Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Mar 21.
Published in final edited form as: Cancer Epidemiol Biomarkers Prev. 2020 Oct 23;30(1):71–79. doi: 10.1158/1055-9965.EPI-20-0556

Epidemiology of basal-like and luminal breast cancers among black women in the AMBER consortium

Halei C Benefield 1, Gary R Zirpoli 2, Emma H Allott 3, Yue Shan 4, Amber N Hurson 5, Angela R Omilian 6, Thaer Khoury 7, Chi-Chen Hong 8, Andrew F Olshan 9, Traci N Bethea 10, Elisa V Bandera 11, Julie R Palmer 12, Christine B Ambrosone 13, Melissa A Troester 14
PMCID: PMC8935955  NIHMSID: NIHMS1640619  PMID: 33097496

Abstract

Background:

Evidence suggests etiologic heterogeneity among breast cancer subtypes. Previous studies with six-marker immunohistochemical classification of intrinsic subtypes included small numbers of black women.

Methods:

Using centralized laboratory results for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor 2 (HER2), proliferation marker Ki-67, epidermal growth factor receptor (EGFR), and cytokeratin (CK)5/6, we estimated case-only and case-control odds ratios (ORs) for established breast cancer risk factors among cases (n=2,354) and controls (n =2,932) in the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. ORs were estimated by ER status and intrinsic subtype using adjusted logistic regression.

Results:

Case-only analyses by ER status showed etiologic heterogeneity by age at menarche, parity (versus nulliparity), and age at first birth. In case-control analyses for intrinsic subtype, increased body mass index (BMI) and waist-to-hip (WHR) ratio were associated with increased risk of luminal A subtype, while older age at menarche and parity, regardless of breastfeeding, were associated with reduced risk. For basal-like cancers, parity without breastfeeding and increasing WHR were associated with increased risk, whereas breastfeeding and age ≥ 25 years at first birth were associated with reduced risk among parous women. Basal-like and ER-/HER2+ subtypes had earlier age-at-incidence distribution relative to luminal subtypes.

Conclusions:

Breast cancer subtypes show distinct etiologic profiles in the AMBER consortium, a study of over 5,000 black women with centrally assessed tumor biospecimens.

Impact:

Among black women, high WHR and parity without breastfeeding are emerging as important intervention points to reduce the incidence of basal-like breast cancer.

INTRODUCTION

Basal-like breast cancer is an aggressive molecular subtype defined by a signature of genes, including those expressed in the basal layer of human breast tissue (1). Basal-like tumors typically have poor clinical outcomes and limited options for targeted treatment due to low or absent expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) (2–7). Previous studies show that the relative frequency of basal-like tumors is highest among younger women and black women, especially premenopausal black women (8–11). Reproductive factors, such as parity and breastfeeding, have been shown to contribute to the risk of basal-like subtype, with parous women who do not breastfeed having an increased risk of estrogen receptor (ER) negative and triple-negative breast cancer (9, 12–14). Late age at menarche has been associated with reduced risk of ER-negative breast cancers among black women (15). Associations with other hormone-related risk factors, such as body mass index and oral contraceptive use, have been inconsistent across studies of black women (16–19).

Accurate and reliable methods for determining breast tumor subtype are important in studies of risk factor heterogeneity. Most prior risk factor studies have relied on ER, PR, and HER2 status from the clinical record for classification of tumors as luminal A (ER+/HER2-), luminal B (ER+/HER2+), ER-/HER2+, and triple-negative (ER-/PR-/HER2-) (20–24). However, laboratory technical variation, changes in expression cutoff values, and intratumoral heterogeneity can contribute to outcome misclassification within this schema, particularly for luminal breast cancers. Moreover, reliance on triple negative status from the clinical record has resulted in misclassification of basal-like breast cancers in some studies (25–28). We recently showed that incorporating centrally-assayed ER, PR, HER2, Ki67, EGFR, and CK5/6 immunohistochemical (IHC)-surrogate classification improved subtype accuracy and produced subtype frequencies similar to those from the RNA-based PAM50 intrinsic subtype assay in the African American Breast Cancer and Risk (AMBER) Consortium (29).

Importantly, this consortium study of black women included a large fraction of younger women, resulting in a more than five-fold larger sample of basal-like breast cancers than previous studies (n = 691 basal-like breast cancers compared to n = 122 in Millikan et al. 2008) (9, 20–24). In this study we sought to improve the precision of risk factors associations for basal-like breast cancer in this population, and compare these estimates to those from previous studies of mostly white women.

METHODS

Study population.

The AMBER consortium includes black cases and controls from observational studies of breast cancer: the Carolina Breast Cancer Study (CBCS) (30), the Black Women’s Health Study (BWHS) (31), the Women’s Circle of Health Study (WCHS) (32, 33) and the Multi-Ethnic Cohort (MEC) (34). Centralized IHC for intrinsic subtype was performed only for CBCS, BWHS and WCHS; MEC participants were not included in the analysis. Sampling schema for each study have been reported previously (35). Each study was approved by institutional review boards at participating hospitals and academic institutions and conducted in accordance with U.S. Common Rule. Informed consent was obtained from each participant. Briefly, the CBCS is a population-based study of breast cancer cases in North Carolina that enrolled women in three phases (Phase 1: 1993–1996, Phase 2: 1996–2001 and Phase 3: 2008–2013) and oversampled young and black women. The study identified cases via rapid case ascertainment and recruited controls using Division of Motor Vehicle and Medicare beneficiary lists. Controls were frequency matched by age and race. Phase 3 did not enroll controls; as a result, use of Phase 3 cases in this study is limited to our analyses of case-only ORs and age-at-incidence curves. Data collection included in-person interview and medical record abstraction. BWHS enrolled 59,000 cancer-free black women via a mailed questionnaire beginning in 1995 and have followed women through biennial questionnaire since. Breast cancer diagnoses are self-reported and confirmed via medical record linkage or through state cancer registries and the National Death Index. Three controls per case were included from the BWHS, frequency-matched to cases by 5-year age category. The WCHS is a case-control study initially conducted in metropolitan New York City and later only in 10 counties in eastern New Jersey (NJ) (32). New York cases with incident breast cancer from hospitals that served large proportions of black cases and controls were identified through random digit dialing. Controls were frequency matched by age and recruitment site. In NJ, cases were identified through the NJ State Cancer Registry and controls using random digit dialing and community-based recruitment (33).

Eligible women for the present study included 1,559 cases from the CBCS (304 from Phase 1, 29 from Phase 2, and 1,226 from Phase 3), 291 cases from the BWHS, and 504 cases from the WCHS. Controls included 788 from Phases 1 and 2 of the CBCS, 873 from the BWHS, and 1,271 from the WCHS. Characteristics of cases and controls are described in Supplemental Table 1.

