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Traditional Breast Cancer Risk Factors in Relation to Molecular
Subtypes of Breast Cancer
Rulla M. Tamimi1,2, Graham A. Colditz3, Aditi Hazra1,2, Heather J. Baer1,2, Susan E.
Hankinson1,2, Bernard Rosner1, Jonathan Marotti4, James L. Connolly4, Stuart J. Schnitt4,
and Laura C. Collins4
1Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital and Harvard
Medical School, Boston, MA, 02115
2Department of Epidemiology, Harvard School of Public Health, Boston, MA, 02115
3Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110
4Department of Pathology, Beth Israel Deaconess Medical Center and Harvard Medical School,
Boston, MA 02115
Abstract
Background—At least four major categories of invasive breast cancer have been reproducibly
identified by gene expression profiling: luminal A, luminal B, HER2-type and basal-like. These
subtypes have been shown to differ in their outcome and response to treatment. Whether this
heterogeneity reflects the evolution of these subtypes through distinct etiologic pathways has not
been clearly defined.
Methods—We evaluated the association between traditional breast cancer risk factors and risk of
previously defined molecular subtypes of breast cancer in the Nurses’ Health Study. This analysis
included 2,022 invasive breast cancer cases for whom we were able to obtain archived breast
cancer tissue specimens. Tissue microarrays (TMAs) were constructed and slides were
immunostained for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth
factor receptor 2 (HER2), cytokeratin 5/6 (CK5/6), and epidermal growth factor receptor (EGFR).
Using immunostain results in combination with histologic grade, cases were grouped into
molecularly defined subtypes. We used Cox proportional hazards models to estimate hazard ratios
(HRs) and 95% confidence intervals (CIs).
Results—We observed differences in the association between risk factors and subtypes of breast
cancer. In general, many reproductive factors were most strongly associated with the luminal A
subtype, although these differences were not statistically significant. Weight gain since age 18
showed significant differences in its association with molecular subtypes (p-heterogeneity=0.05)
and was most strongly associated with the luminal B subtype (p-trend 0.001). Although there was
not significant heterogeneity for lactation across subtypes, an inverse association was strongest for
basal-like tumors (HR=0.6, 95%CI 0.4–0.8; p-heterogeneity=0.88).
Conclusions—These results support the hypothesis that different subtypes of breast cancer have
different etiologies and should not be considered as a single group. Identifying risk factors for less
common subtypes such as luminal B, HER2-type and basal-like tumors has important implications
for prevention of these more aggressive subtypes.
Corresponding Author: Rulla M. Tamimi, Channing Laboratory, 181 Longwood Avenue, Boston, MA 02115, Telephone: (617)
525-0862, rulla.tamimi@channing.harvard.edu.
NIH Public Access
Author Manuscript
Breast Cancer Res Treat. Author manuscript; available in PMC 2013 January 1.
Published in final edited form as:
Breast Cancer Res Treat
. 2012 January ; 131(1): 159–167. doi:10.1007/s10549-011-1702-0.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
Keywords
breast cancer; risk factors; molecular subtype; ER; PR; HER2
INTRODUCTION
Recent studies using microarray technology and unsupervised cluster analysis have provided
new insights into the classification of invasive breast cancers [1–4]. These studies have
resulted in the identification of several breast cancer subtypes that vary in their gene
expression signatures and clinical outcome. The molecularly distinct breast cancer
subgroups identified to date include luminal subtypes A and B (both of which are hormone
receptor-positive), the HER2-type, and a group known as basal-like cancers [1–4]. Although
prognosis and response to treatment has been shown to vary according to these subtypes, it
is unclear if classifying breast cancer in this way may help us to understand better the
etiology of breast cancer. Examination of breast cancer risk factors in relation to these
subtypes may offer the potential to build on traditional classification of tumor types based
on ER and PR status [5, 6] and extend insights into etiology.
Immunohistochemical staining of paraffin sections using antibody panels has been shown to
be a reasonable, albeit imperfect, surrogate for molecular classification of invasive breast
cancers as categorized by gene expression profiling studies [4, 7–10]. Antibodies against
estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor
2 (HER2), cytokeratin 5/6 (CK5/6) and epidermal growth factor receptor (EGFR) have been
particularly useful for this purpose in populations in which material for expression profiling
is not available or for which studying very large numbers of cases would not be feasible.
The heterogeneity of breast tumors may be a reflection of distinct etiologic pathways. For
example, risk factors that influence estrogen levels (i.e, circulating hormones,
postmenopausal hormone use and postmenopausal adiposity), are associated with ER-
positive, but not ER- negative breast cancer [5, 6, 11]. The additional markers used to
classify the molecular phenotypes beyond ER and PR may allow for greater refinement of
tumor subtypes and allow for identification of distinct etiologic pathways. Because luminal
A and B tumor subtypes are both hormone receptor positive and together represent the
majority of breast cancer cases it is not surprising that the majority of identified breast
cancer risk factors are those associated with hormonal exposures. By examining risk factors
in relation to more homogenous subtypes we may identify novel risk factors for less
common subtypes of breast cancer. In the current study, we examine the association between
traditional breast cancer risk factors and the risk of the previously defined molecular
subtypes of breast cancer.
MATERIALS AND METHODS
Study population
Study Design and Population—The Nurses’ Health Study was initiated in 1976 when
121,700 U.S. registered nurses ages 30–55 returned an initial questionnaire. The cohort has
been followed by mailed questionnaires biennially to update exposure information and
ascertain non-fatal incident diseases. Information on body mass index (BMI), reproductive
history, age at menopause, and postmenopausal hormone (PMH) use as well as diagnosis of
cancer and other diseases are updated every two years through questionnaires. The follow-
up rate among this cohort was over 90% through 1996[12].
