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Relationship between crown-like structures and sex-steroid hormones in breast adipose tissue and serum among postmenopausal breast cancer patients
research-article
Author(s):
Maeve Mullooly
1
,
2
,
,
Hannah P. Yang
1 ,
Roni T. Falk
1 ,
Sarah J. Nyante
3 ,
Renata Cora
4 ,
Ruth M. Pfeiffer
1 ,
Derek C. Radisky
5 ,
Daniel W. Visscher
6 ,
Lynn C. Hartmann
6 ,
Jodi M. Carter
6 ,
Amy C. Degnim
6 ,
Frank Z. Stanczyk
7 ,
Jonine D. Figueroa
8 ,
Montserrat Garcia-Closas
1 ,
Jolanta Lissowska
9 ,
Melissa A. Troester
10 ,
Stephen M. Hewitt
11 ,
Louise A. Brinton
1 ,
Mark E. Sherman
12 ,
Gretchen L. Gierach
1
Publication date (Electronic):
19 January 2017
Journal:
Breast Cancer Research : BCR
Publisher:
BioMed Central
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Abstract
Background
Postmenopausal obesity is associated with increased circulating levels of androgens and estrogens and elevated breast cancer risk. Crown-like structures (CLS; microscopic foci of dying adipocytes surrounded by macrophages) are proposed to represent sites of increased aromatization of androgens to estrogens. Accordingly, we examined relationships between CLS and sex-steroid hormones in breast adipose tissue and serum from postmenopausal breast cancer patients.
Methods
Formalin-fixed paraffin embedded benign breast tissues collected for research from postmenopausal women ( n = 83) diagnosed with invasive breast cancer in the Polish Breast Cancer Study (PBCS) were evaluated. Tissues were immunohistochemically stained for CD68 to determine the presence of CLS per unit area of adipose tissue. Relationships were assessed between CD68 density and CLS and previously reported sex-steroid hormones quantified using radioimmunoassays in serum taken at the time of diagnosis and in fresh frozen adipose tissue taken at the time of surgery. Logistic regression analysis was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the relationships between hormones (in tertiles) and CLS.
Results
CLS were observed in 36% of benign breast tissues, with a higher frequency among obese versus lean women (54% versus 17%, p = 0.03). Detection of CLS was not related to individual hormone levels or breast tumor pathology characteristics. However, detection of CLS was associated with hormone ratios. Compared with women in the highest tertile of estrone:androstenedione ratio in fat, those in the lowest tertile were less likely to have CLS (OR 0.12, 95% CI 0.03–0.59). A similar pattern was observed with estradiol:testosterone ratio in serum and CLS (lowest versus highest tertile, OR 0.18, 95% CI 0.04–0.72).
Conclusions
CLS were more frequently identified in the breast fat of obese women and were associated with increased ratios of select estrogens:androgens in the blood and tissues, but not with individual hormones. Additional studies on CLS, tissue and blood hormone levels, and breast cancer risk are needed to understand and confirm these findings.
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Most cited references20
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Body mass index and survival in women with breast cancer—systematic literature review and meta-analysis of 82 follow-up studies
D S M Chan, A. R. Vieira, D. Aune … (2014)
introduction The number of female breast cancer survivors is growing because of longer survival as a consequence of advances in treatment and early diagnosis. There were ∼2.6 million female breast cancer survivors in US in 2008 [1], and in the UK, breast cancer accounted for ∼28% of the 2 million cancer survivors in 2008 [2]. Obesity is a pandemic health concern, with over 500 million adults worldwide estimated to be obese and 958 million were overweight in 2008 [3]. One of the established risk factors for breast cancer development in post-menopausal women is obesity [4], which has further been linked to breast cancer recurrence [5] and poorer survival in pre- and post-menopausal breast cancer [6, 7]. Preliminary findings from randomised, controlled trials suggest that lifestyle modifications improved biomarkers associated with breast cancer progression and overall survival [8]. The biological mechanisms underlying the association between obesity and breast cancer survival are not established, and could involve interacting mediators of hormones, adipocytokines, and inflammatory cytokines which link to cell survival or apoptosis, migration, and proliferation [9]. Higher level of oestradiol produced in postmenopausal women through aromatisation of androgens in the adipose tissues [10], and higher level of insulin [11], a condition common in obese women, are linked to poorer prognosis in breast cancer. A possible interaction between leptin and insulin [12], and obesity-related markers of inflammation [13] have also been linked to breast cancer outcomes. Non-biological mechanisms could include chemotherapy under-dosing in obese women, suboptimal treatment, and obesity-related complications [14]. Numerous studies have examined the relationship between obesity and breast cancer outcomes, and past reviews have concluded that obesity is linked to a lower survival; however, when investigated in a meta-analysis of published data, only the results of obese compared with non-obese or lighter women were summarised [6, 7, 15]. We carried out a systematic literature review and meta-analysis of published studies to explore the magnitude and the shape of the association between body fatness, as measured by body mass index (BMI), and the risk of total and cause-specific mortality, overall and in women with pre- and post-menopausal breast cancer. As body weight may change close to diagnosis and during primary treatment of breast cancer [16], we examined BMI in three periods: before diagnosis, <12 months after diagnosis, and ≥12 months after breast cancer diagnosis. materials and methods data sources and search We carried out a systematic literature search, limited to publications in English, for articles on BMI and survival in women with breast cancer in OVID MEDLINE and EMBASE from inception to 30 June 2013 using the search strategy implemented for the WCRF/AICR Continuous Update Project on breast cancer survival. The search strategy contained medical subject headings and text words that covered a broad range of factors on diet, physical activity, and anthropometry. The protocol for the review is available at http://www.dietandcancerreport.org/index.php [17]. In addition, we hand-searched the reference lists of relevant articles, reviews, and meta-analysis papers. study selection Included were follow-up studies of breast cancer survivors, which reported estimates of the associations of BMI ascertained before and after breast cancer diagnosis with total or cause-specific mortality risks. Studies that investigated BMI after diagnosis were divided into two groups: BMI <12 months after diagnosis (BMI <12 months) and BMI 12 months or more after diagnosis (BMI ≥12 months). Outcomes included total mortality, breast cancer mortality, death from cardiovascular disease, and death from causes other than breast cancer. When multiple publications on the same study population were found, results based on longer follow-up and more outcomes were selected for the meta-analysis. data extraction DSMC, TN, and DA conducted the search. DSMC, ARV, and DNR extracted the study characteristics, tumour-related information, cancer treatment, timing and method of weight and height assessment, BMI levels, number of outcomes and population at-risk, outcome type, estimates of association and their measure of variance [95% confidence interval (CI) or P value], and adjustment factors in the analysis. statistical analysis Categorical and dose–response meta-analyses were conducted using random-effects models to account for between-study heterogeneity [18]. Summary relative risks (RRs) were estimated using the average of the natural logarithm of the RRs of each study weighted by the inverse of the variance and then unweighted by applying a random-effects variance component which is derived from the extent of variability of the effect sizes of the studies. The maximally adjusted RR estimates were used for the meta-analysis except for the follow-up of randomised, controlled trials [19, 20] where unadjusted results were also included, as these studies mostly involved a more homogeneous study population. BMI or Quetelet's Index (QI) measured in units of kg/m2 was used. We conducted categorical meta-analyses by pooling the categorical results reported in the studies. The studies used different BMI categories. In some studies, underweight (BMI <18.5 kg/m2 according to WHO international classification) and normal weight women (BMI 18.5–<25.0 kg/m2) were classified together but, in some studies, they were classified separately. Similarly, most studies classified overweight (BMI 25.0–<30.0 kg/m2) and obese (BMI ≥30.0 kg/m2) women separately but, in some studies, overweight and obese women were combined. The reference category was normal weight or underweight together with normal weight, depending on the studies. For convenience, the BMI categories are referred to as underweight, normal weight, overweight, and obese in the present review. We derived the RRs for overweight and obese women compared with normal weight women in two studies [19, 21] that had more than four BMI categories using the method of Hamling et al. [22]. Studies that reported results for obese compared with non-obese women were analysed separately. The non-linear dose–response relationship between BMI and mortality was examined using the best-fitting second-order fractional polynomial regression model [23], defined as the one with the lowest deviance. Non-linearity was tested using the likelihood ratio test [24]. In the non-linear meta-analysis, the reference category was the lowest BMI category in each study and RRs were recalculated using the method of Hamling et al. [22] when the reference category was not the lowest BMI category in the study. We also conducted linear dose–response meta-analyses, excluding the category underweight when reported separately in the studies, by pooling estimates of RR per unit increase (with its standard error) provided by the studies, or derived by us from categorical data using generalised least-squares for trend estimation [25]. To estimate the trend, the numbers of outcomes and population at-risk for at least three BMI categories, or the information required to derive them using standard methods [26], and means or medians of the BMI categories, or if not reported in the studies, the estimated midpoints of the categories had to be available. When the extreme BMI categories were open-ended, we used the width of the adjacent close-ended category to estimate the midpoints. Where the RRs were presented by subgroups (age group [27], menopausal status [28, 29], stage [30] or subtype [31] of breast cancer, or others [32–34]), an overall estimate for the study was obtained by a fixed-effect model before pooling in the meta-analysis. We estimated the risk increase of death for an increment of 5 kg/m2 of BMI. To assess heterogeneity, we computed the Cochran Q test and I 2 statistic [35]. The cut points of 30% and 50% were used for low, moderate, and substantial level of heterogeneity. Sources of heterogeneity were explored by meta-regression and subgroup analyses using pre-defined factors, including indicators of study quality (menopausal status, hormone receptor status, number of outcomes, length of follow-up, study design, geographic location, BMI assessment, adjustment for confounders, and others). Small study or publication bias was examined by Egger's test [36] and visual inspection of the funnel plots. The influence of each individual study on the summary RR was examined by excluding the study in turn [37]. A P value of <0.05 was considered statistically significant. All analyses were conducted using Stata version 12.1 (Stata Statistical Software: Release 12, StataCorp LP, College Station, TX). results A total of 124 publications investigating the relationship of body fatness and mortality in women with breast cancer were identified. We excluded 31 publications, including four publications on other obesity indices [38–41], 12 publications without a measure of association [42–53], and 15 publications superseded by publications of the same study with more outcomes [54–68]. A further 14 publications were excluded because of insufficient data for the meta-analysis (five publications [69–73]) or unadjusted results (nine publications [74–82]), from which nine publications reported statistically significant increased risk of total, breast cancer or non-breast cancer mortality in obese women (before or <12 months after diagnosis) compared with the reference BMI [69, 71–74, 76, 77, 79, 82], two publications reported non-significant inverse associations [75, 80] and three publications reported no association [70, 78, 81] of BMI with survival after breast cancer. Hence, 79 publications from 82 follow-up studies with 41 477 deaths (23 182 from breast cancer) in 213 075 breast cancer survivors were included in the meta-analyses (Figure 1). Supplementary Table S1, available at Annals of Oncology online shows the characteristics of the studies included in the meta-analyses and details of the excluded studies are in supplementary Table S2, available at Annals of Oncology online. Results of the meta-analyses are summarised in Table 1. Table 1. Summary of meta-analyses of BMI and survival in women with breast cancera BMI before diagnosis BMI <12 months after diagnosis BMI ≥12 months after diagnosis N RR (95% CI) I 2 (%) P h N RR (95% CI) I 2 (%) P h N RR (95% CI) I 2 (%) P h Total mortality Under versus normal weight 10 1.10 (0.92–1.31) 48%0.04 11 1.25 (0.99–0.57) 63%<0.01 3 1.29 (1.02–1.63) 0%0.39 Over versus normal weight 19 1.07 (1.02–1.12) 0%0.88 22 1.07 (1.02–1.12) 21%0.18 4 0.98 (0.86–1.11) 0%0.72 Obese versus normal weight 21 1.41 (1.29–1.53) 38%0.04 24 1.23 (1.12–1.33) 69%<0.01 5 1.21 (1.06–1.38) 0%0.70 Obese versus non-obese – – – 12 1.26 (1.07–1.47) 80%<0.01 – – – Per 5 kg/m2 increase 15 1.17 (1.13–1.21) 7%0.38 12 1.11 (1.06–1.16) 55%0.01 4 1.08 (1.01–1.15) 0%0.52 Breast cancer mortality Under versus normal weight 8 1.02 (0.85–1.21) 31%0.18 5 1.53 (1.27–1.83) 0%0.59 1 1.10 (0.15–8.08) – Over versus normal weight 21 1.11 (1.06–1.17) 0%0.66 12 1.11 (1.03–1.20) 14%0.31 2 1.37 (0.96–1.95) 0%0.90 Obese versus normal weight 22 1.35 (1.24–1.47) 36%0.05 12 1.25 (1.10–1.42) 53%0.02 2 1.68 (0.90–3.15) 67%0.08 Obese versus non-obese – – – 6 1.26 (1.05–1.51) 64%0.02 – – – Per 5 kg/m2 increase 18 1.18 (1.12–1.25) 47%0.01 8 1.14 (1.05–1.24) 66%0.01 2 1.29 (0.97–1.72) 64%0.10 Cardiovascular disease related mortality Over versus normal weight 2 1.01 (0.80–1.29) 0%0.87 – – – – – – Obese versus normal weight 2 1.60 (0.66–3.87) 78%0.03 – – – – – – Per 5 kg/m2 increase 2 1.21 (0.83–1.77) 80%0.03 – – – – – – Non-breast cancer mortality Over versus normal weight – – – 5 0.96 (0.83–1.11) 26%0.25 – – – Obese versus normal weight – – – 5 1.29 (0.99–1.68) 72%0.01 – – – aBMI before and after diagnosis (<12 months after, or ≥12 months after diagnosis) was classified according to the exposure period which the studies referred to in the BMI assessment; the BMI categories were included in the categorical meta-analyses as defined by the studies. P h, P for heterogeneity between studies. Figure 1. Flowchart of search. Studies were follow-up of women with breast cancer identified in prospective aetiologic cohort studies (women were free of cancer at enrolment), or cohorts of breast cancer survivors whose participants were identified in hospitals or through cancer registries, or follow-up of