- Email: [email protected]
Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women
G Model
RETRAM-4; No. of Pages 6 Current Research in Translational Medicine xxx (2016) xxx–xxx
Available online at
ScienceDirect www.sciencedirect.com
Original article
Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women A. Crispo a,*, M. Montella a, G. Buono b, M. Grimaldi a, M. D’Aiuto c, I. Capasso c, E. Esposito c, A. Amore d, F. Nocerino a, L.S.A. Augustin a,e, A. Giudice a, M. Di Bonito f, M. Giuliano b, V. Forestieri b, M. De Laurentiis c, M. Rinaldo c, G. Ciliberto g, S. De Placido b, G. Arpino b a
Unit of epidemiology, National Cancer Institute, G.-Pascale Foundation, Via Mariano Semmola 1, 80131 Naples, Italy Department of Clinical Medicine and Surgery, University of Naples Federico II, Via Sergio Pansini 5, 80131 Naples, Italy c Department of Breast Surgery, National Cancer Institute G. Pascale Foundation, Via Mariano Semmola 1, 80131 Naples, Italy d Department of Surgery, National Cancer Institute G. Pascale Foundation, Via Mariano Semmola 1, 80131 Naples, Italy e Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, 61 Queen St. East, Toronto, Canada f Division of Pathology, National Cancer Institute, G. Pascale Foundation, Via Mariano Semmola 1, 80131 Naples, Italy g Scientific Direction, National Cancer Institute, G. Pascale Foundation, Cappella dei Cangiani 1, 80131 Naples, Italy b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 15 September 2015 Accepted 7 January 2016 Available online xxx
Breast cancer (BC) is the most common malignant tumor in women, obesity is associated with increased BC incidence and mortality and high levels of circulating insulin may negatively impact on cancer incidence. In the present study, we investigated whether the strength of several anthropometric and metabolic parameters varies between BC molecular subtypes. Eligible cases were 991 non-metastatic BC patients recruited between January 2009 and December 2013. Anthropometric, clinical and immunohistochemical features were measured. Multivariate logistic regression models were built to assess HER2 positive BC risk, comparing (a) triple positive (TP) with luminal A, luminal B and triple negative (TN) and (b) HER2-enriched group with luminal A, luminal B and TN. We stratified patients in pre- and post-menopause: significant differences emerged for luminal A in relation to age: they were more likely to be older compared to other groups. Among postmenopausal patients, the adjusted multivariate analysis showed that high BMI and high waist circumference were inversely correlated to TP subtype when compared to luminal B (OR = 0.48 and OR = 0.49, respectively). Conversely, HOMA-IR was a risk factor for TP when compared to luminal A and TN (OR = 2.47 and OR = 3.15, respectively). Our findings suggest a potential role of higher abdominal fat in the development of specific BC molecular subtypes in postmenopausal women. Moreover, they support a potential role of insulin resistance in the development of HER2 positive BC, although this role appears to be stronger when hormone receptors are co-expressed, suggesting a difference in the etiology of these two BC subtypes. ß 2016 Elsevier Masson SAS. All rights reserved.
Keywords: BMI Insulin resistance HOMA-I Breast cancer Molecular subtypes
1. Introduction Breast cancer (BC) is the most common malignant tumor in women, and it is second only to lung cancer as the most frequent cause of cancer related death in Europe [1]. Obesity is associated with increased BC incidence and mortality [2–4], with more advanced stage at the time of diagnosis, and with poorer prognosis [5]. It has been suggested that obesity may promote carcinogenesis both directly and indirectly [6]. The aromatase enzyme synthesizes estrogens
* Corresponding author. E-mail address: [email protected] (A. Crispo).
from circulating androgens in adipose tissue, hence directly stimulating cell proliferation in breast tissue while the suggested indirect effect is linked to chronic compensatory hyperinsulinemia as a consequence of visceral obesity. High levels of circulating insulin may result in aberrant mitogenic and anti-apoptotic effects [7–9] and may negatively impact on cancer incidence [8–11]. Glucose metabolism also seems to play an important role in breast carcinogenesis. Elevated baseline glucose levels have been associated with an increased risk of BC in several studies [12– 14]. Importantly, two distinct meta-analyses of case-control and prospective cohort studies on diabetes and cancer have shown a 1.2-fold increased risk of BC in postmenopausal diabetic patients [15,16].
http://dx.doi.org/10.1016/j.retram.2016.01.004 2452-3186/ß 2016 Elsevier Masson SAS. All rights reserved.