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Breast cancer case confirmation
All women reporting incident diagnoses of cancer were asked for permission to review their
medical records to confirm the diagnosis and to classify cancers as in situ or invasive, by
histologic type, size, and presence or absence of metastases. To identify cases of cancer in
nonrespondents who died, death certificates for all deceased participants and medical
records for the incident cancers were obtained. Following medical record review, 99% of
self-reported breast cancers were confirmed.
Breast cancer tissue block collection and tissue microarray (TMA) construction
Detailed description of the tissue block collection and TMA construction have been
described previously [13, 14]. Briefly, we constructed tissue microarrays from 3,093 cancers
and positive lymph nodes from 2,897 participants. TMAs were constructed in the Dana
Farber Harvard Cancer Center Tissue Microarray Core Facility, Boston, MA. Three 0.6-mm
cores were obtained from each breast cancer and were inserted into the recipient TMA
blocks. We excluded from the current analysis participants with positive lymph nodes only
(n=25), lobular carcinoma in situ (n=31), in situ carcinomas with both ductal and lobular
features (n=13), ductal carcinoma in situ (n=272), and additional rare tumor types including
malignant phyllodes tumors, neuroendocrine carcinoma and angiosarcoma (n=10).
Immunohistochemical analysis
We performed immunohistochemical staining for ER, PR, HER2, CK5/6, and EGFR on 5-
μm paraffin sections cut from the TMA blocks. Immunostains for each marker were
performed in a single staining run on a Dako Autostainer (Dako Corporation, Carpinteria,
CA). These particular biomarkers were selected for analysis because they have been
commonly used as surrogates to classify invasive breast cancers according to their molecular
phenotypes [4, 7–10]. Sources and dilutions of the primary antibodies used in this study are
listed in Appendix 1. Immunostains for ER, PR, HER2, CK 5/6 and EGFR were performed
using methods described in detail previously [13, 15]. Appropriate positive and negative
controls were included in all staining runs.
Immunostained TMA slides were evaluated for ER and PR expression, HER2 protein over-
expression, and expression of CK5/6 and EGFR in each core. Tumor cells that showed any
nuclear staining for ER or PR were considered ER-positive or PR-positive respectively,
whereas all ER-negative or PR-negative cases showed complete absence of tumor cell
staining in all tissue cores. Of note, low positive ER or PR (1–10% of tumor cell nuclei
staining) and positive ER or PR (>10% of tumor cell nuclei staining) were collapsed into a
single “positive” category for the purposes of this analysis. Tumor cells were considered
positive for HER2 protein over-expression when more than 10% of the cells showed
moderate or strong membrane staining (2+ and 3+). The results of analyses in which HER2
positivity was defined as 3+ were very similar to those presented with a definition of 2+ and
3+. Cases were considered basal CK-positive or EGFR-positive if any cytoplasmic and/or
membranous staining was detected in the tumor cells, even if focal. These latter criteria are
similar to those previously used for scoring these markers in invasive basal-like cancers [4,
7, 8].
Classification of Molecular Subtypes
Immunostained TMA sections were reviewed under a microscope and visually scored for
each individual tissue core as described above. We classified a case as positive if there was
staining in any of the three cores from that case and negative if there was no
immunostaining present. Based on RNA expression data [1–3] and previous large scale
epidemiologic studies [16, 17] we used a panel of immunohistochemical markers to classify
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the tumors into molecular subtypes. Cases that were ER-positive and/or PR-positive and
HER2-negative and histologic grade 1 and 2 were classified as luminal A cancers; cases that
were either a.) ER-positive and/or PR-positive and HER2-positive or b.) ER-positive and/or
PR-positive, HER2-negative and histologic grade 3 were classified as luminal B; cases that
were ER-negative, PR-negative, and HER2-positive were classified as HER2 type; and cases
that were negative for ER, PR, and HER2 and positive for CK 5/6 and/or EGFR were
categorized as basal-like. Cases that lacked expression of all 5 markers were considered
“unclassified”. Of the invasive tumors on tissue microarrays, 2249 could be classified into
one of these 5 molecular subtypes.
Statistical Analysis
Information on breast cancer risk factors was obtained from biennial questionnaires. Women
who reported a diagnosis of cancer other than nonmelanoma skin cancer were excluded at
baseline and from subsequent follow-up analysis. Person-time for each participant was
calculated from the date of return of the 1976 questionnaire to the date of breast cancer
diagnosis, date of any other cancer diagnosis (not including nonmelanoma skin cancer),
death from any cause or June 1, 1998, which ever came first.
The primary analysis used incidence rates with person-months in the denominator.. Person-
time for each participant was calculated from the date of return of the 1976 questionnaire to
the date of breast cancer diagnosis, date of any other cancer diagnosis (not including
nonmelanoma skin cancer), death from any cause or June 1, 1998, whichever came first. The
primary analysis used incidence rates with person-months in the denominator. For each
woman, person-months were allocated to each exposure category, beginning in 1976 and
updated every two years. We used Cox proportional hazards models to estimate hazard
ratios (RRs) and 95% confidence intervals (CIs). Multivariate analysis included age at
menopause, family history of breast cancer in a first-degree relative, personal history of
benign breast disease (BBD), body mass index (BMI) at age 18, weight change since age 18,
age at menarche, parity/age at first birth, alcohol consumption, menopausal status/PMH use,
lactation, and smoking. These variables were considered because they are either well
established risk factors for breast cancer [18] or have been reported to be associated with a
particular molecular phenotype of breast cancer[16, 17].
