University of Southern California Dissertations and Theses

The role of heritability and genetic variation in cancer and cancer survival

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The role of heritability and genetic variation in cancer and cancer survival

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Content i

The role of heritability and genetic variation in cancer and
cancer survival
by
Ali Ozhand

A Dissertation Presented to the
Faculty of the USC Graduate School
University of Southern California
In Partial Fulfillment of the
Requirements for the Degree
Doctor of Philosophy
(Epidemiology)

August 2013
Chair: Dr. Thomas Mack,
Member: Dr. Wendy Cozen
Member: Dr. Roberta McKean-Cowdin
Member: Dr. Anna Wu
Outside member: Dr. Sue Martin
ii

Acknowledgements
I would like to acknowledge the committee members of this dissertation who provided
me with the guidance and support to conduct this research.
Specially, I thank Dr Wendy Cozen, Dr Thomas Mack, Dr Giske Ursin, Dr Roberta McKean-
Cowdin, Dr Anna Wu, and Dr Sue Martin. Who have given generously of their time and thought
into the development and review of this research.
I also thank my late father for being my greatest source of encouragement, along with my
mother for her constant love and support.
iii

Table of Contents
Overview .................................................................................................................................. vi
Chapter 1 Background ..................................................................................................................1
Section 1.1 Mammographic density .......................................................................................2
Section 1.1.2 Association with breast cancer .........................................................................2
Section 1.1.3 Determinants .....................................................................................................2
Section 1.1.4 Constitution.......................................................................................................2
Section 2 Growth factors/ cytokines in breast tissue ..............................................................3
Section 3 IL-6 and breast cancer ............................................................................................3
Section 3.1 IL-6 in vitro ..........................................................................................................4
Section 3.2 IL-6 in vivo ..........................................................................................................4
Section 3.3 IL-6 serum levels and breast cancer ....................................................................5
Section 4 References ...............................................................................................................6
Chapter 2: Variation in inflammatory cytokine/growth-factor genes and mammographic
density in premenopausal women aged 50-55 .............................................................................8
Section 1 Abstract ..................................................................................................................9
Section 2 Introduction ..........................................................................................................10
Section 3 Materials and methods ........................................................................................12
Section 3.1 Study population ................................................................................................12
Section 3.1.1 Norwegian Breast Cancer Screening Program (NBCSP) participants ............12
Section 3.2 Mammographic Density Assessment .................................................................14
Section 3.3 Tagging SNP selection and genotyping .............................................................14
Section 3.4 Statistical analysis ............................................................................................16
Section 3.5 Replication study and combined analysis ..........................................................16
Section 4 Results ...................................................................................................................18
Section 4.1 Baseline characteristics of the participants ........................................................18
Section 4.2 Associations between SNPs and MD in NBCSP participants ...........................18
Section 4.3 Associations between SNPs and MD in premenopausal NBCSP participants ..19
Section 4.4 Association between IL6 SNPs and MD in Singapore Chinese women ............19
Section 5 Discussion .............................................................................................................20
Section 6 Conclusion ............................................................................................................24
iv

Section 7 References ............................................................................................................25
Section 8 Tables ....................................................................................................................28
Chapter 3: Heritability of Lymphoid Neoplasms in Twins ....................................................33
Section 1 Abstract ................................................................................................................34
Section 2 Introduction ..........................................................................................................35
Section 3 Methods ...............................................................................................................39
Section 3.1 Recruitment and Data collection ........................................................................39
Section 3.2 Assessment of Zygosity .....................................................................................40
Section 3.3 Assessment of Concordancy ..............................................................................40
Section 3.4 Quantification of Concordancy ..........................................................................41
Section 4 Results ...................................................................................................................41
Section 4.1 Hodgkin’s disease ..............................................................................................42
Section 4.2 Chronic Lymphocytic Leukemia........................................................................44
Section 4.3 Non-Hodgkin Lymphoma ..................................................................................44
Section 4.4 Myeloma ............................................................................................................45
Section 5 Discussion .............................................................................................................46
Section 6 Conclusion ............................................................................................................49
Section 7 References ............................................................................................................50
Section 8 Tables ....................................................................................................................52
Section 8 Figures ...................................................................................................................56
Chapter 4: Short term reduction in mammographic density
and survival in breast cancer ......................................................................................................57
Section 1 Abstract ................................................................................................................58
Section 2 Introduction ..........................................................................................................59
Section 3 Methods ...............................................................................................................60
Section 3.1 Study Setting, Participants, and Recruitment .....................................................60
Section 3.2 Outcome Assessment .........................................................................................62
Section 3.3 Disease Stage and Treatment .............................................................................62
Section 3.4 Anthropometrics .................................................................................................62
Section 3.5 Other Variables ..................................................................................................63
Section 3.6 Mammographic Density .....................................................................................63
v

Section 3.7 Exclusions ..........................................................................................................64
Section 3.8 Statistical Analyses ............................................................................................64
Section 4 Results ...................................................................................................................65
Section 5 Discussion .............................................................................................................66
Section 6 Conclusion ............................................................................................................68
Section 7 References ............................................................................................................69
Section 8 Tables ....................................................................................................................70
Section 8 Figures ...................................................................................................................73

vi

Overview
This dissertation evaluates genetic factors related to cancer and also cancer survival.
Breast cancer and lymphoid neoplasms were the focus of this research.
Chapter 1 provides a background of existing knowledge on breast density and its
importance as a risk factor for breast cancer. The association of breast cancer risk factors with
breast density is also explained. Furthermore, it reviews the available findings about the role of
growth factors and cytokines in development of mammary gland. The role of IL-6 in breast
cancer is also specifically explored.
Chapter 2 is the manuscript for the study of growth-factor/ cytokine pathway genes and
mammographic density, as a marker for breast cancer. The study was conducted on a sample of
premenopausal women participated in Norwegian Breast Cancer Screening Program. The article
“Variation in inflammatory cytokine/growth-factor genes and mammographic density in
premenopausal women aged 50-55” was accepted for publication in the journal PLOS ONE in
May 2013.
Chapter 3 is a prospective cohort study of twins of patients with a lymphoid neoplasm.
The role of heredity in 4 lymphoid neoplasms was assessed by comparing the concordance rates
between mono- and di-zygotic twins enrolled in the International Twin Study. Preliminary
results were published in Blood (ASH Annual Meeting Abstracts), Nov 2012; 120: 3636 and
presented at the 54
th
American Society of Hematology Annual Meeting and Exposition, Atlanta,
Georgia, December 2012.
Chapter 4 is a prospective cohort study of breast cancer survival. The role of breast
density change shortly after breast cancer diagnosis in predicting the overall survival was
vii

assessed using the data from The Health, Eating, Activity, & Lifestyle (HEAL) Study.
Preliminary results were published in the Proceedings of the American Association for Cancer
Research; 2013; 2286 and presented at the American Association for Cancer Research annual
conference, Washington, DC, 2013.
1

Chapter 1

Background

2

Mammographic density

Association with breast cancer
High mammographic density (MD) is an established risk factor for breast cancer. Women with
extensive MD have been found to have four to six times the risk of breast cancer compared to
women with little or no density (1-3). MD is influenced by several breast cancer risk factors
including age, body mass index (BMI), parity, age at first birth, hormone therapy use and
physical activity (4).
Determinants
In premenopausal and postmenopausal women, nulliparity, late age at first birth, younger age,
lower BMI, higher levels of alcohol consumption are associated with increased MD (4). Among
postmenopausal women hormone replacement therapy is associated with increased MD.
Smoking has been reported to be inversely associated with MD (4-6). No association between
physical activity and MD has been suggested (4, 7-9). Percent MD is positively associated with
SES; SES gradients in MD parallels known SES gradients in breast cancer risk(10). Combined,
these factors explained approximately 37% of the variability in the percent density measure in
premenopausal women and 19% in postmenopausal women(4).
Constitution
The histopathological composition of dense breast tissue consists of both stroma and
concentrated epithelial tissue (11). Mammographically dense breasts have been shown to have
higher amounts of collagen, more extensive stromal fibrosis, and higher numbers of epithelial
cells when compared with breasts with little density (1, 11-13). Breast stroma and epithelium
3

interact by means of paracrine cytokines and growth factors, which is a necessary process in the
normal maturation and development of the mammary gland (14-16).
Growth factors/ cytokines in breast tissue
The stroma includes fibrous connective tissue, extracellular matrix (ECM) proteins, fibroblasts,
adipocytes, endothelial cells, and innate immune cells. Stroma provides physical structure for the
gland and stromal cells secrete signals that are important in the development and function of the
epithelium [18]. The extracellular matrix (ECM) together with growth factors/cytokines and cell-
cell interactions, modulate the shape, polarity and behavior (survival, proliferation,
differentiation, or migration) of cells in mammary tissue [19]. The interactions between cells and
ECM are also crucial in determining the organization of the ECM itself [20, 21]. Both cell
behavior and tissue structure is therefore affected by cell-ECM interactions. Growth
factor/cytokines, as the important signals in the mammary tissue microenvironment, play a major
role in determining tissue structure and behavior. Thus, studying the association between growth
factor/cytokines and MD, as a marker of the tissue structure and breast cancer, is crucial for
understanding mechanisms of breast cancer development.
IL-6 and breast cancer
IL-6 has been detected in conditioned medium generated from breast stromal fibroblasts and has
been shown to have paracrine stimulatory effects(17). IL-6 is involved in intercellular signaling
between mesenchyme and breast cancer epithelium(18). Interleukin-6 is a pleotropic cytokine
with both tumor promoting and tumor inhibitory functions. Here, we review the role of IL-6 in
breast tissue based on in vitro and in vivo experiments. We also review studies that investigated
serum levels of IL-6 in breast cancer patients.
4

IL-6 in vitro
Conze et al. showed that multi drug resistant human breast cancer cells (MCF-7/ADR) produce
high levels of IL-6; no IL-6 was detected from the parental sensitive cell line (MCF-7)(19).
Furthermore, exogenous IL-6 was added to the drug resistant cell line and significantly increased
resistance to doxorubicin treatment(19). Chiu et al. showed that regulatory effect of IL-6 on
normal and transformed cell lines is differential and modified by the ER status. The growth of
ER positive breast cancer cell lines was inhibited by IL-6; ER negative lines showed resistance
to the inhibitory effects of IL-6. They also showed that IL-6 secreted from ER(-) lines acts in a
paracrine fashion and suppresses the growth of ER(+) lines(20). Adding IL-6 to the medium
containing breast cancer cell lines showed no effects. However, when estrone sulfate and IL-6
were simultaneously added, proliferation was significantly increased(21). When ER-alpha-
positive breast tumor cell lines MCF-7 were treated with paracrine and autocrine IL-6, an
increased growth rate was observed(22). Adding breast cyst fluid (BCF) with high IL-6 content
obtained from women with gross cystic breast disease (GCB) to breast tumor-derived fibroblasts,
increased aromatase activity and minimally decreased the growth in these cells, suggesting the
role of IL-6 in regulating aromatase activity in breast cancer cells(23).
IL-6 in vivo
Karczewska et al. studied 75 tumor samples from breast carcinoma patients and found a strong
correlation between expression of IL-6 and its receptor subunits with early stages in breast
carcinoma tissues. They also showed that IL-6 expression is positively association with overall
survival and disease free survival (24). Fontanini et al. analyzed the expression of IL-6 in 149
cases of invasive breast carcinoma. They reported an inverse association between expression of
5

this cytokine and histological tumour grade. They also observed a direct correlation between the
percentage of IL-6-positive cells and that of oestrogen and progesterone receptor-positive cells in
tumor samples(25). In breast tumor tissue obtained from breast cancer patients, a significant
correlation was observed between aromatase activity and IL-6 production. Such association was
not found for adipose tissue obtained from the breast quadrants(26).
IL-6 serum levels and breast cancer
Serum concentrations of IL-6 have been shown to be significantly higher in breast cancer
patients compared to healthy women(27, 28). There has also been a strong correlation between
serum IL-6 levels and clinical stage of the disease(27). Higher levels of serum IL-6 has been
shown to be a poor prognosticator in metastatic breast cancer independently associated with
lower overall survival (29-31). In patients with hormone-refractory metastatic breast cancer
(receptor-negative for both estrogen and progesterone), IL-6 levels was shown to be associated
with poor survival(32).

