Background: Earlier epidemiological studies indicate that associations between obesity and breast cancer risk may not only depend on menopausal status and use of exogenous hormones, but might also differ by tumor subtype. Here, we evaluated whether obesity is differentially associated with the risk of breast tumor subtypes, as defined by 6 immunohistochemical markers (ER, PR, HER2, Ki67, Bcl-2 and p53, separately and combined), in the prospective EPIC-Germany Study (n = 27,012). Methods: Formalin-fixed and paraffin-embedded (FFPE) tumor tissues of 657 incident breast cancer cases were used for histopathological analyses. Associations between BMI and breast cancer risk across subtypes were evaluated by multivariable Cox regression models stratified by menopausal status and hormone therapy (HT) use. Results: Among postmenopausal non-users of HT, higher BMI was significantly associated with an increased risk of less aggressive,i.e.ER+,PR+,HER2-,Ki67 , Bcl-2+ and p53- tumors (HR per 5 kg/m : 1.44 [1.10, 1.90], p =0.009), but not low with risk of more aggressive tumor subtypes. Among postmenopausal users of HT, BMI was significantly inversely associated with less aggressive tumors (HR per 5 kg/m : 0.68 [0.50, 0.94], p = 0.018). Finally, among pre- and perimenopausal women, Cox regression models did not reveal significant linear associations between BMI and risk of any tumor subtype, although analyses by BMI tertiles showed a significantly lower risk of less aggressive tumors for women in the highest tertile (HR: 0.55 [0.33, 0.93]). Conclusion: Overall, our results suggest that obesity is related to risk of breast tumors with lower aggressiveness, a finding that requires replication in larger-scale analyses of pooled prospective data. Keywords: Breast cancer, Obesity, Tumor subtypes, Estrogen receptor, Ki-67, p53, Bcl-2 Background women [3–5]. Moreover, it has been proposed that obes- Associations between etiological factors and cancer risk ity is related to more slowly proliferating tumors, as have been shown to be differential across molecular defined by low expression of the Ki67 protein in tumor tumor subtypes in earlier epidemiological studies [1, 2]. cells . Thus, mechanisms to link obesity with breast With respect to relationships between anthropometric cancer, especially altered estrogen and Insulin-like factors and breast cancer risk, there is evidence to sug- growth factor 1 (IGF-1) signaling , could drive overall gest that obesity, as measured by body mass index less aggressive tumors with a distinct molecular profile. (BMI), increases the risk of estrogen receptor positive However, despite the notion that a better understanding (ER+) rather than ER- breast tumors in postmenopausal of risk factor associations with tumor subtypes is needed to improve personalized medicine and prevention , prospective data on the relationship between anthropo- * Correspondence: firstname.lastname@example.org Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), metric parameters and the risks of breast cancer by Im Neuenheimer Feld 280, Heidelberg, Germany Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Nattenmüller et al. BMC Cancer (2018) 18:616 Page 2 of 8 subtypes beyond those defined by hormone receptor sta- carried out using antibodies routinely employed for diag- tus are sparse . nostic purposes (Additional file 2: Table S2) and an immu- Theaim of thepresent studywas to examinetheassocia- nostaining device (DAKO, Techmate 500plus). All TMA tions between obesity with breast cancer risk across more slides were examined by at least one pathologist (E.H., refined tumor subtypes. For this purpose, we assessed six M.K.) with special expertise in breast cancer pathology. In well-established immunohistochemical markers (ER, PR, case of a discrepancy between the scores derived from the HER2, Ki67, Bcl-2 and p53) in tumor samples of breast can- first and second core of the same patient, the pathologists cer cases from the prospective European Prospective Inves- re-examined both cores and made a final decision. When- tigation into Cancer and Nutrition (EPIC)-Germany Study. ever TMA analysis did not yield a conclusive result for a We hypothesized that obesity would be particularly related marker, it was assigned a missing value (ER: 2.0%; PR: to the development of less aggressive tumors (i.e. ER+, PR+, 2.7%; HER2: 1.7%; Ki67: 6.1%; Bcl-2: 4.1%; p53: 6.7%). HER2-, Ki67 , Bcl-2+ and p53- tumors). Tumors were categorized as ER positive/negative and PR low positive/negative using the Allred Score . HER2 was Methods determined according to staining pattern and intensity, Study population and scored as negative (0 and 1+) or positive (2+ and 3+) EPIC is a multi-center prospective cohort study with . Ki67 proliferation activity was scored by percentage more than 500,000 participants across Europe. In of positive tumor nuclei (< 20%: low proliferative activity; Germany, 53,088 participants (30,270 women) in the age ≥20%: high proliferative activity) . Bcl-2 