Tumor biomarkers.

Eligible cases for the current analysis were women diagnosed with invasive breast cancer and for whom tumor tissue was available for centralized laboratory analysis (n=2,354). For all cases, IHC biomarker stains were carried out on paraffin-embedded tumor sections or tumor microarrays at the Translational Pathology Lab at the University of North Carolina at Chapel Hill using assay procedures and cutpoints as previously described (29). A 10% ER positivity threshold was used to delineate ER-positive versus -negative. Subtypes were defined using 6 biomarkers: luminal A (ER+ and/or PR+, Ki-67 < 7.1%), luminal B ( ER+ and/or PR+, Ki-67 ≥ 7.1%]), ER-/HER2+, or basal-like (ER- and PR- and HER2- and [EGFR+ or CK5/6+]). When ER+ cases were missing Ki-67 (333 cases from CBCS; 0 cases from BWHS; 51 cases from WCHS), subtypes were defined using 5 biomarkers and included in case-control and case-only analyses: luminal A (ER+ and/or PR+ and grade 1 or grade 2), luminal B (ER+ and/or PR+ and grade 3), ER-/HER2+, or basal-like (ER- and PR- and HER2- and [EGFR+ or CK5/6+]). 20 cases were additionally missing grade, and were included only in analyses stratified on ER status.

Statistical analyses.

ORs were calculated as the measure of association between risk factor exposure and breast cancer subtype. Multivariable binomial logistic regression was used to calculate case-control and case-only ORs and 95% confidence intervals (CIs). Multivariable models were adjusted for age (continuous linear), first degree family history of breast cancer (yes or no), parity (nulliparous, 1 – 2 children, or ≥ 3 children), breastfeeding duration (never, < 6 months, or ≥ 6 months), and study (CBCS, WCHS, BWHS). We conducted sensitivity analyses of fully adjusted models (adjusting for all risk factors under consideration) and found the magnitude and direction of point estimates did not change appreciably; however, the width of confidence intervals was larger. P-values were two-sided with α = 0.05.

To compare the joint impact of parity and breastfeeding we used a composite variable categorizing parous women by breastfeeding status (1 or 2 children, never lactated; 3 or more children, never lactated; 1 or 2 children, ever lactated; 3 or more children, ever lactated) and calculated case-control ORs with nulliparous controls as the referent group.

Bimodality in age at diagnosis has been used to investigate etiologically distinct subtypes of breast cancer, therefore we assessed bimodal age-at-diagnosis distributions to evaluate for etiological heterogeneity among intrinsic subtypes (36). We use two-component statistical mixture models to estimate the proportions of early-onset and late-onset cases within each of the intrinsic subtypes, as previously described (36, 37). Within each subtype we compared the fit of single-density models versus two-component mixture models. For each type of model we implemented both normal density and semi-nonparametric density models (adding polynomial multiplier to the normal distribution to allow for skewness and heavy tails in the distributions), resulting in a total of four models for each subtype. The four models fitted for each subtype were compared using Akaike information criterion (AIC) values, with smaller AIC values indicating a better fit. We identified the best fitting single-density model and the best fitting two-component mixture model, and then compared the goodness of fit between these two models using the difference in their AIC values (ΔAIC). ΔAIC >10 indicated a substantial difference in the goodness of fit between the two models. For each subtype we plotted the smoothed density curve estimated from the best model overlaid with the empirical age-at-diagnosis distribution (i.e. histogram) for early onset, late onset and overall distribution. All analyses were performed using SAS 9.4 (SAS Institute, Cary, NC) and R (version 3.4.3, R Foundation for Statistical Computing, Vienna, Austria).

RESULTS

Case-only odds ratios for ER-positive and ER-negative tumors.

Estrogen receptor is a strong marker of etiologic heterogeneity in previous studies of breast cancer in black and white women. To first establish the similarity of this dataset to previous studies with respect to ER heterogeneity, we evaluated case-only ORs for a range of risk factors comparing ER-negative tumors to ER-positive tumors. Multivariable ORs are presented in Table 1. Older age at menarche, increasing parity, age at first full term birth, and breastfeeding status showed etiologic heterogeneity by ER status. Family history, oral contraceptive use, BMI, and WHR did not show heterogeneity by ER status with ORs close to 1.

Table 1:

Case-only odds ratios comparing ER-negative relative to ER-positive breast cancer. Cases are from BWHS, WCHS, and CBCS Phases 1, 2, and 3.

ER-Positive (n=1393) ER-Negative (n=956)
Risk Factor n (%) n (%) OR (95%CI)a
Family historyb
No 1147 (82) 793 (83) 1.00
Yes 246 (18) 163 (17) 1.03 (0.82 – 1.29)
Age at menarche
<11 164 (12) 96 (10) 1.00
11–12 630 (45) 413 (43) 1.15 (0.86 – 1.54)
>=13 597 (43) 446 (47) 1.35 (1.01 – 1.80)
Missing 2 1
Parity
Nulliparous 242 (17) 114 (12) 1.00
1–2 children 661 (47) 492 (51) 1.87 (1.43 – 2.44)
>=3 children 490 (35) 350 (37) 2.05 (1.54 – 2.73)
Missing 0 0
Age at first full-term birthc
<25 808 (70) 643 (76) 1.00
>=25 337 (30) 196 (23) 0.74 (0.60 – 0.92)
Missing 6 3
Lifetime breastfeeding durationc
Never 640 (56) 538 (64) 1.00
<6 mos 205 (18) 144 (17) 0.75 (0.59 – 0.96)
6+ mos 297 (26) 154 (18) 0.61 (0.49 – 0.78)
Missing 9 6
Oral contraceptive use
Never 559 (40) 339 (35) 1.00
Ever 823 (59) 611 (64) 1.02 (0.85 – 1.23)
Missing 11 0
Body mass index
<25 228 (16) 155 (16) 1.00
25–29 367 (26) 285 (30) 1.22 (0.94 – 1.59)
>=30 782 (56) 506 (53) 1.04 (0.82 – 1.33)
Missing 16 10
Waist-to-hip ratio
<0.77 164 (12) 116 (12) 1.00
0.77–0.83 343 (25) 287 (30) 1.18 (0.87 – 1.59)
>=0.84 837 (60) 528 (55) 0.97 (0.73 – 1.29)
Missing 49 25
Open in a new tab
a

Model includes: age, family history, parity, breastfeeding duration, and study.

b

Family history of breast cancer in first-degree relative

c

Includes parous women only

Case-control odds ratios for IHC intrinsic subtype.