To determine if the association between exposures is differentially associated with tumor
subtypes, we used competing risks models[19, 20]. Specifically, this approach uses the data
augmentation method described by Lunn and McNeil [21] to create a separate observation
for each subject for each type of outcome and then stratifies on event type, allowing for
estimation of separate associations of each risk factor with the relative hazard of each type
of outcome [20]. Likelihood ratio tests were conducted to compare models assuming
different associations of exposures with each subtype of tumor to models assuming the same
association with all types; a significant p value for this test of heterogeneity would indicate
that the associations are different for the different tumor subtypes. All analyses were
conducted with SAS version 9.2 (SAS, Cary, North Carolina). All statistical tests were two-
sided and p-values <0.05 were considered statistically significant. This study was approved
by the Committee on the Use of Human Subjects in Research at Brigham and Women’s
Hospital.
RESULTS
During the course of follow-up, 2,022 invasive breast cancer cases were identified that could
be classified into one of five molecular phenotypes. Based on immunostaining data from the
five markers used in conjunction with histologic grade (which was used as a surrogate for
Ki67 proliferation index), 1,267 tumors were classified as luminal A; 321 were luminal B;
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113 were HER2; 226 were basal-like and 95 tumors were unclassifiable (ER-/PR-/HER2-/
EGFR-/CK5/6-). In addition, 3,549 breast cancers occurred among women for whom we
were unable to classify into these phenotypes due to lack of either tissue availability or
staining results for one or more of the five markers. Compared with women with tissue
specimens, the women for whom we were unable to obtain specimens were very similar
with respect to breast cancer risk factors and tumor characteristics [13].
The mean age at diagnosis ranged from 55.3 years for women with basal like tumors to 58.0
years for women with luminal A tumors (Table 1). In general, the luminal A tumors were
smaller, less likely to have nodal involvement, were of lower stage and were more often of
lower histologic grade than the other four molecular subtypes. The luminal B and basal like
tumors were more often high grade, while the HER2 type was most likely to be stage III/IV
(33.0%) and the unclassified type was most likely to be metastatic at diagnosis (8.1%)
relative to the other subtypes.
We observed differences in the association between breast cancer risk factors and molecular
subtypes of breast cancer (Table 2). In general, reproductive risk factors for breast cancer
including age at menarche, parity, and age at first birth tended to be most strongly associated
with the luminal A subtype, although there was no evidence of statistical heterogeneity
across the subtypes. For example, compared to nulliparous women, having three or more
children was inversely associated with luminal A breast cancer (HR=0.7, 95%CI 0.5–1.0; p-
trend=0.01). However, this inverse association was not observed among the other subtypes.
We found that hormonal exposures in later adult life exhibited the greatest heterogeneity in
association with these molecular subtypes. Weight gain since age 18 was positively
associated with both luminal A (p-trend=0.05) and B tumor (p-trend=0.001) subtypes, but
not with the ER- subtypes (p-heterogeneity=0.05). The association between weight gain
since age 18 and luminal B tumors was significantly stronger than the association with
luminal A tumors (p-heterogeneity=0.0007). There was suggestive evidence that the
association between postmenopausal hormone use and breast cancer may also vary by
subtype (p-heterogeneity=0.08). We observed a significant association between estrogen
only hormone therapy (HR=1.4, 95%CI 1.1–1.7) and estrogen plus progestin therapy
(HR=1.5, 95%CI 1.2–2.0) and risk of luminal A tumors, but not luminal B tumors (P-
heterogeneity=0.004). Estrogen plus progestin therapy was also associated with basal-like
(HR=1.8, 95% CI 1.0–3.4) and unclassified (HR=2.9, 95% CI 1.1–7.6) tumors, but not
luminal B or HER2-type.
Family history of breast cancer was differentially associated with breast cancer subtypes
(p=0.01). Having one first degree relative with breast cancer was significantly associated
with luminal A and B subtypes only, while having two first degree relatives with breast
cancer was associated with an increased risk of luminal A (HR=2.3, 95% CI 1.3–4.2),
HER2-type (HR=2.5, 95% CI 0.3–18.1), and basal-like (HR=2.9, 95% CI 0.7–11.7) tumors,
and not the others.
There was little evidence that the association between the other risk factors we considered
differed by molecular subtypes. Having a prior benign breast disease was associated with a
20–70% increased risk of all breast cancer subtypes (p-heterogeneity=0.69). BMI at age 18
was inversely associated with luminal A, basal-like and unclassified subtypes of breast
cancer (p-heterogeneity=0.49). Although there were no significant differences in the
association between lactation across molecular subtypes, an inverse association was
strongest for the basal-like tumors (HR=0.6, 95%CI 0.4–0.9; p-heterogeneity=0.88).
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DISCUSSION
In this study of over 2,000 breast cancer cases, we found significant differences in the
association between breast cancer risk factors and molecular subtypes of tumors. As
expected many reproductive risk factors including age at menarche, parity, age at first birth,
age at menopause, and postmenopausal hormone use were associated with luminal A
tumors, the most common type of breast of cancer. In general, hormonally related risk
factors in later adult life demonstrated the most heterogeneity. The association between
postmenopausal hormone use and luminal A tumors was significantly stronger than the
relation with luminal B tumors (the other ER+ subtype). Interestingly, weight gain since age
18 was more strongly associated with luminal B tumors than luminal A tumors.