was scored as range between 35 and 65 years were recruited at the negative if less than 10% of the cells were positive and study centers in the cities of Heidelberg and Potsdam staining intensity was weak, otherwise Bcl-2 was scored as between 1994 and 1998 [7, 8]. At baseline, anthropomet- positive . Cases with more than 10% of cells stained ric measurements were carried out by trained personnel, were rated p53 positive, the remaining cases were rated and data on diet, physical activity, smoking, alcohol con- p53 negative, as in most previous studies using this anti- sumption, medication use, reproductive factors and gen . Categorization of subtypes was based on visual socio-economic status were obtained . estimation counting at least 100 tumor cells. Incident cases of breast cancer were either self-reported during follow-up or derived from cancer Statistical analyses registries. Each case was validated by a study physician Relationships between BMI at recruitment and breast using the information given by the patient’s treating phy- cancer risk were evaluated separately among 1) women, sicians and hospitals. Overall, 1095 cases of primary who were pre- or perimenopausal at baseline 2) women, breast cancer had occurred until Dec 31st 2010, the who were postmenopausal at baseline and used hormone closure date for the present analyses. After exclusion of therapy (HT), and 3) women, who were postmenopausal prevalent cases of cancer (n = 1669), individuals lost to at baseline and did not use HT, as differential risk asso- follow-up (n = 947), individuals with unclear breast can- ciations with BMI across these subgroups have been cer status (n = 23), individuals with missing covariate reported [14, 15]. Statistical analyses on breast cancer information (n = 181), and incident cases without tumor risk by tumor subtype were carried out using multivari- blocks (n = 438) from the EPIC-Germany cohort, the able Cox proportional hazards regression analyses to es- study population for the present analyses comprised timate hazard ratios (HR) and 95% confidence intervals 27,012 women (Additional file 1: Figure S1). (CI) across tertiles of BMI (created based on data of the full cohort), with age as the underlying time scale. All Laboratory methods models were adjusted for height (continuous), number Formalin-fixed paraffin-embedded (FFPE) tumor tissue of full-term pregnancies (continuous), educational material was available for a total of 657 cases (60.0%). level (university degree vs. no university degree), There were no significant statistical differences regarding smoking status (never, former, current), and study age, reproductive factors and lifestyle factors between center (Heidelberg, Potsdam). Analyses among pre- these cases and those for which no tumor blocks were and perimenopausal women were further adjusted for available, even though there were slightly more in situ current use of oral contraceptives. The inclusion of and grade I tumors in the latter group (Additional file 2: other potential confounders (alcohol consumption, Table S1). A board-certified senior pathologist (E.H.) breast feeding, age at menarche, age at first preg- selected representative tumor areas to construct tissue nancy) only marginally affected risk associations and microarrays (TMA) on a hematoxylin and eosin stained were not included in final Cox regression models. slide of each tumor block. A TMA machine (AlphaMetrix Linear trends were estimated by entering BMI as a Biotech, Roedermark, Germany) was used to extract continuous term into the same model rescaling HRs to tandem 1 mm cylindrical core samples. IHC staining was reflect a 5 kg/m increase. Observations were Nattenmüller et al. BMC Cancer (2018) 18:616 Page 3 of 8 left-truncated and censored at end of follow-up, death, and Additional file 1: Figure S1). Overall, 40.8% of the or cancer diagnosis, whichever occurred first. In order women were postmenopausal at baseline. Among the to assess patterns of IHC markers, unsupervised hier- postmenopausal women, 46.0% reported to use HT. The archical clustering was used to group cancer cases average follow-up duration was 13.0 (±3.1) years. according to the similarity / dissimilarity of the IHC Median age at diagnosis among the 657 breast cancer staining results for ER, PR, HER2, Ki67, Bcl-2, and p53, cases was 60.2 (range: 38.9–78.6) years. as previously published [16, 17]. In addition to BMI, we Tumor stages and grades at diagnosis were as follows; evaluated waist circumference and hip circumference as In situ: 7.0%, Stage I: 38.7%, Stage II: 41.0%, Stage III: anthropometric markers of obesity in relation to breast 11.3%, Stage IV: 2.0%; Grade I: 12.4%, Grade II: 56.8%, cancer risk. Heterogeneity in associations between Grade III: 30.8% (Additional file 2: Table S1). Of the in- anthropometric factors and breast cancer risk across vasive tumors, 70.5% were carcinoma of no special type subtypes was tested for using a competing risk frame- (NST), 18.3% lobular