6

References
1. Boyd N, Lockwood G, Martin L, et al. Mammographic densities and breast cancer risk. Breast
Dis 1998;10(3-4):113-26.
2. Boyd N, Guo H, Martin L, et al. Mammographic density and the risk and detection of breast
cancer. N Engl J Med 2007;356(3):227-36.
3. McCormack V, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast
cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 2006;15(6):1159-69.
4. Vachon C, Kuni C, Anderson K, et al. Association of mammographically defined percent breast
density with epidemiologic risk factors for breast cancer (United States). Cancer Causes Control
2000;11(7):653-62.
5. Cabanes A, Pastor-Barriuso R, García-López M, et al. Alcohol, tobacco, and mammographic
density: a population-based study. Breast Cancer Res Treat 2011;129(1):135-47.
6. Butler LM, Gold EB, Conroy SM, et al. Active, but not passive cigarette smoking was inversely
associated with mammographic density. Cancer Causes Control 2010;21(2):301-11.
7. Conroy SM, Butler LM, Harvey D, et al. Physical activity and change in mammographic density:
the Study of Women's Health Across the Nation. Am J Epidemiol 2010;171(9):960-8.
8. Woolcott CG, Courneya KS, Boyd NF, et al. Mammographic density change with 1 year of
aerobic exercise among postmenopausal women: a randomized controlled trial. Cancer Epidemiol
Biomarkers Prev 2010;19(4):1112-21.
9. Qureshi SA, Ellingjord-Dale M, Hofvind S, et al. Physical activity and mammographic density in
a cohort of postmenopausal Norwegian women; a cross-sectional study. Springerplus
2012;1(1):75.
10. Aitken Z, Walker K, Stegeman BH, et al. Mammographic density and markers of socioeconomic
status: a cross-sectional study. BMC Cancer 2010;10:35.
11. Hawes D, Downey S, Pearce CL, et al. Dense breast stromal tissue shows greatly increased
concentration of breast epithelium but no increase in its proliferative activity. Breast Cancer Res
2006;8(2):R24.
12. Guo Y, Martin L, Hanna W, et al. Growth factors and stromal matrix proteins associated with
mammographic densities. Cancer Epidemiol Biomarkers Prev 2001;10(3):243-8.
13. Alowami S, Troup S, Al-Haddad S, et al. Mammographic density is related to stroma and stromal
proteoglycan expression. Breast Cancer Res 2003;5(5):R129-35.
14. Cullen KJ, Lippman ME. Stromal-epithelial interactions in breast cancer. Cancer Treat Res
1992;61:413-31.
15. Dickson RB, Lippman ME. Growth factors in breast cancer. Endocr Rev 1995;16(5):559-89.
16. Sakakura T. New aspects of stroma-parenchyma relations in mammary gland differentiation. Int
Rev Cytol 1991;125:165-202.
17. Adams EF, Rafferty B, White MC. Interleukin 6 is secreted by breast fibroblasts and stimulates
17 beta-oestradiol oxidoreductase activity of MCF-7 cells: possible paracrine regulation of breast
17 beta-oestradiol levels. Int J Cancer 1991;49(1):118-21.
18. Hutchins D, Steel CM. Regulation of ICAM-1 (CD54) expression in human breast cancer cell
lines by interleukin 6 and fibroblast-derived factors. Int J Cancer 1994;58(1):80-4.
19. Conze D, Weiss L, Regen PS, et al. Autocrine production of interleukin 6 causes multidrug
resistance in breast cancer cells. Cancer Res 2001;61(24):8851-8.
20. Chiu JJ, Sgagias MK, Cowan KH. Interleukin 6 acts as a paracrine growth factor in human
mammary carcinoma cell lines. Clin Cancer Res 1996;2(1):215-21.
21. Honma S, Shimodaira K, Shimizu Y, et al. The influence of inflammatory cytokines on estrogen
production and cell proliferation in human breast cancer cells. Endocr J 2002;49(3):371-7.
7

22. Sasser A, Sullivan N, Studebaker A, et al. Interleukin-6 is a potent growth factor for ER-alpha-
positive human breast cancer. FASEB J 2007;21(13):3763-70.
23. Reed MJ, Coldham NG, Patel SR, et al. Interleukin-1 and interleukin-6 in breast cyst fluid: their
role in regulating aromatase activity in breast cancer cells. J Endocrinol 1992;132(3):R5-8.
24. Karczewska A, Nawrocki S, Breborowicz D, et al. Expression of interleukin-6, interleukin-6
receptor, and glycoprotein 130 correlates with good prognoses for patients with breast carcinoma.
Cancer 2000;88(9):2061-71.
25. Fontanini G, Campani D, Roncella M, et al. Expression of interleukin 6 (IL-6) correlates with
oestrogen receptor in human breast carcinoma. Br J Cancer 1999;80(3-4):579-84.
26. Purohit A, Ghilchik MW, Duncan L, et al. Aromatase activity and interleukin-6 production by
normal and malignant breast tissues. J Clin Endocrinol Metab 1995;80(10):3052-8.
27. Kozłowski L, Zakrzewska I, Tokajuk P, et al. Concentration of interleukin-6 (IL-6), interleukin-8
(IL-8) and interleukin-10 (IL-10) in blood serum of breast cancer patients. Rocz Akad Med
Bialymst 2003;48:82-4.
28. Jiang XP, Yang DC, Elliott RL, et al. Reduction in serum IL-6 after vacination of breast cancer
patients with tumour-associated antigens is related to estrogen receptor status. Cytokine
2000;12(5):458-65.
29. Bozcuk H, Uslu G, Samur M, et al. Tumour necrosis factor-alpha, interleukin-6, and fasting
serum insulin correlate with clinical outcome in metastatic breast cancer patients treated with
chemotherapy. Cytokine 2004;27(2-3):58-65.
30. Salgado R, Junius S, Benoy I, et al. Circulating interleukin-6 predicts survival in patients with
metastatic breast cancer. Int J Cancer 2003;103(5):642-6.
31. Zhang GJ, Adachi I. Serum interleukin-6 levels correlate to tumor progression and prognosis in
metastatic breast carcinoma. Anticancer Res 1999;19(2B):1427-32.
32. Bachelot T, Ray-Coquard I, Menetrier-Caux C, et al. Prognostic value of serum levels of
interleukin 6 and of serum and plasma levels of vascular endothelial growth factor in hormone-
refractory metastatic breast cancer patients. Br J Cancer 2003;88(11):1721-6.

8

Chapter 2

Variation in inflammatory cytokine/growth-factor genes and
mammographic density in premenopausal women aged 50-
55

9

Abstract
Background: Mammographic density (MD) has been found to be an independent risk factor for
breast cancer. Although data from twin studies suggest that MD has a strong genetic component,
the exact genes involved remain to be identified. Alterations in stromal composition and the
number of epithelial cells are the most predominant histopathological determinants of
mammographic density. Interactions between the breast stroma and epithelium are critically
important in the maturation and development of the mammary gland and the cross-talk between
these cells are mediated by paracrine growth factors and cytokines. The potential impact of
genetic variation in growth factors and cytokines on MD is largely unknown.
Methods: We investigated the association between 89 single nucleotide polymorphisms (SNPs)
in 7 cytokine/growth-factor genes (FGFR2, IGFBP1, IGFBP3, TGFB1, TNF, VEGF, IL6) and
percent MD in 301 premenopausal women (aged 50 to 55 years) participating in the Norwegian
Breast Cancer Screening Program. We evaluated the suggestive associations in 216
premenopausal Singapore Chinese Women of the same age.
Results: We found statistically significant associations between 9 tagging SNPs in the IL6 gene
and MD in Norwegian women; the effect ranged from 3-5% in MD per variant allele (p-values =
0.02 to 0.0002). One SNP in the IL6 (rs10242595) significantly influenced MD in Singapore
Chinese women.
Conclusion: Genetic variations in IL6 may be associated with MD and therefore may be an
indicator of breast cancer risk in premenopausal women.

10

Introduction
High mammographic density (MD) is an established risk factor for breast cancer. Women with
extensive MD have been found to have four to six times the risk of breast cancer compared to
women with little or no density (1-3). MD is influenced by several breast cancer risk factors
including age, body mass index (BMI), parity, age at first birth, hormone therapy use and
physical activity; these variables jointly explain approximately 30% of the variability in MD (4).
It is likely that genetic variation is another key factor influencing variability in MD. Twin
studies suggest that genetic factors account for 30-60% of the variance in MD (5-7). However,
the genetic determinants of MD have not yet been identified. In a recent combined meta-
analysis of data from five genome wide association studies (GWAS) among women of European
descent, one locus (ZNF365- rs10995190) was reported as highly associated with MD after
correction for age and BMI. Although highly statistically significant (combined P=9×6·10−
10
),
this SNP explains only 0.5% of the variance in MD (8). It seems likely that there will be
multiple other loci involved not detected in the GWAS given the low statistical power of
GWAS (9, 10).
The histopathological composition of dense breast tissue consists of both stroma and
concentrated epithelial tissue (11). Mammographically dense breasts have been shown to have
higher amounts of collagen, more extensive stromal fibrosis, and higher numbers of epithelial
cells when compared with breasts with little density (11-14). Breast stroma and epithelium
interact by means of paracrine cytokines and growth factors, which is a necessary process in the
normal maturation and development of the mammary gland (15-17).
11

The stroma includes fibrous connective tissue, extracellular matrix (ECM) proteins, fibroblasts,
adipocytes, endothelial cells, and innate immune cells. Stroma provides physical structure for the
gland and stromal cells secrete signals that are important in the development and function of the
epithelium (18). The extracellular matrix (ECM) together with growth factors/cytokines and cell-
cell interactions, modulate the shape, polarity and behavior (survival, proliferation,
differentiation, or migration) of cells in mammary tissue (19). The interactions between cells and
ECM are also crucial in determining the organization of the ECM itself (20, 21). Both cell
behavior and tissue structure is therefore affected by cell-ECM interactions. Thus, studying the
growth factor/cytokines, as the important signals in the mammary tissue microenvironment, and
their role in determining mammographic density, as a marker of the tissue structure and breast
cancer, is crucial for understanding mechanisms of breast cancer development.

A number of studies have suggested an association between growth factors and cytokines and
MD. Specifically, serum levels of IGF-I and IGF binding proteins have been associated with
MD (22-24); findings have been more consistent in premenopausal than in postmenopausal
women (24-26). Further, quantitative microscopy using immunoreactive staining has shown
higher amounts of IGF-I in dense breasts compared with lower density breasts, especially in
women younger than 50 years of age (13). Genetic variations in IGF and IGF binding proteins
have been associated with MD in several studies (7, 27-30). The role of other growth factors and
cytokines such as transforming growth factor-beta (TGF-β), interleukins and tumor-necrosis-
factor-alpha (TNF-α) with MD has not been well described. A gene expression analysis found
decreased levels of TGF-β signaling in women with increased MD (31). One study observed a
positive association between serum levels of interleukin-6, TNF-α, and C-reactive protein (CRP)
12

with MD. Although that association did not remain statistically significant after adjusting for
BMI (32), the sum of the findings to date was supportive, and we decided to further study the
association between growth factor genetic variants and MD.
Given the biological constituents of MD, the known role of hormone therapy on MD (33, 34),
and the individual variability in such hormonal effects, we recently investigated the association
between genetic variants in 23 hormone metabolism genes and 7 growth factor genes and MD in
postmenopausal participants of the Norwegian Breast Cancer Screening Program (NBCSP). That
analysis suggested that there was an association with genetic variants in PRL and CYP1B1 in
hormone users (most of whom had used norethisterone acetate preparations). In women who had
never used hormone therapy, it was not a hormone gene, but a growth factor gene that was most
important (genetic variants in TNF-α.) This suggests that genetic determinants of MD may vary
depending on women’s hormonal milieu, and indicated that in never users of hormone therapy
growth factor genes may play a role (35).
We therefore explored the role of variation in growth factor genes in premenopausal women
participating in NBCSP and compared the results with our previous findings in postmenopausal
women. We also decided to test any association in an independent sample of similarly aged
premenopausal Singapore Chinese women.

Materials and methods
Study population
Norwegian Breast Cancer Screening Program (NBCSP) participants
The NBCSP is a governmentally funded program which provides biennial screening
mammograms to all Norwegian women 50-69 years of age. The screening program began as a
13

four-year pilot project in 1995-96 in four counties of Norway. The project was expanded to all
19 counties and became a nationwide program in 2004. As part of the NBCSP, all women of the
appropriate age are sent an invitation letter to receive a bilateral two-view mammogram
biennially. Each woman is given an appointment time and location for receiving the
mammogram. During the first 10 years (1996–2005), 76.2% of invited women participated in
the screening program (36).
In 2004, 17,050 female residents of the three largest counties in Norway (Oslo, Akershus,
and Hordaland) were invited to participate in the current study at the same time as they were
mailed the official NBCSP invitation letter. This study has previously been described (37). In
brief, participants were asked to complete a risk factor questionnaire which included questions
on menstrual and reproductive history, oral contraceptive and menopausal hormone use, family
history of breast cancer, current weight and height, alcohol and smoking. Subjects were asked
to bring the completed questionnaire and informed consent to the clinic on the day of their
scheduled mammogram. Approximately 71% (N=12,056) of the invited women attended the
scheduled mammographic examination and 66% of the attendees aged 50 to 69 (N=7,941)
completed the risk factor questionnaire.

Buccal kits were mailed to 7,174 of the 7,941 women who completed the mammogram
and questionnaire to collect DNA for genetic testing. A total of 3,728 women (51% of the 7,174
women) provided a buccal sample. We requested mammograms from the radiological facilities
on all 3,728 women with a completed questionnaire and a buccal sample. After excluding
women with only a digital mammogram (n=300), we were able to obtain analog mammograms
from the year 2004 on 2,876 women. Of these, 121 women were excluded for the following
14

reasons; history of breast or any cancers (N=17), undetermined breast area (N=3), missing age
(N=28), missing BMI (N=73) (height=46/weight=67). After the exclusions, a total of 2,755
women aged 50 to 69 had usable analog mammogram and complete risk factor data. All the
participants signed an informed consent and the study was approved by the USC institutional
review board, the Norwegian regional ethics committee and the Norwegian Data Inspectorate.