Case-control ORs were estimated for subtype based on 6-marker central IHC (Table 2). Subtype distribution by study site has been previously reported (29). Luminal A subtype was positively associated with increasing BMI and increasing WHR. Older age at menarche and parity (versus nulliparity), among both women who breastfed and women who did not, were associated with reduced risk of luminal A subtype. The magnitude of point estimates for luminal B breast cancer were close to the null, except for family history of breast cancer, which was significantly associated with luminal B risk. The direction of associations for ER-/HER2+ tumors were similar to those for luminal A, with family history, oral contraceptive use and age ≥ 25 years at first birth strongly positively associated and parity with breastfeeding inversely associated with risk of ER-/HER2+. For basal-like breast cancer, increasing WHR (OR: 1.49, 95% CI: 1.00 – 2.21 for WHR 0.77 – 0.83; OR: 1.45, 95% CI: 0.99 – 2.12 for WHR ≥ 0.84) and higher parity without breastfeeding (OR: 1.70, 95% CI: 1.11 – 2.60 for 1 – 2 children and never breastfed, OR: 1.84, 95% CI: 1.17 – 2.90 for 3+ children, never breastfed) were significantly associated with increased risk, while breastfeeding and age ≥ 25 years at first birth were protective among parous women.

Table 2:

Case-control odds ratios for six-markera breast cancer subtypes among cases from BWHS, WCHS, and CBCS Phases 1 and 2.

Controls (n=2932) Luminal A (n=471) Luminal B (n=240) ER-/HER2+ (n=122) Basal (n=295)
Risk Factor N (%) N (%) ORb (95% CI) N (%) ORb (95%CI) N (%) ORb (95%CI) N (%) ORb (95%CI)
Family historyc
No 2616 (89) 386 (82) 1.00 202 (84) 1.00 101 (83) 1.00 247 (84) 1.00
Yes 316 (11) 85 (18) 1.76 (1.34 – 2.30) 38 (16) 1.44 (1.00 – 2.10) 21 (17) 1.87 (1.15 – 3.05) 48 (16) 1.67 (1.20 – 2.35)
Age at menarche
<11 298 (10) 55 (12) 1.00 32 (13) 1.00 13 (11) 1.00 31 (11) 1.00
11–12 1201 (41) 221 (47) 0.97 (0.70 – 1.34) 98 (41) 0.78 (0.51 – 1.19) 53 (43) 1.03 (0.56 – 1.93) 127 (43) 1.04 (0.68 – 1.59)
>=13 1423 (49) 110 (46) 0.70 (0.51 – 0.98) 110 (46) 0.71 (0.47 – 1.07) 56 (46) 0.95 (0.51 – 1.77) 137 (46) 0.93 (0.61 – 1.42)
Missing 10 1 1 0 0
Age at first full-term birthd
<25 1733 (71) 255 (69) 1.00 146 (71) 1.00 57 (63) 1.00 213 (81) 1.00
>=25 716 (29) 117 (31) 1.17 (0.91 – 1.49) 61 (29) 1.06 (0.77 – 1.46) 33 (37) 1.66 (1.06 – 2.62) 50 (19) 0.62 (0.44 – 0.86)
Missing 19 4 1 2 1
Parity and breastfeeding
Nulliparous 464 (16) 95 (20) 1.00 32 (13) 1.00 30 (25) 1.00 31 (11) 1.00
1–2, never 833 (28) 128 (27) 0.74 (0.55 – 0.99) 81 (34) 1.37 (0.90 – 2.11) 40 (33) 0.72 (0.44 – 1.18) 99 (34) 1.70 (1.11 – 2.60)
3+, never 520 (18) 77 (16) 0.68 (0.49 – 0.95) 40 (17) 1.06 (0.65 – 1.73) 22 (18) 0.62 (0.34 – 1.10) 70 (24) 1.84 (1.17 – 2.90)
1–2, ever 582 (20) 81 (17) 0.69 (0.50 – 0.95) 44 (18) 1.06 (0.66 – 1.70) 14 (11) 0.38 (0.20 – 0.72) 43 (15) 1.10 (0.68 – 1.78)
3+, ever 510 (17) 81 (17) 0.72 (0.51 – 0.99) 42 (18) 1.13 (0.69 – 1.83) 16 (13) 0.51 (0.27 – 0.97) 48 (16) 1.40 (0.87 – 2.26)
Missing 23 9 1 0 4
Lifetime breastfeeding durationd
Never 1353 (55) 205 (55) 1.00 121 (58) 1.00 62 (67) 1.00 169 (64) 1.00
<6 mos 444 (18) 52 (14) 0.79 (0.57 – 1.10) 32 (15) 0.85 (0.57 – 1.28) 11 (12) 0.57 (0.29 – 1.10) 34 (13) 0.65 (0.44 – 0.95)
6+ mos 648 (27) 110 (29) 1.11 (0.86 – 1.43) 54 (15) 0.90 (0.64 – 1.27) 19 (21) 0.71 (0.41 – 1.20) 57 (13) 0.73 (0.53 – 1.00)
Missing 23 9 1 1 4
Oral contraceptive use
Never 1376 (47) 214 (45) 1.00 112 (47) 1.00 43 (35) 1.00 125 (42) 1.00
Ever 1546 (53) 254 (54) 1.19 (0.97 – 1.47) 128 (53) 1.09 (0.83 – 1.44) 79 (65) 1.50 (1.01 – 2.24) 170 (58) 1.13 (0.88 – 1.47)
Missing 10 3 0 0 0
Body mass index
<25 578 (20) 74 (16) 1.00 41 (17) 1.00 21 (17) 1.00 62 (21) 1.00
25–29 870 (30) 131 (28) 1.16 (0.85 – 1.58) 65 (27) 1.02 (0.68 – 1.53) 42 (34) 1.51 (0.88 – 2.60) 93 (32) 1.03 (0.73 – 1.45)
>=30 1425 (50) 260 (55) 1.41 (1.07 – 1.88) 129 (54) 1.19 (0.82 – 1.73) 57 (47) 1.26 (0.75 – 2.12) 133 (45) 0.87 (0.62 – 1.20)
Missing 59 6 5 2 7
Waist-to-hip ratio
<0.77 576 (20) 74 (16) 1.00 32 (13) 1.00 23 (19) 1.00 41 (14) 1.00
0.77–0.83 778 (28) 116 (25) 1.24 (0.90 – 1.71) 54 (23) 1.08 (0.68 – 1.72) 35 (29) 1.18 (0.68 – 2.06) 89 (30) 1.49 (1.00 – 2.21)
>=0.84 1472 (52) 262 (56) 1.43 (1.06 – 1.94) 143 (60) 1.31 (0.85 – 2.01) 58 (48) 1.25 (0.74 – 2.11) 154 (52) 1.45 (0.99 – 2.12)
Missing 106 19 11 6 11
Open in a new tab
a

Includes 384 cases missing Ki67, which were classified using grade as a surrogate as described in Allott et al. 2018(29)

b

Model adjusts for age, family history, parity, breastfeeding duration, and study site.

c

Family history of breast cancer in first-degree relative

d

Among parous women only

Case-only odds ratios for IHC intrinsic subtype.