carcinoma, and 11.1% other; of the work, as proposed by Wang et al. . As the evidence in situ tumors, 67.4% were ductal carcinoma, 13.0% were on associations between BMI and in situ breast tumors lobular carcinoma, and 19.6% other (Additional file 2: is not consistent [19, 20], we decided to exclude cases of Table S3). The proportions of subtypes indicating in situ tumors in sensitivity analyses. All statistical ana- more favorable prognosis were 84.8% for ER+, 70.7% lyses were carried out using SAS, version 9.4 (SAS Insti- for PR+, 87.5% for HER2-, 83.1% for Ki67 , 66.0% low tute, Cary, NC, USA). For unsupervised hierarchical for Bcl-2+ and 80.1% for p53-. Frequencies of luminal clustering and for the generation of a dendogram / heat A (ER+ and/or PR+, HER2- and Ki67 ), luminal B low map to visualize clusters of tumor markers we used the (ER+ and/or PR+, HER2- and Ki67 ), Her2+, and high d3heatmap package in R . triple negative (ER-, PR-, and HER2-) tumors were 68.6, 8.4, 9.7, and 13.3%. Results The results of the unsupervised hierarchical cluster- Characteristics of the study population ing of breast cancer cases according to IHC staining The analytical cohort for the present analyses comprised profiles are shown in Fig. 1. The three main clusters 27,012 women at a median baseline age of 48.4 (range: identified by hierarchical clustering can be character- 35.2–65.2) years, and a median BMI of 24.7 (see Table 1, ized as follows: Cluster 1 (42.7% of all cases) contains tumors with a profile of individual markers indicative of low aggressiveness (all cases are ER+, PR+, HER2-, Table 1 Characteristics of the study population Ki67 , Bcl-2+ and p53-). Cluster 2 (19.0% of all cases) low N 27,012 contains ER- tumors and ER+ tumors that are Bcl-2 Age at recruitment 48.4 (41.2, 57.0) negative. Cluster 3 (38.3% of all cases) mainly contains Anthropometric parameters ER+ tumors that, unlike the ER+ tumors in cluster 1, BMI (kg/m ) 24.7 (22.3, 28.0) show at least one criterion pointing to higher aggres- siveness (i.e. p53 positivity, Bcl-2 negativity, high Ki67 Height (cm) 163.2 (159.0, 167.5) expression, or HER2 positivity). Menopausal Status Pre- and perimenopausal (%) 59.2 BMI and risk of breast cancer by tumor subtype Postmenopausal (%) 40.8 Among postmenopausal non-users of HT, BMI was Hormone therapy (%) directly associated with higher overall breast cancer risk User at baseline (%) 46.0 (HR per 5 kg/m : 1.27 [95% CI: 1.07, 1.50], p = 0.005), while a significant inverse association was observed Non-user at baseline (%) 54.0 c among HT users (HR: 0.80 [0.66, 0.98], p = 0.024) Number of full-term pregnancies 1.7 (0, 8) (Table 2). BMI was not significantly associated with Smoking Status overall breast cancer risk in pre- and perimenopausal Never smokers (%) 55.7 women (HR: 0.98 [0.85, 1.12], p = 0.72). Former smokers (%) 25.6 Analyses stratified by tumor subtypes as derived from Current smokers (%) 18.7 hierarchical clustering are shown in Table 3. Among postmenopausal non-users of HT, each 5 kg/m incre- Education Level ment of BMI was directly and significantly associated University Degree (%) 34.4 with the risk of less aggressive cluster 1 tumors, i.e. No University Degree (%) 65.6 tumors that were ER+, PR+, HER2-, Ki67 , Bcl-2+ and low Median values (p25, 75) are shown for continuous variables b p53-, with a HR per 5 kg/m of 1.44 [95% CI: 1.10, 1.90], Postmenopausal women only Mean value (Minimum, Maximum) p = 0.009). BMI was not associated with more aggressive Nattenmüller et al. BMC Cancer (2018) 18:616 Page 4 of 8 Fig. 1 Frequencies of combined tumor subtypes as derived from hierarchical clustering, with the top three clusters marked in the dendrogram; light bars indicate positivity (or high proliferation activity in case of Ki67) cluster 2 and cluster 3 tumors (Table 3). Among tumors (Additional file 2: Table S5); there were no sig- HT-users, BMI was significantly associated with lower nificant associations with luminal B and triple negative risk of less aggressive cluster 1 tumors (HR per 5 kg/m : tumors. 0.68 [0.50, 0.94], p = 0.018); again, no significant associa- In analyses on breast tumor subtypes defined by indi- tions with the risks of more aggressive cluster 2 and vidual markers, BMI was significantly positively associ- cluster 3 tumors were observed. While risk analyses per ated with risk of ER+, PR+, HER2-, Ki67 , Bcl-2+ and low 5 kg/m did not reveal significant associations between p53- tumors among postmenopausal non-users of HT BMI and risks of any tumor subtype in pre- and peri- (Additional file 2: Table S6). By contrast, no significant menopausal women, it is of note that women in the associations with ER-, PR-, HER2+, Ki67 , Bcl-2- and high highest BMI tertile showed a significantly lower risk of p53+ tumors were observed. With respect to postmeno- less aggressive cluster 1 tumors as compared to women pausal users of HT, Cox regression analyses showed in the lowest BMI tertile (HR : 0.55 [0.33, significant inverse