biomarkers of methylation

Biomarkers of methylation in studies of breast cancer risk

Yoon Hee Cho, M.P.H., Ph.D.

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

Contents

Epigenetics

The study of heritable changes in phenotype (appearance)

or gene expression caused by mechanisms other than

changes in the underlying DNA sequence

• DNA methylation

• Histone modification

Epigenetic Mechanisms

Nature 441, 143-145, 2006

DNA methylation and histone modifications

http://www.ncc.go.jp/en/nccri/divisions/14carc/14carc01.html

http://en.wikipedia.org/wiki/Epigenetics

Epigenetic Mechanisms

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

Nature Reviews Genetics 8, 286, 2007

The number of methyl(CH3) groups attached to –C- in CpG island in specific gene promoter -> regulate the expression of key genes.

The loss of methylation in genomic DNA promote chromosomal instability and increased cell proliferation through alteration in the expression of proto-oncogenes.

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.

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

LIBCSP

Study Purpose:

Population-based study undertaken to identify environmental

factors associated with breast cancer among women on Long

Island, NY

Population-based case-control study1508 cases and 1556 controls, residents of Nassau or Suffolk

county were collected from 1996 to 1997

Long Island Follow-up Study (case series)

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.

Informatics centers Biospecimens repositories

NCI program management

Population-based Clinic-based

San Francisco

Ontario Utah

Philadelphia

New YorkMelbourne

Sydney

http://epi.grants.cancer.gov/CFR/

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.

Turkish patients

ControlsCases

Tumor DNAAdjacent normal

tissue DNAWBC DNATumor DNA

Adjacent normal

tissue DNAWBC DNA WBC DNA

Are epigenetic changes in tumors prognostic markers for breast cancer?

Study I

LIBCSP Follow-Up study

Breast 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 record

Specific Aims to:

Determine associations of gene specific hypermethylation markers

in tumors with prognosis of breast cancer.

Data Collection from (1) case-control in-home interview, (2)

follow-up telephone interview, (3) medical record abstraction

and (4) the National Death Index (NDI)Step 1

MethyLight assay with Tumor tissue DNA (765 cases)

10 Breast cancer-related tumor suppressor genes : APC, p16, RASSF1A,

GSTP1, CyclinD2, DAPK1, TWIST1, HIN1, CDH1 and RARβ

Step 2

Analysis of associations between breast cancer-specific/

all-cause mortality and methylation levelsStep 3

** Vital status was followed through the end of 2005 with a mean follow up time of

8.0 years

** 172 deaths were observed.

Variables

HIN1 RASSF1A DAPK1 GSTP1 CyclinD2 TWIST1 RARβ

No. +

(%)P

No. +

(%)P

No. +

(%)P

No. +

(%)P

No. +

(%)P

No. +

(%)P

No. +

(%)P

Total481

(62.9)

652

(85.2)

108

(14.1)

213

(27.8)

150

(19.6)

117

(15.3)

211

(27.6)

Age at diagnosis (y)

< 50126

(65.0)

165

(85.1)

16

(8.3)

54

(27.8)

27

(13.9)

26

(13.4)

46

(23.7)

> 50355

(62.20.49

487

(85.3)0.94

92

(16.1)0.007

159

(27.9)0.99

123

(21.5)0.02

91

(15.9)0.40

165

(28.9)0.16

Menopausal status

Pre-144

(66.4)

190

(87.6)

24

(11.1)

65

(30.0)

31

(14.3)

22

(10.1)

59

(27.1)

Post-331

(62.2)0.29

450

(84.6)0.30

83

(15.6)0.11

144

(27.1)0.42

117

(22.0)0.02

91

(17.1)0.02

150

(28.2)0.78

Cancer type

In situ70

(73.7)

82

(86.3)

11

(11.6)

32

(33.7)

17

(17.9)

13

(13.7)

34

(35.8)

Invasive411

(61.3)0.02 570

(85.10.75 97

(14.5)

0.45 181

(27.0)0.17 133

(19.9)0.65 104

(15.5)0.64 177

(26.4)0.06

Table1. Association between gene promoter methylation & general characteristics

in a population-based cohort on Long Island, N.Y.