breast cancer patients enrolled in case–control studies or randomised clinical trials. Some studies included only premenopausal women [83–85] or postmenopausal women [21, 27, 86–94], but most studies included both. Menopausal status was usually determined at time of diagnosis. Year of diagnosis was from 1957–1965 [70] to 2002–2009 [74]. Patient tumour characteristics and stage of disease at diagnosis varied across studies, and some studies included carcinoma in situ. No all studies provided clinical information on the tumour, treatment, and co-morbidities. Most of the studies were based in North America or Europe. There were three studies from each of Australia [79, 95, 96], Korea [97, 98] and China [99–101]; two studies from Japan [71, 102]; one study from Tunisia [103] and four international studies [19, 104–106]. Study size ranged from 96 [107] to 24 698 patients [97]. Total number of deaths ranged from 56 [93] to 7397 [108], and the proportion of deaths from breast cancer ranged from 22% [27] to 98% [84] when reported. All but eight studies [30, 93, 94, 98, 99, 109–111] had an average follow-up of more than 5 years. BMI and total mortality categorical meta-analysis For BMI before diagnosis, compared with normal weight women, the summary RRs were 1.41 (95% CI 1.29–1.53, 21 studies) for obese women, 1.07 (95% CI 1.02–1.12, 19 studies) for overweight women, and 1.10 (95% CI 0.92–1.31, 10 studies) for underweight women (Figure 2). For BMI <12 months after diagnosis and the same comparisons, the summary RRs were 1.23 (95% CI 1.12–1.33, 24 studies) for obese women, 1.07 (95% CI 1.02–1.12, 22 studies) for overweight women, and 1.25 (95% CI 0.99–1.57, 11 studies) for underweight women (supplementary Figure S1, available at Annals of Oncology online). Substantial heterogeneities were observed between studies of obese women and underweight women (I 2 = 69%, P < 0.01; I 2 = 63%, P < 0.01, respectively). For BMI ≥12 months after diagnosis, the summary RRs were 1.21 (95% CI 1.06–1.38, 5 studies) for obese women, 0.98 (95% CI 0.86–1.11, 4 studies) for overweight women, and 1.29 (95% CI 1.02–1.63, 3 studies) for underweight women (supplementary Figure S2, available at Annals of Oncology online). Twelve additional studies reported results for obese versus non-obese women <12 months after diagnosis, and the summary RR was 1.26 (95% CI 1.07–1.47, I 2 = 80%, P < 0.01). Figure 2. Categorical meta-analysis of pre-diagnosis BMI and total mortality. dose–response meta-analysis There was evidence of a J-shaped association in the non-linear dose–response meta-analyses of BMI before and after diagnosis with total mortality (all P < 0.01; Figure 3), suggesting that underweight women may be at slightly increased risk compared with normal weight women. The curves show linear increasing trends from 20 kg/m2 for BMI before diagnosis and <12 months after diagnosis, and from 25 kg/m2 for BMI ≥12 months after diagnosis. When linear models were fitted excluding the underweight category, the summary RRs of total mortality for each 5 kg/m2 increase in BMI were 1.17 (95% CI 1.13–1.21, 15 studies, 6358 deaths), 1.11 (95% CI 1.06–1.16, 12 studies, 6020 deaths), and 1.08 (95% CI 1.01–1.15, 4 studies, 1703 deaths) for BMI before, <12 months after, and ≥12 months after diagnosis, respectively (Figure 4). Substantial heterogeneity was observed between studies on BMI <12 months after diagnosis (I 2 = 55%, P = 0.01). Figure 3. Non-linear dose–response curves of BMI and mortality. Figure 4. Linear dose–response meta-analysis of BMI and total mortality. BMI and breast cancer mortality categorical meta-analysis BMI was significantly associated with breast cancer mortality. Compared with normal weight women, for BMI before diagnosis, the summary RRs were 1.35 (95% CI 1.24–1.47, 22 studies) for obese women, 1.11 (95% CI 1.06–1.17, 21 studies) for overweight women, and 1.02 (95% CI 0.85–1.21, 8 studies) for underweight women (Figure 5). For BMI <12 months after diagnosis, the summary RRs were 1.25 (95% CI 1.10–1.42, 12 studies) for obese women, 1.11 (95% CI 1.03–1.20, 12 studies) for overweight women, and 1.53 (95% CI 1.27–1.83, 5 studies) for underweight women (supplementary Figure S3, available at Annals of Oncology online). Substantial heterogeneity was observed between studies of obese women (I 2 = 53%, P = 0.02). For BMI ≥12 months after diagnosis, the summary RRs of the two studies identified were 1.68 (95% CI 0.90–3.15) for obese women and 1.37 (95% CI 0.96–1.95) for overweight women (supplementary Figure S4, available at Annals of Oncology online). The summary of another six studies that reported RRs for obese versus non-obese <12 months after diagnosis was 1.26 (95% CI 1.05–1.51, I 2 = 64%, P = 0.02). Figure 5. Categorical meta-analysis of pre-diagnosis BMI and breast cancer mortality. dose–response meta-analysis There was no significant evidence of a non-linear relationship between BMI before, <12 months after, and ≥12 months after diagnosis and breast cancer mortality (P = 0.21, P = 1.00, P = 0.86, respectively) (Figure 3). When linear models were fitted excluding data from the underweight category, statistically significant increased risks of breast cancer mortality with BMI before and <12 months after diagnosis were observed (Figure 6). The summary RRs for each 5 kg/m2 increase were 1.18 (95% CI 1.12–1.25, 18 studies, 5262 breast cancer deaths) for BMI before diagnosis and 1.14 (95% CI 1.05–1.24, 8 studies, 3857 breast cancer deaths) for BMI <12 months after diagnosis, with moderate (I 2 = 47%, P = 0.01) and substantial (I 2 = 66%, P = 0.01) heterogeneities between studies, respectively. Only two studies on BMI ≥12 months after diagnosis and breast cancer mortality (N = 220 deaths) were identified. The summary RR was 1.29 (95% CI 0.97–1.72). Figure 6. Linear dose–response meta-analysis of BMI and breast cancer mortality. BMI and other mortality outcomes Only two studies reported results for death from cardiovascular disease (N = 151 deaths) [27, 112]. The summary RR for obese versus normal weight before diagnosis was 1.60 (95% CI 0.66–3.87). No association was observed for overweight versus normal weight (summary RR = 1.01, 95% CI 0.80–1.29). For each 5 kg/m2 increase in BMI, the summary RR was 1.21 (95% CI 0.83–1.77). Five studies reported results for deaths from any cause other than breast cancer (N = 2704 deaths) [21, 34, 108, 113, 114]. The summary RRs were 1.29 (95% CI 0.99–1.68, I 2 = 72%, P = 0.01) for obese women, and 0.96 (95% CI 0.83–1.11, I 2 = 26%, P = 0.25) for overweight women compared with normal weight women. subgroup, meta-regression, and sensitivity analyses The results of the subgroup and meta-regression analyses are in supplementary Tables S3 and S4, available at Annals of Oncology online. Subgroup analysis was not carried out for BMI ≥12 months after diagnosis as the limited number of studies would hinder any meaningful comparisons. Increased risks of mortality were observed in the meta-analyses by menopausal status. While the summary risk estimates seem stronger with premenopausal breast cancer, there was no significant heterogeneity between pre- and post-menopausal breast cancer as shown in the meta-regression analyses (P = 0.28–0.89) (supplementary Tables S3 and S4, available at Annals of Oncology online). For BMI before diagnosis and total mortality, the summary RRs for obese versus normal weight were 1.75 (95% CI 1.26–2.41, I 2 = 70%, P < 0.01, 7 studies) in women with pre-menopausal breast cancer and 1.34 (95% CI 1.18–1.53, I 2 = 27%, P = 0.20, 9 studies) in women with post-menopausal breast cancer. Studies with larger number of deaths [105, 115], conducted in Europe [28, 115], or with weight and height assessed through medical records [28, 104, 115, 116] tended to report weaker associations for BMI <12 months after diagnosis and total mortality compared with other studies (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S3, available at Annals of Oncology online); while studies with larger number of deaths [101], conducted in Asia [101, 102], or adjusted for co-morbidity [101, 102] reported weaker associations for BMI <12 months after diagnosis and breast cancer mortality (meta-regression P = 0.01, 0.02, 0.01, respectively) (supplementary Table S4, available at Annals of Oncology online). Analyses stratified by study designs, or restricted to studies with invasive cases only, early-stage non-metastatic cases only, or mammography screening detected cases only, or controlled for previous diseases did not produce results that were materially different from those obtained in the overall analyses (results not shown). Summary risk estimates remained statistically significant when each study was omitted in turn, except for BMI ≥12 months after diagnosis and total mortality. The summary RR was 1.06 (95% CI 0.98–1.15) per 5 kg/m2 increase when Flatt et al. [117] which contributed 315 deaths was omitted. small studies or publication bias Asymmetry was only detected in the funnel plots of BMI <12 months after diagnosis and total mortality, and breast cancer mortality, which suggests that small studies with an inverse association are missing (plots not shown). Egger's tests were borderline significant (P = 0.05) or statistically significant (P = 0.03), respectively. discussion The present systematic literature review and meta-analysis of follow-up studies clearly supports that, in breast cancer survivors, higher BMI is consistently associated with lower overall and breast cancer survival, regardless of when BMI is ascertained. The limited number of studies on death from cardiovascular disease is also consistent with a positive association. For before, <12 months after, and 12 months or more after breast cancer diagnosis, compared with normal weight women, obese women had 41%, 23%, and 21% higher risk for total mortality, and 35%, 25%, and 68% increased risk for breast cancer mortality, respectively. The findings were supported by the positive associations observed in the linear dose–response meta-analysis. All associations were statistically significant, apart from the relationship between BMI ≥12 months after diagnosis and breast cancer mortality. This may be due to limited statistical power, with only 220 breast cancer deaths from two follow-up studies. Positive associations, in some cases statistically significant, were also observed in overweight, and underweight women compared with normal weight women. Women with BMI of 20 kg/m2 before, or <12 months after diagnosis, and of 25 kg/m2 12 months or more after diagnosis appeared to have the lowest mortality risk in the non-linear dose–response analysis. Co-morbid conditions may cause the observed increased risk in underweight women. Thorough investigation within the group and on their contribution to the shape of the association is hindered, as not all studies in this review reported results for this group. The increased risk associated with obesity was similar in pre- or post-menopausal breast cancer. We did not find any evidence of a protective effect of obesity on survival after pre-menopausal breast cancer, contrary to what has been observed for the development of breast cancer in pre-menopausal women [4]. A large body of evidence with 41 477 deaths (23 182 from breast cancer) in over 210 000 breast cancer survivors was systematically reviewed in the present study. We carried out categorical, linear, and non-linear dose–response meta-analyses to examine the magnitude and the shape of the associations for total and cause-specific mortality in underweight, overweight, and obese women by time periods before and after diagnosis that is important in relation to the population-at-risk and breast cancer survivors. Our findings agree with and further extend the results from previous meta-analyses. A review published in 2010 reported statistically significant increased risks of 33% of both total and breast cancer mortality for obesity versus non-obesity around diagnosis [7]. These estimates are slightly higher than ours, which may be explained by the different search periods and inclusion criteria for the articles (33 studies and 15 studies included in the analyses, respectively). Another review published in 2012 further reported consistent positive associations of total and breast cancer mortality with higher versus lower BMI around diagnosis [6]. No significant differences were observed by menopausal status or hormone receptor status. The After Breast Cancer Pooling Project of four prospective cohort studies found differential effects of levels of pre-diagnosis obesity on survival [118]. Compared with normal weight women, significant or borderline significant increased risks of 81% of total and 40% of breast cancer mortality were only observed for morbidly obese (≥40 kg/m2) women and not for women in other obesity categories. We observed statistically significant increased risks also for overweight women, probably because of a larger number of studies. We were unable to investigate the associations with severely and morbidly obese women because only two studies included in this review reported such results [19, 113]. Overall, our findings are consistent with previous meta-analyses in showing elevated total and breast cancer mortality associated with higher BMI and support the current guidelines for breast cancer survivors to stay as lean as possible within the normal range of body weight [4], for overweight women to avoid weight gain during treatment and for obese women to lose weight after treatment [119]. The present review is limited by the challenges and flaws encountered by the individual epidemiological studies evaluating the body fatness–mortality relationship in breast cancer survivors. Most studies did not adjust for co-morbidities and assess intentional weight loss. Women with more serious health issues, and especially smokers, may lose weight but are at an increased risk of mortality, and this might cause an apparent increased risk in underweight women. Body weight information through the natural history of the disease and treatment information were usually not complete or available. Increase of body weight post-diagnosis is common in women with breast cancer, particularly during chemotherapy [16]. Chemotherapy under-dosing is a common problem in obese women and may contribute to their increased mortality [120]. Although several studies with pre-diagnosis BMI adjusted for underlying illnesses or excluded the first few years of follow-up, reverse causation may have affected the results in studies that assessed BMI in women with cancer and other illnesses. However, in these studies, the associations were similar to other studies. Possible survival benefit (subjects with better prognostic factors survive) may be present in the survival cohorts, in which the range of BMI could be narrower, and may cause an underestimation of the association. Follow-up studies with variable characteristics were pooled in the meta-analysis. Women identified in clinical trials may have had specific tumour subtypes, with fewer co-morbidities, and were more likely to receive protocol treatments with high treatment completion rates. Women who were recruited through mammography screening programmes may have had healthier lifestyles or access to medical facilities, and more likely to be diagnosed with in situ or early-stage breast cancer. Cancer detection methods, tumour classifications and treatment regimens change over time, and may vary within (if follow-up is long) and between studies, and could not be simply examined by using the diagnosis or treatment date. We cannot rule out the effect of unmeasured or residual confounding in our analysis. Nevertheless, most results were adjusted for multiple