associations with risks of ER+, HER2-, Tertile3 vs. Tertile1 0.93]). Sensitivity analyses excluding in situ cases yielded Ki67 , Bcl-2+ and p53- tumors, and a non-significant low similar highly similar results (Additional file 2: Table S4). tendency for an inverse association with PR+ breast Associations between BMI and risk of luminal A tumors cancer (Additional file 2: Table S7). Again, there were no were similar to those between BMI and risk of cluster 1 significant associations with risk of ER-, PR-, HER2+, Table 2 Hazard ratios of overall breast cancer across tertiles of BMI b b b Postmenopausal non-users of HT Postmenopausal users of HT Pre- and perimenopausal women Cases (n) HR CI (95%) Cases (n) HR CI (95%) Cases (n) HR CI (95%) Tertile 1 14 1 65 1 141 1 Tertile 2 43 1.87 (1.00,3.49) 92 0.97 (0.70,1.34) 85 0.76 (0.57,1.00) Tertile 3 79 2.28 (1.23,4.16) 56 0.69 (0.47,1.00) 82 0.93 (0.70,1.24) Per 5 kg/m 1.27 (1.07,1.50) 0.80 (0.66,0.98) 0.98 (0.85,1.12) p trend 0.005 0.024 0.72 Median (p25, p75) values of BMI: Tertile 1: 21.4 (20.4, 22.3), Tertile 2: 24.8 (23.9, 25.7); Tertile 3: 29.9 (28.1, 32.7) From Cox regression models adjusted for height, number of full-term pregnancies, pill use, education level, smoking status, and study center At baseline (HT hormone therapy) Nattenmüller et al. BMC Cancer (2018) 18:616 Page 5 of 8 Table 3 Hazard ratios of breast cancer across tertiles of BMI by clusters of breast tumors from hierarchical clustering (see Fig. 1) Postmenopausal Postmenopausal Pre- and perimenopausal b b b non-users of HT users of HT women Cases HR CI (95%) Cases HR CI (95%) Cases HR CI (95%) (n) (n) (n) Cluster 1 Tertile 1 4 1 Tertile 1 30 1 Tertile 1 59 1 (ER+, PR+, HER2-, Ki67 , bcl-2+, Tertile 2 8 1.02 (0.31,3.40) Tertile 2 32 0.74 (0.44,1.22) Tertile 2 31 0.64 (0.41,1.00) low and p53-) Tertile 3 33 2.50 (0.86,7.23) Tertile 3 24 0.61 (0.35,1.06) Tertile 3 21 0.55 (0.33,0.93) 2 2 2 Per 5 kg/m 1.44 (1.10,1.90) Per 5 kg/m 0.68 (0.50,0.94) Per 5 kg/m 0.85 (0.67,1.08) p trend 0.009 p trend 0.018 p trend 0.19 Cluster 2 Tertile 1 5 1 Tertile 1 10 1 Tertile 1 18 1 (ER- or ER+ that are Bcl-2-) Tertile 2 6 0.77 (0.23,2.56) Tertile 2 18 1.14 (0.52,2.53) Tertile 2 9 0.59 (0.26,1.32) Tertile 3 16 1.40 (0.49,4.04) Tertile 3 6 0.43 (0.15,1.21) Tertile 3 20 1.52 (0.77,3.00) 2 2 2 Per 5 kg/m 1.15 (0.78,1.70) Per 5 kg/m 0.83 (0.52,1.32) Per 5 kg/m 1.22 (0.91,1.62) p trend 0.47 p trend 0.42 p trend 0.18 Cluster 3 Tertile 1 5 1 Tertile 1 20 1 Tertile 1 48 1 (ER+ with at least one other marker Tertile 2 21 2.98 (1.01,8.75) Tertile 2 33 1.20 (0.68,2.12) Tertile 2 26 0.72 (0.44,1.18) indicative of higher aggressiveness) Tertile 3 16 1.57 (0.51,4.83) Tertile 3 17 0.77 (0.39,1.51) Tertile 3 31 1.13 (0.70,1.82) 2 2 2 Per 5 kg/m 1.00 (0.71,1.42) Per 5 kg/m 0.82 (0.58,1.15) Per 5 kg/m 0.94 (0.74,1.19) p trend 0.99 p trend 0.24 p trend 0.60 Median (p25, p75) values of BMI: Tertile 1: 21.4 (20.4, 22.3), Tertile 2: 24.8 (23.9, 25.7); Tertile 3: 29.9 (28.1, 32.7) No statistical heterogeneity of HRs across subtypes was observed a b From Cox regression models adjusted for height, number of full-term pregnancies, pill use, education level, smoking status, and study center At baseline (HT hormone therapy) Ki67 , Bcl-2- and p53+ tumors. Among pre- and peri- immunohistochemical markers. Among postmenopausal high menopausal women, BMI was not significantly associ- women who did not use HT at the time of recruitment, ated with risks of any tumor subtype defined by higher BMI was significantly associated with increased individual markers (Additional file 2: Table S8). The risk of less aggressive tumors, as either defined by indi- results on BMI and risks of tumor subtypes defined by vidual markers (ER+, PR+, HER2-, Ki67 , Bcl-2+, p53-) low individual markers were similar after exclusion of in situ or a combination of these markers derived from hier- cases (see Additional file 2: Table S9, Table S10, and archical cluster analysis (cluster 1). By contrast, we Table S11). observed no significant associations between BMI and The directions of associations with risk of tumor sub- risk of more aggressive tumors, irrespective of whether types were highly similar when using waist and hip subtype classification was based on single markers or on circumference as anthropometric indices of obesity in- marker combinations (clusters 2 and 3). Among HT stead of BMI, while the associations between users, higher BMI was linearly associated with reduced waist-to-hip ratio and breast cancer risk were weaker relative risk of less aggressive (hormone receptor posi- and non-significant (data not shown). Risk associations tive, HER-, Ki67 , Bcl-2+, or cluster 1) tumors, while low among premenopausal women only were very similar as there were no significant associations with more aggres- the presented associations among peri- and premeno- sive tumors. Analyses by single markers did not reveal pausal women (data not shown). Importantly, no formal any significant associations among pre- and