Case-only ORs were calculated to allow evaluation of etiologic heterogeneity by 6-marker subtype. Risk factor profiles for luminal B, ER-/HER2+ and basal-like breast tumors were estimated relative to luminal A subtype (Table 3). Overall, consistent with case-control findings, the luminal A and luminal B subtypes showed similar risk factor profiles, demonstrated by the non-significant case-only ORs for luminal B tumors. Case-only analyses highlighted differences between luminal A and ER-/HER2+ and basal-like etiology. Longer breastfeeding duration was associated with decreased odds for both ER-/HER2+ and basal-like breast cancers relative to luminal A. Parity (versus nulliparity), particularly among women who did not breastfeed, was associated with higher odds of basal-like compared to luminal A subtype (OR: 3.04, 95% CI: 2.11 – 4.37 for 1 – 2 children, never breastfed). Similar to case-control analyses, age ≥ 25 years at first birth was protective for basal-like cancers among parous women. Age at menarche, oral contraceptive use, BMI, and WHR did not show significant heterogeneity by intrinsic subtype with case-only ORs not significantly different from 1.

Table 3:

Case-only odds ratios comparing six-markera subtypes of breast cancer among cases from BWHS, WCHS, and CBCS Phases 1–3.

Luminal A (n=827) Luminal B (n=626) ER-/HER2+ (n=210) Basal (n=691)
Risk Factor N N OR (95%CI) N OR (95%CI) N OR (95%CI)
Family historyb
No 664 535 1.00 177 1.00 568 1.00
Yes 163 91 0.70 (0.53 – 0.93) 33 0.84 (0.55 – 1.27) 123 0.93 (0.71 – 1.21)
Age at menarche
<11 90 79 1.00 22 1.00 70 1.00
11–12 386 269 0.81 (0.57 – 1.14) 91 0.97 (0.57 – 1.64) 298 0.99 (0.69 – 1.42)
>=13 350 277 0.92 (0.65 – 1.31) 97 1.23 (0.73 – 2.09) 322 1.19 (0.83 – 1.71)
Missing 1 1 0 1
Age at first full-term birthc
<25 483 361 1.00 111 1.00 498 1.00
>=25 193 156 1.12 (0.87 – 1.46) 50 1.13 (0.77 – 1.67) 135 0.70 (0.54 – 0.92)
Missing 5 1 2 1
Parity and breastfeeding
Nulliparous 146 108 1.00 47 1.00 57 1.00
1–2, never 218 190 1.16 (0.84 – 1.59) 63 1.00 (0.64 – 1.55) 252 3.04 (2.11 – 4.37)
3+, never 158 107 0.89 (0.62 – 1.27) 38 0.90 (0.55 – 1.49) 153 2.58 (1.74 – 3.81)
1–2, ever 152 124 1.06 (0.75 – 1.50) 28 0.55 (0.32 – 0.93) 118 1.89 (1.27 – 2.80)
3+, ever 144 96 0.91 (0.63 – 1.31) 33 0.85 (0.51 – 1.42) 107 2.06 (1.37 – 3.09)
Missing 9 1 1 4
Lifetime breastfeeding durationc
Never 376 297 1.00 101 1.00 405 1.00
<6 mos 111 101 1.12 (0.82 – 1.53) 28 0.81 (0.50 – 1.31) 110 0.84 (0.62 – 1.14)
6+ mos 185 119 0.84 (0.64 – 1.11) 33 0.64 (0.42 – 1.00) 115 0.60 (0.45 – 0.79)
Missing 9 1 1 4
Oral contraceptive use
Never 336 252 1.00 69 1.00 243 1.00
Ever 484 370 0.89 (0.71 – 1.12) 140 1.13 (0.81 – 1.58) 443 1.01 (0.81 – 1.27)
Missing 7 4 1 5
Body mass index
<25 132 104 1.00 37 1.00 110 1.00
25–29 224 160 0.94 (0.67 – 1.32) 74 1.32 (0.83 – 2.09) 196 1.11 (0.80 – 1.55)
>=30 462 355 1.01 (0.75 – 1.37) 96 0.89 (0.57– 1.38) 378 1.04 (0.77 – 1.41)
Missing 9 7 3 7
Waist-to-hip ratio
<0.77 101 74 1.00 32 1.00 73 1.00
0.77–0.83 204 158 0.89 (0.61 – 1.31) 64 1.03 (0.62 – 1.70) 206 1.24 (0.85 – 1.82)
>=0.84 493 373 0.90 (0.63 – 1.28) 107 0.84 (0.52 – 1.37) 395 1.06 (0.74 – 1.52)
Missing 29 21 7 17
Open in a new tab
a

Includes 384 cases missing Ki67, which were classified using grade as a surrogate as described in Allott et al. 2018(29)

b

Family history of breast cancer in first-degree relative

c

Includes parous women only

Age-at-incidence curves.

Bimodal frequency distributions for age at incidence have been interpreted as evidence of etiologic heterogeneity for basal-like vs. luminal A tumors (36). We found that luminal cancers were best represented by a two-component mixture model (Supplemental Table 2). For the basal-like group, ΔAIC lay between 4–10, still indicating that the two-component mixture model provided better fit, albeit with slightly lower certainty than for luminal cancers. For ER-/HER2+, we could not distinguish which model provided the better fit, with ΔAIC<4. Broadly, luminal A tumors showed a strongly bimodal pattern in age at diagnosis, with enrichment for both early and late onset disease. Luminal B subtype had a less pronounced late onset peak. ER-/HER2+ subtype was skewed toward earlier age at incidence, with a strong early-onset peak. Basal-like subtype was similarly enriched for early-onset disease, showing a small late-onset peak (Figure 1).

Figure 1.

Figure 1.

Open in a new tab

Density plots showing age frequency distributions at diagnosis for invasive breast cancer cases from AMBER, overall and by 6-marker IHC-based subtype. Smoothed density curve is plotted in black, early-onset density is plotted in blue and late-onset density is plotted in green, with dotted line representing median age at diagnosis for early- and late-onset density curves. Bar plot shows empirical distribution of age at diagnosis. Includes 384 cases missing Ki67, which were classified using grade as a surrogate as described in Allott et al. 2018(29)

DISCUSSION

We estimated associations between breast cancer risk factors and tumor subtypes defined by six-marker IHC classification among black women in the AMBER consortium. We found breastfeeding to be protective for basal-like and ER-/HER2+ breast cancer in both case-control and case-only analyses. Parity and later age at menarche were associated with reduced risk of luminal A breast cancer, while increased BMI and WHR conferred increased risk. Luminal B subtype was not significantly associated with any risk factors other than family history, although the direction of association was similar for Luminal A breast cancers, and in case-only analyses did not exhibit a risk factor profile distinct from luminal A. Age-at-incidence curves overall showed bimodal distributions with pronounced early onset peaks for all subtypes, but ER-/HER2+ and basal-like subtypes showed earlier age at diagnosis compared to luminal subtypes, in keeping with their distinct risk factor profiles. In a large, black population with centrally-assessed, six-marker IHC-based breast cancer subtypes, risk factor profiles by intrinsic subtype suggest distinct risk factors for basal-like breast cancer and highlight breastfeeding as a plausible intervention to reduce risk of this aggressive subtype of breast cancer.