Mammographic Density Assessment
Left craniocaudal mammograms were scanned using a Kodak Lumisys 85 scanner. MD was
assessed by a trained reader (GU) using a previously validated computer-assisted method (the
University of Southern California Madena software) (38). The reader assessed the absolute MD
by outlining all dense areas within the breast except white artifacts, prominent fibrous strands,
vasculature or the pectoralis muscle. The total area of the breast was assessed by a research
assistant who was trained by GU. MD was calculated as the absolute density divided by the total
area of the breast.

Tagging SNP selection and genotyping
We selected genes encoding growth factors (VEGF), growth factor receptors (FGFR2, GHRHR),
growth factor binding proteins (IGFBP1; IGFBP3), and cytokines (TGFB1, TNF, IL6). For
VEGF, IGFBP1;IGFBP3, TGFB1, TNF, and IL6, we selected tagging SNPs to capture the
genetic variation in each gene with an R
2
>0.80. Tagging SNPs were selected from 20kb
upstream of 5’ untranslated region (UTR) to 10kb downstream of 3’ UTR that tagged all
common SNPs (minor allele frequency ≥5%) among the non-Hispanic white or Chinese
population. This selection was done using the Snagger (39) software and a custom database of
15

the Hapmap CEU data (http://hapmap.ncbi.nlm.nih.gov); release 24) merged with the Affymetrix
500K panel as well as the Hapmap CHB data release 24. For FGFR2 and GHRHR, we selected
one SNP of interest for each gene.
Due to restricted funding, DNA extraction and genotyping were performed on 3,317 of
the 3,728 participants who donated buccal samples. DNA was extracted from buccal swabs
using the standard protocol for the QIAamp blood DNA kit (Qiagen, Valencia, CA). We
genotyped the selected SNPs using an Illumina BeadLab System (San Diego, CA) with
GoldenGate®. Genotyping was completed in the USC Genomics Center under the direction of
Dr. David Van Den Berg. Briefly, samples were run in a 96-well format using the Illumina
Sentrix Array technology, scanned on a BeadArray Reader, and analyzed using BeadStudio
Software (v.3.0.9) with Genotyping Module (v.3.0.27) (Illumina). The SNPs with <85% call
rates were excluded: this resulted in the exclusion of 4% of SNPs. The genotyping concordance
rate based on 57 duplicate samples was 98%. Out of 97 SNPs in this pathway, 8 SNPs were
excluded due to departure from Hardy-Weinberg equilibrium (HWE) (P<0.001), leaving 89
SNPs for further analysis.
Of the genotyped 3,317 samples, 241 samples were excluded from the analysis due to
low overall genotype call-rates (less than 80%). In total, 2,397 women (2,055 postmenopausal,
342 peri- or premenopausal at the time of mammography) had complete information on
genotype, MD and breast cancer risk factors. Of the 342 peri- or premenopausal women, 301
were premenopausal and aged 55 or younger at the time of mammogram (they were still
menstruating and were not taking any type of hormones).

16

Statistical analysis
We explored the association between MD and potential risk factors (age, BMI, age at full-term
pregnancy, number of children, age at menarche, family history of breast cancer, and level of
education) using categorical variables. We used analysis of covariance (ANCOVA) to calculate
age adjusted least-square mean of MD in each category. A test of trend across these categories
was generated using linear regression models after adjusting for age; BMI was further included
in the models (40).

We investigated the association between each genetic variant and MD based on additive models,
which estimate the difference in the continuous dependent variable (MD) per copy of the minor
allele of each polymorphism after adjustments for age and BMI. In order to explore the potential
modifying effect of BMI on the findings, we repeated this analysis separately in women with
BMI below as well as above 25 kg/m
2
. We considered a two-sided P value of < 0.05 as
statistically significant.

Replication study and combined analysis
We evaluated the statistically significant associations observed in the NBCSP participants using
data from 163 premenopausal Singapore Chinese women of similar age, who were participants
of the genetic study component of the Mammography Subcohort of the Singapore Chinese
Health Study (SCHS). Participants of the Mammography Subcohort were enrolled in both the
SCHS and the Singapore Breast Screening Project (SBSP); details have been described
17

previously (41, 42). Briefly, 35,298 Chinese women and 27,959 men, ages 45-74 years, enrolled
in SCHS during 1993-1998. Subjects were residents of government housing estates; during the
enrollment period 86% of the Singapore population resided in such housing facilities. During
1994 to 1997, Singaporean women ages 50-64 years were invited for a screening mammography
as part of the SBSP (43). Through a computer linkage, a total of 3,777 women common to the
SBSP and SCHS databases were identified. Of these, mammograms were successfully retrieved
from 3,702 women. We excluded 6 women due to missing information on key variables; 1
woman who was later found not to be a Singapore resident. Mammograms of the
Mammography Subcohort of the SCHS were scanned using a Cobrascan 812T scanner
(Radiographic Digital Imaging Inc., Compton, California). Images were read using the same
procedures and software by GU. The total breast area was assessed by two assistants and the
average of the two readings was used. Of the 3,695 women in the Mammography Subcohort
(41, 44, 45), DNA samples were available on 2,164 women (1,848 blood, 316 buccal). Twenty
tagging SNPs in the IL6 locus were selected and genotyped using the same methods used for the
NBCSP participants; 1 SNP with a genotyping call rate <85% and 7 SNPs with a MAF<0.01 in
Chinese population were excluded, leaving 12 IL6 SNPs for statistical analyses. 2,038 samples
of the 2,164 genotyped samples had a genotyping success rate (call rate ≥ 85%). The mean age
of the 2,038 participants were 57.2 (SD 4.3). Two hundred and sixteen women self-reported as
premenopausal at time of mammography; of these, 163 women who were aged 55 or younger at
mammography (range 46-55) and had never used hormone therapy were included in the current
analysis. Genotyping concordance based on the 42 random duplicate samples was >99.9%.
None of the 12 IL6 SNPs departed significantly from HWE (P≥0.01).
18

We combined the Norwegian and Singapore samples and assessed the association between 12
IL6 tagging SNPs and MD. In the combined analysis, we defined the risk allele as the minor
allele in the Norwegian sample. We adjusted the models for age at mammogram (continuous),
BMI at mammogram (continuous), and ethnic and dialect group (Norwegian, Cantonese,
Hokkien).

Results

Baseline characteristics of the participants
The baseline characteristics of the postmenopausal sample have previously been described (35).
In brief, mean age at screening was 58.4 years, mean BMI 25.1, mean age at menarche 13.2
years, mean age at first pregnancy 22.0 years, mean number of children 2.0, and mean years of
education 12.8. In premenopausal women (Table 1), mean MD decreased with increasing BMI
after adjustment for age (P<0.0001). Older age at full term pregnancy was associated with higher
MD after adjustment for age and BMI (P=0.02). Higher level of education was associated with
higher percent MD after adjustment for age (P=0.011) but the association was no longer
statistically significant after we further adjusted the model for BMI.

Associations between SNPs and mammographic density in NBCSP participants
The effect of growth factor gene variants on MD was significantly modified by menopausal
status (Table S1; see (35) for detailed results on postmenopausal women). The majority of
statistically significant associations were observed among the premenopausal women only. In the
remaining part of the results, we limit the analysis to this group of women.
19

Associations between SNPs and mammographic density in premenopausal NBCSP
participants
In the additive genetic model, IL6 tagging SNPs rs6952003, rs10242595, rs11766273,
rs1880241, rs1880242, rs2069833, rs2069840, rs4552807 and rs7776857 were associated with
MD with P values less than 0.05 (Table 2 and Table 3). The estimated difference in MD per
minor allele of each IL6 SNP ranged from 3-5%, with p-values ranging from 0.04 to 0.0002. One
TNF tagging SNP (rs2857605) was also significantly associated with MD (beta=2.99), however
the level of significance was relatively low (P= 0.046). We did not find any statistically
significant associations between the polymorphisms in VEGF, GHRHR, IGFBP1, IGFBP3,
FGFR2, and TGFB1 and MD (Table 2 and Table S2).

In addition, we examined the associations separately in women with low and high BMI (using
25kg/m
2
as the cut-off value). The association between IL6 SNPs and MD appeared to be
restricted to women with a BMI less than 25 kg/m
2
; 8 of 9 tagging IL6 SNPs that showed
significant results in the overall analysis remained significant only in the low BMI group. For 5
of these 8 SNPs, the effect modification by BMI was statistically significant (Table 4).

Association between IL6 SNPs and MD in Singapore Chinese women
Of the 12 evaluated IL6 SNPs, only rs10242595 was associated MD in the replication sample,
with an estimated 10.6% increase in MD per A-allele (Table 5). In the pooled analysis with data
from the Singapore Chinese women and the NBCSP, rs10242595 A-allele was associated with a
6.2% increase in MD (P=0.0001).
20

Discussion

We studied the association between MD and the SNPs in 7 growth factor or cytokine genes
including IGFBP1, IGFBP3, TNF, FGFR2, VEGF, GHRHR, and IL6. We observed statistically
significant effect modification by menopausal status. While there were no significant
associations for SNPs in 6 of the genes, 9 SNPs in the IL6 region (rs6952003, rs10242595,
rs11766273, rs1880241, rs1880242, rs2069833, rs2069840, rs4552807, rs7776857) were each
significantly associated with MD in premenopausal women. MD varied between 3.4% to 5.8%
per allele for these SNPs. Several of the associations were statistically significantly modified by
BMI; the associations were limited to women with low BMI. The association with rs10242595
was replicated in an independent study of Singapore Chinese women. Given that each 1%
increment in MD has been shown to be associated with a 2% higher relative risk of breast cancer
(46), the magnitude of these associations suggest that these variants could be clinically
significant.

The lack of association we found between SNPs in most of these growth factor and cytokine
genes (IGFBP1, IGFBP3, FGFR2, VEGF and GHRHR) and MD is consistent with results from
the few studies that have been conducted on these genes and MD. Consistent with our findings,
the majority of previous studies investigating IGFBP1 and IGFBP3 SNPs reported a lack of
significant association between IGFBP1/IGFBP3 SNPs including rs2854746, rs1553009,
rs1065780, rs2132570, rs3110697, rs35539615, rs4619, and rs6670 and MD. These studies
include a cross-sectional study among 1,121 of premenopausal and postmenopausal women from
21

the Nurses' Health Study cohort investigating 13 tagging SNPs (27), a study of 819 pre- and
postmenopausal women of Hawaiian, European, and Japanese ancestry from the Multiethnic
Cohort study investigating 22 tagging SNPs (30), and a study of 1,916 premenopausal women
within the Prospect-EPIC cohort investigating 11 tagging SNPs (47). In the study by Tamimi et
al., rs4619 in IGFBP1/IGFBP3 region was positively associated with increased MD in a mixed
population of premenopausal and postmenopausal women (27), however, this association was
not observed in another study (30) nor in our study. Results from the Multiethnic Cohort study
showed no association between IGFBP1/IGFBP3 rs10228265, rs1496497 and rs3110697 and
MD in the overall analysis, but a significant association was found when the analysis was limited
to women with Hawaiian and Japanese descent (30). In that study, the results were based on data
from premenopausal and postmenopausal women pooled together. Our finding of no significant
association between FGFR2 rs2981582 and MD is consistent with a study of 516 white (429
non-Hispanic, 87 Hispanic) women in the age range of 20 to 49 years (48) and in a study of 825
pre-and postmenopausal women within the Multiethnic Cohort study (49). We looked at only
one SNP in GHRHR gene (rs4988496) and found no significant association. Similarly, in a
study of 177 premenopausal women (50) a different polymorphism, GHRHR A57T, was reported
as not significantly associated with MD.

While there have been no previous studies on IL6 SNPs and MD, there are experimental data
suggesting that our significant findings are biologically plausible. Cultures of normal mammary
epithelial cells obtained from healthy women were shown to release interleukin-6 and express
interleukin-6 receptor (51). Data coming from in vitro studies supports the pleiotropic (having
both tumor promoting and tumor-counteracting effects) nature of interleukin-6 in breast tissue
22

(52). It seems plausible that variations in the IL6 gene could have effects on cell growth and alter
MD and eventually breast cancer risk.
Another plausible way to explain the effects of interleukin-6 on MD is indirectly through
estrogen. Interleukin-6 has an important role in regulating estrogen synthesis in normal and
malignant breast tissues. The activities of aromatase, estradiol 17β-hydroxysteroid
dehydrogenase and estrone sulfatase have been shown to be influenced by interleukin-6 in these
tissues (53).

Mammographic density is inversely associated with the amount of fat tissue in the breast. It is
possible that genetic factors could influence MD by influencing the amount of fat in the breast.
IL6 rs10242595-A allele was associated with decreased total body fat mass in one study where
fat mass was measured with dual energy X-ray absorptiometry (54). Our finding is consistent
with this result; we found a significant positive association between this polymorphism and MD.
IL6 rs1880242 has been significantly associated with decreased risk of obstructive sleep apnea
syndrome; obesity is a strong risk factor for this syndrome (55). Consistent with results from
this study, we found a significant positive association between this polymorphism and MD.
However, this may suggest that the observed association between this SNP and MD is driven by
its association with non-dense breast area rather than the absolute density. When we tested the
association with absolute density for this IL-6 SNP, the observed association was similar to
percent MD.
In this study we found both positive and inverse associations with different IL6 SNPs. To what
extent the IL-6 tagging SNPs modify IL-6 protein levels, and the direction of effect on protein
23

levels is not yet clear. The negative association observed for some of these SNPs and MD does
not necessarily represent a negative association between serum or tissue levels or function of IL-
6 and MD. Future studies with available breast tissue samples, blood samples and
mammographic density measurements would allow us to explore whether any of these
associations represent tissue specific effects. However, it would be a challenge to assemble a
large enough group of healthy women with breast tissue samples for such investigations.
In this study the association between 5 IL6 SNPs and MD was significantly modified by BMI
(Table 4). Higher magnitude and more significant associations in women with the BMI of 25 or
less, suggests that the role of IL6 variants in predicting MD is less important in obese women.