perimeno- heterogeneity of associations between anthropometric pausal women, whereas risk of cluster 1 tumors was factors and breast cancer risk across tumor subtypes, as lower among women in the highest BMI tertile com- either derived from hierarchical clustering or defined by pared to those in the lowest. individual IHC markers, was observed. Various studies have shown associations between obesity and an increased risk of breast cancer among Discussion postmenopausal non-users of HT, particularly of ER+ Here, we examined associations between BMI and breast / PR+ breast cancer, but not ER- / PR- breast cancer cancer risk by tumor subtypes characterized by six [4, 22, 23]. Our present data confirm the association Nattenmüller et al. BMC Cancer (2018) 18:616 Page 6 of 8 with hormone-receptor positive breast cancer and characteristics. In addition, larger epidemiological data- additionally indicate that postmenopausal obesity may sets are needed to stratify ER positive and ER negative be related to an overall less aggressive molecular sub- tumors by p53 or Bcl-2 status, which was not possible type of breast cancer characterized by a lower prolif- due to sample size restrictions in the present study. eration rate (Ki67 ), Bcl-2 positivity and p53 Our findings among postmenopausal non-users of HT low negativity – immunohistochemical characteristics that might suggest better prognosis in obese breast cancer are each associated with better prognosis [12, 24–26]. patients, as they may be more likely to have less aggres- The inverse overall association between obesity and sive tumor subtypes than lean patients. Yet, prospective breast cancer risk among HT users that we observed analyses in cohorts of breast cancer patients have clearly is in agreement with previous data from the full shown that breast cancer-specific survival is negatively EPIC-Europe cohort . Our results suggest that impacted by obesity irrespective of menopausal status or this inverse association might be strongest for (if not hormone receptor status of the tumor [35, 36]. These restricted to) the less aggressive tumor subtypes, paradoxical observations may be explained by lower effi- which is in contrast, however, with earlier observa- ciency of anticancer drugs, particularly aromatase inhibi- tions in the EPIC-Europe Study, which were suggest- tors, in obese patients and by better compliance to ive of an inverse association between BMI and breast treatment among normal weight patients ; still, fur- cancer risk among users of HT for ER- / PR- but not ther studies are needed to resolve the paradox as to why ER+ / PR+ tumors . Thus, and given the lack of obesity may be related to an increased risk of less further studies on obesity and breast cancer risk by aggressive breast tumors, while at the same time being tumor subtypes among HT users , the associa- associated with worse prognosis irrespective of the tions observed in the present study require replica- tumor subtype. tion. Our observation of a lower risk of less Several limitations apply to our study. First, by using aggressive tumors among pre- and perimenopausal TMAs from preserved tumor material to assess tumor women in the highest BMI tertile is consistent with subtypes, we ensured homogeneity of testing conditions. results of a meta-analysis, in which BMI was signifi- However, when compared to full-slice IHC staining done cantly inversely associated with the risk of ER+/PR+ for diagnostic purposes, IHC performed on TMAs may tumors but not ER-/PR- tumors in premenopausal be more prone to misclassification of subtypes, especially women . when the tumor tissue exhibits heterogeneous expres- Biological mechanisms that may underlie the associ- sion of the markers in question and visual estimation of ation between obesity and breast cancer include altered positive tumor cells is used. To minimize such misclassi- sex hormone metabolism, adipokine signaling, subclin- fication, we used two tissue cores per tumor. Neverthe- ical inflammation, hyperglycaemia, hyperinsulinaemia, less, we cannot rule out that misclassification of tumor and increased IGF-1 signaling [15, 29]. Differential asso- subtypes diluted associations in our study to some ciations of obesity and breast cancer risk by hormone degree. Second, case numbers in our study may have receptor status likely reflect a greater responsiveness of been too low to detect weaker associations in some sub- ER+ / PR+ tumors to these mechanisms [4, 30]. How- groups, especially for the more rare and aggressive ever, it is largely unknown why obesity should predis- cancer subtypes. Due to lower numbers of these tumors, pose to p53- and Bcl-2+ tumor subtypes in tests for statistical heterogeneity in the associations postmenopausal women, as indicated by our data. The between obesity and breast cancer risk across tumor expression of p53 in breast adipose stromal cells is subtypes were limited. In this context, it is worth men- downregulated by obesity-induced prostaglandin E