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
G Model
RETRAM-4; No. of Pages 6 2
A. Crispo et al. / Current Research in Translational Medicine xxx (2016) xxx–xxx
Recently, BC has been categorized into molecularly-defined subtypes with different clinical and biological characteristics [17]. The prognosis seems to differ according to subtype [18–20], and it has been suggested that the underlying etiology may also differ [21,22]. To date, a limited number of studies have evaluated the potential role of body weight, anthropometric characteristics and glucose metabolism on the risk of developing a specific molecular subtype of BC. In the present study, we investigated whether the strength of several anthropometric and metabolic parameters varies between BC subtypes. 2. Materials and methods 2.1. Study population and laboratory assays Eligible cases were 991 non-metastatic BC patients who were consecutively treated with mastectomy or breast-conserving surgery at the National Cancer Institute ‘‘G. Pascale Foundation’’ and Federico II University of Naples, (Southern Italy), between January 2009 and December 2013. For each patient, anthropometric features including weight in kilograms, height in meters, waist and hip circumference (WC and HC) in centimeters were measured, and venous blood was collected on study entry. Body mass index (BMI) (kg/m2) was calculated from weight and height and evaluated according to the World Health Organization classification ( 25 kg/m2 = underweight/normal; > 25 kg/m2 = overweight/obese). The circumferences of waist (measured 2 cm above the umbilicus) and hip (measured at the maximal protrusion) was measured at the time of interview. Waist to hip ratio (WHR) was calculated as the ratio between these measures. Fasting plasma glucose and insulin levels were assessed from blood samples according to the NCEP ATP III criteria [23]. Diabetes was considered an exclusion criterium and was determined from laboratory data when fasting plasma glucose was 126 mg/dl according to the American Diabetes Association guidelines [24]. We used HOMA-IR as a measure of insulin resistance computed according to the formula: [fasting serum insulin (mU/mL) fasting plasma glucose (mmol/L)/22.5] [25]. Patients were divided according to HOMA index into three categories: < 2.5; 2.5–5.4; and 5.5 [26]. The study was approved by Federico II Institutional Review Board (IRB approval # 743/15). Since data were extracted from a pre-existing computerized database, the IRB waved the need for patient informed consent for this study. Patient records and information were anonymized and de-identified prior to analysis. 2.2. Immunochemistry Data on tumor size (T), lymph node invasion and tumor grade (G) were collected for all patients. At each participating centers an experienced pathologist using light microscopy evaluated antigen expression. For each sample, at least five fields (inside the tumor and in the area exhibiting invasion) and > 500 cells were analyzed. Using a semi-quantitative scoring system, the intensity, extent and subcellular distribution of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2, also known as c-erb B2) and Ki-67, were evaluated. The cutoff used to distinguish ‘‘positive’’ from ‘‘negative’’ cases was 1% ER/PR positive tumor cells. Immunohistochemical analyses of HER2 expression describe the intensity and staining pattern of tumor cells evaluated using scores from 0 to 3+ in agreement with the Herceptest kit scoring guidelines. The Food and Drug Administration (FDA)-approved test, the HerceptestTM (DAKO), distinguishes between four categories: no staining or weak staining in fewer than 10% of the tumor cells (0); weak staining in part of the membrane in more than 10% of the tumor cells (1+); complete staining of the membrane with weak or moderate intensity in more than 10% of the neoplastic cells (2+); and strong staining in more than 10% (3+). Scores of 0 or 1+ were considered negative for HER2 expression, 2+ was uncertain, and 3+ was positive. Scores of 2+ undergo FISH analysis for HER2 gene amplification. The proliferative index Ki-67 was defined as the percentage of immune-reactive tumor cells out of the total number of cells. The percentage of positive cells per case was scored into 2 groups: ‘‘low’’: < 20% (low proliferative activity) and ‘‘high’’: 20% (high proliferative activity). 2.3. Molecular subtype classification BCs molecular subtypes were identified and categorized based on the 13th St Gallen International Breast Cancer Conference (2013) Expert Panel [18]. Briefly, cases were distinguished into:
luminal A: ER and PR positive, HER2 negative, Ki-67 ‘‘low’’ i.e., < 20%; luminal B-like (HER2 negative): ER positive, HER2 negative and with Ki-67 ‘high’ (i.e. 20%), and/or PR ‘negative or low’ (i.e., 20%); triple positive (TP): ER positive, HER2 over-expressed or amplified (HER2 positive), any Ki-67, any PR; HER2-enriched: HER2 over-expressed or amplified, ER and PR absent; triple negative (TN): ER and PR absent, HER2 negative.