There were several strengths of our study. The study sample was selected from a population
based study conducted within a national screening program, and the population studied is
ethnically homogeneous. Further, we used previously validated MD assessment techniques, and
collected detailed information regarding key MD risk factors. We also replicated our findings in
a different ethnic group. BMI was considered a potentially confounding factor in this study; we
controlled for this variable in all the analyses presented. Many previous studies of MD
combined pre- and postmenopausal women, which could mask any findings in premenopausal
women. Our analysis conducted separately in premenopausal and postmenopausal women, may
have helped to clarify results in premenopausal women. A limitation of our study was using
buccal samples for genotyping of the NBCSP samples, which resulted in a relatively lower call-
rate compared to the results from studies using blood samples. Further, given the nature of these
screening programs, relatively few women were eligible for our study of premenopausal women.
Finally, although it could have been more informative to examine the association between
24

genetic variants, serum or tissue levels of growth factors/cytokines as intermediates, and MD, we
did not have serum or tissue available to perform such analyses.

Conclusions
Our study suggests that SNPs in the IL6 region may be associated with MD in premenopausal
women. Future studies should be conducted to relate these SNPs and interleukin-6
concentrations as well as IL6 gene expression in the mammographically dense tissue to elucidate
the mechanisms underlying this association.

25

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28

Tables
Table 1 Mean percentage of mammographic density (MD) by descriptive characteristics (n=301)

N % %MD
a
P
b
%MD
d
P
Age (years) 50 107 35.55 25.35

25.32

51 92 30.56 26.92

27.58

52-55 102 33.89 25.42 0.8516 24.86 0.8157
BMI (Kg/m2) <20 18 5.98 44.78

34.04

20-23 95 31.56 32.44

26.01

23-25 67 22.26 26.32

24.74

25-30 88 29.24 20.04

24.26

Age at first full term pregnancy >30 33 10.96 11.13 <0.0001 27.44 0.1848

=<20 46 15.97 19.45

19.64

21-24 78 27.08 24.32

25.21

25-29 72 25 29.94

28.3

>=30 64 22.22 25.51 0.0364
c
26.48 0.0223
c

Nulliparous 28 9.72 30.69 0.011 29.91 0.0079
Number of children Nulliparous 28 9.3 30.63

29.76

One 46 15.28 26.22

28.67

Two 145 48.17 24.2

26.06

Three or more 82 27.24 26.94 0.4829 24.09 0.1787
Age at menarche Younger than 12 38 12.62 22.46

26

12 68 22.59 25.25

26.17

13 88 29.24 26.01

25.31

14 59 19.6 25.28

24.96

Older than 14 48 15.95 29.8 0.1013 27.4 0.8585
Family history of breast cancer No 218 72.43 26

25.6

Yes 83 27.57 25.48 0.8247 26.51 0.6621
Level of education Secondary & below 49 17.25 19.93

24.24

Higher secondary 100 35.21 26.44

26.25

College/university 135 47.54 28.28 0.0102 26.86 0.3775
a
Percent MD adjusted for age at mammogram (continuous)
b
test for trend
c
test for trend excluding the nulliparous group
d
Percent MD adjusted for age at mammogram (continuous) and BMI at mammogram (continuous)

29

Table 2 the association between the most significant SNP within each growth factor gene and MD in Norwegian women
(N=310).
Gene name
Number of
SNPs tested
Most
significant SNP
WW
a
WV
b

VV
c

MAF
d
beta
e
SE P
IL6 18 rs1880241 85 158 54 0.45 4.98 1.34 0.0002
TNF 15 rs2857605 182 92 21 0.22 2.99 1.49 0.046
VEGF 19 rs3025030 217 78 3 0.13 3.51 1.98 0.07
IGFBP1;IGFBP3 26 rs13232606 261 25 1 0.05 4.91 3.10 0.12
TGFB1 9 rs12983047 209 70 16 0.16 1.04 1.62 0.52
GHRHR 1 rs4988496 263 25 0 0.05 -4.33 3.36 0.20
FGFR2 1 rs2981582 113 139 42 0.36 0.78 1.36 0.57
a
Number of women with wild-wild genotype
b
Number of women with wild-variant genotype
c
Number of women with variant-variant genotype
d
Minor allele frequency
e
Percent MD per variant allele based on additive model adjusted for age at mammogram (continuous) and BMI at mammogram
(continuous)

30

Table 3 Association between 9 IL6 tagging SNPs (with P-value less than 0.05) and MD after adjustment for age and BMI,
based on an additive genetic model (N=301)
Gene name SNP Chromosome Position
a
Alleles WW
b
WV
c
VV
d
MAF
e
beta
f
SE P

IL6 rs1880241 22759469 A:G 85 158 54 0.45 4.98 1.34 0.0002
IL6 rs10242595 22774231 G:A 154 125 15 0.25 5.60 1.56 0.0004
IL6 rs7776857 22754768 T:G 110 150 37 0.38 -4.87 1.39 0.0005
IL6 rs2069833 22767664 T:C 81 157 56 0.47 -4.61 1.36 0.0008
IL6 rs1880242 22759607 G:T 70 162 66 0.46 3.84 1.37 0.0053
IL6 rs4552807 22751019 T:A 110 146 39 0.42 3.54 1.37 0.010
IL6 rs11766273 22775663 G:A 247 48 1 0.08 -5.84 2.40 0.015
IL6 rs2069840 22768572 C:G 132 133 31 0.31 3.40 1.41 0.016
IL6 rs6952003 22752705 T:A 173 113 10 0.23 3.44 1.66 0.0399
a
based on map to Genome Build 37.3
b
Number of women with wild-wild genotype
c
Number of women with wild-variant genotype
d
Number of women with variant-variant genotype
e
Minor allele frequency
f
Percent MD per variant allele based on additive model adjusted for age at mammogram (continuous) and BMI at mammogram
(continuous)
31

Table 4 Association between 8 IL6 tagging SNPs from table 3 and MD in low and high BMI groups

BMI<=25 n=180

BMI>25 n=121

gene name SNP beta
a
SE P

WW
b
WV
c
VV
d

beta
a
SE P

WW WV VV

P
e
IL6 rs10242595 8.66 2.04 <.0001 101 67 10

1.47 2.28 0.52 53 58 5 0.04
IL6 rs2069833 -7.71 1.80 <.0001 48 93 35

-1.17 1.96 0.55 33 64 21 0.07
IL6 rs7776857 -7.57 1.81 <.0001 68 85 24

-1.75 2.08 0.40 42 65 13 0.72
IL6 rs4552807 6.32 1.82 0.0007 70 85 22

-0.21 1.99 0.92 40 61 17 0.06
IL6 rs1880242 6.07 1.79 0.0009 41 92 45

1.31 2.04 0.52 29 70 21 0.02
IL6 rs11766273 -10.00 3.13 0.0017 142 34 0

-2.38 3.69 0.52 105 14 1 0.88
IL6 rs1880241 5.60 1.86 0.0029 50 96 32

4.72 1.83 0.0112 35 62 22 0.01
IL6 rs6952003 5.97 2.27 0.0091 104 67 6

-0.41 2.33 0.86 69 46 4 0.03
IL6 rs2069840 3.69 1.90 0.054 71 85 21

3.44 2.00 0.09 61 48 10 0.03
a
Percent MD per variant allele based on additive model adjusted for age at mammogram (continuous) and BMI at mammogram (continuous)
b
Number of women with wild-wild genotype
c
Number of women with wild-variant genotype
d
Number of women with variant-variant genotype
e
P-value for interaction

32

Table 5 Association between IL6 SNPs and MD in Norwegian women, Singapore Chinese women, and the combined analysis including both populations
a
From linear regression models adjusting for age at mammogram (continuous) and BMI at mammogram (continuous). Additive genetic model was used.
b
From linear regression models adjusting for age at mammogram (continuous) and BMI at mammogram (continuous) and dialect group (Cantonese, Hokkien). Additive genetic
model was used. rs11766273, rs1880241, rs2069833 and rs7776857were excluded from the Singapore study because of low MAF
c
From linear regression models adjusting for age at mammogram (continuous) and BMI at mammogram (continuous) and ethnic and dialect group (Norwegian, Cantonese,
Hokkien). Additive genetic model was used.
d
Risk allele; the risk allele was defined based on the minor allele in the Norwegian sample.
e
Riske allele frequency

Norwegian
a
Study

Singapore
b
Study

Combined
c

SNP

Alleles

RA
d
RAF
e
beta SE P

Alleles RA
e
RAF beta SE P

beta SE P
rs10242595

G/A A 0.26 5.60 1.56 0.0004

A/G A 0.96 10.65 4.62 0.02

6.18 1.50 <.0001
rs12700386

C/G G 0.19 1.90 1.74 0.27

C/G G 0.04 -2.83 4.79 0.56

1.27 1.66 0.44
rs17147230

T/A A 0.01 2.09 9.37 0.82

A/T A 0.53 2.15 1.91 0.26

2.14 1.80 0.23
rs1880242

G/T T 0.49 3.84 1.37 0.0053

G/T T 0.21 -3.45 2.40 0.15

1.87 1.20 0.12
rs2056576

C/T T 0.29 1.33 1.51 0.38

C/T T 0.03 -3.95 5.35 0.46

0.88 1.47 0.55
rs2066992

G/T T 0.03 5.33 4.31 0.22

T/G T 0.80 2.56 2.42 0.29

3.13 2.06 0.13
rs2069837

A/G G 0.09 2.72 2.31 0.24

A/G G 0.16 -0.78 2.63 0.77

1.11 1.72 0.52
rs2069840

C/G G 0.33 3.40 1.41 0.02

C/G G 0.03 -7.45 5.94 0.21

2.74 1.40 0.05
rs4552807

T/A A 0.38 3.54 1.37 0.01

A/T A 0.48 2.71 7.88 0.73

3.50 1.39 0.01
rs6949149

G/T T 0.04 6.06 3.47 0.08

T/G T 0.68 2.21 2.03 0.28

3.05 1.70 0.07
rs6952003

T/A A 0.22 3.44 1.66 0.04

T/A A 0.25 -2.41 2.16 0.27

1.10 1.32 0.40
rs6969502

G/A A 0.15 0.99 1.79 0.58

A/G A 0.68 2.47 2.02 0.22

1.67 1.33 0.21
33

Chapter 3

Heritability of Lymphoid Neoplasms in Twins

34

Abstract
Background: Hodgkin lymphoma, especially the young adult type, is one of the most heritable
cancers. We previously reported a high risk of Hodgkin lymphoma to monozygotic (MZ), but not
dizygotic (DZ) co-twins of cases, and only a modest difference in risk between MZ and DZ co-
twins of non-Hodgkin lymphoma (NHL) cases (Mack, 1995). After an additional 18 years of
follow-up, we have now updated the observed occurrence of hematologic malignancies in the
initially unaffected co-twins of HL, NHL, multiple myeloma (MM), and chronic lymphocytic
leukemia (CLL) twin probands.
Methods: The number of calendar and age-specific person-years at risk for each co-twin was
calculated from the date of diagnosis of the proband to the date of last follow-up of the co-twin,
defined by the last date of contact, date of death ascertained directly or from linkage with the
National Death Index, or evidence of current vital status from a national tracing program. The
expected number of cases was calculated by applying the calendar and age-specific incidence
rates by 5-year interval categories for each hematologic neoplasm from the Surveillance,
Epidemiology and End-Results Program to the person-years of risk. Diagnoses of hematological
neoplasms in the co-twins by age and year were ascertained by direct follow-up augmented by a
linkage with the National Death Index, using diagnoses categorized by the ICD-9 codes. The
standardized incidence ratio (SIR) was computed as the observed to expected number of cases;
95% confidence intervals (CI), and the risk ratio (RR) (ratio of the SIR in MZ co-twins
compared to that in DZ co-twins) were calculated. Whereas the SIR for DZ co-twins measures
the heritable and acquired components of risk to first-degree family members, the RR provides
evidence of genetic heritability, based on the additional genomic commonality of MZ twins.
35

Results: A total of 366 (HL), 516 (NHL), 99 (MM), and 44 (CLL) co-twins of probands
contributed to the analysis. The risk of developing the same hematologic neoplasm as the
proband was generally higher in the MZ compared to the DZ co-twins, with the highest RR
observed for HL (13.3) and the lowest for NHL (1.75). Although more than 10,000 person-years
were added since the original paper, the RR’s for HL and NHL did not change substantially from
those reported in 1995. The RR for CLL was 3.3 suggesting moderately strong heritability. One
MZ co-twin developed MM producing a large SIR; however chance cannot be easily ruled out.
The findings of most interest are the continued very high risk of HL in MZ compared to DZ
twins confirming the strong heritability of this neoplasm, and the relatively low RR for NHL.
MZ and DZ co-twins of NHL probands had increased but similar SIR’s, suggesting that shared
environmental factors are more important than heritability. Subtype–specific information on the
NHL type was not available from the ICD-9 codes used by the Death Index to identify new
cases, so it is possible that stronger NHL heritability would be evident if subtypes were
considered separately.

