biomarkers of methylation
TRANSCRIPT
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
Unmethylated 113 21 1.00 (Ref.) 9 1.00 (Ref.)
Methylated652
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.)
Methylated108
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.)
Methylated213
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.)
Methylated150
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.)
Methylated117
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.)
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)
4-5 genes are methylated (31 events/ 215 cases)
6-10 genes are methylated (15 events/ 72 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
H. Yazici et al., Cancer Epidemiol Biomarkers Prev 2009;18:2723-2725
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
H. Yazici et al., Cancer Epidemiol Biomarkers Prev 2009;18:2723-2725
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.
YH Cho et al., Anticancer Res. 2010; 30(7):2489-2496
Tumor Adjacent tissue Blood (Case) Blood (Control)
% M
ethy
latio
n in
Sat
2M1
0
50
100
150
200
250
300
* † §
† †
Tumor Adjacent tissue Blood (Case) Blood (Control)
% M
ethy
latio
n in
Alu
M2
20
40
60
80
100
Tumor Adjacent tissue Blood (Case) Blood (Control)
% M
ethy
latio
n in
LIN
E1
20
40
60
80
100
120
140
* †
YH Cho et al., Anticancer Res. 2010; 30(7):2489-2496
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
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)
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