2.4. Statistical analyses Mean and standard deviations were used for age (continuous data) while frequencies and percentage values were used for categorical data. Age differences were evaluated using the Student-t or One Way ANOVA test (post-hoc tests) according to the number (2 or more) of groups compared. We used Pearson’s Chi2 test of independence (2-tailed) to assess the relationship between BMI, WHR, WC and HOMA-IR and molecular subtypes either in pre- or post-menopause. Multivariate logistic regression models were then built to assess HER2 positive BC risk, comparing: TP with luminal A, luminal B and TN (Table 3); HER2-enriched group with luminal A, luminal B and TN (Table 4), by exclusively including those factors, which tested significant in the univariate analysis (data not shown). We considered P values less or equal to 0.05 as statistically significant. All statistical analyses were performed with the SPSS statistical software version 21 (SPSS Inc., Chicago IL, USA).
3. Results Clinical and tumor characteristics, according to BC subtypes, are reported in Table 1. In the overall study cohort, 43.2% of patients had luminal A, 28.3% luminal B-like, 15.9% HER2 positive, 11.5% TP, and 4.4% HER2-entriched tumors. Triple negative phenotype accounted for the 12.6% of the total. Tumor characteristics (tumor size, lymph node status, stage and grading) and menopausal status significantly differed according to BC subtypes (P = 0.001, P = 0.05, respectively). We also stratified patients in pre- and post-menopause (Table 2A and Table 2B, respectively). Among premenopausal patients (Table 2A) mean age was statistically significant (P = 0.01), in particular luminal A were less young compared to luminal B and TN (44.6 years, 42.3 years and 40.5 years, respectively). There was no statistically significant difference for BMI, WHR, WC and HOMA-IR, according to molecular subtypes in premenopausal women. Among postmenopausal patients age was significantly different among BC subtypes, particularly the youngest age was found among women with the HER2-enriched subtype (56.8 years) compared to all others (P = 0.002) (Table 2B). BMI showed statistically significant differences in distribution: TP, HER2-enriched and TN were more likely to be normal weight (BMI 25) compared to luminal A (43.1%, 46.7% and 44.1% vs. 33%, respectively P = 0.03). Also the WHR distribution was significantly different: TP and HER2-enriched subtypes were more likely to have WHR 80 cm compared to luminal A (42.3%, 58.6% vs. 29.5%, respectively P = 0.01). In relation to insulin resistance in postmenopausal women we found no statistically significant difference in distribution in HOMA-IR levels between molecular subtypes, although numerically, patients with the highest HOMA-IR (HOMA 5.5) were most likely TP (27.5%). The multivariate analysis was performed only in postmenopausal patients and included the following variables: BMI, WC and HOMA-IR (Table 3). High BMI and high WC were inversely associated with the TP subtype when compared to luminal B (OR = 0.48, 95% CI: 0.25–0.94; P = 0.03 and OR = 0.49, 95% CI: 0.25– 0.98; P = 0.05, respectively). Conversely, HOMA-IR was a risk factor for TP when compared to luminal A and TN (OR = 2.47, 95% CI: 1.04–5.89; P = 0.04 and OR = 3.15, 95% CI: 1.03–9.63, P = 0.04, respectively).
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
G Model
RETRAM-4; No. of Pages 6 A. Crispo et al. / Current Research in Translational Medicine xxx (2016) xxx–xxx
3
Table 1 Tumor characteristics, anthropometrics and HOMA-IR according to breast cancer molecular subtypesa. HER2+
HER2–
Total patients in group n = 991 Age (years) at diagnosis (mean SD) Age (years) < 40 40–45 46–55 56–65 66–75 > 75 Tumor pathology Tumor size T1 T2 T3–T4 Lymph node status Negative Positive Pathological stage Stage 0 or I Stage II, III Grading I II III Menopausal status PrePostBMI (Kg/m2) 25 > 25 WHR 80 > 80 WC (cm) 88 > 88 HOMA_IR < 2.5 2.5–5.4 5.5
Triple negative n (%)
Luminal A n (%)
Luminal B n (%)
Triple positive n (%)
Her2-enriched n (%)
428 (43.2)
280 (28.3)
114 (11.5)
44 (4.4)
125 (12.6)
56.6 12.8