Introduction
Both genetic and environmental factors have been suggested to influence risk of lymphoid
neoplasms. The role of heredity in the pathogenesis of lymphoid neoplasms is studied using
family history, Genome wide Association (GWA) and twin studies.
Comparing the rate of illnesses in relatives of affected subjects with those of a control group
provides a fair but not sufficient evidence to support the importance of genetic susceptibility in
36

lymphoid neoplasms. Environmental factors can also aggregate in the families and lead to the
observed excess risk of a trait in a family history study.
Hodgkin’s disease has a bimodal distribution in modern countries. The first peak is in young
adulthood (between the ages of 15 to 35) and the second one is in older ages (above 50 years).
The age specific incidence rates of Hodgkin’s disease vary by histologic subtypes. Nodular
sclerosis subtype is more common in young adults and the mixed-cellularity subtype is more
common in older ages (1). Both genetic and environmental factors have been suggested in the
pathogenesis of these subtypes. Grufferman et al.(2), in an incidence survey, observed an
unexpectedly higher concordance of the disease in unaffected siblings of patients with Hodgkin’s
disease. In a national cohort study of over 3 million young individuals (ages 0-37) in Sweden,
with a total of 943 incident Hodgkin lymphoma cases, family history of Hodgkin lymphoma in a
sibling or parent was strongly and independently associated with increased risk of Hodgkin’s
disease (3). The results were similar for nodular sclerosis and mixed cellularity subtypes. A
family cancer database in Sweden quantified the magnitude of the genetic effect in Hodgkin’s
disease by studying 12 parent-child pairs and two pairs of siblings diagnosed with Hodgkin
lymphoma (4). The extent to which parents diagnosed with Hodgkin’s disease transmit it to their
offspring was estimated as 28 percent. Significantly younger age-of-onset in the offspring of the
parents who were affected with Hodgkin’s disease suggested the anticipation of the disease in the
successive generation. Based on the results from these studies, familiality plays an important role
in Hodgkin’s disease.
A pooled analysis of 10 211 non-Hodgkin lymphoma (NHL) cases and 11 905 controls from the
International Lymphoma Epidemiology Consortium (InterLymph) reported elevated risk of NHL
for individuals who had first-degree relatives with NHL (OR: 1.5; 95% CI: 1.2-1.9), Hodgkin
37

lymphoma (OR: 1.6; 95% CI: 1.1-2.3), and leukemia (OR: 1.4; 95% CI: 1.2-2.7) (5). Base on the
results of this study, NHL confers a strong familial association which is uniform across its
subtypes.
Goldin et al. assessed the family history of 5918 CLL cases and 11 778 controls from Swedish
Family-Cancer Database and found significantly higher risk of CLL among first degree relatives
of patients with CLL (OR: 7.52 95% CI: 3.63-15.56). (6)
Relatives of individuals with monoclonal gammopathy of undetermined significance, a
premalignant disorder characterized by the presence of a monoclonal immunoglobulin in
subjects lacking evidence of multiple myeloma (MM) or other lymphoproliferative malignancies,
have been shown to be at higher risk of developing MM (RR: 2.9; 1.9-4.3) (7). Furthermore,
individuals with a parental history of MM have also been shown to be at higher risk of
developing MM OR: (2.45 95% CI, 1.63–3.55) (8).
Recently, genetic contribution has been largely studied using Genome Wide Association (GWA)
studies. GWA studies capture the common variants that explain the variability of the health
outcomes in the population and provide us with some evidence for the contribution of genetic
factors. Based on GWA studies various portions of the risk associated with lymphoid neoplasms
is attributed to genetic factors. Multiple loci related to HLA, REL, PVT1, and GATA3 have
been shown statistically significant associations with Hodgkin’s disease in 2 GWA studies (OR
ranged from 0.4 to 1.7) (9, 10). GWA studies have also suggested the role of heterogeneity in
HLA class II in follicular none-Hodgkin lymphoma (FNHL) (11-14) and diffuse large B-cell
lymphomas (DLBCL) (13). Based on these studies, various loci related to HLA area changed the
risk of NHL 20-95%. A GWAS of 1,529 cases and 3,115 controls identified six CLL risk loci,
38

providing suggestive evidence for the role of genetic factors in susceptibility to a this
malignancy(15).
3 Genomic regions associated with multiple myeloma risk have been identified in one GWA
study (OR ranged from 1.29 to 1.38) (16).
A comparison of the levels of concordance in pairs of monozygotic and dizygotic twins raised
together permits an evaluation of the role of heredity. We have previously analyzed the incidence
of malignant lymphomas in a cohort of twins (17). We compared the concordance rate for
Hodgkin’s disease in monozygotic versus dizygotic twins. 10 of the 179 pairs of monozygotic
became concordant for Hodgkin’s disease, whereas none of the 187 dizygotic twins did. Our
results strongly suggested that genetic susceptibility is important in the pathogenesis of the
disease. We did not find significantly enough evidence for the role of genetic factors in Non-
Hodgkin’s lymphomas and other lymphoid cancers. However, the duration of our observation
was relatively short to detect other types of lymphoid neoplasms, considering the relatively
young median age of the cohort.
Few twin studies have reported concordance of other lymphoid neoplasms in twins. A study
based upon a data from Childhood Cancer Survivor Study (CCSS) in collaboration with
International Twin Study (ITS), reported 6 twin pairs concordant for leukemia (out of 27 MZ and
38 DZ pairs with a leukemic proband) and one twin pair concordant for non-Hodgkin lymphoma
(out of 5 MZ and 8 DZ pairs with a proband diagnosed with the disease) (18). This study differed
from ours in that it was largely directed at a young population (median age at diagnosis was 2.5
years for cases with twins concordant for leukemia). Intraplacental metastasis or intrauterine
transmission of the leukemic cells through common placental circulations, rather than heritability
39

is proposed as the mechanism for increased risk of leukemia among these young monozygotic
twins (19).
In a follow up study based upon a data from National Academy of Sciences-National Research
Council Twin Registry, Braun et al. assessed the concordance of cancer mortality among male
twin pairs who served in World War II (20). No concordant pairs were reported among 94 twin
pairs with a proband who died from non-Hodgkin lymphoma (41 MZ, 53 DZ). Only one DZ twin
pair was concordant for leukemia as the cause of death, among 63 pairs (17 MZ, 45 DZ). The
results of this study do not reflect the true effect of genetic predisposition in mortality from
leukemia nor non-Hodgkin lymphoma.

After an additional 18 years of follow-up, we have now updated the observed occurrence of
lymphoid malignancies in the initially unaffected co-twins of HL, NHL, multiple myeloma
(MM), and chronic lymphocytic leukemia (CLL) twin probands.
Methods
Recruitment and Data collection
Using weekly advertisements in large newspapers and magazines in the United States and
Canada between 1980 and 1992, we invited “twins with cancer “to participate in our study.
Participants called in response to advertisements and voluntarily signed up for the study.
Respondents were almost always a twin or the spouse or a first degree relative of the twin. We
briefly interviewed the respondent via telephone. They provided us with information about
affected pair’s birth date, sex, and perceived zygosity; the date and place of any diagnosis of
40

chronic disease; and the date and place of death if either or both twins had died. We asked for
permission to obtain the patient’s medical records, either from the patient or a relative. We coded
the diagnosis on the medical records based on the International Classification Diseases for
Oncology
(21)
. Furthermore, we obtained the histopathological slides and confirmed the
diagnosis. Diagnoses of hematological neoplasms in the co-twins by age and year were
ascertained by direct follow-up; we mailed a questionnaire to each twin pair and followed them
every 18 months. We augmented outcome assessment in co-twins by a linkage with the National
Death Index, using diagnoses categorized by the ICD-9 codes. The date of diagnosis in the twin
of the proband, death, or last contact, was considered as the end point. The last contact occurred
in 1990 or later in 87 percent o f the MZ twin and 84 percent of the DZ twins.
Assessment of Zygosity
We asked the respondent about the perceived zygosity of the twins. Self perceptions of zygosity
have been repeatedly reported to be 90 percent accurate (22). The high accuracy of self reported
zygosity has also been validated using molecular methods (23, 24).
Assessment of Concordancy
We made additional efforts to assess the twin pairs reported to be concordant for a diagnosis. We
directly contacted each pair and reconfirmed their diagnosis and zygosity. We also collected
additional information about their medical and family history and obtained the diagnostic slides
and pathology reports. The slides were blinded and reviewed. The reviewers then classified the
slides using a modified Lukes–Butler system (25). Diagnoses of hematological neoplasms in co-
twins were augmented by a linkage with the National Death Index, using diagnoses categorized
by the ICD-9 codes.
41

Quantification of Concordancy
Since the risk of the studied lymphoid neoplasms and the probability of ascertainment are age
dependent and environmental influences are part of the familial risk associated with these
conditions, we used the standardized incidence ratio (SIR) to assess the risk of the disease in
groups of monozygotic and dizygotic twins. In order to calculate SIR in each group, we
compared the observed number of secondary cases to the expected numbers. In 5 years age
increments and diagnosis calendar year periods; we calculated the number of person years at risk
between the date of initial diagnosis and the end point according to zygosity and sex. The end
point was defined as the date of diagnosis, death, or last contact in the twin of the proband.
Calendar year periods were defined based on the trends of national incidence rates; in each
period the age specific incident rates remained approximately constant. We estimated the
number of cases expected to occur in the healthy twins of each zygosity/sex group, using age ,
sex and calendar year specific U.S. national incidence rates based on the data from the
Surveillance, Epidemiology, and End Results program (26)
Results
Between 1980 and 1995, a total number of 1025 twin pairs (220 MZ and 129 DZ male same-sex;
249 MZ and 24 DZ female same-sex; 234 DZ opposite-sex) with a lymphoid neoplasm were
identified. We classified these twins based on the diagnosis of the proband twin (Hodgkin’s
disease, Non Hodgkin lymphoma, CLL and Myeloma) and analyzed them separately (Table-1).

42

Hodgkin’s disease (HD)
HD was initially diagnosed in 366 twins (171 MZ and 195 DZ) between 1936 and 1995. 78
percent of DZ twins and 75 percent of the DZ twins were diagnosed between 1970 and 1989. 67
percent of the MZ twins and 70 percent of the DZ twins were initially diagnosed between 15 to
35 years of age.
We evaluated the risk of Hodgkin’s disease in twins of the patients initially diagnosed with
Hodgkin’s disease in respect to their zygosity, age at diagnosis (in 5 years increments), sex, and
year of diagnosis (Table 2). 171 MZ and 195 DZ unaffected twins were observed after an
average of 22 and 20 years of follow-up, respectively, after the initial diagnosis in the proband.
0.114 MZ and 0.117 DZ case would have been expected to occur by chance after the
aforementioned period in each zygosity group, based on SEER-based age-specific and period-
specific incidence rates. Among the MZ twins we observed 13 cases of Hodgkin’s (9 male and 4
female), giving the SIR of 114.2 (95% CI: 60.74, 195.29). In contrast, we observed only 1 case
of Hodgkin’s disease among the DZ twins, giving a statistically significantly lower SIR of 8.55
(95% CI: 0.11, 47.59). The risk of Hodgkin’s disease among MZ twins was approximately 13
times as likely as the corresponding risk in DZ twins. We repeated this analysis with only the
cases ascertained prospectively; 0.11 case was expected in each zygosity group. 8 cases were
observed in the MZ twins, giving a SIR of 70.69 (95% CI: 30.44, 139.30); 1 case was observed
among DZ twins (SIR: 8.55, 95% CI: 0.11, 47.59). The risk of Hodgkin’s disease among
prospectively ascertained MZ twins was approximately 8 times as likely as the corresponding
risk in DZ twins; the observed difference was not beyond chance. We also restricted the analysis
to the twins who were initially diagnosed in their adolescence/young adulthood; 0.06 case was
43

expected in each zygosity group. 11cases were observed in MZ twins, giving a SIR of 187.62
(95% CI: 93.53, 335.72); we observed no case among DZ twins.
7 pairs of MZ twins were concordant for nodular sclerosis (NS), one MZ pair was concordant for
mixed cellularity (MC), and one MZ twin pair was ambiguously concordant. 3 MZ pairs were
discordant in respect to their histological subtypes. Histopathology was not available for one MZ
and one DZ HD concordant pair. Age at diagnosis for the twin of the proband in NS concordant
pairs was similar to the subtype discordant pairs (roughly 24 years of age). The initial diagnosis
for the index twin in all three subtype discordant pairs was NS; 2 co-twins were later diagnosed
with MC and one with lymphocyte predominant (LP) HD (Table-4). The average interval from
the diagnosis in the index twin was significantly lower in subtype discordant pairs when
compared with NS concordant pairs (2.3 vs. 7.3 years, one sided p=0.026).
Using Kaplan Meier curves, we evaluated the cumulative Hodgkin’s disease-free survival in
concordant twin pairs in which the index twin was diagnosed at younger ages and separately in
all HD concordant pairs; the results were similar between the two groups (Figure-1).
We did not observe any difference with respect to the risk of other lymphoid neoplasms between
MZ and DZ twin pairs in which one was initially diagnosed with Hodgkin’s disease (Table-2).
We stratified the twin pairs in each zygosity group by their gender and repeated this analysis; the
risk of Hodgkin’s disease in MZ female twins was 3.8 times as likely as the risk in DZ female
same-sex twins, but the difference was not statistically significant. We also observed a higher
risk in MZ male twins when compared with MZ female twins (SIR of 135 vs. 86 respectively),
however, the difference was not statistically significant (Table-3).