Unexpectedly, we found that some hormonal factors were associated with hormone receptor
negative subtypes. For example, age at menopause was significantly associated with the
HER2-type and unclassified subtype, and current estrogen plus progestin hormone use was
strongly associated with both basal-like and unclassified tumor types.
A number of other studies have evaluated the association between breast cancer risk factors
and tumor subtypes, although only a handful have evaluated markers beyond ER, PR and
HER2. The Polish Breast Cancer Study (n=804 breast cancer cases) also found that most
established breast cancer risk factors were associated with luminal A tumors [16]. However,
they reported that age at menarche was more strongly inversely associated with basal-like
tumors than luminal A tumors (p-heterogeneity =0.0009), which we did not observe in the
current study. Similar to our study, the Polish Breast Cancer Study found that having a
family history of breast cancer was a risk factor for almost all subtypes although the
magnitude of the effect was greatest for basal-like and HER2-type breast cancers.
The Carolina Breast Cancer Study (CBCS) (n=1424 breast cancer cases) also examined the
association between risk factors for molecular subtypes of breast cancer in a case-control
study of both Caucasian and African American women[17]. Millikan et al. found that
increasing parity was associated with reduced risk of luminal A tumors and an increased risk
of basal-like tumors. Our results with respect to parity are consistent with this finding. In
addition, The CBCS reported an inverse association between lactation and basal-like tumors.
Although there was no significant heterogeneity between lactation and subtype in our study,
we did find a strong inverse association between lactation and basal-like tumors. For women
with total breast feeding of 4+ months, we found a 40% reduced risk of basal-like breast
cancer, which is in line with the 30% reduced risk observed in the CBCS.
In addition, studies examining risk factors in relation to tumors classified with information
on ER, PR and HER2 only have also been conducted. A combined study of the LACE and
Pathways studies within Kaiser Permanente Northern California examined breast cancer risk
factors in relation to subtypes defined by ER, PR, and HER2. In this study of 2544 invasive
breast cancer cases, Kwan et al [22]found that relative to luminal A cases (ER+ and/or PR+/
HER2-), luminal B cases (ER+ and/or PR+/HER2+) were less likely to consume alcohol and
use HRT. Breast feeding for at least four months was associated with a lower risk of triple
negative cases (ER-/PR-/HER2-) compared with luminal A. Similarly, two other studies
Phipps et al [23](n=1130 total cases) and Gaudet et al [24](n=890 total cases), also reported
an inverse association between breastfeeding for 6 or more months and triple negative breast
tumors.
Of interest, a number of risk factors in our study did not demonstrate heterogeneity across
tumor subtypes including age at menarche, BMI at age 18, previous BBD, and alcohol
consumption. It is possible that these factors are having a similar effect on risk across the
different subtypes and this may be indicating how these factors are affecting breast cancer
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etiology. For example, having a prior BBD may indicate having early proliferative lesions
which could have developed through a number of different pathways. BBD is believed to be
a general marker of breast cancer risk, and thus may reflect the culmination of many risk
factors and not be specific to any one pathway. It is also possible that we may not have had
enough power to detect the difference across subtypes for some exposures.
Our classification of tumor subtypes was similar although not identical to those used in
previous epidemiologic studies [16, 17]. Both of the prior studies utilized
immunohistochemical markers to define molecular subtypes, while we also incorporated
histologic grade. Others have shown that the distinction between luminal A and B tumors
can be refined by adding the proliferation marker Ki67 to ER, PR, and HER2[25]. Given
that Ki67 data were not available for our cases, we used histologic grade as a surrogate for
proliferation rate given the close correlation between proliferation rate and histologic grade.
Thus, our definitions for luminal A and B are different than the two previous studies, but
more in keeping with the most recently proposed classification scheme[25]. This may limit
our ability to compare across studies and explain some of the differences observed.
The results of the current study taken together with the previous studies suggest that many of
the traditional breast cancer risk factors are associated with luminal A tumors, and that there
may be some differences with other subtypes. The associations with luminal A tumors is not
surprising since these are the most common tumor type and may also reflect the selection of
exposures we have focused in the current study. We have chosen to examine traditional
breast cancer risk factors; these were initially identified because they are the risk factors
shown to be most commonly associated with breast cancer. The majority of these risk
factors have also been shown to be associated with the most common type of breast cancer,
namely ER+ breast cancers. While it is reassuring that these risk factors are associated with
the luminal A subtype, it does not help us to further our understanding of risk factors
associated with ER- subtypes. Adding the additional markers and dividing the cases into
smaller subsets limits our power to detect associations for the rare subtypes. One of the most
consistent findings from this study and other studies is the inverse association between
breast feeding and triple negative or basal like tumors. This finding supports the hypothesis
that molecular classification of tumors may help us to better understand etiology and/or
provide insights into the mechanisms by which these less common molecular subtypes
develop.
This study has a number of strengths including the large study population with over 2,000
invasive breast cancer cases, the prospectively collected nature of the exposure variables,
and uniform staining and scoring of molecular markers. Despite the large number of cases,
we were still limited by the number of less common subtypes in particular the HER2-type
and the unclassified tumors. Thus, our power to detect significant differences by these
subtypes was limited. In addition, we lacked adequate power to examine these associations
among premenopausal breast cancer cases. It is worth noting that the frequency of receptor
status positivity and molecular subtype frequency among invasive tumors in our study
population was very similar to other populations suggesting that samples included in this
study are representative of the overall US population.