Breastfeeding has consistently demonstrated a protective effect for triple negative and basal-like breast cancers in prior literature (38–45). In our study, we observed that women who were parous but did not breastfeed had the highest ORs for basal-like breast cancer, further reinforcing the importance of breastfeeding to reduce risk of basal-like breast cancer. A number of prior studies have found similar results, including earlier analyses with clinical markers in the AMBER consortium, which found breastfeeding to be associated with reduced risk for ER-negative, basal-like, and triple-negative subtypes, while parous women who did not breastfeed were at increased risk for these subtypes (9, 13, 39, 46). We also found increased WHR to be associated with increased risk of basal-like subtype, in agreement with earlier analyses in the AMBER consortium (16, 18). A previous study among participants of the Women’s Health Initiative showed no association between WHR and risk for triple negative breast cancer; however, this study included a small number of black women (n=199 cases) (47). The relationship between adiposity and breast cancer risk appears to vary by race and ethnicity, and WHR, commonly used to represent central adiposity, may more strongly influence breast cancer risk than BMI among black women (48). It will be important to consider obesity-related biomarkers in future studies to better understand how body mass distribution and race influence breast cancer etiology.

Compared to luminal and basal-like subtypes, risk factor profiles for luminal B and ER-/HER2+ breast cancer have been less consistently reported, likely due to the lower prevalence of these subtypes. However, considering the magnitude of associations in prior literature, there appears to be an overlapping risk factor profile for luminal A and B subtypes, in line with our case-only ORs, which were very close to 1 (38, 40, 41, 49). As for the ER-/HER2+ subtype, we found several significant associations, including a protective effect of parity with breastfeeding. This is in contrast to a study among a multiethnic cohort of women which found parity (versus nulliparity) to be associated with 43% (95% CI: 1.08–1.89) higher odds of ER-/HER2+ breast cancer relative to luminal A (46). That study included a larger number (n=493) of ER-/HER2+ tumors and a lower proportion (less than 6%) of black women relative to our study. In the Nurses Health Study, a cohort of largely non-Hispanic white women, Fortner et al. also found parity (versus nulliparity) to be associated with increased risk of ER-/HER2+ tumors (40). Our discrepant findings may be due to differences in tumor classification schema and/or patient population, as reproductive factors are known to vary by race and ethnicity (50, 51). Of note, we found an increased risk for ER-/HER2+ subtype with oral contraceptive use, a finding that has been previously reported among white and Asian women, but not among black women (52–54).

Age at incidence curves offer additional perspectives on etiologic heterogeneity among breast tumor subtypes. Recent evidence has suggested that breast cancer can be divided into two etiologic subtypes defined by age at onset, and that the difference in the relative distribution of those two subtypes underpins the biological characteristics of any given breast cancer categorization (55). We saw that, compared to the luminal subtypes, ER-/HER2+ and basal-like subtypes exhibited strong early onset peaks, which supports the distinct risk factor associations found in case-only analyses. However, it is notable that predominant early onset enrichment was observed among all subtypes in this study of black women. This is in contrast to an earlier study using data from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), which enrolled mostly white women, and showed luminal A and luminal B subtypes to have predominant late onset modes (55). Our findings confirm a consistent trend toward earlier age at breast cancer incidence for black women compared to white women and suggest that it persists across all subtypes (36, 56, 57).

Strengths of this study include a large population of black women from throughout the United States and the use of central laboratory, 6-marker IHC classification. The sample size of ER-/HER2+ and basal-like breast cancers is also higher than in previous studies of these subtypes in diverse populations. However, despite being one of the largest cohorts of black women with breast cancer, we still lacked precision in measuring risk factor associations for the less common luminal B and ER-/HER2+ subtypes. Likewise, the sample size of our study did not allow sufficient power for analysis stratified by menopausal status. For example, examining parity and lactation or breastfeeding duration and stratifying by menopausal status would have resulted in fewer than 10 women in multiple categories. Additionally, some associations were not statistically significant in case-control analyses, but the magnitude of these associations are important for interpretation of case-only analyses, which include larger sample sizes. It is also important to note that the modes for early and late age at incidence cannot be generalized to women in the source populations because some studies in the AMBER consortium oversampled for younger women. However, the modes were stable across intrinsic subtype, allowing for comparison of age-at-incidence curves by subtype.

Our findings suggest that previously identified risk factors for basal-like breast cancer also hold for black women. In earlier analyses, aggregation of risk factor patterns by race, including a trend toward earlier births and lower breastfeeding rates in black women, remained a concern for etiologic inference. This study shows that even among a population entirely composed of black women, reproductive and body size patterns were associated with this more aggressive breast cancer subtype. Future research to understand the mechanisms underlying these associations are needed. Additional approaches such as studies of second primary breast cancers may offer more direct evidence for etiologic heterogeneity. However, the current results suggest that continued promotion of breastfeeding as well as improved understanding of the biologic mechanisms linking adiposity and basal-like breast cancer risk should be part of a comprehensive strategy to address breast cancer disparities.

Supplementary Material

1

ACKNOWLEDGEMENTS

This work was supported by grants from the National Institutes of Health (P01-CA151135 (C. Ambrosone, J. Palmer, A. Olshan), R01-CA058420 (J. Palmer), UM1-CA164974 (J. Palmer), R01-CA098663 (J. Palmer), R01-CA100598 (C. Ambrosone), P50-CA58223 (M. Troester, A. Olshan), U01-CA179715 (M. Troester), P30-ES010126 (M. Troester), T32 CA057726 (H. Benefield, A. Hurson), F30-CA236199 (H. Benefield), P30-CA16056 (C. Ambrosone), the Breast Cancer Research Foundation (C. Ambrosone), a grant from the UNC Lineberger Comprehensive Cancer Center funded by the University Cancer Research Fund (LCCC2017T204), and the Susan G. Komen Foundation (M. Troester, J. Palmer). H. Benefield is a recipient of the Gertrude B. Elion Mentored Medical Student Research Award of Triangle Community Foundation.