54.9 13
53.3 12.9
52.3 10.2
54.7 12.8
26 (6.1) 64 (15.0) 133 (31.1) 93 (21.7) 82 (19.2) 30 (7.0)
36 40 69 73 45 17
15 (13.2) 22 (19.3) 26 (22.8) 31 (27.2) 13 (11.4) 7 (6.1)
2 (4.5) 9 (20.5) 15 (34.1) 13 (29.5) 5 (11.4) –
18 (14.4) 12 (9.6) 26 (20.8) 44 (35.2) 19 (15.2) 6 (4.8)
(12.9) (14.3) (24.6) (26.1) (16.1) (6.1)
P*
0.08 .004
<.0001 285 (68.3) 111 (26.6) 21 (5.0)
144 (52.0) 121 (43.7) 12 (4.3)
56 (50.5) 48 (43.2) 7 (6.3)
15 (37.5) 21 (52.5) 4 (10.0)
61 (49.2) 56 (45.2) 7 (5.6)
255 (61.0) 163 (39.0)
127 (46.9) 144 (53.1)
51 (45.9) 60 (54.1)
20 (46.5) 23 (53.5)
73 (59.8) 49 (40.2)
203 (48.6) 215 (51.4)
79 (29.2) 192 (70.8)
35 (31.5) 76 (68.5)
10 (23.3) 33 (76.7)
40 (32.8) 82 (67.2)
52 (12.4) 269 (63.9) 100 (23.8)
3 (1.1) 82 (29.9) 189 (69.0)
2 (1.8) 32 (28.8) 77 (69.4)
1 (2.4) 12 (28.6) 29 (69.0)
1 (0.8) 17 (13.7) 106 (85.5)
157 (36.7) 271 (63.3)
113 (40.4) 167 (59.6)
56 (49.1) 58 (50.9)
14 (31.8) 30 (68.2)
42 (33.6) 83 (66.4)
185 (45.2) 225 (54.8)
113 (40.9) 163 (59.1)
59 (52.2) 54 (47.8)
23 (52.3) 21 (47.7)
57 (49.6) 58 (50.4)
173 (41.8) 241 (58.2)
107 (40.1) 160 (59.9)
46 (43.8) 59 (56.2)
27 (62.8) 16 (37.2)
47 (43.9) 60 (56.1)
193 (46.5) 222 (53.5)
106 (39.7) 161 (60.3)
56 (53.3) 49 (46.7)
20 (46.5) 23 (53.5)
53 (49.5) 54 (50.5)
154 (53.5) 105 (36.5) 29 (10.1)
121 (60.8) 56 (28.1) 22 (11.1)
48 (58.5) 20 (24.4) 14 (17.1)
16 (50) 12 (37.5) 4 (12.5)
51 (56.7) 32 (35.6) 7 (7.8)
.001
<.0001
<.0001
.05
.2
.08
.1
.3
a Molecular subtypes were defined as luminal A (hormone receptor positive (ER+, PR 20), low Ki-67 (< 20), HER2–); luminal B-like (hormone receptor positive (ER+, PR < 20), high Ki-67 (> 20), HER2–); human epidermal growth factor receptor 2 positive (triple positive: HER2+ hormone receptor positive; Her2-enriched: HER2+ hormone receptor negative/absent); triple negative (HER2–, hormone receptor negative/absent). * Chi2 test was used to compare the molecular subtypes and characteristics of the patients.
No significant associations were observed for HER2-enriched compared to all other subtypes in relation to BMI, WC and HOMAIR (Table 4). 4. Discussion Knowledge on clinical outcome of different molecular BC subtypes is progressively improving. However, little is known on the role of metabolism and anthropometric parameters on the activation of different pathways leading to these distinct BC subtypes. The present study confirms that age at diagnosis and clinical tumor characteristics differ according to specific BC molecular subtypes. Overall, women with HER2 positive BC were younger and had more advanced disease at diagnoses. Patients with luminal A were the eldest and were more likely to have a low stage, low grade BC at diagnosis. Among postmenopausal women, patients with HER2-enriched tumors were the youngest and patients with luminal A or B who tended to be older, not surprisingly, were more likely to have a higher BMI and higher WHR. Interestingly, TN postmenopausal patients, although not presenting with a large BMI at diagnoses, were more likely to have
a high WHR, confirming the finding of a peculiar body fat deposition in this subgroup. Women with TP subtype were less likely to have a high BMI compared to patients with luminal B, however they were more likely to have a higher HOMA-IR index when compared to patients with luminal A and TN subtypes. The positive relationship between BMI and luminal tumors among postmenopausal women is well characterized in the literature [27–35]. Indeed, in agreement with prior studies assessing the association between BMI and BC risk using ER/PR status [27–34], we also observed a positive association between BMI > 25 kg/m2 and risk of luminal BC among postmenopausal women. This association could be largely attributed to the positive relationship between BMI and endogenous estrogen levels since adipose tissue is the primary source of estrogen in postmenopausal women [36]. WHR is a well-known measure of central obesity in women. To our knowledge, at least one large study, the Carolina Breast Cancer (CBC) study [21], has investigated the relationship between anthropometric characteristics including WHR and risk of different BC molecular subtypes. In the CBC study, postmenopausal patients with a high WHR were more likely to develop TN rather than other BC subtypes [21]. Consistent with this finding, in our