tioning that in previous analyses of the full European (PGE ), which results in a local upregulation of aroma- EPIC cohort, heterogeneity in BMI breast cancer risk tase activity and estrogen production , and estrogen associations by ER/PR status was restricted to women receptor has also been demonstrated to downregulate older than 65 years at diagnosis , and that our sample p53 and cause tumor cell proliferation [31, 32]. Bcl-2 size was not sufficient to further stratify analyses by age proteins, by contrast, have been proposed to exert groups. Thus, our main observation – associations of pro-apoptotic effects [12, 25, 33] and influence obesity with less aggressive breast cancer subtypes – re- p53-mediated cell-death [31, 34]. Thus, ER positivity, quires replication in larger-scale studies and pooled ana- Bcl-2 positivity and p53 negativity, which co-occurred in lyses. This is also true with regard to further a majority of breast cancer cases in the present analyses, stratification of analyses by histological types of breast all appear to be part of a more general molecular con- cancer and cancer stage (e.g. invasive vs. in situ or ductal stellation that could be driven by obesity, even though vs. lobular), for which case numbers in the present study more experimental insight is needed to better under- were not sufficient. Another limitation is that we did not stand the interplay between obesity and these tumor have data on family history of breast cancer for Nattenmüller et al. BMC Cancer (2018) 18:616 Page 7 of 8 statistical adjustment. Finally, as many similar cohort Availability of data and materials Publication of data from EPIC-Germany in public repositories is not covered studies on BMI and breast cancer risk, we could not by the informed consent and participant information of the study. Pseudony- address changes in weight over time, even though weight mized data can be made available for statistical validation upon request. changes in our population are moderate according to Authors’ contributions self-reports . RK, HB, and PS initiated the tumor collection for the EPIC cohorts in Heidelberg and Potsdam and obtained the funding. EH managed the EPIC-Germany tumor collection. JK, EH, MB, TK and TJ organized the tumor collection. EH marked the Conclusion tumor areas and monitored the preparation and staining of TMAs. MK, CJN and In the present study, we evaluated associations between EH evaluated the TMAs. HPS, PS and BW supported the evaluation. HB, RK, VK, TK, and MB managed the follow-up activities of EPIC-Germany. TK initiated and obesity and breast cancer risk by tumor subtypes, as designed the present project, with conceptual support from CJN, RK, MK, AS defined by six immunohistochemical markers used in clin- and RTF. CJN and TK wrote the manuscript. CJN, DS and TK ran the statistical ical routine to guide treatment and determine prognosis. analyses. All authors read and critically revised the manuscript and approved its final version. Our data suggests that obesity is related to ER+, PR+, HER2-, Ki67 , Bcl-2+ and p53- tumors, i.e. such with low Ethics approval and consent to participate lower aggressiveness, in postmenopausal women. Further All participants gave written informed consent and the study was approved by the responsible ethics committees at both study centers (Potsdam: Ethics mechanistic studies are needed to determine which bio- Committee of the Medical Association of the State of Brandenburg; logical mechanisms underlie the detected associations, Heidelberg: Ethics Committee of the Heidelberg University Hospital) . and larger pooled analyses of prospective cohort data will Tissue samples were provided by the tissue bank of the National Center for Tumor Diseases (NCT, Heidelberg, Germany) in accordance with the regulations be required to further investigate relationships between of the tissue bank and the approval of the ethics committee of the Heidelberg obesity and molecular breast tumor subtypes, and particu- University Hospital. larly the less frequent subtypes, in more detail. Competing interests The authors declare that they have no competing interests. Additional files Publisher’sNote Springer Nature remains neutral with regard to jurisdictional claims in Additional file 1: Figure S1. Flow Chart. (DOCX 29 kb) published maps and institutional affiliations. Additional file 2: Table S1. Characteristics of breast cancer cases with and without available immunohistochemistry (IHC) markers; Author details Table S2. Antibodies; Table S3. Frequency of histological tumor Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), types; Table S4. Hazard ratios of breast cancer across tertiles of BMI Im Neuenheimer Feld 280, Heidelberg, Germany. Institute of Pathology, by clusters of breast tumors from hierarchical clustering, after exclusion of situ University Hospital Heidelberg, Heidelberg, Germany. Department of tumors; Table S5. Hazard ratios of luminal A breast cancer across tertiles of Epidemiology, German