation in LINE1 100 80 60 Figure 2. Comparison of (A) LINE1, (B) Sat2M1 40 and (C) AluM2 hypomethylation levels from 20 tumor (n =40)- normal adjacent tissues (n =27) , Tumor Adjacent tissue Blood (Case) Blood (Control) and WBC DNA (n =40 for both cases and controls). Hypomethylation levels in LINE1 andB Sat2M1 in tumor tissue was significantly 300 * † § decreased compared with those in WBC DNA (*both P<0.0001, Wilcoxon test). Significant % Methylation in Sat2M1 † † 250 200 correlations in methylation of LINE1 between 150 tumor and WBC DNA (†Rho =0.46; P =0.0031, 100 Spearman’s rank correlation test) and 50 methylation of Sat2M1 between tumor and 0 adjacent normal tissues (†Rho=0.78; P<0.0001), tumor and WBC DNA (†Rho =0.32; P =0.046) or Tumor Adjacent tissue Blood (Case) Blood (Control)C 100 adjacent normal tissue and WBC DNA (†Rho=0.67; P=0.002) were shown. Methylation % Methylation in AluM2 80 of Sat2M1 in WBC DNA was significantly 60 different between cases and control (§P=0.01, Wilcoxon test). Data represent the means ± SD 40 (error bars). 20 Tumor Adjacent tissue Blood (Case) Blood (Control) YH Cho et al., Anticancer Res. 2010; 30(7):2489-2496
  • 38. Conclusions Tumor and adjacent tissues showed frequent hypermethylation for all genes tested, while WBC DNA was rarely hypermethylated. For HIN1, RASSF1A, APC and TWIST1 there was agreement between hypermethylation in tumor and adjacent tissues. Significant correlations in methylation of Sat2M1 between tumor and adjacent tissues and WBC DNA were found. There also was a significant difference in methylation of Sat2M1 between cases and controls. These results suggest that further studies of WBC methylation, including prospective studies, may provide biomarkers of breast cancer risk.
  • 39. LIBCSP Promoter hypermethylation of 3 known tumor-suppressor genes (BRCA1, CDH1 and RARβ) was analyzed in white blood cell (WBC) DNA from 1026 breast cancer patients and 1038 population-based controls by the MethyLight assay Gene specific promoter methylation in 519 tumor tissue DNA was also analyzed to determine the correlation of methylation levels with bloodSpecific aims are to;1. determine promoter hypermethylation in tumor tissues and paired mononuclear cells from beast cancer patients.2. determine the correlation between methylation status in tumors and non-tumor tissues.3. compare levels of methylation in WBC DNAs between patients and population-based healthy controls.
  • 40. Table1. General characteristics and promoter hypermethylation levels of whiteblood cell DNA in cases and controls Number of subjects (%) OR Variables P-value Cases Controls (95% CI) (n = 1026) (n = 1038) Age (mean ± S.D, yr) 58.7± 12.6 55.8± 12.4 <0.0001 Race White 965(94.2) 962(92.7) Black 42(4.1) 47(4.5) 0.19 Other 17(1.7) 29(2.8) Menopausal status Pre- 329(32.9) 355(35.8) 0.18 Post- 672(67.1) 638(64.3) BMI (mean± SD, kg/m2) 26.6±5.6 26.4±5.8 0.27 Lifetime Alcohol intake (g/day) Non-drinkers 385(37.5) 374(36.0) < 15 479(46.7) 515(49.7 0.36 ≥15 162(15.8) 148(14.3)
  • 41. Number of subjects (%) ORVariables P-value Cases Controls (95% CI) (n = 1026) (n = 1038)Smoking Never 473(46.1) 472(45.6) Former 358(34.9) 370(65.7) 0.93 Current 195(19.0) 194(18.7)Family history of cancer No 808(81.2) 870(85.8) 0.006 Yes 187(18.8) 144(14.2)BRCA1 Unmethylated 1007 (98.2) 1025 (98.8) 1(ref) Methylated 19 (1.8) 13 (1.2) 1.43(0.69-2.95)CDH1 Unmethylated 1009 (98.7) 1027 (98.9) 1(ref) Methylated 17 (1.3) 11 (1.1) 1.50(0.68-3.31)RARβ Unmethylated 1013 (98.7) 1022 (98.5) 1(ref) Methylated 13 (1.3) 16 (1.5) 0.73(0.34-1.53)
  • 42. Table2. Hypermethylation of a two gene panel in white blood cell DNA inbreast cancer cases and controls Number of subject (%) Gene panel OR (95%CI)* Case Control BRCA1 / CDH1 Both negative 992 (96.7) 1014 (97.7) 1.0 (Ref.) At least any one positive 34 (3.3) 24 (2.3) 1.38 (0.80-2.38)* OR adjusted for age and family history of breast cancer.
  • 43. Table 3. Promoter hypermethylation in breast tumor and paired white bloodcell DNA from breast cancer patients Number of patients (%) Methylation status BRCA1 CDH1 RARβ Tumor, UMa / WBC, UM 483 (93.1) 395 (76.1) 284 (54.7) Tumor, UM / WBC, Mb 6 (1.1) 3 (0.6) 4 (0.8) Tumor, M / WBC, UM 27 (5.2) 118 (22.7) 230 (44.3) Tumor, M / WBC M 3 (0.6) 3 (0.6) 1 (0.2) Kappa coefficient 0.13(-0.02,0.28) 0.03(-0.02,0.07) -0.01(-0.03,0.007) P-value 0.0002 0.06 0.13 a UM = Unmethylated. b M = Methylated.
  • 44. Conclusions Hypermethylation of BRCA1, CDH1 and RARβ in WBC DNA were not significantly associated with breast cancer risk. Hypermethylation in the genes panel showed 38% increased risk of breast cancer, but it was not statistically significant. Nor was there concordance between tumor tissue and paired WBC DNA methylation. These results suggest that hypermethylation in blood is not a useful biomarker of breast cancer risk, but further studies with additional genes are needed.
  • 45. Acknowledgement LIBCSP  BCFRUNC: MD Gammon (PI), Columbia: RM Santella PT Bradshaw MB Terry PI, RS former PIColumbia: RM Santella, I Gurvich MB Terry, YJ Zhang Breast Cancer Family Registry J Shen, HC Wu  Turkish sampleMt. Sinai: SL Teitelbaum, J Chen Istanbul U: H Yazici X Xu