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
G Model
RETRAM-4; No. of Pages 6 A. Crispo et al. / Current Research in Translational Medicine xxx (2016) xxx–xxx
4
Table 2A Anthropometric characteristics and HOMA-IR in premenopausal women according to breast cancer molecular subtypesa. HER2+
HER2–
Total patients in group n = 382 Age (years) at diagnosis (mean SD) BMI (kg/m2) 25 > 25 WHR 80 > 80 WC (cm) 88 > 88 HOMA_IR < 2.5 2.5–5.4 5.5
Triple negative
P*
Luminal A
Luminal B
Triple positive
Her2-enriched
157 (41.1)
113 (29.6)
56 (14.7)
14 (3.7)
42 (11.0)
44.6a 4.9
42.3b 6.4
42.7ba 6.3
42.6ba 7.8
40.5b 8.0
0.01
99 (66.9) 49 (33.1)
68 (60.7) 44 (39.3)
34 (61.8) 21 (38.2)
9 (64.3) 5 (35.7)
23 (60.5) 15 (39.5)
0.8
56 (37.6) 93 (62.4)
29 (26.9) 79 (73.1)
14 (26.4) 39 (73.6)
3 (21.4) 11 (78.6)
15 (41.7) 21 (58.3)
0.1
107 (71.3) 43 (28.7)
60 (55.6) 48 (44.4)
33 (62.3) 20 (37.7)
8 (57.1) 6 (42.9)
26 (72.2) 10 (27.8)
75 (68.2) 29 (26.4) 6 (5.5)
59 (77.6) 16 (21.1) 1 (1.3)
30 (71.4) 9 (21.4) 3 (7.1)
5 (62.5) 3 (37.5) 0
23 (67.6) 10 (29.4) 1 (2.9)
0.08
0.7
BMI: body mass index; WHR: waist to hip ratio; WC: waist circumference; HP: hip circumference. a Molecular subtypes were defined as luminal A (hormone receptor positive (ER+, PR 20), low Ki-67 (< 20), HER2–); luminal B-like (hormone receptor positive (ER+, PR < 20), high Ki-67 (> 20), HER2–); human epidermal growth factor receptor 2 positive (triple positive: HER2+, hormone receptor positive; Her2-enriched: HER2+ hormone receptor negative/absent); triple negative (HER2–, hormone receptor negative/absent). * Chi2 test was used to compare molecular subtypes and characteristics of the patients for categorical variables and ANOVA post-hoc for continuous variables: values with different letters are significantly different from each other (P 0.05). Table 2B Anthropometric characteristics and HOMA-IR in postmenopausal women according to breast cancer molecular subtypesa. HER2–
Total patients in group n = 609 Age (years) at diagnosis (mean SD) BMI (kg/m2) 25 > 25 WHR 80 > 80 WC (cm) 88 > 88 HOMA_IR < 2.5 2.5–5.4 5.5
HER2+
Triple negative n (%)
Luminal A n (%)
Luminal B n (%)
Triple positive n (%)
Her2-enriched n (%)
271 (44.5)
167 (27.4)
58 (9.5)
30 (4.9)
a
a
a
b
P*
83 (13.6)
63.4 9.2
63.5 8.7
63.6 8.8
56.8 7.8
61.9a 7.8
86 (33.0) 175 (67.0)
45 (27.4) 119 (72.6)
25 (43.1) 33 (56.9)
14 (46.7) 16 (53.3)
34 (44.2) 43 (55.8)
78 (29.5) 186 (70.5)
50 (31.4) 109 (68.6)
22 (42.3) 30 (57.7)
17 (58.6) 12 (41.4)
21 (29.6) 50 (70.4)
86 (32.5) 179 (67.5)
46 (28.9) 113 (71.1)
23 (44.2) 29 (55.8)
12 (41.4) 17 (58.6)
27 (38.0) 44 (62.0)
79 (44.4) 76 (42.7) 23 (12.9)
62 (50.4) 40 (32.5) 21 (17.1)
18 (45.0) 11 (27.5) 11 (27.5)
11 (45.8) 9 (37.5) 4 (16.7)
28 (50.0) 22 (39.3) 6 (10.7)
0.002 0.03
0.01
0.2
0.3
BMI: body mass index; WHR: waist to hip ratio; WC: waist circumference; HP: hip circumference. a Molecular subtypes were defined as luminal A (hormone receptor positive (ER+, PR 20), low Ki-67 (< 20), HER2–); luminal B-like (hormone receptor positive (ER+, PR < 20), high Ki-67 (> 20), HER2–); human epidermal growth factor receptor 2 positive (triple positive: HER2+, hormone receptor positive; Her2-enriched: HER2+ hormone receptor negative/absent); triple negative (HER2–, hormone receptor negative/absent). * Chi2 test was used to compare molecular subtypes and characteristics of the patients for categorical variables and ANOVA post-hoc for continuous variables: values with different letters are significantly different from each other (P 0.05). Table 3 Multivariate logistic regression models testing variables associated with HER2 positivity in triple positive breast cancer among postmenopausal women. Factors
Multivariate analysis Triple positive(1) vs. luminal A a
OR (95% CI) BMI (kg/m2) 25 > 25 WC (cm) 88 > 88 HOMA_IR 5.5 > 5.5
Triple positive(2) vs. luminal B P
a
OR (95% CI)
0.1 1b 0.60 (0.32–1.10)
P
ORa (95% CI)
0.03 1b 0.48 (0.25–0.94)
0.08 1b 0.56 (0.29–1.07)