Institute of Human Nutrition (DIfE) BMI; Table S6. Hazard ratios of breast cancer subtypes across tertiles of BMI Postdam-Rehbrücke, Nuthetal, Germany. Tissue Bank of the National Center among postmenopausal non-users of hormone therapy; Table S7. Hazard for Tumor Diseases (NCT), Heidelberg, Germany. ratios of breast cancer subtypes across tertiles of BMI among postmenopausal users of hormone therapy; Table S8. Hazard ratios of breast cancer sub- Received: 15 September 2017 Accepted: 23 May 2018 types across tertiles of BMI among pre- and perimenopausal women; Table S9. Hazard ratios of breast cancer subtypes across tertiles of BMI among postmenopausal non-users of hormone therapy, after exclusion References of situ tumors; Table S10. Hazard ratios of breast cancer subtypes across 1. Ogino S, Fuchs CS, Giovannucci E. How many molecular subtypes? tertiles of BMI among postmenopausal users of hormone therapy, after Implications of the unique tumor principle in personalized medicine. Expert exclusion of situ tumors; Table S11. Hazard ratios of breast cancer subtypes Rev Mol Diagn. 2012;12(6):621–8. across tertiles of BMI among pre- and perimenopausal women, after 2. Barnard ME, Boeke CE, Tamimi RM. Established breast cancer risk factors and exclusion of situ tumors. (DOCX 84 kb) risk of intrinsic tumor subtypes. Biochim Biophys Acta. 2015;1856(1):73–85. 3. Neuhouser ML, Aragaki AK, Prentice RL, et al. Overweight, obesity, and postmenopausal invasive breast cancer risk: a secondary analysis of the Abbreviations women’s health initiative randomized clinical trials. JAMA Oncol. 2015;1(5): Bcl-2: B-cell lymphoma 2; BMI: Body mass index; CI: Confidence interval; 611–21. EPIC: European Prospective Investigation into Cancer and Nutrition; 4. Ritte R, Lukanova A, Berrino F, Dossus L, Tjonneland A, Olsen A, Overvad TF, ER: Estrogen receptor; FFPE: formalin-fixed paraffin-embedded; HER2: Human Overvad K, Clavel-Chapelon F, Fournier A, et al. Adiposity, hormone epidermal growth factor receptor 2; HR: Hazard ratio; HT: Hormone therapy; replacement therapy use and breast cancer risk by age and hormone IGF-1: Insulin-like growth factor 1; IHC: Immunohistochemistry; receptor status: a large prospective cohort study. Breast Cancer Res. 2012; PR: Progesterone receptor; TMA: Tissue microarray 14(3):R76. 5. Borgquist S, Jirstrom K, Anagnostaki L, Manjer J, Landberg G. Anthropometric factors in relation to different tumor biological subgroups Acknowledgements of postmenopausal breast cancer. Int J Cancer. 2009;124(2):402–11. The authors thank Veronika Geißler and David Jansen for preparing the TMAs 6. Friedenreich CM. Review of anthropometric factors and breast cancer risk. used for the present study. Eur J Cancer Prev. 2001;10(1):15–32. 7. Boeing H, Wahrendorf J, Becker N. EPIC-Germany–A source for studies into Funding diet and risk of chronic diseases. European investigation into Cancer and The present study was funded by the German Federal Ministry of Education nutrition. Ann Nutr Metab. 1999;43(4):195–204. and Research (BMBF, grant numbers 01ER0808 and 01ER0809). The funders 8. Boeing H, Korfmann A, Bergmann MM. Recruitment procedures of EPIC- had no involvement in the design of the study, the conduct of the study, or Germany. European investigation into Cancer and nutrition. Ann Nutr the submission of the manuscript for publication. Metab. 1999;43(4):205–15. Nattenmüller et al. BMC Cancer (2018) 18:616 Page 8 of 8 9. Harvey JM, Clark GM, Osborne CK, Allred DC. Estrogen receptor status 29. Park J, Morley TS, Kim M, Clegg DJ, Scherer PE. Obesity and cancer– by immunohistochemistry is superior to the ligand-binding assay for mechanisms underlying tumour progression and recurrence. Nat Rev predicting response to adjuvant endocrine therapy in breast cancer. Endocrinol. 2014;10(8):455–65. J Clin Oncol. 1999;17(5):1474–81. 30. Ritte R, Lukanova A, Tjonneland A, Olsen A, Overvad K, Mesrine S, Fagherazzi G, 10. Wolff AC, Hammond ME, Hicks DG, Dowsett M, McShane LM, Allison Dossus L, Teucher B, Steindorf K, et al. Height, age at menarche and risk of KH, Allred DC, Bartlett JM, Bilous M, Fitzgibbons P, et al. hormone receptor-positive and -negative breast cancer: a cohort study. Int J Recommendations for human epidermal growth factor receptor 2 Cancer. 2013;132(11):2619–29. testing in breast cancer: American Society of Clinical Oncology/College 31. Wang X, Simpson ER, Brown KA. p53: protection against tumor growth of American Pathologists clinical practice guideline update. J Clin Onco. beyond effects on cell cycle and apoptosis. Cancer Res. 2015;75(23):5001–7. 2013;31(31):3997–4013. 32. Bailey ST, Shin H, Westerling T, Liu XS, Brown M. Estrogen receptor prevents p53-dependent apoptosis in breast cancer. Proc Natl Acad Sci U S A. 2012; 11. Coates AS, Winer EP, Goldhirsch A, Gelber RD, Gnant M, Piccart-Gebhart M, 109(44):18060–5. Thürlimann B, Senn H-J. Tailoring therapies—improving the management of 33. Zinkel S, Gross A, Yang E. BCL2 family in DNA damage and cell cycle control. early breast cancer: St Gallen international expert consensus on the primary Cell Death Differ. 