Variables

HIN1 RASSF1A DAPK1 GSTP1 CyclinD2 TWIST1 RARβ

No. +

(%)P

No. +

(%)P

No. +

(%)P

No. +

(%)P

No. +

(%)P

No. +

(%)P

No. +

(%)P

BMI

< 25202

(58.6)

293

(84.9)

42

(12.2)

92

(26.7)

63

(18.3)

50

(14.5)

96

(27.8)

0.89≥ 25279

(66.4)

0.07 359

(85.5)

0.83 66

(15.7)

0.16 121

(28.8)

0.51 87

(20.7)

0.40 67

(16.0)

0.58 115

(27.4)

Family history of breast cancer

No375

(62.1)

511

(84.6)

83

(13.7)

171

(28.3)

123

(20.4)

94

(15.6)

165

(27.3)

Yes93

(68.4)0.17

119

(87.5)0.39

21

(15.4)0.61

35

(25.7)0.54

22

(16.2)0.27

21

(15.4)0.97

36

(26.5)0.84

ER status

ER positive61

(44.9)

101

(74.3)

14

(10.3)

37

(27.2)

21

(15.4)

22

(16.2)

41

(30.1)

ER negative282

(65.9)0.01

383

(89.5)0.01

73

(17.1)0.06

122

(28.5)0,77

96

(22.4)0.08

70

(16.4)0.96

117

(27.3)0.52

PR status

PR positive106

(51.5)

167

(81.1)

28

(13.6)

65

(31.6)

47

(22.8)

41

(19.9)

69

(33.5)

PR negative237

(66.2)0.01

317

(88.5)0.01

59

(16.5)0.36

95

(26.3)0,18

70

(19.6)0.36

51

(14.3)0.08

89

(24.9)0.03

When < 765 data unknown or missing

Table 2. Age-adjusted hazard ratios (HRs) and 95% confidence intervals (CI) for

methylation status of selected tumor markers and mortality after 8 years of follow up.

No. of

cases

All-cause mortality Breast cancer-specific mortality

No. of

death

Age-adjusted HR

(95% CI)

No. of

death

Age-adjusted HR

(95% CI)

HIN1

Unmethylated 284 62 1.00 (Ref.) 31 1.00 (Ref.)

Methylated481

110 1.05 (0.77-1.44) 59 1.12 (0.72-1.73)

RASSF1A


Methylated652

151 1.24 (0.78-1.95) 81 1.61 (0.81-3.21)

DAPK1


Methylated108

29 1.12 (0.75-1.67) 16 1.33 (0.77-2.29)

GSTP1


Methylated213

59 1.43 (1.05-1.97) 34 1.66 (1.09-2.54)

CyclinD2


Methylated150

44 1.23 (0.87-1.74) 21 1.27 (0.77-2.08)

TWIST1


Methylated117

34 1.28 (0.88-1.87) 20 1.69 (1.02-2.78)

RARβ


Methylated211

58 1.37 (1.00-1.89) 34 1.69 (1.10-2.59)

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 cohort

of women diagnosed with breast cancer in 1996-1997, Long Island Breast

Cancer Study Project

* 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

No of genes

Methylated*

No. of

cases

All-cause mortality Breast cancer-specific mortality

No. of

deathHR** (95% CI)

No. of

deathHR** (95% CI)

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)

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

for 8 years. Black: carrying

0-1 methylated gene. Red:

carrying 2-3 methylated

genes. Blue: carrying 4-5

thylated genes. Yellow:

carrying 6-10 methylated

genes.

0-1 methylated gene (14 events/ 149 cases)

2-3 genes are methylated (30 events/ 329 cases)



Bre

ast cancer

specific

su

rviv

al p

rob

ab

ility

Follow-up years after breast cancer diagnosis

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.

On going Studies

Tumor 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

Is methylation in plasma DNA a

diagnostic marker for breast cancer?

Study II

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 early

biomarker for breast cancer diagnosis by comparing methylation

frequencies in cases and unaffected sisters and population-based

controls.

Sites Subjects No. of

subjects

No. of

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)

Table 1. Frequency of RASSF1A methylation in breast cancer cases and

controls

*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

Characteristics No. of subjects No. of positive (%)

Years prior to diagnosis

<1

1-2

>2

15

17

29

2 (13)

3 (18)

6 (21) p=0.91

Age at blood collection

<40

40-49

50-59

>=60

7

16

22

16

1 (14)

1 (6)

6 (27)

3 (19) p=0.42

Age at diagnosis

<40

40-49

50-59

>=60

6

16

16

23

1 (17)

1 (6)

4 (25)

5 (22) p=0.55

ER Status

Positive

Negative

14

4

2 (14)

2 (50) p=0.20

PR Status

Positive

Negative

8

11

1 (13)

3 (27) p=0.60

Table 2. Distribution of methylated RASSF1A according to years before

diagnosis, age, hormonal status among breast cancer cases


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)

Table 3. Frequency of RASSF1A methylation according to menopausal

and smoking habits among cases and controls


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.

