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. 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
4. Epigenetic Mechanisms
DNA methylation and histone modifications
http://www.ncc.go.jp/en/nccri/divisions/14carc/14carc01.html
Nature 441, 143-145, 2006
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, 2007
The number of methyl(CH3) groups The loss of methylation in genomic
attached to –C- in CpG island in DNA promote chromosomal
specific gene promoter -> regulate instability and increased cell
the 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 environmental
factors associated with breast cancer among women on Long
Island, 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 management
http://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 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.
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 RARβ
Step 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 RARβ
Variables 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 94 165
No
(62.1) (84.6) (13.7) (28.3) (20.4) (15.6) (27.3)
93 119 21 35 22 21 36
Yes 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 41
ER positive
(44.9) (74.3) (10.3) (27.2) (15.4) (16.2) (30.1)
282 383 73 122 96 70 117
ER 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 69
PR positive
(51.5) (81.1) (13.6) (31.6) (22.8) (19.9) (33.5)
237 317 59 95 70 51 89 0.03
PR 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
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 cohort
of women diagnosed with breast cancer in 1996-1997, Long Island Breast
Cancer 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 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
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 aim
To 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.
25. Table 1. Frequency of RASSF1A methylation in breast cancer cases and
controls
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 before
diagnosis, 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 menopausal
and 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 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
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 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.
33. 40 tumor tissue, adjacent normal tissue and blood pairs from
Step 1 breast carcinoma patients (aged 34-73) and 40 ethnicity
matched controls from the Oncology Institute, University of Istanbul
between 1991 and 1997.
MethyLight assay
Step 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 breast
Step 3
cancer risk
34. Table 1. General and clinicopathologic characteristics in breast cancer
patients 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 difference
between cases and control (t-test).
YH Cho et al., Anticancer Res. 2010; 30(7):2489-2496
35. 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).
YH Cho et al., Anticancer Res. 2010; 30(7):2489-2496
36. 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
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 and
B 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
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.
40. Table1. General characteristics and promoter hypermethylation levels of white
blood 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 (%)
OR
Variables 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 in
breast 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 blood
cell 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 BCFR
UNC: MD Gammon (PI), Columbia: RM Santella
PT Bradshaw MB Terry PI,
RS former PI
Columbia: RM Santella, I Gurvich
MB Terry,
YJ Zhang Breast Cancer Family Registry
J Shen,
HC Wu
Turkish sample
Mt. Sinai: SL Teitelbaum,
J Chen Istanbul U: H Yazici
X Xu