Abbreviations:

AMBER

African American Breast Cancer Epidemiology and Risk

ER

estrogen receptor

PR

progesterone receptor

HER2

human epidermal growth factor receptor 2

EGFR

epidermal growth factor receptor

ORs

cytokeratin (CK)5/6, odds ratios

BMI

body mass index

WHR

waist-to-hip ratio

IHC

immunohistochemical

CBCS

Carolina Breast Cancer Study

BWHS

the Black Women’s Health Study

WCHS

the Women’s Circle of Health Study

MEC

the Multi-Ethnic Cohort

NJ

New Jersey

CIs

confidence intervals

AIC

Akaike information criterion

METABRIC

Molecular Taxonomy of Breast Cancer International Consortium

Footnotes

Conflicts of interest: Authors have no conflicts of interest to disclose.

Contributor Information

Halei C. Benefield, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Gary R. Zirpoli, Slone Epidemiology Center at Boston University, Boston, MA, USA

Emma H. Allott, Patrick G Johnston Centre for Cancer Research, Queen’s University Belfast, Belfast, United Kingdom

Yue Shan, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Amber N. Hurson, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Angela R. Omilian, Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

Thaer Khoury, Department of Pathology, Roswell Park Cancer Institute, Buffalo, NY, USA.

Chi-Chen Hong, Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA.

Andrew F. Olshan, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Traci N. Bethea, Slone Epidemiology Center at Boston University, Boston, MA, USA

Elisa V. Bandera, Cancer Epidemiology and Health Outcomes, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

Julie R. Palmer, Slone Epidemiology Center at Boston University, Boston, MA, USA

Christine B. Ambrosone, Department of Cancer Prevention and Control, Roswell Park Comprehensive Cancer Center, Buffalo, NY, USA