confounding factors, including tumour stage or other-related variables and stratified analyses by several key factors showed similar summary risk estimates. Small study or publication bias was observed in the analyses of BMI <12 months after diagnosis. However, the overall evidence is supported by large, well-designed studies and is unlikely to be changed. We did not conduct analyses by race/ethnicity and treatment types as only limited studies had published results. Future studies of body fatness and breast cancer outcomes should aim to account for co-morbidities, separate intended and unintended changes of body weight, and collect complete treatment information during study follow-up. Randomised clinical trials are needed to test interventions for weight loss and maintenance on survival in women with breast cancer. In conclusion, the present systematic literature review and meta-analysis extends and confirms the associations of obesity with an unfavourable overall and breast cancer survival in pre- and post-menopausal breast cancer, regardless of when BMI is ascertained. Increased risks of mortality in underweight and overweight women were also observed. Given the comparable elevated risks with obesity in the development (for post-menopausal women) and prognosis of breast cancer, and the complications with cancer treatment and other obesity-related co-morbidities, it is prudent to maintain a healthy body weight (BMI 18.5–<25.0 kg/m2) throughout life. funding This work was supported by the World Cancer Research Fund International (grant number: 2007/SP01) (http://www.wcrf-uk.org/). The funder of this study had no role in the decisions about the design and conduct of the study; collection, management, analysis, or interpretation of the data; or the preparation, review, or approval of the manuscript. The views expressed in this review are the opinions of the authors. They may not represent the views of the World Cancer Research Fund International/American Institute for Cancer Research and may differ from those in future updates of the evidence related to food, nutrition, physical activity, and cancer survival. disclosure DCG reports personal fees from World Cancer Research Fund/American Institute for Cancer Research, during the conduct of the study; grants from Danone, and grants from Kelloggs, outside the submitted work. AM reports personal fees from Metagenics/Metaproteomics, personal fees from Pfizer, outside the submitted work. All remaining authors have declared no conflicts of interest. Supplementary Material Supplementary Data
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Obesity and Cancer
Kathleen Wolin, Kenneth Carson, Graham Colditz (2010)
Introduction Because weight, weight gain, and strategies to manage weight are important both for the risk for developing cancer and for survival among cancer patients, we divided this review into two sections relating to the patient population. We then present a set of strategies that can be useful to guide clinical advice to patients for whom weight control is an important adjunct to risk management or to improve quality of life and disease-free survival after diagnosis. Weight, Weight Gain, and Risk for Cancer Although evidence shows that adult overweight and obesity are related to risk for many cancers, the growing epidemic of obesity provides a challenge to clinical practice and the implementation of guidelines for the management of weight. Historical data from the past 25 years point to obesity as a cause of approximately 14% of cancer deaths in men and up to 20% of cancer deaths in women [1]. These may be conservative estimates because the population has gained substantial weight over this time period and the prevalence of overweight and obesity has increased from 15% in 1980 to 35% in 2005 [2] (Fig. 1). Some now estimate that the total health burden of overweight and obesity may exceed that for cigarette smoking [3]. Figure 1. Trends in obesity, U.S. Abbreviation: BMI, body mass index. Source: NCHS 2008. Available at http://www.cdc.gov/nchs/data/hestat/overweight/overweight_adult.htm. A major review of weight, physical activity, and cancer incidence by the International Agency for Research on Cancer (IARC) used obesity prevalence data from Europe and relative risks from a meta-analysis of published studies and concluded, in 2002, that obesity was a cause of 11% of colon cancer cases, 9% of postmenopausal breast cancer cases, 39% of endometrial cancer cases, 25% of kidney cancer cases, and 37% of esophageal cancer cases [4]. In addition, data from the American Cancer Society suggested that overweight and obesity were related to mortality from liver cancer, pancreatic cancer, non-Hodgkin's lymphoma, and myeloma [1]. This effect on mortality reflects both the excess incidence and excess mortality among those with cancer. Since the 2002 IARC report, substantial new evidence has supported a cause-and-effect relation between overweight and obesity and the onset of these cancers, further increasing the burden of cancer resulting from obesity [5]. The American Institute for Cancer Research and World Cancer Research Fund reported that there is convincing evidence for a relation between obesity and esophageal, pancreatic, colorectal, postmenopausal breast, endometrial, and kidney cancers, with probable evidence for gallbladder cancer. In addition, they found probable evidence that abdominal fatness, in particular, increases the risk for pancreatic, endometrial, and postmenopausal breast cancer. Finally, emerging evidence suggests that obesity increases risk for aggressive prostate cancer [6]. Overall, we estimate that overweight and obesity cause approximately 20% of all cancer cases. Previously Doll and Peto [7] included overnutrition (overweight) with diet causing a combined 35% of all cancer cases. In Figure 2, we break out overweight and obesity from diet and provide updated estimates for the causes of cancer. Figure 2. Estimated proportion of cancer in the U.S. that could have been avoided by changes in each category of nongenetic cancer causes. To conclude that a cause-and-effect relation exists between obesity and cancer at each site, one often pursues studies of mechanisms that confirm the underlying biology of this relation and provide insights into prevention strategies. Take, for example, postmenopausal breast cancer. Among postmenopausal women, obesity is directly related to circulating estradiol levels [8], which themselves are directly related to breast cancer risk [9, 10]. When the action of estrogens is interrupted by estrogen receptor modulators in randomized controlled trials, breast cancer incidence is approximately 50% lower [11, 12]. Just as smoking cessation leads to a reduction in the risk for lung cancer, adding to the evidence of a cause-and-effect relation, a documented 50% reduction in the risk for breast cancer among women who lost ≥10 kg after menopause and kept it off [13] adds to our understanding of this causal relation. In addition, focusing on weight loss after menopause, a time in life when obesity clearly increases the risk for breast cancer, provides important evidence on the time frame for change in cause (weight) and subsequent change in cancer incidence. This lower incidence of postmenopausal breast cancer follows the decline in circulating estrogen after weight loss. For colon cancer, growing evidence points to insulin pathways mediating the effect of body mass index BMI and risk [14]. Studies of blood glucose levels and colon cancer show a direct relation between higher glucose and subsequent risk [14]. Providing further biologic rationale, c-peptide [15], a marker of insulin production, also shows this positive relation, and animal models using insulin injection versus saline show a significantly higher incidence of colon cancer among those injected with insulin [16]. Finally, preclinical