In conclusion, in this study we found that traditional breast cancer risk factors demonstrated
different relations with the molecular subtypes of breast cancer. In general, many of the
reproductive factors were most strongly associated with the luminal A subtype. In addition,
we confirmed a previously reported strong inverse association between lactation and basal-
like tumors. It is unclear whether classifying breast tumors according to the molecular
phenotypes (ie. subtypes that are known to have prognostic importance) permits
identification of differences in risk factor profiles. Additional work to determine if the
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differences that have been observed are due to single markers or to the molecular phenotype
is necessary. It remains to be seen whether non-traditional breast cancer risk factors may
exist that are associated with less common tumor subtypes. Identifying risk factors for these
less common subtypes such as HER2 and basal-like tumors, which also have a poorer
prognosis, has important implications for prevention of these tumor subtypes.
Acknowledgments
Funding/Support: Supported by GlaxoSmithKline (WE234 (EPI40307)); Public Health Service Grants CA087969,
and SPORE in Breast Cancer CA089393, from the National Cancer Institute, National Institutes of Health,
Department of Health and Human Services and Breast Cancer Research Fund. Dr. Graham Colditz is supported in
part by an American Cancer Society Cissy Hornung Clinical Research Professorship.
We thank the participants and staff of the Nurses' Health Study cohort, for their valuable contributions. We thank
the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA,
ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WY. The study
sponsors had no role in the design of the study; the collection, analysis, and interpretation of the data; the writing of
the manuscript; or the decision to submit the manuscript for publication.
References
1. Perou CM, Jeffrey SS, van de Rijn M, Rees CA, Eisen MB, Ross DT, Pergamenschikov A,
Williams CF, Zhu SX, Lee JC, et al. Distinctive gene expression patterns in human mammary
epithelial cells and breast cancers. Proc Natl Acad Sci U S A. 1999; 96(16):9212–9217. [PubMed:
10430922]
2. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M,
Jeffrey SS, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with
clinical implications. Proc Natl Acad Sci U S A. 2001; 98(19):10869–10874. [PubMed: 11553815]
3. Sorlie T, Tibshirani R, Parker J, Hastie T, Marron JS, Nobel A, Deng S, Johnsen H, Pesich R,
Geisler S, et al. Repeated observation of breast tumor subtypes in independent gene expression data
sets. Proc Natl Acad Sci U S A. 2003; 100(14):8418–8423. [PubMed: 12829800]
4. Brenton JD, Carey LA, Ahmed AA, Caldas C. Molecular classification and molecular forecasting of
breast cancer: ready for clinical application? J Clin Oncol. 2005; 23 (29):7350–7360. [PubMed:
16145060]
5. Colditz GA, Rosner BA, Chen WY, Holmes MD, Hankinson SE. Risk factors for breast cancer
according to estrogen and progesterone receptor status. J Natl Cancer Inst. 2004; 96(3):218–228.
[PubMed: 14759989]
6. Ma H, Bernstein L, Pike MC, Ursin G. Reproductive factors and breast cancer risk according to
joint estrogen and progesterone receptor status: a meta-analysis of epidemiological studies. Breast
Cancer Res. 2006; 8(4):R43. [PubMed: 16859501]
7. Nielsen TO, Hsu FD, Jensen K, Cheang M, Karaca G, Hu Z, Hernandez-Boussard T, Livasy C,
Cowan D, Dressler L, et al. Immunohistochemical and clinical characterization of the basal-like
subtype of invasive breast carcinoma. Clin Cancer Res. 2004; 10(16):5367–5374. [PubMed:
15328174]
8. Abd El-Rehim DM, Ball G, Pinder SE, Rakha E, Paish C, Robertson JF, Macmillan D, Blamey RW,
Ellis IO. High-throughput protein expression analysis using tissue microarray technology of a large
well-characterised series identifies biologically distinct classes of breast cancer confirming recent
cDNA expression analyses. Int J Cancer. 2005; 116(3):340–350. [PubMed: 15818618]