Melissa A. Troester, Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

REFERENCES

  • 1.Parker JS, Mullins M, Cheang MCU, Leung S, Voduc D, Vickery T, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol [Internet]. 2009. Mar 10 [cited 2017 Jul 18];27(8):1160–7. Available from: 10.1200/JCO.2008.18.1370 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Carey LA, Perou CM, Livasy CA, Dressler LG, Cowan D, Conway K, et al. Race, Breast Cancer Subtypes, and Survival in the Carolina Breast Cancer Study. JAMA [Internet]. 2006. Jun 7 [cited 2017 Sep 29];295(21):2492. Available from: 10.1001/jama.295.21.2492 [DOI] [PubMed] [Google Scholar]
  • 3.Kroenke CH, Sweeney C, Kwan ML, Quesenberry CP, Weltzien EK, Habel LA, et al. Race and breast cancer survival by intrinsic subtype based on PAM50 gene expression. Breast Cancer Res Treat [Internet]. 2014. Apr 7 [cited 2017 Jul 27];144(3):689–99. Available from: 10.1007/s10549-014-2899-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Caan BJ, Sweeney C, Habel LA, Kwan ML, Kroenke CH, Weltzien EK, et al. Intrinsic subtypes from the PAM50 gene expression assay in a population-based breast cancer survivor cohort: prognostication of short- and long-term outcomes. Cancer Epidemiol Biomarkers Prev [Internet]. 2014. May 17 [cited 2019 Feb 12];23(5):725–34. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24521998 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Khoury MJ, Flanders WD. Nontraditional Epidemiologic Approaches in the Analysis of Gene Environment Interaction: Case-Control Studies with No Controls! Am J Epidemiol [Internet]. 1996. Aug 1 [cited 2017 Sep 12];144(3):207–13. Available from: 10.1093/oxfordjournals.aje.a008915 [DOI] [PubMed] [Google Scholar]
  • 6.Liedtke C, Mazouni C, Hess KR, André F, Tordai A, Mejia JA, et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol [Internet]. 2008. Mar 10 [cited 2019 Dec 22];26(8):1275–81. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18250347 [DOI] [PubMed] [Google Scholar]
  • 7.Lin NU, Vanderplas A, Hughes ME, Theriault RL, Edge SB, Wong YN, et al. Clinicopathologic features, patterns of recurrence, and survival among women with triple-negative breast cancer in the National Comprehensive Cancer Network. Cancer. 2012. Nov 15;118(22):5463–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Nielsen TO, Hsu FD, Jensen K, Cheang M, Karaca G, Hu Z, et al. Immunohistochemical and clinical characterization of the basal-like subtype of invasive breast carcinoma. Clin Cancer Res. 2004. Aug 15;10(16):5367–74. [DOI] [PubMed] [Google Scholar]
  • 9.Millikan RC, Newman B, Tse C-K, Moorman PG, Conway K, Smith LV., et al. Epidemiology of basal-like breast cancer. Breast Cancer Res Treat [Internet]. 2008. May 20 [cited 2018 Dec 5];109(1):123–39. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17578664 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Chollet-Hinton L, Olshan AF, Nichols HB, Anders CK, Lund JL, Allott EH, et al. Biology and etiology of young-onset breast cancers among premenopausal African American women: Results from the AMBER Consortium. Cancer Epidemiol Biomarkers Prev. 2017. Dec 1;26(12):1722–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Sweeney C, Bernard PS, Factor RE, Kwan ML, Habel LA, Quesenberry CP, et al. Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics. Cancer Epidemiol Biomarkers Prev [Internet]. 2014. May 12 [cited 2019 Feb 12];23(5):714–24. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24521995 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Ambrosone CB, Zirpoli G, Ruszczyk M, Shankar J, Hong C-C, Mcilwain D, et al. Parity and breastfeeding among African-American women: differential effects on breast cancer risk by estrogen receptor status in the Women’s Circle of Health Study. Cancer Causes Control. 2014;25:259–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Palmer JR, Boggs DA, Wise LA, Ambrosone CB, Adams-Campbell LL, Rosenberg L. Parity and lactation in relation to estrogen receptor negative breast cancer in African American women. Cancer Epidemiol Biomarkers Prev [Internet]. 2011. [cited 2019 Nov 3];20(9):1883–91. Available from: www.aacrjournals.org [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Warner ET, Colditz GA, Palmer JR, Partridge AH, Rosner BA, Tamimi RM. Reproductive factors and risk of premenopausal breast cancer by age at diagnosis: Are there differences before and after age 40? Breast Cancer Res Treat. 2013;142(1):165–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Ambrosone CB, Zirpoli G, Hong C-C, Yao S, Troester MA, Bandera EV., et al. Important Role of Menarche in Development of Estrogen Receptor–Negative Breast Cancer in African American Women. JNCI J Natl Cancer Inst [Internet]. 2015. Sep [cited 2019 Aug 2];107(9). Available from: 10.1093/jnci/djv172 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Bertrand KA, Bethea TN, Adams-Campbell LL, Rosenberg L, Palmer JR. Differential Patterns of Risk Factors for Early-Onset Breast Cancer by ER Status in African American Women. Cancer Epidemiol Biomarkers Prev [Internet]. 2017. [cited 2019 Nov 24];26(2):270–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/27756774 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Bethea TN, Rosenberg L, Hong C-C, Troester MA, Lunetta KL, Bandera EV, et al. A case-control analysis of oral contraceptive use and breast cancer subtypes in the African American Breast Cancer Epidemiology and Risk Consortium. Breast Cancer Res [Internet]. 2015. Feb 21 [cited 2019 Nov 24];17:22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25849024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Bandera EV, Chandran U, Hong C-C, Troester MA, Bethea TN, Adams-Campbell LL, et al. Obesity, body fat distribution, and risk of breast cancer subtypes in African American women participating in the AMBER Consortium. Breast Cancer Res Treat [Internet]. 2015. Apr 26 [cited 2019 Aug 2];150(3):655–66. Available from: 10.1007/s10549-015-3353-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Ma H, Ursin G, Xu X, Lee E, Togawa K, Malone KE, et al. Body mass index at age 18 years and recent body mass index in relation to risk of breast cancer overall and ER/PR/HER2-defined subtypes in white women and African-American women: a pooled analysis. Breast Cancer Res [Internet]. 2018. [cited 2019 Nov 24];20(1):5. Available from: http://www.ncbi.nlm.nih.gov/pubmed/29357906 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Kwan ML, Kushi LH, Weltzien E, Maring B, Kutner SE, Fulton RS, et al. Epidemiology of breast cancer subtypes in two prospective cohort studies of breast cancer survivors. Breast Cancer Res. 2009. May 22;11(3). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Stead LA, Lash TL, Sobieraj JE, Chi DD, Westrup JL, Charlot M, et al. Triple-negative breast cancers are increased in black women regardless of age or body mass index. Breast Cancer Res [Internet]. 2009. [cited 2020 Apr 6];11(2). Available from: http://breast-cancer-research.com/content/11/2/R18Thisarticleisonlineat:http://breast-cancer-research.com/content/11/2/R18 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Li CI, Beaber EF, Tang MTC, Porter PL, Daling JR, Malone KE. Reproductive factors and risk of estrogen receptor positive, triple-negative, and HER2-neu overexpressing breast cancer among women 20–44 years of age. Breast Cancer Res Treat. 2013. Jan 9;137(2):579–87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Trivers KF, Lund MJ, Porter PL, Liff JM, Flagg EW, Coates RJ, et al. The epidemiology of triple-negative breast cancer, including race. Cancer Causes Control [Internet]. 2009. Sep 3 [cited 2019 Feb 26];20(7):1071–82. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19343511 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Shinde SS, Forman MR, Kuerer HM, Yan K, Peintinger F, Hunt KK, et al. Higher parity and shorter breastfeeding duration. Cancer [Internet]. 2010. Nov 1 [cited 2020 Apr 6];116(21):4933–43. Available from: 10.1002/cncr.25443 [DOI] [PubMed] [Google Scholar]