44

Chronic Lymphocytic Leukemia (CLL)
CLL was originally diagnosed in 44 twin pairs (24 MZ and 20 DZ) between 1937 and 1988. In
46 percent of the MZ twins and 65 percent of the DZ twins the index case was diagnosed at 55
years of age or older (Table-1).
The risk of CLL in twins of the index cases diagnoses with CLL was assessed in respect to the
zygosity, age at diagnosis (in 5 years increments), sex, and year of diagnosis. 24 MZ and 20 DZ
unaffected twins were observed for an average of 15.59 and 15.3 years of follow-up,
respectively. Based on national age-specific and period-specific incidence rates obtained from
SEER, 0.015 MZ and 0.013 DZ case would have been expected to occur by chance after the
aforementioned period in each zygosity group. Among the MZ twins we observed 4 cases of
CLL (2 male and 2 female), giving the SIR of 260.93 (95% CI: 70.20, 668.03). Only 1 case of
CLL among the DZ twins was observed, giving the SIR of 79.81 (95% CI: 1.04, 444.04). In
pairs of twins in which one had CLL, CLL occurred in the other twin 3.3 times as often in the
MZ group as in the DZ group. However, the observed difference in the risk between MZ and DZ
pairs was not statistically significant (Table-2).

Non-Hodgkin Lymphoma
Between 1937 and 1992 we identified 516 (219 MZ and 297 DZ) twin pairs which had an index
case diagnosed with NHL. 84 percent of DZ twins and 82 percent of the DZ twins were
diagnosed between 1970 and 1989. 34 percent of the MZ twins and 36 percent of the DZ twins
were initially diagnosed between 35 to 55 years of age; similar portion of the MZ and DZ pairs
were diagnosed with NHL at 55 years of age or older (%39 and %37 respectively) (Table-1).
45

217 MZ and 297 MZ unaffected twins were observed after an average of 14.77 and 15.97 years
of follow-up, respectively, after the initial NHL diagnosis in the pro-band. 0.51 MZ and 0.74
case would have been expected to occur by chance after the aforementioned period in each
zygosity group, based on national age-specific and period-specific incidence rates. Among the
MZ twins we observed 6 cases of NHL (3 male and 3 female), giving the SIR of 11.78 (95% CI:
4.30, 25.65). We observed 5 cases of NHL (one male same-sex, one female same-sex, and 3
opposite-sex pairs) among the DZ twins, giving the SIR of 6.72 (95% CI: 2.16, 15.68). The risk
of NHL among MZ twins was less than 2 times as likely as the corresponding risk in DZ twins;
the observed difference was not statistically significant. We observed a considerably higher risk
of other lymphoid neoplasms among MZ twins of the NHL probands in comparison with the
corresponding risk in DZ twins (MZ
SIR
/DZ
SIR
=8.7); however, our finding was not statistically
significant (Table-2).

Myeloma
55 MZ and 44 DZ twins with an index case diagnosed with myeloma were identified between
1944 and 1992. Age at the time of diagnosis of the index case in 58 percent of the MZ pairs and
67 percent of the DZ pairs was 55 or older.
Unaffected MZ and DZ twins were observed for an average of 13.55 and 12.51 years
respectively. Based on national age-specific and period-specific incidence rates obtained from
SEER, 0.035 MZ and 0.025 case would have been expected to occur by chance after the
aforementioned period in each zygosity group. Among MZ pairs we only observed one case of
myeloma, giving the SIR of 28.8 (95% CI: 0.38, 160.25); no myeloma case was observe among
DZ pairs.
46

Discussion
In this study we evaluated the risk of the lymphoid neoplasms (HD, CLL, NHL, and myeloma)
among MZ and DZ twins of the patients diagnosed with the same disease by comparing the
number of observed cases in each group to the expected numbers based on national incidence
rates. Compared to the risk in general population and irrespective of zygosity, we observed a
higher risk of lymphoid neoplasms in twins of the patients diagnosed with the same neoplasm.
We consistently observed a higher risk of these neoplasms among monozygotic twins of the
patients with the same disease, when compared with the risk in dizygotic twins. However, the
observed difference between the risk in MZ and DZ pairs was statistically different only in twins
of the patients with Hodgkin’s disease. The risk of other lymphoid neoplasms in the MZ twins
of patient with NHL was higher than the risk in DZ twins (SIR
MZ
/SIR
DZ
= 8.7). However, given
the widely overlapping confidence intervals, the difference may well be due to chance.
We observed a similar disparity between the observed and expected number of cases in MZ
twins of patients with Hodgkin’s disease when we restricted the analysis to the prospectively
ascertained cases, suggesting that the observed elevated risk in MZ twins, could not be an artifact
of ascertainment. However, the difference in the risk of HD between MZ and DZ was no longer
statistically significant in this analysis.

The higher risk of Hodgkin’s disease in twins relative to the risk in general population is
attributed to familiality. Familiality in the etiology of health outcomes is representative of the
joint effects of genetic and environmental factors. DZ twins raised together, share their
environment and half of their genetic material. In contrast, MZ twins raised together, share their
environment and all of their genetic material. Significantly higher concordance of Hodgkin’s
47

disease in MZ twins compared to DZ twins provides a fair evidence for the role of heredity in the
etiology of this disease; relatively young mean age at diagnosis and short average interval
between the diagnoses in the twins concordant for HD are consistent with this observation.
In spite of the significantly higher concordance of HD among MZ compared to DZ pairs, 96
percent of the twins with a diseased index case remained unaffected. It is likely that the
susceptible genotype has a low overall penetrance and only a subgroup of the population was
genetically susceptible.
Majority of the twins concordant for HD were pathologically diagnosed with the NS subtype. In
agreement with our finding, family history studies have reported a significantly high rate of NS
among sibships with multiple cases of HD (2, 27-39). GWAS have also suggested several
genotypes related to immune system to affect the susceptibility to HD (9, 10). Included are HLA-
DRB1 (MHC Class II), REL (involved in B-cell proliferation, development and survival (40) ),
PVT1 (a none-protein coding chromosomal rearrangement associated with multiple myeloma
(41)), and GATA3 (a transcription factor involved in differentiation of TH1 and TH2 cells (42)).

Compared to the general population and regardless of zygosity, we observed statistically
significantly higher occurrence of NHL and CLL in twins of patients diagnosed with the same
disease (table 2). This observation is consistent with the results of the family history studies (5)
and provides further evidence for the role of familiality as a predictor of the risk in these
conditions.
The role of familiality was not significantly different between MZ and DZ pairs with NHL.
Thus, our findings do not contribute to the role of heredity in susceptibility to this illness.
However, GWAS have reported multiple loci in the HLA region associated with NHL and based
48

on their findings 20-60% of the risk of the disease could be attributed to the heterogeneity in
HLA class II gene (11-14). Indeed, contrary to the results of a GWAS(15), our findings do not
provide evidence for the role of heredity in susceptibility to CLL. One possible explanation for
the differing observations is that the diagnosis of these malignancies is misclassified among
twins. Since we used the same method in classification of the diagnosis in MZ and DZ pairs, this
possibility is unlikely to have been a problem. Another possible explanation is that the effect
size obtained from GWAS overestimates the true effect. Since multiple genetic markers are
tested and anticipated to be conferred by most common genetic variations, small to moderate
increases in risk reported in GWAS could have been falsely positive discoveries where the null
hypothesis is true. On the other hand, in GWAS one position on the genome is independently
tested for an association with the trait, ignoring all other positions and causal environmental
factors. However, twin studies provide us with a fair understanding of the familiality as the net
effect of genetic and environmental factors. Indeed, the role of heredity is estimated by
comparing the concordance of a trait between MZ and DZ twins, while the role of the
environmental factors is also inclusively considered. Majority of the findings coming from
GWAS are subtype specific and limited to follicular NHL. Additionally, NHL subtypes were not
available for most of our twins and the role of heredity might have been stronger in the subtype
specific analysis.
MZ twins of NHL patients appear to be more susceptible to other lymphoid neoplasms (HL,
leukemia not otherwise specified, CLL and myeloma); this observation is consistent with the
results of the family history studies (5). However, the susceptibility to lymphoid neoplasms was
statistically different than DZ twins and this finding does not provide enough evidence for the
role of genetic factors.
49

Conclusion
Hodgkin’s disease is highly heritable and genetic risk factors play an important role in the
susceptibility to this disease.

50

References
1. Diehl V, Tesch H. Hodgkin's disease--environmental or genetic? N Engl J Med 1995;332(7):461-
2.
2. Grufferman S, Cole P, Smith PG, et al. Hodgkin's disease in siblings. N Engl J Med
1977;296(5):248-50.
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52

Tables
Table 1 Demographic characteristic of twins with lymphoid neoplasms, International Twin Study.
HD CLL NHL MM
MZ DZ MZ DZ MZ DZ MZ DZ
N(%) N(%) N(%) N(%) N(%) N(%) N(%) N(%)
Age Age Age Age
<15 18(11) 14(7) <15 15(7) 23(8)
15-35 114(67) 136(70) 15-35 44(20) 56(19)
35+ 39(23) 45(23) <55 13(54) 7(35) 35-55 75(34) 108(36) <55 23(42) 16(36)
55+ 11(46) 13(65) 55+ 85(39) 110(37) 55+ 32(58) 28(64)

PY follow-up
(years) 22.35 19.97 15.59 15.3 14.77 15.97 13.55 12.51
Gender
Proband/co-twin
M/M 99(58) 34(17) 11(46) 9(45) 91(42) 72(24) 19(35) 14(32)
F/F 72(42) 81(42) 13(54) 7(35) 128(58) 90(30) 36(65) 15(34)
M/F 45(23) 3(15) 77(26) 8(18)
F/M 35(18) 1(5) 58(20) 7(16)

53

Table 2 Occurrence of the same and other hematologic neoplasms in co-twins of twin probands, International Twin
Study.
a

Malignant
neoplasm
Zygosity
Number
at risk
Cases
observed
Cases
expected
SIR 95% CI
Updated Previous
SIR (MZ) SIR(MZ)
/SIR (DZ) /SIR (DZ
Proband Twin
All HL All HL MZ 171 13 0.11384 114.2 60.74, 195.29
All HL All HL DZ 195 1 0.11691 8.55 0.11, 47.59 13.35 ∞

AYA HL AYA HL MZ 114 11 0.05863 187.62 93.53, 335.72
AYA HL AYA HL DZ 136 0 0.06338 - - ∞ NA
PA HL PA HL MZ 165 8 0.11317 70.69 30.44, 139.30
PA HL PA HL DZ 194 1 0.11691 8.55 0.11, 47.59 8.26 ∞

CLL CLL MZ 24 4 0.01533 260.93 70.20, 668.03
CLL CLL DZ 20 1 0.01253 79.81 1.04, 444.04 3.27 NA

NHL NHL MZ 219 6 0.50915 11.78 4.30, 25.65
NHL NHL DZ 297 5 0.74428 6.72 2.16, 15.68 1.75 1.7

MM MM MZ 55 1 0.03472 28.8 0.38, 160.25
MM MM DZ 44 0 0.02521 0 ∞ NA

All HL
Any
Lymphoid MZ 171 2
b
0.99447 2.01 0.23, 7.26
All HL
Any
Lymphoid DZ 195 3
c
0.9796 3.06 0.62, 8.95 0.66 0.6

NHL
Any
Lymphoid MZ 219 6
d
0.39012 15.38 5.62, 33.48
NHL
Any
Lymphoid DZ 297 1
e
0.56699 1.76 0.02, 9.81 8.72 NA
a
There were no additional lymphoid neoplasms in co-twins of MM or CLL cases.
b
NHL and leukemia
c
2 NHL and leukemia
d
One each CLL, MM, and HL, and 3 leukemia not otherwise specified.
e
Leukemia
AYA= adolescent/young adult (AYA HL is a subset of All HL)
PA=Prospectively Ascertained (PA HL is a subset of All HL)

54

Table 3 Occurrence of the same and other hematologic neoplasms in co-twins of twin probands by gender

Zygosity Malignant neoplasm
Number
at risk
Cases
observed
Cases
expected
SIR 95% CI
Proband/Sex Twin/ Sex lower upper
MZ HD/M HD/M 99 9 0.0664 135.54 61.85 257.32
HD/F HD/F 72 4 0.0464 86.30 23.22 220.95
DZ HD/M HD/M 34 0 0.0213 0.00 - -
HD/M HD/F 45 0 0.0232 0.00 - -
HD/F HD/M 35 0 0.0227 0.00 - -
HD/F HD/F 81 1 0.0435 22.99 0.30 127.90
MZ NHL/M NHL/M 91 3 0.2382 12.59 2.53 36.79
NHL/F NHL/F 128 3 0.2635 11.39 2.29 33.27
DZ NHL/M NHL/M 72 1 0.1849 5.41 0.07 30.10
NHL/M NHL/F 77 0 0.1820 0.00 - -
NHL/F NHL/M 58 3 0.1427 21.03 4.23 61.43
NHL/F NHL/F 90 1 0.2202 4.54 0.06 25.27
MZ CLL/M CLL/M 17 2 0.0093 215.75 24.23 778.96
CLL/F CLL/F 10 2 0.0062 321.54 36.11 1160.93
DZ CLL/M CLL/M 9 1 0.0050 200.40 2.62 1115.00
CLL/M CLL/F 7 0 0.0011 0.00 - -
CLL/F CLL/M 3 0 0.0004 0.00 - -
CLL/F CLL/F 1 0 0.0055 0.00 - -
55

Table 4 Characteristics of 14 Pairs of Twins Concordant for Hodgkin Disease
Zygosity Sex Year of Birth Age at Diagnosis (YR) Histological Sub-type

Twin Twin

Twin Twin Twin Twin

A B

A B A B
MZ M M 1936 37 50 NS NS
MZ M M 1946 28 29 MC MC
MZ M M 1948 23 25 NS MC
MZ M M 1953 17 32 NS NS
MZ M M 1955 34 39 NA NA
MZ M M 1955 16 21 NS LP
MZ M M 1957 21 28 NS NS
MZ F F 1960 24 28 NS/LD NS/LD
MZ F F 1960 23 31 NS NS
MZ F F 1962 18 23 NS NS
MZ M M 1962 33 33 NS MC
MZ M M 1964 18 20 NS NS
DZ F F 1965 24 36 NS NA
MZ F F 1965 29 33 NS NS
* NS denotes nodular sclerosis, NA not available, LD lymphocyte-depleted, and MC mixed cellularity.