9. Abd El-Rehim DM, Pinder SE, Paish CE, Bell J, Blamey RW, Robertson JF, Nicholson RI, Ellis IO.
Expression of luminal and basal cytokeratins in human breast carcinoma. J Pathol. 2004; 203(2):
661–671. [PubMed: 15141381]
10. van de Rijn M, Perou CM, Tibshirani R, Haas P, Kallioniemi O, Kononen J, Torhorst J, Sauter G,
Zuber M, Kochli OR, et al. Expression of cytokeratins 17 and 5 identifies a group of breast
carcinomas with poor clinical outcome. Am J Pathol. 2002; 161(6):1991–1996. [PubMed:
12466114]
Tamimi et al. Page 8
Breast Cancer Res Treat. Author manuscript; available in PMC 2013 January 1.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript
11. Missmer SA, Eliassen AH, Barbieri RL, Hankinson SE. Endogenous estrogen, androgen, and
progesterone concentrations and breast cancer risk among postmenopausal women. J Natl Cancer
Inst. 2004; 96(24):1856–1865. [PubMed: 15601642]
12. Colditz GA, Hankinson SE. The Nurses' Health Study: lifestyle and health among women. Nat Rev
Cancer. 2005; 5(5):388–396. [PubMed: 15864280]
13. Tamimi RM, Baer HJ, Marotti J, Galan M, Galaburda L, Fu Y, Deitz AC, Connolly JL, Schnitt SJ,
Colditz GA, et al. Comparison of molecular phenotypes of ductal carcinoma in situ and invasive
breast cancer. Breast Cancer Res. 2008; 10(4):R67. [PubMed: 18681955]
14. Collins LC, Marotti JD, Baer HJ, Tamimi RM. Comparison of estrogen receptor results from
pathology reports with results from central laboratory testing. J Natl Cancer Inst. 2008; 100(3):
218–221. [PubMed: 18230800]
15. Collins LC, Botero ML, Schnitt SJ. Bimodal frequency distribution of estrogen receptor
immunohistochemical staining results in breast cancer: An analysis of 825 cases. Am J Clin
Pathol. 2005; 123:16–20. [PubMed: 15762275]
16. Yang XR, Sherman ME, Rimm DL, Lissowska J, Brinton LA, Peplonska B, Hewitt SM, Anderson
WF, Szeszenia-Dabrowska N, Bardin-Mikolajczak A, et al. Differences in risk factors for breast
cancer molecular subtypes in a population-based study. Cancer Epidemiol Biomarkers Prev. 2007;
16(3):439–443. [PubMed: 17372238]
17. Millikan RC, Newman B, Tse CK, Moorman PG, Conway K, Smith LV, Labbok MH, Geradts J,
Bensen JT, Jackson S, et al. Epidemiology of basal-like breast cancer. Breast Cancer Res Treat.
2008; 109(1):123–139. [PubMed: 17578664]
18. Hankinson, S.; Hunter, DJ. Breast Cancer. In: Adami, HOHD.; Trichopoulos, D., editors. Textbook
of Cancer Epidemiology. New York: Oxford University Press; 2002. p. 301-339.
19. Glynn RJ, Rosner B. Methods to evaluate risks for composite end points and their individual
components. J Clin Epidemiol. 2004; 57(2):113–122. [PubMed: 15125620]
20. Glynn RJ, Rosner B. Comparison of risk factors for the competing risks of coronary heart disease,
stroke, and venous thromboembolism. Am J Epidemiol. 2005; 162(10):975–982. [PubMed:
16207808]
21. Lunn M, McNeil D. Applying Cox regression to competing risks. Biometrics. 1995; 51 (2):524–
532. [PubMed: 7662841]
22. Kwan ML, Kushi LH, Weltzien E, Maring B, Kutner SE, Fulton RS, Lee MM, Ambrosone CB,
Caan BJ. Epidemiology of breast cancer subtypes in two prospective cohort studies of breast
cancer survivors. Breast Cancer Res. 2009; 11(3):R31. [PubMed: 19463150]
23. Phipps AI, Malone KE, Porter PL, Daling JR, Li CI. Reproductive and hormonal risk factors for
postmenopausal luminal, HER-2-overexpressing, and triple-negative breast cancer. Cancer. 2008;
113(7):1521–1526. [PubMed: 18726992]
24. Gaudet MM, Press MF, Haile RW, Lynch CF, Glaser SL, Schildkraut J, Gammon MD, Douglas
Thompson W, Bernstein JL. Risk factors by molecular subtypes of breast cancer across a
population-based study of women 56 years or younger. Breast Cancer Res Treat. 2011 Jun 11.