data provide additional support for the insulin–insulin-like growth factor (IGF) hypothesis of cancer risk, as outlined in several excellently detailed recent reviews [17, 18]. The phosphoinositide 3-kinase/Akt pathway likely compromises the downstream target of insulin, and is one of the pathways most commonly altered in epithelial tumors [18, 19]. In sum, strong evidence points to hyperinsulinemia as the direct pathway from adiposity to colon cancer. For each cancer site, we present summary estimates of relative risk from the rigorous meta-analysis by Renehan et al. [5] and the likely pathway or mechanism for a causal relation between obesity and cancer (Tables 1 and 2). Table 1. RR for cancer per 5 kg/m2 higher BMI and most likely causal mechanism: Males Shown is the RR for a five-point greater BMI—for example, the RR linked to a BMI of 28 compared with a BMI of 23, or a BMI of 32 compared with a BMI of 27. a p 2 kg/m2 after a breast cancer diagnosis had a relative risk for death during 9 years of follow-up that was 1.64 (95% CI, 1.07–2.51), compared with stable weight women (Fig. 4). Insulin pathways have been suggested as one mechanism for this effect [35]. Weight gain following diagnosis may be particularly problematic because research suggests that it is largely an increase in fat mass and not muscle mass [36]. Furthermore, evidence that physical activity after diagnosis reduces the risk for breast cancer recurrence [37] and intervention trials of diet and physical activity that showed longer disease-free survival times among the intervention group, the members of which lost substantially more weight than the control group [38], all point to the importance of energy balance after breast cancer. Figure 4. Sustained weight loss and breast cancer risk in postmenopausal women who never used postmenopausal hormones. Based on Table 3 of Eliassen AH, Colditz G, Rosner B et al. Adult weight change and risk of postmenopausal breast cancer. JAMA 2006;296:193–201. Although obesity is not associated with prostate cancer incidence overall, it is associated with fatal and aggressive disease [6]. There is also emerging evidence that prostate cancer is more difficult to detect in obese men [6]. Obese men have a higher risk for recurrence following prostatectomy [6, 39, 40], but there is no evidence for higher rates of recurrence among obese men undergoing brachytherapy [6, 39, 41]. Obese men also have higher rates of death from prostate cancer [6]. However, some reports suggest that obese patients with metastatic prostate cancer may have better postdiagnosis outcomes, perhaps because obesity acts as a cachexia preventive [42]. Evidence for an effect of obesity on colon cancer survival is mixed. Meyerhardt et al. [43] found no association with disease-free survival time or overall mortality (patients all received weight-based treatment, so this could not be attributed to chemotherapy underdosing). Subsequent reports have indicated that those who are very obese (class 2 obesity) have greater overall mortality and shorter disease-free survival intervals [44–46]. However, the one study to look at the effects of weight change found no association with survival outcomes [44]. In addition, in one study [45], chemotherapy dose was capped for the very obese, which may confound the findings. The implications of worse outcomes for the morbidly obese are particularly worrisome because this is a rapidly increasing segment of the population. In addition to the impact of obesity on disease incidence and progression, concern has been raised regarding the potential for dosing of therapy to be poorly matched to weight in heavier cancer patients. The narrow therapeutic index associated with many chemotherapeutic drugs prompts a rational concern on the part of the medical oncologist that the high doses of chemotherapy required by the very obese will result in excess toxicity. Research in this area has suggested that obese patients are frequently treated at lower chemotherapy dose intensities than the nonobese. Paradoxically, studies have not demonstrated greater chemotherapy-related toxicity in obese patients treated at full dose intensities [47]. This refutes the notion that obese patients should uniformly receive dose-reduced therapy. For obese patients being treated with curative intent, dose reductions should be approached with particular caution. A similar problem with regard to chemotherapy in obese patients surrounds the common use of body surface area (BSA) for dose calculation. Historically, cytotoxic chemotherapeutic agents have been dosed this way based on the assumption that metabolism is proportional to BSA, a theory first described by Rubner in 1883. One of the more commonly used formulae is based on measurements performed on a small number of individuals, reported nearly 100 years ago [48]. Although the BSA approach may be helpful when extrapolating doses between mice and humans during the translational period from preclinical to early-phase clinical studies, some have questioned whether this approach is appropriate in clinical practice [49]. For example, a man who is 180-cm tall with an ideal body weight of 71 kg has a BSA of 1.9, according to the Dubois formula, whereas another man who is 180-cm tall and who weighs twice as much (142 kg) has a BSA of 2.55. The BSA in the latter man is 34% greater than the BSA in the man with ideal body weight, whereas the weight of the latter man is 100% greater. Particularly for drugs with any degree of fat solubility (higher volume of distribution), BSA dosing is likely to result in systematic underdosing, compared with weight-based dosing. With increasing attention being paid to chemotherapy dose intensity as a predictor of treatment response in numerous tumor types, careful reconsideration of the optimal dosing in obese patients may be appropriate for some drugs [47, 50]. Obesity may also interfere with the ability to deliver other forms of treatment. Wong et al. [51] found that there was a shift in the delivery of external-beam radiation in obese patients, resulting in the target location not receiving the full dose. In addition, research has suggested that, among men undergoing prostatectomy, surgical margins may not be as clean in obese men and they may have fewer nerve bundles preserved [52]. Quality of life among cancer patients and those free from cancer is reduced by higher BMI. Limited data suggest that weight loss is associated with improved quality of life. More substantial data point to an increase in physical activity among cancer survivors leading to significant increases in quality of life [53]. Mechanisms Among cancer survivors, the likely pathways from obesity leading to recurrence and death again may vary by tumor site. Insulin receptors are increasingly being studied as a mechanism for obesity to have adverse effects among breast cancer survivors. The insulin receptor is overexpressed and may bind both insulin and IGF-II [54]. Among colon cancer patients, the insulin pathway has again been identified as potentially mediating adverse outcomes, and recent research addressing obesity and STMN1 (stathmin or oncoprotein-18), which destabilizes microtubules and reorganizes cytoskeleton and functions in cell progression and cell migration, indicates that the adverse effect of BMI among colon cancer patients may be limited to those who are STMN1+ [55]. Metformin is associated with a lower cancer incidence among diabetics [56] and with lower cancer mortality among patients with type 2 diabetes [57]. Among mechanisms for such a benefit are the inhibition of cancer cell growth, suppression of ErbB-2 oncoprotein overexpression, and inhibition of mammalian target of rapamycin [58–60]. This rapidly expanding area of laboratory and human evidence points to a role for metformin among diabetic patients, with clinical trials