0.8
0.05
0.4 1b 0.70 (0.32–1.53)
0.1 1b 1.93 (0.80–4.65)
P
1b 0.92 (0.44–1.92)
1b 0.49 (0.25–0.98) 0.04
1b 2.47 (1.04–5.89)
Triple positive(3) vs. TN
0.04 1b 3.15 (1.03–9.63)
In the multivariate models the triple positive were compared with: luminal A(1); luminal B-like (HER2 negative)(2); triple negative (TN)(3). a Adjusted for those factors which were statistically significant in the univariate analysis. b Reference category.
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
G Model
RETRAM-4; No. of Pages 6 A. Crispo et al. / Current Research in Translational Medicine xxx (2016) xxx–xxx
5
Table 4 Multivariate logistic regression models testing variables associated with HER2 positivity in Her2-enriched breast cancer among postmenopausal women. Factors
Multivariate analysis HER2-enriched
(1)
a
OR (95% CI) BMI (kg/m2) 25 > 25 WC (cm) 88 > 88 HOMA_IR 5.5 >5.5
vs. luminal A
HER2-enriched P
(2)
OR (95% CI)
0.3 1b 0.65 (0.28–1.47)
P
ORa (95% CI)
0.2 1b 0.57 (0.24–1.37)
0.8 1b 0.92 (0.38–2.18)
P 0.8
1b 1.12 (0.43–2.91) 0.7
1b 0.83 (0.34–2.07) 0.3
1b 1.18 (0.83–6.26)
HER2-enriched(3) vs. TN
vs. luminal B
a
0.8 1b 1.14 (0.42–3.08)
0.8 1b 1.08 (0.30–3.89)
0.2 1b 2.50 (0.55–11.3)
In the multivariate models the triple positive were compared with: luminal A(1); luminal B-like (HER2 negative)(2); triple negative (TN)(3). a Adjusted for those factors which were statistically significant in the univariate analysis. b Reference category.
study, more than 70% of TN postmenopausal women had a WHR higher than 80, suggesting a potential role of higher abdominal fat in the development of specific BC molecular subtypes in postmenopausal women. Insulin, insulin-like growth factor-1 (IGF-1), hyperglycemia and type 2 diabetes are risk factors for breast cancer [9,37–39] and anti-hyperglycemic agents have been found protective in reducing BC recurrence [40]. Recent studies provide novel evidence for the predictive role of pretreatment elevated fasting blood glucose levels in the progression of HER2 positive BC [41]. An interplay between factors related to glucose metabolism and molecular targets such as HER2 status had been previously hypothesized [42] as well between the IGF-1 receptor (IGF-1R) and the insulin receptor substrate-1 (IRS-1) signaling in breast carcinogenesis [43]. Furthermore, suggestions of a tight interplay between IGF-IR and the epidermal growth factor receptor (EGFR) emerges from a number of studies. IGF-1R activation is crucial for the mitogenic and transforming activity of EGFR [44,45] and increased signaling from IGF-1R has been consistently described among the molecular mechanisms driving resistance to the humanized anti-HER2 antibody trastuzumab [46–48]. We have previously shown that insulin resistance may be associated to the development of more aggressive BC (e.g. HER2 positive) [34]. Given the bulk of existing evidence, in the present study, we suggest that a metabolic environment favoring higher insulin levels would be associated to greater risk of HER2 positive BC. In our patient population, especially in postmenopausal women, patients with TP tumor were more likely to have a higher HOMA score compared to patients with luminal A and TN BC. Another anthropometric parameter strongly associated to TP subtype was BMI. However, TP subtype was associated to lower BMI, which is difficult to explain in light of their higher HOMA-IR index. No evidence of such associations was found for HER2-enriched molecular subtype. HER2-enriched and TP subtypes are considered different entities with regard to prognoses and sensitivity to therapy. Our findings therefore may suggest a potential role for insulin resistance in the development of HER2 positive BC when hormone receptors are coexpressed, suggesting a difference in the etiology of these BC subtypes. Our study has some limitations. Given the retrospective design of the study we were unable to retrieve data on potentially relevant risk factors such as smoking status, medication use, family history or lifestyle habits. Importantly, we could not explore the role of several other potentially relevant markers of altered glucose metabolism such as IGF-1, IGF binding proteins and EGFR. However, our study also has several important strengths. First, for each of our patients, the standard assay of pretreatment fasting glucose and insulin levels were run by the central laboratory of the