[Epub ahead of print].
25. Cheang MC, Chia SK, Voduc D, Gao D, Leung S, Snider J, Watson M, Davies S, Bernard PS,
Parker JS, et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J
Natl Cancer Inst. 2009; 101(10):736–750. [PubMed: 19436038]
Tamimi et al. Page 9
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Table 1
Tumor characteristics according to breast cancer phenotypes among women with invasive breast cancer, Nurses’ Health Study (1976–1996).
Characteristic Luminal A Luminal B HER2 Basal-like Unclassified
N (%) 1267 (62.7) 321 (15.9) 113 (5.6) 226 (11.2) 95 (4.7)
Mean age at diagnosis, yrs 58.0 56.5 56.1 55.3 55.6
Median age at diagnosis, yrs 59.0 57.0 56.0 56.0 55.0
Tumor size1
0.1 to 1.0 cm 323 (26.7) 47 (15.3) 18 (17.0) 27 (12.9) 19 (21.6)
1.1 to 2.0 cm 503 (41.6) 116 (37.8) 27 (25.5) 74 (35.2) 31 (35.2)
2.1 to 4.0 cm 281 (23.2) 100 (32.6) 44 (41.5) 87 (41.4) 24 (27.3)
4.1+ cm 103 (8.5) 44 (14.3) 17 (16.0) 22 (10.5) 14 (15.9)
Missing 57 14 7 16 7
Lymph node status1
No Nodes 787 (66.3) 165 (55.4) 47 (43.9) 119 (56.9) 50 (57.5)
1–3 Nodes 234 (19.7) 65 (21.8) 28 (26.2) 54 (25.8) 14 (16.1)
4–9 Nodes 86 (7.2) 38 (12.8) 14 (13.1) 22 (10.5) 9 (10.3)
10+ Nodes 55 (4.6) 23 (7.7) 13 (12.2) 7 (3.4) 7 (8.1)
Metastatic at diagnosis 26 (2.2) 7 (2.4) 5 (4.7) 7 (3.4) 7 (8.1)
Missing 79 23 6 17 8
Stage at diagnosis1
I/II 1026 (84.6) 230 (74.7) 71 (67.0) 173 (80.8) 66 (73.3)
III/IV 187 (15.4) 78 (25.3) 35 (33.0) 41 (19.2) 24 (26.7)
Missing 54 13 7 12 5
Grade2
Well differentiated 366 (28.9) 8 (2.7) 3 (2.8) 7 (3.1) 16 (16.8)
Int. differentiated 901 (71.1) 57 (18.9) 54 (49.5) 57 (25.3) 37 (39.0)
Poorly differentiated 0 237 (78.5) 52 (47.7) 161 (71.6) 42 (44.2)
Missing 0 19 4 1 0
Histology1
Invasive ductal 1002(79.4) 300 (94.0) 111 (99.1) 210 (94.6) 79 (85.9)
Invasive lobular 180 (14.3) 10 (3.1) 0 3 (1.4) 8 (8.7)
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Characteristic Luminal A Luminal B HER2 Basal-like Unclassified
N (%) 1267 (62.7) 321 (15.9) 113 (5.6) 226 (11.2) 95 (4.7)
Invasive ductal and lobular 70 (5.6) 5 (1.6) 0 0 2 (2.2)
Invasive not specified 10 (0.8) 4 (1.3) 1 (0.9) 9 (4.1) 3 (3.3)
Missing 5 2 1 4 3
Race
White 1209 (95.4) 304 (94.7) 109 (96.5) 210 (92.9) 90 (94.7)
Black 11 (0.9) 7 (2.2) 1 (0.9) 8 (3.5) 2 (2.1)
American Indian 2 (0.2) 0 0 0 0
Asian 4 (0.3) 0 1 (0.9) 2 (0.9) 0
Hawaiian 0 0 0 1 (0.4) 0
Other 31 (2.5) 8 (2.5) 0 4 (1.8) 3 (3.2)
Multiracial 10 (0.8) 2 (0.6) 2 (1.8) 1 (0.4) 0
1Information obtained from pathology records
2Information obtained from centralized pathology review of slides
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Table 2
Relative risk1 of invasive breast cancer molecular subtypes breast cancer according to breast cancer risk factors
Luminal A Luminal B HER2 Basal Unclassified
Cases 1267 321 113 226 95
Person-years 2008898 2009767 2009968 2009865 2009986
Age at menarche
<12 1.0 (ref) 1.0 (Ref) 1.0 (Ref) 1.0 (Ref) 1.0 (Ref)
12 1.0 (0.8–1.1) 1.1 (0.8–1.5) 1.4 (0.8–2.3) 0.8 (0.5–1.1) 0.8 (0.4–1.3)
13 0.8 (0.7–1.0) 1.1 (0.8–1.5) 0.9 (0.5–1.5) 0.7 (0.5–1.0) 0.5 (0.3–0.9)
14 0.7 (0.6–0.8) 1.0 (0.7–1.5) 0.9 (0.4–1.8) 0.8 (0.5–1.3) 0.6 (0.3–1.2)
14+ 0.7 (0.5–0.9) 1.0 (0.6–1.6) 1.1 (0.5–2.3) 0.8 (0.5–1.3) 0.8 (0.4–1.7)
P for Trend 0.002 0.14 0.31 0.72 0.11
P-Heterogeneity 0.92
BMI at age 18
<20 1.0 (ref) 1.0 (ref) 1.0 (ref) 1.0 (Ref) 1.0 (Ref)
20–21.9 0.9 (0.7–1.0) 1.0 (0.8–1.3) 0.6 (0.4–1.0) 0.8 (0.5–1.1) 0.5 (0.3–0.9)
22.0–23.9 0.8 (0.7–1.0) 0.8 (0.6–1.2) 0.7 (0.4–1.3) 0.9 (0.6–1.3) 0.6 (0.3–1.2)
24.0–26.9 0.6 (0.5–0.8) 1.0 (0.6–1.5) 0.5 (0.2–1.1) 0.7 (0.4–1.3) 0.8 (0.4–1.8)
27- 0.5 (0.4–0.8) 0.7 (0.3–1.4) 0.6 (0.2–1.7) 0.4 (0.1–1.1) 0.2 (0.0–1.2)
P for Trend <0.0001 0.56 0.27 0.04 0.02