now proposed [61]. Inflammation is also a source of great interest. Adipocytes release inflammatory markers, which are increasingly being found to be associated with a worse postdiagnosis prognosis. In the Healthy Eating, Activity, and Living (HEAL) study, serum amyloid a and c-reactive protein levels were associated with shorter overall survival in breast cancer patients [62]. As with the hypothesized mechanisms linking obesity to cancer risk, ample preclinical data detail the linkages in these pathways [17, 18]. Weight Loss and Intermediate Endpoints Many randomized controlled trials of weight loss and measures of insulin, glucose, and blood pressure show a strong response to an increase in activity and a reduction in adiposity [63]. Lack of energy expenditure in the form of physical activity is a leading determinant of higher body mass. For those who are already overweight, physical activity, in combination with dietary changes, is important for achieving, and particularly for maintaining, weight loss [63]. There is increasing evidence [64] that attaining >9,000 daily steps as an indicator of physical activity is associated with a lower likelihood of being obese, including among lower income and racial/ethnic minority populations. TV viewing is a highly prevalent sedentary activity; Americans watch at least 4 hours of TV each day and TV viewing is the most prevalent activity after work and sleep. TV viewing is positively associated with excess body weight among children and adults [65, 66]. TV viewing may also promote obesity through the promotion of consumption of calorically dense foods through advertising and other media content [67]. The strong and consistent association between TV viewing and obesity suggests the importance of including reduction in TV hours as a target in behavioral weight reduction efforts. Benefits of Weight Loss and Increased Physical Activity Among Cancer Survivors Strong evidence supports the idea that an increase in physical activity leads to improved quality of life among cancer survivors [53]. Among men after prostate surgery, being lean and physically active is associated with superior symptoms of incontinence, compared with obese and inactive men, suggesting a role for weight loss after prostate resection to improve quality of life [68]. The National Heart, Lung and Blood Institute evidence review on weight loss [63] evaluated 86 randomized, controlled trials and concluded that “low calorie diets are recommended for weight loss in overweight and obese persons” (evidence category A) and that “Physical activity is recommended as part of a comprehensive weight loss therapy and weight control program because it modestly contributes to weight loss” (evidence category A) [63]. Specific recommended intake levels vary based on a number of factors, including current weight, activity levels, and weight loss goals. There are many other behavioral factors that influence weight loss and may be more effectively intervened upon over an extended intervention period to achieve sustained weight loss. We outline several of these in Figure 5. Rather than focus on specific calorie calculations, we recommend behavioral targets that should lead to an energy deficit as well as increase and maintain patient motivation. Figure 5. Physical activity, diet, and behavioral goals for sustained weight loss. Counseling for Weight Loss Providers play a central role in guiding their patients to follow strategies to reduce or maintain their weight and thus improve their quality of life and reduce their risk for developing a primary malignancy, or improve survival among those diagnosed with cancer. Given the prevalence of overweight and obesity, weight loss is a sound strategy for reducing the risk for cancer. Here, we briefly outline strategies that a provider can use to counsel patients to maintain weight and achieve a sustained weight loss. For effective weight change interventions, one would like to have on hand strategies to address individual behaviors, social factors that might reinforce individual behavior change, and environmental strategies (such as safe sidewalks and effective transportation systems, etc.) [69] that promote or discourage a given behavior. The Diabetes Prevention Trial demonstrated the efficacy of an intensive health care provider–based intervention to reduce weight and increase physical activity over a 24-month period and thereby prevent diabetes [70]. Translating this efficacy study into effective approaches that work in the broader clinical setting remains to be demonstrated. One approach, derived from the methodology originally developed by the National Cancer Institute to guide physicians in counseling their patients to quit smoking, the “Five As” model guides clinicians through a counseling session, with each “A” corresponding to a brief behavioral intervention—assess, advise, agree, assist, arrange — which together have been shown to be effective. Clinicians may use the Five As approach to deliver behavior change strategy messages (such as those in Fig. 5). Interactive technologies can also address a wide variety of health behavior domains simultaneously. Thus, interactive technology can provide a streamlined, consistent method for conducting many aspects of evidence-based behavior change counseling, including assessing current health behaviors, identifying barriers to change, allowing the patient to set goals and select relevant activities, and arranging follow-up support. Evidence from randomized trials demonstrates that the use of interactive technologies supports lifestyle behavior change [71], although most of this research was conducted using a single-disease and/or single behavior change focus. Practice-based research indicates that clinicians who have limited financial, space, and personnel resources favor tools that address comorbid conditions and health behaviors simultaneously. In addition, tools that can be accessed via patients' homes or from community settings are useful and effective and appeal to clinicians who face barriers to providing self-management care tools [71]. Thus, there is great interest in the ability of interactive and Web-based tools to deliver and reinforce behavior change strategies in a cost-effective manner. Increasingly, Web-based tools for supporting positive lifestyle changes are making their way into the clinical setting. However, it is unclear whether such interventions are effective. An increasing number of studies are beginning to assess their potential to reduce weight, both in the health care setting and beyond. Summary of Provider Role Because the factors that influence energy balance range from the individual to the environment, efforts to shift the population distribution of physical activity upward and diet toward healthier choices will require emphasis on all the intervention points: health care settings, communities, and environment. The challenge for providers is to engage the patient in understanding the importance of weight control and increased physical activity to begin on the path to behavior change. Safety Issues Although the benefits of weight maintenance and greater physical activity are well established for many chronic diseases, the risks must also be considered. These include injuries, alteration in glucose control for diabetics, and sustained increases in joint pain. In general, these risks are outweighed by the vast benefits of weight loss and increased physical activity and can generally be reduced with simple steps. To date, drug-based strategies to aid with weight loss have had side effects that reduce adherence. Conclusion Obesity causes a substantial proportion of all cancers, and emerging evidence suggests that adult weight loss reduces cancer risk. Increasing physical activity and avoiding weight gain after cancer, as in adult life, have substantial benefits.