56

Figures

0.0
0
0.2
5
0.5
0
0.7
5
1.0
0
Cumulative Hodgkin disease-free survival
0 5 1
0
1
5
Interval from Hodgkin disease in index twin(years)
Young HD All HD
Figure 1 Kaplan–Meier curve of freedom from Hodgkin disease in co-twins (all and young) of index cases.
57

Chapter 4

Short term reduction in mammographic density and
survival in breast cancer

58

Abstract

Back ground: Identification of the factors that predict response to treatment in breast cancer
patients early after diagnosis is important in guiding the treatment strategy. High mammographic
density (MD) is a risk factor for breast cancer. However no study has examined the association
between change in MD and death in breast cancer survivors. We hypothesized that a short-term
change in breast density may be a surrogate biomarker predicting risk of death from breast
cancer and all causes.
Methods: We evaluated the relationship between reduction in MD and risk of death from all
causes within the Health, Eating, Activity, and Lifestyle (HEAL) Study. In a prospective
observational study, we studied 391 women diagnosed with primary invasive breast carcinoma
between 1995 and 1998 and followed until death or September 2009. We collected
mammograms and prognostic, demographic, and lifestyle factors as well as treatments at the
time of diagnosis and two years after the diagnosis was made. Mammograms were digitized and
MD was measured on cranio-caudal (CC) images of the unaffected breast using a computer
assisted program developed at the University of Toronto. MD reduction (MDR) was evaluated
based on two mammograms; the first was taken 12 months before diagnosis, and the second
approximately 24 months after diagnosis. MDR was defined as the difference between the MD
of these two images (% MDR = % preMD -% postMD). Reduction in MD was categorized into a
binary variable as women who had a MDR ≥5% compared to those with less than 5% reduction
in MD. Cox proportional hazards models were used to estimate the Hazard ratios and 95%
confidence intervals.

59

Results: Breast cancer patients with 5% or more reduction in MD were younger (mean age was
55.9 compared to 59.0) and more likely to be premenopausal at diagnosis (37% compared to
24.0%). Women with MDR ≥5% were 42% less likely to die from any cause after adjustment for
age, BMI, menopausal status at baseline, and study center (HR=0.58 CI: 0.35-0.94). The results
were stronger when we restricted the analysis to 97 women who had higher breast density at
baseline (HR=0.21 CI: 0.06-0.75). When we restricted the analysis to women who had taken
tamoxifen, a similar direction was observed but the results were not statistically significant.
Conclusion: Result from our data suggests that reduction in mammographic density few years
after breast cancer diagnosis may be used as a predictor of overall survival.
Introduction

High mammographic density (MD) is a risk factor for breast cancer. The risk of breast cancer in
women with high MD has been shown to be four to six times the risk in those with little or no
density (1-3). The attributable risk of breast cancer due to mammographic density is higher than
the attributable risk due to any other known risk factor. Several exposures that affect breast
cancer risk influence MD; included are age, body mass index (BMI), parity, age at first birth,
hormone therapy use and physical activity (4). MD is decreased by tamoxifen and increased by
hormone therapy (5).
Changes in mammographic density over 3 years have been suggested to be a better predictor of
breast cancer risk than a single measurement of mammographic density. The risk of breast
cancer was reduced in women who had a reduction in radiographic breast density within 3 years
and was elevated in those who had an increase in breast density (6). It has been suggested that
response to tamoxifen in the preventive setting, could be predicted by a reduction in MD over a
60

12- to 18-month period. Women who used tamoxifen for primary prevention of breast cancer
and had a 10% or more reduction in MD were at lower risk of breast cancer; no significant
change in breast cancer risk was observed in tamoxifen users with less reduction in MD (7).
Change in mammographic density during short-term use of adjuvant Tamoxifen/Aromatase
inhibitors has been suggested to be a significant predictor of long-term recurrence in women with
ER-positive breast cancer (8). Women who used ET for at least 2 years and had 5% or more
reduction in MD over 8 to 20 months after breast cancer diagnosis had lower risk of recurrence
compared to those with less reduction. Reduction in absolute density following breast cancer
diagnosis has been suggested to be a predictor of breast cancer survival. Women who had a 11-
to 20% reduction in absolute MD following breast cancer diagnosis were 44 percent less likely to
die from breast cancer compared to those with no change; the survival benefit of MDR was more
strong among tamoxifen users (9).
Currently, there are no means to accurately measure the survival benefit of treatments early after
breast cancer diagnosis. No study to date, has evaluated the association between change in MD
early after breast diagnosis and overall survival. Using 391 breast cancer patients from the
Health, Eating, Activity, and Lifestyle (HEAL) Study, we evaluated the relationship between
reduction in percent MD shortly after breast cancer diagnosis and overall survival.
Methods
Study Setting, Participants, and Recruitment
The Health, Eating, Activity, and Lifestyle (HEAL) Study is a population based, multicenter,
multiethnic prospective cohort study that has enrolled 1,183 breast cancer patients who are being
observed to determine whether weight, physical activity, diet, sex hormones, mammographic
61

density, and other factors affect breast cancer prognosis. Women were recruited into the HEAL
study through Surveillance, Epidemiology, and End Results (SEER) registries in New Mexico,
Los Angeles County in California, and western Washington. Details of the aims, study design,
and recruitment procedures have been published previously (10-13).
Briefly, in New Mexico, we recruited 615 women, age 18 years or older, diagnosed with in situ
to stage IIIA breast cancer between July 1996 and March 1999 and living in Bernalillo, Sante Fe,
Sandoval, Valencia, or Taos Counties. In western Washington, we recruited 202 women,
between the age of 40 and 64 years, diagnosed with in situ to stage IIIA breast cancer between
September 1997 and September 1998 and living in King, Pierce, or Snohomish Counties. In Los
Angeles County, we recruited 366 black women with stage 0 to IIIA primary breast cancer who
had participated in the Los Angeles portion of the Women’s Contraceptive and Reproductive
Experiences Study, a case-control study of invasive breast cancer, or who had participated in a
parallel case-control study of in situ breast cancer. Los Angeles participants were US-born,
English-speaking women age 35 to 64 years and diagnosed with breast cancer between May
1995 and May 1998.
Participants completed in-person interviews at baseline (within their first year after diagnosis; on
average, 7.5 months after diagnosis) and 24 months after the baseline visit (within their third
year of diagnosis; on average, 31 months after diagnosis). Written informed consent was
obtained from each patient. The study was performed with the approval of the institutional
review boards of participating centers, in accord with an assurance filed with and approved by
the US Department of Health and Human Services.

62

Outcome Assessment
Information on vital status was obtained from SEER. If alive, individuals were observed through
their last follow-up assessment or SEER vital status update, whichever was most recent.
Information on breast cancer recurrences and new primary breast cancers was obtained by self-
report at 24-month, 5-year and 10-year interviews and confirmed using a combination of medical
records and SEER data. Overall survival time was the time from breast cancer diagnosis to death
from any cause or end of follow-up; events were limited to data collected through September,
2009. Individuals lost to follow-up were censored at last date of contact.
Disease Stage and Treatment
We obtained data on disease stage from the local SEER registries before recruitment.
Participants were classified as having in situ, stage I (localized), or stage II to IIIA (regional)
breast cancer based on American Joint Committee on Cancer stage of disease classification
contained within SEER. Estrogen receptor (ER) and progesterone receptor (PR) status of tumors
was categorized as positive, negative, or unknown/borderline. Treatment and additional clinical
data were obtained from medical records. Adjuvant treatment was categorized into the following
four mutually exclusive groups: surgery only; surgery and radiation; surgery and chemotherapy;
or surgery, radiation, and chemotherapy.
Anthropometric Measurements
With the women wearing light clothing and no shoes, weight was measured at the 24-month
follow-up assessment to the nearest 0.1 kg using a balance-beam laboratory scale at New Mexico
and Washington and a portable Thinner Digital Electronic Scale (Conair, East Windsor, NJ) at
Los Angeles. Height was measured, without shoes, to the nearest 0.1cm using a stadiometer at
63

New Mexico and Washington and a tape measure at Los Angeles. All measurements were
performed twice and averaged. Body mass index (BMI) was computed as kilograms per meter
squared.
Other Variables
Standardized questionnaire information was collected at baseline and 24-month follow-up on
medical history and demographic characteristics. Postmenopausal status was defined as age ≥55
years, not menstruating in the last 12 months, an oophorectomy, or a hysterectomy. Use of oral
hormone replacement therapy was defined as any use of estrogen or progesterone since
diagnosis. Women were defined as users of tamoxifen if they reported current use at the follow-
up interview. History of medical conditions related to cardiovascular disease and inflammation
(heart failure, myocardial infarction) was self-reported at the follow-up interview. Information
on physical activity collected at follow-up was used to compute total average metabolic
equivalent task (MET) hours per week of moderate and/or vigorous sport and recreational
activities for the year before follow-up (11).
Mammographic Density
Two mammographic films were retrieved from individual providers that each woman had
specified; the first one corresponding to 12 months before diagnosis, and the second
approximately 24 months after diagnosis. Each film was digitized using either an Epson 1680
scanner (Epson America Inc, Long Beach, CA; used in Washington) or Cobrascan CX-812M
large format 12-bit x-ray scanner (Radiographic Digital Imaging, Torrance, CA; used in New
Mexico and Los Angeles). We measured the craniocaudal view of the unaffected breast for
mammographic percentage density and dense area. The density readings were performed using
64

Cumulus 108, a computer-assisted mammogram-reading program developed at the University of
Toronto (Ontario, Canada). This method has been described in detail elsewhere ref (14). Briefly,
the reader uses a sliding scale to outline the breast edge and then the dense breast area based on
pixel brightness. Percentage density is the proportion of dense breast area relative to the total
area of the breast.
Exclusions
Among the 1,183 eligible women enrolled at baseline, 944 women were alive and completed the
follow-up survey (Fig 1). Reasons for nonparticipation included death (n=44), refusal (n=104),
spouse refusal (n =1), unable to contact (n =17), unable to locate (n =55), moved from study area
(n =16), and illness (n =2). 912 women did not have a non-fatal breast cancer event in the 9
months prior to the 24-month interview. Of these, 206 had in situ as the initial breast cancer
diagnosis and were excluded. Of the remaining 706 participants, mammographic density
measurements were available at baseline and 24 month in 455 women. Of these, 64 women
lacked covariates (BMI and/or menopausal status) resulting in a sample size of 391 participants
for analyses of overall survival.
Statistical Analyses
Previous studies of MDR have reported a strong effect on breast cancer risk/survival when the
cut-off values for absolute MDR is set at 5 or 10 percent (7, 8). When we categorized all
individuals into two groups with the cut point at a MDR of %10, the statistical power was limited
by the small sample size and number of deaths. We therefore categorized MDR into a binary
variable as women who had a MDR ≥5% compared to those with less than 5% reduction in MD.
We examined the association between MDR and characteristics of the subjects by comparing the
65

means or percentages of covariates between the MDR groups. Differences in means were
compared using t-tests. Categorical variables were compared using chi-square analysis. We
analyzed time to death by MDR groups using Kaplan–Meier plots. Hazard ratios (HRs) and 95%
CIs for MDR≥ 5% were estimated using Cox proportional hazards regression. We explored the
association between MDR and breast cancer survival separately for all participants; pre- and
post-menopausal women; women who used tamoxifen; as well as women with very low, low,
high and very high baseline MD (obtained from quantiles of baseline MD). Models were
adjusted for age at diagnosis, BMI at baseline, center and menopausal status. All statistical
analysis was performed using STATA 11.
Results
Characteristics of the participants by MDR group are listed in Table 1. Breast cancer patients
with 5% or more reduction in MD were younger (mean age was 55.9 compared to 59.0, p=
0.004) (don’t match the numbers in Table 2), more likely to have a higher density at baseline,
and more likely to be premenopausal at diagnosis (37% compared to 24.%, p= 0.007). The other
characteristics of the participants were similar between MDR groups. The primary tumor
characteristics of the patients by MDR group are summarized in Table 2. MDR groups were
similar with respect to the level of differentiation, histology, ER/PR status and stage. The
Kaplan-Meier curves display the effect of MDR on mortality, with the higher overall survival
rate noted in patients who had 5 percent or more reduction in MD shortly after breast cancer
diagnosis (Figure 2).
HRs and 95% CIs comparing MDR groups for overall survival are listed in Table 4. Higher
reduction in MD was significantly associated with higher overall survival. Women who had 5%
66