2006;13(8):1351–9. therapy of early breast Cancer 2015. Ann Oncol. 2015;26(8):1533–46. 34. Nakano K, Vousden KH. PUMA, a novel proapoptotic gene, is induced by 12. Hwang KT, Woo JW, Shin HC, Kim HS, Ahn SK, Moon HG, Han W, Park IA, p53. Mol Cell. 2001;7(3):683–94. Noh DY. Prognostic influence of BCL2 expression in breast cancer. Int J 35. Chan DSM, Vieira AR, Aune D, Bandera EV, Greenwood DC, McTiernan A, Cancer. 2012;131(7):E1109–19. Navarro Rosenblatt D, Thune I, Vieira R, Norat T. Body mass index and 13. Boyle DP, McArt DG, Irwin G, Wilhelm-Benartzi CS, Lioe TF, Sebastian E, survival in women with breast cancer—systematic literature review and McQuaid S, Hamilton PW, James JA, Mullan PB, et al. The prognostic meta-analysis of 82 follow-up studies. Ann Oncol. 2014;25(10):1901–14. significance of the aberrant extremes of p53 immunophenotypes in breast 36. Niraula S, Ocana A, Ennis M, Goodwin PJ. Body size and breast cancer cancer. Histopathology. 2014;65(3):340–52. prognosis in relation to hormone receptor and menopausal status: a meta- 14. Fortner RT, Katzke V, Kuhn T, Kaaks R. Obesity and breast Cancer. Recent analysis. Breast Cancer Res Treat. 2012;134(2):769–81. Results Cancer Res. 2016;208:43–65. 37. Lennon H, Sperrin M, Badrick E, Renehan AG. The obesity paradox in Cancer: a 15. Renehan AG, Zwahlen M, Egger M. Adiposity and cancer risk: new review. Curr Oncol Rep. 2016;18(9):56. mechanistic insights from epidemiology. Nat Rev Cancer. 2015;15(8):484–98. 38. Haftenberger M, Mensink GB, Herzog B, Kluttig A, Greiser KH, Merz B, Nothlings 16. Weigelt B, Horlings HM, Kreike B, Hayes MM, Hauptmann M, Wessels LF, de U, Schlesinger S, Vogt S, Thorand B, et al. Changes in body weight and obesity Jong D, Van de Vijver MJ, Van't Veer LJ, Peterse JL. Refinement of breast status in German adults: results of seven population-based prospective studies. cancer classification by molecular characterization of histological special Eur J Clin Nutr. 2016;70(3):300–5. types. J Pathol. 2008;216(2):141–50. 17. Makretsov NA, Huntsman DG, Nielsen TO, Yorida E, Peacock M, Cheang MC, Dunn SE, Hayes M, van de Rijn M, Bajdik C, et al. Hierarchical clustering analysis of tissue microarray immunostaining data identifies prognostically significant groups of breast carcinoma. Clin Cancer Res. 2004;10(18 Pt 1): 6143–51. 18. Wang M, Spiegelman D, Kuchiba A, Lochhead P, Kim S, Chan AT, Poole EM, Tamimi R, Tworoger SS, Giovannucci E, et al. Statistical methods for studying disease subtype heterogeneity. Stat Med. 2016;35(5):782–800. 19. Mullooly M, Khodr ZG, Dallal CM, Nyante SJ, Sherman ME, Falk R, Liao LM, Love J, Brinton LA, Gierach GL. Epidemiologic risk factors for in situ and invasive breast cancers among postmenopausal women in the National Institutes of Health-AARP diet and health study. Am J Epidemiol. 2017; 186(12):1329–40. 20. Reeves GK, Pirie K, Green J, Bull D, Beral V. Comparison of the effects of genetic and environmental risk factors on in situ and invasive ductal breast cancer. Int J Cancer. 2012;131(4):930–7. 21. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: Foundation for Statistical Computing; 2014. http://www.R-project.org/. Accessed Dec 10 2017 22. Suzuki R, Orsini N, Saji S, Key TJ, Wolk A. Body weight and incidence of breast cancer defined by estrogen and progesterone receptor status–a meta-analysis. Int J Cancer. 2009;124(3):698–712. 23. Bhaskaran K, Douglas I, Forbes H, dos-Santos-Silva I, Leon DA, Smeeth L. Body-mass index and risk of 22 specific cancers: a population-based cohort study of 5·24 million UK adults. Lancet. 2014;384(9945):755–65. 24. de Azambuja E, Cardoso F, de Castro G, Jr., Colozza M, Mano MS, Durbecq V, Sotiriou C, Larsimont D, Piccart-Gebhart MJ, Paesmans M: Ki-67 as prognostic marker in early breast cancer: a meta-analysis of published studies involving 12,155 patients. Br J Cancer 2007, 96(10):1504–1513. 25. Dawson SJ, Makretsov N, Blows FM, Driver KE, Provenzano E, Le Quesne J, Baglietto L, Severi G, Giles GG, McLean CA, et al. BCL2 in breast cancer: a favourable prognostic marker across molecular subtypes and independent of adjuvant therapy received. Br J Cancer. 2010;103(5):668–75. 26. Soussi T, Beroud C. Assessing TP53 status in human tumours to evaluate clinical outcome. Nat Rev Cancer. 2001;1(3):233–40. 27. Lahmann PH, Hoffmann K, Allen N, van Gils CH, Khaw KT, Tehard B, Berrino F, Tjonneland A, Bigaard J, Olsen A, et al. Body size and breast cancer risk: findings from the European prospective investigation into Cancer and nutrition (EPIC). Int J Cancer. 2004;111(5):762–71. 28. Munsell MF, Sprague BL, Berry DA, Chisholm G, Trentham-Dietz A. Body mass index and breast cancer risk according to postmenopausal estrogen- progestin use and hormone receptor status. Epidemiol Rev. 2014;36:114–36.
BMC Cancer – Springer Journals
Published: May 31, 2018
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