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Inflammation and increased aromatase expression occur in the breast tissue of obese women with breast cancer.
Patrick Morris, Clifford Hudis, Dilip Giri … (2011)
Obesity is a risk factor for the development of hormone receptor-positive breast cancer in postmenopausal women and has been associated with an increased risk of recurrence and reduced survival. In humans, obesity causes subclinical inflammation in visceral and subcutaneous adipose tissue, characterized by necrotic adipocytes surrounded by macrophages forming crown-like structures (CLS). Recently, we found increased numbers of CLS, activation of the NF-κB transcription factor, and elevated aromatase levels and activity in the mammary glands of obese mice. These preclinical findings raised the possibility that the obesity → inflammation axis is important for the development and progression of breast cancer. Here, our main objective was to determine if the findings in mouse models of obesity translated to women. Breast tissue was obtained from 30 women who underwent breast surgery. CLS of the breast (CLS-B) was found in nearly 50% (14 of 30) of patient samples. The severity of breast inflammation, defined as the CLS-B index, correlated with both body mass index (P < 0.001) and adipocyte size (P = 0.01). Increased NF-κB binding activity and elevated aromatase expression and activity were found in the inflamed breast tissue of overweight and obese women. Collectively, our results suggest that the obesity → inflammation → aromatase axis is present in the breast tissue of most overweight and obese women. The presence of CLS-B may be a biomarker of increased breast cancer risk or poor prognosis.
Author and article information
Contributors
Maeve Mullooly: 240-276-6287
,
maeve.mullooly@nih.gov
Hannah P. Yang: yanghan@mail.nih.gov
Roni T. Falk: falkr@exchange.nih.gov
Sarah J. Nyante: sarah_nyante@med.unc.edu
Renata Cora: renata.cora@nih.gov
Ruth M. Pfeiffer: pfeiffer@mail.nih.gov
Derek C. Radisky: Radisky.Derek@mayo.edu
Daniel W. Visscher: Visscher.Daniel@mayo.edu
Lynn C. Hartmann: hartmann.lynn@mayo.edu
Jodi M. Carter: Carter.Jodi@mayo.edu
Amy C. Degnim: Degnim.Amy@mayo.edu
Frank Z. Stanczyk: stanczyk@usc.edu
Jonine D. Figueroa: Jonine.figueroa@ed.ac.uk
Montserrat Garcia-Closas: montserrat.garcia-closas@nih.gov
Jolanta Lissowska: lissowsj@coi.waw.pl
Melissa A. Troester: troester@unc.edu
Stephen M. Hewitt: hewitts@mail.nih.gov
Louise A. Brinton: brintonl@exchange.nih.gov
Mark E. Sherman: shermanm@exchange.nih.gov
Gretchen L. Gierach: gierachg@mail.nih.gov
Journal
Journal ID (nlm-ta): Breast Cancer Res
Journal ID (iso-abbrev): Breast Cancer Res
Title:
Breast Cancer Research : BCR
Publisher:
BioMed Central
(London
)
ISSN
(Print):
1465-5411
ISSN
(Electronic):
1465-542X
Publication date
(Electronic):
19
January
2017
Publication date PMC-release: 19
January
2017
Publication date
(Print):
2017
Volume: 19
Electronic Location Identifier: 8
Affiliations
[1
]ISNI 0000 0004 1936 8075, GRID grid.48336.3a, , Division of Cancer Epidemiology and Genetics, National Cancer Institute, ; 9609 Medical Center Drive, Bethesda, MD 20892 USA
[2
]ISNI 0000 0004 1936 8075, GRID grid.48336.3a, , Cancer Prevention Fellowship Program, Division of Cancer Prevention, National Cancer
Institute, ; Bethesda, MD USA
[3
]ISNI 0000000122483208, GRID grid.10698.36, Department of Radiology, , School of Medicine, University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
[7
]ISNI 0000 0001 2156 6853, GRID grid.42505.36, Department of Obstetrics and Gynecology, , Keck School of Medicine, University of Southern California, ; Los Angeles, CA USA
[8
]ISNI 0000 0004 1936 7988, GRID grid.4305.2, Usher Institute of Population Health Sciences and Informatics, , The University of Edinburgh, Medical School, ; Teviot Place, Edinburgh, UK
[9
]Department of Cancer Epidemiology and Prevention, Cancer Center and M. Sklodowska-Curie
Institute of Oncology, Warsaw, Poland
[10
]ISNI 0000000122483208, GRID grid.10698.36, Department of Epidemiology and Lineberger Comprehensive Cancer Center, , University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
Article
Publisher ID: 791DOI: 10.1186/s13058-016-0791-4
PMC ID: 5244534
PubMed ID: 28103902
SO-VID: 81842e47-c345-44a0-9ae6-cb0293d97f0c
Copyright © © The Author(s). 2017
License:
Open AccessThis 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.
History
Date
received
: 7
September
2016
Date
accepted
: 5
December
2016
Funding
Funded by: Intramural Research Program of the National Cancer Institute
Categories
Subject:
Research Article
Custom metadata
issue-copyright-statement © The Author(s) 2017
ScienceOpen disciplines: Oncology & Radiotherapy