P-Heterogeneity 0.49
Weight Gain since 18
Loss <2 kg 0.9 (0.6–1.1) 0.9 (0.5–1.7) 1.1 (0.4–2.9) 0.8 (0.4–1.7) 3.1 (0.8–11.9)
Stable 1.0 (ref) 1.0 (Ref) 1.0 (ref) 1.0 (ref) 1.0 (Ref)
Gain 2.1–5kg 0.9 (0.7–1.1) 0.9 (0.5–1.5) 0.6 (0.2–1.6) 1.4 (0.8–2.5) 3.1 (0.9–10.8)
Gain 5.1–10kg 0.9 (0.7–1.1) 1.1 (0.7–1.8) 1.4 (0.6–3.0) 1.1 (0.6–2.0) 2.6 (0.7–8.8)
Gain 10.1–20kg 0.9 (0.8–1.2) 1.3 (0.8–2.1) 1.3 (0.6–2.7) 1.2 (0.7–2.1) 3.5 (1.1–11.5)
Gain 20.1–25kg 0.9 (0.7–1.3) 1.2 (0.7–2.1) 1.3 (0.5–3.3) 1.0 (0.5–2.2) 2.4 (0.6–9.6)
Gain 25+ kg 1.1 (0.8–1.4) 1.5 (0.9–2.5) 1.2 (0.5–3.2) 1.5 (0.8–2.8) 2.5 (0.6–9.6)
P for trend 0.05 0.001 0.31 0.11 0.35
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Luminal A Luminal B HER2 Basal Unclassified
P-Heterogeneity 0.05
Parity
Nulliparous 1.0 (Ref) 1.0 (Ref) 1.0 (Ref) 1.0 (Ref) 1.0 (Ref)
1 child 0.8 (0.6–1.3) 2.2 (0.8–5.9) 1.1 (0.3–4.2) 0.6 (0.2–1.8) 1.4 (0.3–8.2)
2 children 0.7 (0.5–1.1) 1.2 (0.5–3.0) 1.0 (0.3–3.5) 0.9 (0.3–2.6) 1.1 (0.2–5.4)
3+ children 0.7 (0.5–1.0) 1.1 (0.5–2.6) 0.7 (0.2–2.3) 1.1 (0.4–2.9) 1.4 (0.3–6.0)
P for trend 0.01 0.01 0.12 0.04 (+) 0.65
P-Heterogeneity 0.60
Age at first birth
Per 1 year increase 1.018 (1.007–1.030) 0.994 (0.964–1.024) 1.024 (0.991–1.059) 1.002 (0.968–1.037) 1.006 (0.959–1.056)
P-Heterogeneity 0.36
Age at Menopause
Per year increase 1.039 (1.021–1.058) 1.067(1.024–1.111) 1.075 (1.006–1.150) 1.012 (0.972–1.053) 1.089 (1.006–1.179)
P-Heterogeneity 0.10
Previous BBD
No 1.0 (Ref) 1.0 (ref) 1.0 (ref) 1.0 (Ref) 1.0 (Ref)
Yes 1.4 (1.2–1.5) 1.7 (1.3–2.2) 1.2 (0.8–1.9) 1.5 (1.1–2.0) 1.5 (0.9–2.4)
P-Heterogeneity 0.69
Menopausal status/PMH Use
Premenopausal 1.2 (1.0–1.5) 1.5 (1.0–2.2) 1.1 (0.6–2.1) 1.1 (0.7–1.9) 1.7 (0.8–3.7)
Post Never Use 1.0(REF) 1.0 (Ref) 1.0 (Ref) 1.0 (Ref) (Ref)
Post Past Use 0.9 (0.8–1.1) 1.2 (0.8–1.7) 1.3 (0.7–2.4) 0.8 (0.5–1.4) 1.5 (0.7–3.2)
Post Current E only 1.4 (1.1–1.7) 1.0 (0.7–1.7) 1.1 (0.6–2.3) 1.4 (0.8–2.3) 2.0 (0.9–4.4)
Post Current E+P 1.5 (1.2–2.0) 1.3 (0.8–2.4) 0.3 (0.0–2.2) 1.8 (1.0–3.4) 2.9 (1.1–7.6)
P-Heterogeneity 0.08
Alcohol Consumption
None 1.0 (Ref) 1.0 (Ref) 1.0 (Ref) 1.0 (Ref) 1.0 (Ref)
<5 g/week 1.1 (1.0–1.3) 0.8 (0.6–1.1) 1.1 (0.6–1.8) 0.8 (0.6–1.2) 0.5 (0.3–1.0)
5–10 1.0 (0.8–1.3) 1.1 (0.7–1.7) 1.3 (0.7–2.7) 0.7 (0.4–1.3) 0.9 (0.5–1.9)
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Luminal A Luminal B HER2 Basal Unclassified
10–15 1.2(1.0–1.5) 0.8 (0.5–1.3) 1.4 (0.7–2.8) 0.7 (0.4–1.2) 0.6 (0.3–1.3)
15+ 1.3 (1.0–1.6) 1.0 (0.7–1.5) 1.4 (0.7–2.8) 0.8 (0.5–1.4) 0.8 (0.4–1.6)
P-for trend 0.04 0.91 0.13 0.31 0.58
P-Heterogeneity 0.32
Lactation
Never 1.0 (Ref) 1.0 (Ref) 1.0 (ref) 1.0 (Ref) 1.0 (Ref)
0–3 months 0.8 (0.7–0.9) 1.0 (0.7–1.4) 0.8 (0.4–1.3) 0.8 (0.6–1.2) 1.0 (0.6–1.8)
4+ months 0.8 (0.7–1.0) 0.8 (0.6–1.1) 0.9 (0.6–1.5) 0.6 (0.4–0.9) 0.6 (0.4–1.1)
P-Heterogeneity 0.88
Family History of breast cancer
None 1.0 (Ref) 1.0 (ref) 1.0 (Ref) 1.0 (Ref) 1.0 (Ref)
1 1st degree relative 1.6 (1.4–1.9) 1.5 (1.0–2.1) 1.5 (0.8–2.6) 1.0 (0.6–1.6) 0.9 (0.4–1.9)
2+ 1st degree relat 2.3 (1.3–4.2) -- 2.5 (0.3–18.1) 2.9 (0.7–11.7) --
P-Heterogeneity 0.01
1Mutually adjusted for all variables in the table
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Appendix 1
Sources and dilutions of primary antibodies used in this study.
Antibody Clone Manufacturer Dilution
ER 1D5 Dako 1:200
PR PgR 636 Dako 1:50
HER2 A0485 (rabbit polyclonal) Dako 1:400
CK 5/6 D5/16B4 Dako 1:50
EGFR 2-18C9 Dako pre-diluted (pharmDX kit)
ER=estrogen receptor; PR=progesterone receptor; HER2=human epidermal growth factor receptor 2; CK5/6=cytokeratin 5/6; EGFR=epidermal
growth factor receptor
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