pertinent participating institutions. Second, tumor histological characterization was available for all the study participants and data produced were highly reliable given the stringent quality control ongoing at the involved Institutions. Furthermore, an experienced medical assistant in close collaboration carried out data retrieval from the medical records with the medical oncologists who had prospectively followed the patients included in our analyses. Third, the large number of patients included in the present analyses strengthens the consistency of our results. 5. Conclusions In conclusion, the present study provides novel evidence of the predictive role of BMI and insulin resistance on the risk of developing specific subtypes of BC. These findings, if additional prospective and experimental studies corroborate our data, may have important clinical implications, especially in BC prevention. Ethic approval The study was approved by Federico II Institutional Review Board (IRB approval # 743/15). Since data were extracted from a pre-existing computerized database, the IRB waved the need for patient informed consent for this study. Disclosure of interest The authors declare that they have no competing interest. References [1] Ferlay J, Autier P, Boniol M, Heanue M, Colombet M, Boyle P. Estimates of the cancer incidence and mortality in Europe in 2006. Ann Oncol 2007;18(3): 581–92. [2] Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 2008;371(9612):569–78. [3] Basen-Engquist K, Chang M. Obesity and cancer risk: recent review and evidence. Curr Oncol Rep 2011;13(1):71–6. [4] Haakinson DJ, Leeds SG, Dueck AC, Gray RJ, Wasif N, Stucky CC, et al. The impact of obesity on breast cancer: a retrospective review. Ann Surg Oncol 2012;19(9):3012–8. [5] Crispo A, Barba M, D’Aiuto G, De Laurentiis M, Grimaldi M, Rinaldo M, et al. Molecular profiles of screen detected vs. symptomatic breast cancer and their impact on survival: results from a clinical series. BMC Cancer 2013;13:15. [6] Khandekar MJ, Cohen P, Spiegelman BM. Molecular mechanisms of cancer development in obesity. Nat Rev Cancer 2011;11(12):886–95. [7] Tran TT, Medline A, Bruce WR. Insulin promotion of colon tumors in rats. Cancer Epidemiol Biomarkers Prev 1996;5(12):1013–5. [8] Giovannucci E. Insulin and colon cancer. Cancer Causes Control 1995;6(2): 164–79. [9] Kaaks R, Lukanova A. Energy balance and cancer: the role of insulin and insulin-like growth factor-I. Proc Nutr Soc 2001;60(1):91–106.
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
G Model
RETRAM-4; No. of Pages 6 6
A. Crispo et al. / Current Research in Translational Medicine xxx (2016) xxx–xxx
[10] Vigneri P, Frasca F, Sciacca L, Pandini G, Vigneri R. Diabetes and cancer. Endocr Relat Cancer 2009;16(4):1103–23. [11] Capasso I, Esposito E, Pentimalli F, Montella M, Crispo A, Maurea N, et al. Homeostasis model assessment to detect insulin resistance and identify patients at high risk of breast cancer development: National Cancer Institute of Naples experience. J Exp Clin Cancer Res 2013;32:14. [12] Jee SH, Ohrr H, Sull JW, Yun JE, Ji M, Samet JM. Fasting serum glucose level and cancer risk in Korean men and women. JAMA 2005;293(2):194–202. [13] Rapp K, Schroeder J, Klenk J, Ulmer H, Concin H, Diem G, et al. Fasting blood glucose and cancer risk in a cohort of more than 140,000 adults in Austria. Diabetologia 2006;49(5):945–52. [14] Stattin P, Bjor O, Ferrari P, Lukanova A, Lenner P, Lindahl B, et al. Prospective study of hyperglycemia and cancer risk. Diabetes Care 2007;30(3):561–7. [15] Larsson SC, Mantzoros CS, Wolk A. Diabetes mellitus and risk of breast cancer: a meta-analysis. Int J Cancer 2007;121(4):856–62. [16] Larsson SC, Orsini N, Wolk A. Diabetes mellitus and risk of colorectal cancer: a meta-analysis. J Natl Cancer Inst 2005;97(22):1679–87. [17] Prat A, Parker