  • 25.Allott EH, Cohen SM, Geradts J, Sun X, Khoury T, Bshara W, et al. Performance of Three-Biomarker Immunohistochemistry for Intrinsic Breast Cancer Subtyping in the AMBER Consortium. Cancer Epidemiol Biomarkers Prev [Internet]. 2016. Mar [cited 2019 Mar 1];25(3):470–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26711328 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Nielsen TO, Parker JS, Leung S, Voduc D, Ebbert M, Vickery T, et al. A Comparison of PAM50 Intrinsic Subtyping with Immunohistochemistry and Clinical Prognostic Factors in Tamoxifen-Treated Estrogen Receptor–Positive Breast Cancer. Clin Cancer Res [Internet]. 2010. Nov 1 [cited 2019 May 18];16(21):5222–32. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20837693 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Bastien RR, Rodríguez-Lescure Á, Ebbert MT, Prat A, Munárriz B, Rowe L, et al. PAM50 breast cancer subtyping by RT-qPCR and concordance with standard clinical molecular markers. BMC Med Genomics. 2012;5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thürlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. 2013. [cited 2020 Mar 19]; Available from: http://guidance.nice.org.uk/DT/4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Allott EH, Geradts J, Cohen SM, Khoury T, Zirpoli GR, Bshara W, et al. Frequency of breast cancer subtypes among African American women in the AMBER consortium. Breast Cancer Res [Internet]. 2018. Dec 6 [cited 2019 Nov 3];20(1):12. Available from: 10.1186/s13058-018-0939-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Newman B, Moorman PG, Millikan R, Qaqish BF, Geradts J, Aldrich TE, et al. The Carolina Breast Cancer Study: integrating population-based epidemiology and molecular biology. Breast Cancer Res Treat [Internet]. 1995. Jul [cited 2018 Dec 5];35(1):51–60. Available from: http://www.ncbi.nlm.nih.gov/pubmed/7612904 [DOI] [PubMed] [Google Scholar]
  • 31.Rosenberg L, Adams-Campbell L, Palmer JR. The Black Women’s Health Study: a follow-up study for causes and preventions of illness. J Am Med Womens Assoc. 1995. Mar;50(2):56–8. [PubMed] [Google Scholar]
  • 32.Ambrosone CB, Ciupak GL, Bandera EV., Jandorf L, Bovbjerg DH, Zirpoli G, et al. Conducting molecular epidemiological research in the age of HIPAA: A multi-institutional case-control study of breast cancer in African-American and European-American women. J Oncol. 2009;15. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Bandera EV, Chandran U, Zirpoli G, McCann SE, Ciupak G, Ambrosone CB. Rethinking sources of representative controls for the conduct of case-control studies in minority populations. BMC Med Res Methodol. 2013;13(1):71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Kolonel LN, Henderson BE, Hankin JH, Nomura AMY, Wilkens LR, Pike MC, et al. A multiethnic cohort in Hawaii and Los Angeles: Baseline characteristics. Am J Epidemiol. 2000. Feb 15;151(4):346–57. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Palmer JR, Ambrosone CB, Olshan AF. A collaborative study of the etiology of breast cancer subtypes in African American women: the AMBER consortium. Cancer Causes Control [Internet]. 2014. Mar 17 [cited 2019 Feb 26];25(3):309–19. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24343304 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Anderson WF, Pfeiffer RM, Dores GM, Sherman ME. Comparison of Age Distribution Patterns for Different Histopathologic Types of Breast Carcinoma. Cancer Epidemiol Biomarkers Prev [Internet]. 2006. Oct 1 [cited 2019 Mar 12];15(10):1899–905. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17035397 [DOI] [PubMed] [Google Scholar]
  • 37.Pfeiffer RM, Carroll RJ, Wheeler W, Whitby D, Mbulaiteye S. Combining assays for estimating prevalence of human herpesvirus 8 infection using multivariate mixture models. Biostatistics [Internet]. 2008. Jan [cited 2019 Mar 12];9(1):137–51. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17566074 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Tamimi RM, Colditz GA, Hazra A, Baer HJ, Hankinson SE, Rosner B, et al. Traditional breast cancer risk factors in relation to molecular subtypes of breast cancer. Breast Cancer Res Treat. 2012. Jan;131(1):159–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Palmer JR, Viscidi E, Troester MA, Hong CC, Schedin P, Bethea TN, et al. Parity, lactation, and breast cancer subtypes in African American Women: Results from the AMBER Consortium. J Natl Cancer Inst. 2014. Oct 1;106(10). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Fortner RT, Sisti J, Chai B, Collins LC, Rosner B, Hankinson SE, et al. Parity, breastfeeding, and breast cancer risk by hormone receptor status and molecular phenotype: results from the Nurses’ Health Studies. Breast Cancer Res [Internet]. 2019. Dec 12 [cited 2019 Nov 4];21(1):40. Available from: 10.1186/s13058-019-1119-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Holm J, Eriksson L, Ploner A, Eriksson M, Rantalainen M, Li J, et al. Assessment of Breast Cancer Risk Factors Reveals Subtype Heterogeneity. Cancer Res [Internet]. 2017. Jul 1 [cited 2019 Mar 12];77(13):3708–17. Available from: http://www.ncbi.nlm.nih.gov/pubmed/28512241 [DOI] [PubMed] [Google Scholar]
  • 42.Kwan ML, Bernard PS, Kroenke CH, Factor RE, Habel LA, Weltzien EK, et al. Breastfeeding, PAM50 Tumor Subtype, and Breast Cancer Prognosis and Survival. J Natl Cancer Inst. 2015. Jul 1;107(7). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Islami F, Liu Y, Jemal A, Zhou J, Weiderpass E, Colditz G, et al. Breastfeeding and breast cancer risk by receptor status-a systematic review and meta-analysis. Ann Oncol. 2015. Dec 1;26(12):2398–407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Ma H, Ursin G, Xu X, Lee E, Togawa K, Duan L, et al. Reproductive factors and the risk of triple-negative breast cancer in white women and African-American women: A pooled analysis. Breast Cancer Res. 2017. Jan 13;19(1). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Sisti JS, Collins LC, Beck AH, Tamimi RM, Rosner BA, Eliassen AH. Reproductive risk factors in relation to molecular subtypes of breast cancer: Results from the nurses’ health studies. Int J cancer [Internet]. 2016. May 15 [cited 2019 Nov 24];138(10):2346–56. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26684063 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Chen L, Li CI, Tang MTC, Porter P, Hill DA, Wiggins CL, et al. Reproductive factors and risk of luminal, HER2-overexpressing, and triple-negative breast cancer among multiethnic women. Cancer Epidemiol Biomarkers Prev. 2016. Sep 1;25(9):1297–304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Phipps AI, Chlebowski RT, Prentice R, Mctiernan A, Stefanick ML, Wactawski-Wende J, et al. Body Size, Physical Activity, and Risk of Triple-Negative and Estrogen Receptor-Positive Breast Cancer. 2011. [cited 2019 Nov 24]; Available from: http://cebp.aacrjournals.org/ [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Bandera EV, Maskarinec G, Romieu I, John EM. Racial and Ethnic Disparities in the Impact of Obesity on Breast Cancer Risk and Survival: A Global Perspective. Adv Nutr. 2015. Nov 1;6(6):803–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Gaudet MM, Gierach GL, Carter BD, Luo J, Milne RL, Weiderpass E, et al. Pooled Analysis of Nine Cohorts Reveals Breast Cancer Risk Factors by Tumor Molecular Subtype. Cancer Res [Internet]. 2018. Sep 5 [cited 2019 Mar 12];78(20):6011–21. Available from: http://www.ncbi.nlm.nih.gov/pubmed/30185547 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Allen JA, Li R, Scanlon KS, Perrine CG, Chen J, Odom E, et al. Progress in increasing breastfeeding and reducing racial/ethnic differences - United States, 2000–2008 births. Vol. 62, Morbidity and Mortality Weekly Report. Department of Health and Human Services; 2013. p. 77–80.23388550 [Google Scholar]
  • 51.Martin JA, Hamilton BE, Osterman MJK. Births in the United States, 2018 Key findings Data from the National Vital Statistics System [Internet]. 2018. [cited 2019 Nov 24]. Available from: https://www.cdc.gov/nchs/products/index.htm. [Google Scholar]
  • 52.Park B, Choi JY, Sung HK, Ahn C, Hwang Y, Jang J, et al. Attribution to Heterogeneous Risk Factors for Breast Cancer Subtypes Based on Hormone Receptor and Human Epidermal Growth Factor 2 Receptor Expression in Korea. Med (United States). 2016. Apr 1;95(14). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Gammon MD, Hibshoosh H, Terry MB, Bose S, Schoenberg JB, Brinton LA, et al. Oral contraceptive use and other risk factors in relation to HER-2/neu overexpression in breast cancer among young women. Cancer Epidemiol Biomarkers Prev [Internet]. 1999. May [cited 2019 Nov 24];8(5):413–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/10350436 [PubMed] [Google Scholar]
  • 54.Cerne JZ, Ferk P, Frkovic-Grazio S, Leskosek B, Gersak K. Risk factors for HR- and HER2-defined breast cancer in Slovenian postmenopausal women. Climacteric. 2012. Feb;15(1):68–74. [DOI] [PubMed] [Google Scholar]
  • 55.Anderson WF, Rosenberg PS, Prat A, Perou CM, Sherman ME. How Many Etiological Subtypes of Breast Cancer: Two, Three, Four, Or More? JNCI J Natl Cancer Inst [Internet]. 2014. Aug 12 [cited 2019 Mar 12];106(8):dju165-dju165. Available from: http://www.ncbi.nlm.nih.gov/pubmed/25118203 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Matsuno RK, Anderson WF, Yamamoto S, Tsukuma H, Pfeiffer RM, Kobayashi K, et al. Early- and late-onset breast cancer types among women in the United States and Japan. Cancer Epidemiol Biomarkers Prev. 2007. Jul 1;16(7):1437–42. [DOI] [PubMed] [Google Scholar]
  • 57.Allott EH, Shan Y, Chen M, Sun X, Garcia-Recio S, Kirk EL, et al. Bimodal age distribution at diagnosis in breast cancer persists across molecular and genomic classifications. Breast Cancer Res Treat. 2019. Sep 18 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1640619-supplement-1.docx (20.9KB, docx)

RESOURCES