On going Studies

Plasma 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

Is methylation in blood DNA

associated with breast cancer risk?

Study III

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

• 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 assay

Specific 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.

Turkish breast cancer patients

40 tumor tissue, adjacent normal tissue and blood pairs from

breast carcinoma patients (aged 34-73) and 40 ethnicity

matched controls from the Oncology Institute, University of Istanbul

between 1991 and 1997.

Step 1

MethyLight assay

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)

Step 2

Analysis of associations between methylation status and breast

cancer risk Step 3

Table 1. General and clinicopathologic characteristics in breast cancer

patients and controls

Number of subjects (%)

P-valueCases 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)

0.63*41-60 22 (61.1) 27 (67.5)

> 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 difference

between cases and control (t-test).YH Cho et al., Anticancer Res. 2010; 30(7):2489-2496

YH Cho et al., Anticancer Res. 2010; 30(7):2489-2496

Figure 1. Map of gene promoter

methylation in blood, normal adjacent-

and tumor tissues.

Box color represents the degree of

methylation (light gray, 1≤ % methylation

<4; dark gray, 4 ≤ % methylation <10;

black, 10 ≥ % methylation).

Table II. Promoter hypermethylation in breast tumor, paired normal adjacent

tissue 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 was

not available in subject who was positive for blood.


Tumor Adjacent tissue Blood (Case) Blood (Control)

% M

ethy

latio

n in

Sat

2M1

0

50

100

150

200

250

300

* † §

† †


% M

ethy

latio

n in

Alu

M2

20

40

60

80

100


% M

ethy

latio

n in

LIN

E1

20

40

60

80

100

120

140

* †


Figure 2. Comparison of (A) LINE1, (B) Sat2M1

and (C) AluM2 hypomethylation levels from

tumor (n =40), normal adjacent tissues (n =27)

and WBC DNA (n =40 for both cases and

controls). Hypomethylation levels in LINE1 and

Sat2M1 in tumor tissue was significantly

decreased compared with those in WBC DNA

(*both P<0.0001, Wilcoxon test). Significant

correlations in methylation of LINE1 between

tumor and WBC DNA (†Rho =0.46; P =0.0031,

Spearman’s rank correlation test) and

methylation of Sat2M1 between tumor and

adjacent normal tissues (†Rho=0.78; P<0.0001),

tumor and WBC DNA (†Rho =0.32; P =0.046) or

adjacent normal tissue and WBC DNA

(†Rho=0.67; P=0.002) were shown. Methylation

of Sat2M1 in WBC DNA was significantly

different between cases and control (§P=0.01,

Wilcoxon test). Data represent the means ± SD

(error bars).

A

B

C

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.

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 blood

Specific 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.

Table1. General characteristics and promoter hypermethylation levels of white

blood cell DNA in cases and controls

Variables

Number of subjects (%)

OR

(95% CI) P-value

Cases

(n = 1026)

Controls

(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)

0.19Black 42(4.1) 47(4.5)

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)

0.36< 15 479(46.7) 515(49.7

≥15 162(15.8) 148(14.3)

Variables

Number of subjects (%)OR

(95% CI)P-value

Cases

(n = 1026)

Controls

(n = 1038)

Smoking

Never 473(46.1) 472(45.6)

0.93Former 358(34.9) 370(65.7)

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


Methylated 17 (1.3) 11 (1.1) 1.50(0.68-3.31)

RARβ


Methylated 13 (1.3) 16 (1.5) 0.73(0.34-1.53)

Table2. Hypermethylation of a two gene panel in white blood cell DNA in

breast cancer cases and controls

Gene panel

Number of subject (%)

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.

Table 3. Promoter hypermethylation in breast tumor and paired white blood

cell DNA from breast cancer patients

Methylation status

Number of patients (%)

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

P-value

0.13(-0.02,0.28)

0.0002

0.03(-0.02,0.07)

0.06

-0.01(-0.03,0.007)

0.13

a UM = Unmethylated.b M = Methylated.

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.

Acknowledgement

LIBCSP

UNC: MD Gammon (PI),

PT Bradshaw

Columbia: RM Santella,

MB Terry,

YJ Zhang

J Shen,

HC Wu

Mt. Sinai: SL Teitelbaum,

J Chen X Xu

BCFR

Columbia: RM Santella

MB Terry PI,

RS former PI

I Gurvich

Breast Cancer Family Registry

Turkish sample

Istanbul U: H Yazici

biomarkers of methylation

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