or more reduction in mammographic density 2 years after diagnosis were 42 percent less likely
to die from any cause after adjustment for age at diagnosis, baseline BMI, center and menopausal
status at baseline (HR= 0.58, 95% CI: 0.35,0.94). In a separate model we additionally adjusted
the model for quintiles (or quartiles?) of baseline MD and we observed similar results. When we
restricted the analysis to women who were pre- or post-menopausal at diagnosis, the protective
effect of higher MDR was observed but it was no longer statistically significant. Indeed, when
we restricted the analysis to women who had taken tamoxifen, a similar direction was observed
but the results were not statistically significant. MDR of 5% or higher was associated with a
statistically significant lower mortality in women who had a very high (MD≥34%)
mammographic density at baseline.
Discussion
To our knowledge, this is the first population-based cohort study to date examining associations
between short-term MDR and overall survival among breast cancer patients. We observed
significant associations between reduced overall survival and 5% or more reduction in
mammographic density measured approximately 24 months after diagnosis. All associations
were independent of age at diagnosis, BMI at baseline, center, menopausal status at baseline and
quantiles of MD at baseline and did not seem to be modified by menopausal status or tamoxifen
use. Reduction in mammographic density seemed to better predict overall survival in breast
cancer patients who had very high mammographic density at baseline.
In agreement with other studies, our results show the benefit of mammographic density
reduction; MDR has been shown to be associated with reduced risk of breast cancer and better
survival after diagnosis (6-9). Results from other studies (8, 9)suggest that an association
67

between MDR and survival maybe present only in patients who were prescribed tamoxifen.
However, the results from our population-based study indicate that this association is present in
all breast cancer patients, independent of their treatment regimen; among tamoxifen users MDR
was associated with a better survival but the association was not statistically significant. The
discrepancy between our work and that of Kim et al1(8) may be attributable to the type of
outcome, which was overall survival in our study but recurrence free survival in the study by
Kim et al. Li et al.(9) suggested that MDR is a predictor of breast cancer survival in tamoxifen
users. They found a significant association between MDR and breast cancer survival in the
overall analysis including tamoxifen users and non-users; the results were similar but not
statistically significant among women who did not receive tamoxifen. However, their study
differs from ours in two important respects. First, they found stronger effects with absolute dense
area compared to percent density which was used in our study. Second, they used the medio-
lateral oblique views of the breast to measure MD, which could be a source of error because of
the overlapping shadows of anterior pectoral muscles; we used cranio-caudal images in our
study. Consistent with the results of the Cuzick(7) and Kerlikowske (6) study, we found that
among women in the highest baseline breast density category those who experience a reduction
in MD, will have a better health outcome.
The mechanism by which MDR is related to breast cancer prognosis is unclear. One possible
explanation is that in premenopausal and perimenopausal women, a good treatment response
(Chemotherapy/hormone therapy/radiation) could cause effective estrogen deprivation and as a
result, could reduce breast density and improve overall survival. The histopathological
composition of dense breast tissue consists of stroma and concentrated epithelial tissue(15).
Mammographically dense breasts have been shown to have higher amounts of collagen, more
68

extensive stromal fibrosis, and higher numbers of epithelial cells when compared with breasts
with little density (15-18). It is also likely that MDR represents a change in the constitution of
breast tissue which could affect the behavior of distant micro-metastatic cancer cells and
improve the survival after breast cancer diagnosis.
A few limitations of this study deserve consideration. First and foremost, is that aromatase-
inhibitors were not yet available when HEAL patients were diagnosed, and data on HER-2/neu
status were not routinely collected in SEER; therefore, we could not assess their effects on the
associations observed in this study. The second limitation is the variable timing of the
mammograms, limiting the external validity of the findings. The strengths of this study, in
contrast, lie in its cohort design and the integration of multiple data sources for validating the
accuracy of variables.
Conclusion
This observational data gives suggestive evidence that a reduction in mammographic density
after breast cancer diagnosis maybe an early prognostic marker for improved long-term survival
in patients. This data needs to be validated using existing or future clinical trials.

69

References
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cancer. N Engl J Med 2007;356(3):227-36.
3. McCormack V, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast
cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 2006;15(6):1159-69.
4. Vachon C, Kuni C, Anderson K, et al. Association of mammographically defined percent breast
density with epidemiologic risk factors for breast cancer (United States). Cancer Causes Control
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5. Atkinson C, Warren R, Bingham SA, et al. Mammographic patterns as a predictive biomarker of
breast cancer risk: effect of tamoxifen. Cancer Epidemiol Biomarkers Prev 1999;8(10):863-6.
6. Kerlikowske K, Ichikawa L, Miglioretti DL, et al. Longitudinal measurement of clinical
mammographic breast density to improve estimation of breast cancer risk. J Natl Cancer Inst
2007;99(5):386-95.
7. Cuzick J, Warwick J, Pinney E, et al. Tamoxifen-Induced Reduction in Mammographic Density
and Breast Cancer Risk Reduction: A Nested Case-Control Study. J Natl Cancer Inst 2011.
8. Kim J, Han W, Moon HG, et al. Breast density change as a predictive surrogate for response to
adjuvant endocrine therapy in hormone receptor positive breast cancer. Breast Cancer Res
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of Response to Adjuvant Tamoxifen Therapy in Postmenopausal Patients With Breast Cancer. J
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10. Irwin ML, Crumley D, McTiernan A, et al. Physical activity levels before and after a diagnosis of
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survivors. Med Sci Sports Exerc 2004;36(9):1484-91.
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breast cancer survivors. J Clin Oncol 2003;21(10):1961-6.
13. Pierce BL, Ballard-Barbash R, Bernstein L, et al. Elevated biomarkers of inflammation are
associated with reduced survival among breast cancer patients. J Clin Oncol 2009;27(21):3437-
44.
14. Boyd NF, Byng JW, Jong RA, et al. Quantitative classification of mammographic densities and
breast cancer risk: results from the Canadian National Breast Screening Study. J Natl Cancer Inst
1995;87(9):670-5.
15. Hawes D, Downey S, Pearce CL, et al. Dense breast stromal tissue shows greatly increased
concentration of breast epithelium but no increase in its proliferative activity. Breast Cancer Res
2006;8(2):R24.
16. Boyd NF, Lockwood GA, Byng JW, et al. Mammographic densities and breast cancer risk.
Cancer Epidemiology, Biomarkers & Prevention 1998;7(12):1133-44.
17. Guo Y, Martin L, Hanna W, et al. Growth factors and stromal matrix proteins associated with
mammographic densities. Cancer Epidemiol Biomarkers Prev 2001;10(3):243-8.
18. Alowami S, Troup S, Al-Haddad S, et al. Mammographic density is related to stroma and stromal
proteoglycan expression. Breast Cancer Res 2003;5(5):R129-35.

70

Tables
Table 2 Physiological, demographic and treatment characteristics of the participants by MDR group (n=391)
MDR <5% (n=195) MDR ≥5% (n=196) P
Mean age at breast cancer DX 59.01 55.86 0.0037
Mean BMI at baseline 27.14 26.61 0.3554
Level of education
High school or less 54(28) 49(25)
Some college 75(38) 68(35)
College grad or higher 66(34) 79(40) 0.417
Race
Hispanic 25(13) 31(16)
White, Non-Hispanic 144(74) 128(65)
Black/African-America 21(11) 31(16)
American Indian 2(1) 2(1)
Asian/Pacific 3(2) 3(2)
Other 0(0) 1(1) 0.464
Baseline MD
≤9.4 87(45) 11(6)
>9.4, ≤20.8 55(28) 43(22)
>20.8, ≤34 34(17) 64(33)
>34 19(10) 78(40) 0.000
Mean time between mammograms (m) 35.89 37.95 0.16
Any cause death (N) 45 26
Breast cancer death (N) 18 9
Menopausal status at baseline
Premenopausal 47(24) 72(37)
post menopausal 148(76) 124(63) 0.007
Estrogen at the time of BC dx 91(47) 97(49) 0.576
Progesterogen at the time of BC dx 50(26) 49(25) 0.884
Level of physical activity
0 Minutes/Week 65(33) 60(31)
>0 - < 150 Minutes/Week 63(32) 58(30)
150+ Minutes/Week 67(34) 78(39) 0.577
Menopausal status at 24m
Premenopausal 25(13) 34(17)
Post menopausal 170(87) 162(83) 0.26
Smoked more than 100 cigarettes in life 99(51) 104(53) 0.65
Chemotherapy 63(32) 67(34) 0.69
Radiation 138(71) 138(70) 0.94
Center
FHCRC 52(27) 35(18)
UNM 122(63) 130(66)
USC 21(11) 31(16) 0.064
Tamoxifen use 120(62) 127(65) 0.5

71

Table 3 Tumor characteristics of the participants by MDR group (n=391)

MDR <5%
(n=195)
MDR ≥5%
(n=196)
P
Level of Differentiation
Well differentiated, grade I 49(25) 60(31)
Moderately differentiated, grade II 64(33) 67(34)
Poorly differentiated, grade III 67(34) 55(28)
Missing 15(8) 14(7) 0.50
Histology
Ductal carcinoma 136(70) 137(70)
other 59(30) 59(30) 0.97
ER status
Positive 142(73) 139(71)
Negative 31(16) 34(17)
Missing 22(11) 23(12) 0.91
PR status
Positive 112(57) 113(58)
Negative 58(30) 46(23)
Missing 25(13) 37(19) 0.16
Stage
Stage I 121(64) 112(59)
Stage IIA 49(26) 51(27)
Stage IIB + (no Stage 4) 18(10) 27(14) 0.34

72

Table 4 HRs for Overall Survival by MDR group
Sample
Hazard
Ratio
Std.
Err. P 95% Conf. Interval
All (model 1)
MDR <5% (n=195) 1.00 (N=391, death=71)*
MDR ≥5% (n=196) 0.58 0.14 0.028 0.35 0.94
All (model 2)
(N=391, death=71)** MDR <5% (n=195)
1.00

MDR ≥5% (n=196) 0.57 0.16 0.05 0.33 0.995
Premenopausal (at DX)
MDR ≥5% (n=196) 1.00 (N=119, death=14)***
MDR ≥5% (n=72) 0.37 0.22 0.09 0.12 1.17
Postmenopausal (at DX)
MDR <5% (n=148) 1.00 (N=272, death=57)****
MDR ≥5% (n=124) 0.68 0.19 0.166 0.39 1.17
Tamoxifen users
MDR <5% (n=120) 1.00

(N=247, death=45)*
MDR ≥5% (n=127) 0.72 0.22 0.279 0.39 1.31
BMD >34%
(N=97, death=12)* MDR <5% (n=19)
1.00
MDR ≥5% (n=78) 0.21 0.14 0.02 0.06 0.75
BMD: 20.8-34%
(N=98, death=13)* MDR <5% (n=34)
1.00
MDR ≥5% (n=64) 2.17 1.61 0.30 0.51 9.32
BMD 9.4-20.8%
(N=98, death=23)* MDR <5% (n=55)
1.00
MDR ≥5% (n=43) 0.65 0.29 0.34 0.27 1.57
BMD <9.4%

(N=98, death=23)* MDR <5% (n=87) 1.00

MDR ≥5% (n=11) 0.19 0.19 0.11 0.02 1.42
*Adjusted for age at DX, BMI at baseline, center and menopausal status at baseline
**Adjusted for age at DX, BMI at baseline, center, menopausal status at baseline and quantiles of MD at baseline
***Adjusted for age at DX, BMI at baseline, center and change in menopausal status from baseline
****Adjusted for age at DX, BMI at baseline and center
BMD: Baseline mammographic density

73

Figures

Figure 1 Cohort definition and exclusions
74

0.00 0.25 0.50 0.75 1.00
Cum ulative survival
0 50 100 150 200
Time from Dx to death (months)
less than 5% MDR (n=195) 5% MDR and more (n=196)
Kaplan-Meier survival estimates

Figure 2 Kaplan Meier survival curves by MDR group

Abstract (if available)

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Asset Metadata

Creator Ozhand, Ali (author)

Core Title The role of heritability and genetic variation in cancer and cancer survival

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Epidemiology

Publication Date 07/19/2013

Defense Date 05/24/2013

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag breast cancer,concordance,cytokines,growth factors,Hodgkin's,IL6,mammographic density,OAI-PMH Harvest,SNP,Survival,Twins

Language English

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Advisor Mack, Thomas M. (committee chair), Cozen, Wendy (committee member), Martin, Sue Ellen (committee member), McKean-Cowdin, Roberta (committee member), Wu, Anna (committee member)

Creator Email aliozhand@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c3-293098

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