JS, Karginova O, Fan C, Livasy C, Herschkowitz JI, et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res 2010;12(5):R68. [18] Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 2013;24(9):2206–23. [19] Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature 2000;406(6797):747–52. [20] Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001;98(19):10869–74. [21] Millikan RC, Newman B, Tse CK, Moorman PG, Conway K, Dressler LG, et al. Epidemiology of basal-like breast cancer. Breast Cancer Res Treat 2008;109(1):123–39. [22] Tamimi RM, Colditz GA, Hazra A, Baer HJ, Hankinson SE, Rosner B, et al. Traditional breast cancer risk factors in relation to molecular subtypes of breast cancer. Breast Cancer Res Treat 2012;131(1):159–67. [23] National Cholesterol Education Program Expert Panel on Detection E, Treatment of High Blood Cholesterol in A. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002;106(25):3143–421. [24] Goodwin PJ, Ennis M, Pritchard KI, Trudeau ME, Koo J, Madarnas Y, et al. Fasting insulin and outcome in early-stage breast cancer: results of a prospective cohort study. J Clin Oncol 2002;20(1):42–51. [25] Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28(7):412–9. [26] Gayoso-Diz P, Otero-Gonzalez A, Rodriguez-Alvarez MX, Gude F, Garcia F, De Francisco A, et al. Insulin resistance (HOMA-IR) cut-off values and the metabolic syndrome in a general adult population: effect of gender and age: EPIRCE cross-sectional study. BMC Endocr Disord 2013;13:47. [27] Colditz GA, Rosner BA, Chen WY, Holmes MD, Hankinson SE. Risk factors for breast cancer according to estrogen and progesterone receptor status. J Natl Cancer Inst 2004;96(3):218–28. [28] Enger SM, Ross RK, Paganini-Hill A, Carpenter CL, Bernstein L. Body size, physical activity, and breast cancer hormone receptor status: results from two case-control studies. Cancer Epidemiol Biomarkers Prev 2000;9(7):681–7. [29] Potter JD, Cerhan JR, Sellers TA, McGovern PG, Drinkard C, Kushi LR, et al. Progesterone and estrogen receptors and mammary neoplasia in the Iowa Women’s Health Study: how many kinds of breast cancer are there? Cancer Epidemiol Biomarkers Prev 1995;4(4):319–26.
[30] Cotterchio M, Kreiger N, Theis B, Sloan M, Bahl S. Hormonal factors and the risk of breast cancer according to estrogen- and progesterone-receptor subgroup. Cancer Epidemiol Biomarkers Prev 2003;12(10):1053–60. [31] Huang WY, Newman B, Millikan RC, Schell MJ, Hulka BS, Moorman PG. Hormone-related factors and risk of breast cancer in relation to estrogen receptor and progesterone receptor status. Am J Epidemiol 2000;151(7): 703–14. [32] Rosenberg LU, Einarsdottir K, Friman EI, Wedren S, Dickman PW, Hall P, et al. Risk factors for hormone receptor-defined breast cancer in postmenopausal women. Cancer Epidemiol Biomarkers Prev 2006;15(12):2482–8. [33] Suzuki R, Rylander-Rudqvist T, Ye W, Saji S, Wolk A. Body weight and postmenopausal breast cancer risk defined by estrogen and progesterone receptor status among Swedish women: a prospective cohort study. Int J Cancer 2006;119(7):1683–9. [34] Capasso I, Esposito E, de Laurentiis M, Maurea N, Cavalcanti E, Botti G, et al. Metabolic syndrome-breast cancer link varies by intrinsic molecular subtype. Diabetol Metabol Syndr 2014;6(1):105. [35] Phipps AI, Malone KE, Porter PL, Daling JR, Li CI. Body size and risk of luminal, HER2-overexpressing, and triple-negative breast cancer in postmenopausal women. Cancer Epidemiol Biomarkers Prev 2008;17(8):2078–86. [36] Wang X, Simpson ER, Brown KA. Aromatase overexpression in dysfunctional adipose tissue links obesity to postmenopausal breast cancer. J Steroid Biochem Mol Biol 2015. [37] Bosetti C, Rosato V, Polesel J, Levi F, Talamini R, Montella M, et al. Diabetes mellitus and cancer risk in a network of case-control studies. Nutr Cancer 2012;64(5):643–51. [38] Campbell PT, Newton CC, Patel AV, Jacobs EJ, Gapstur SM. Diabetes and causespecific mortality in a prospective cohort of one million U.S. adults. Diabetes Care 2012;35(9):1835–44. [39] Boyle P, Koechlin A, Pizot C, Boniol M, Robertson C, Mullie P, et al. Blood glucose concentrations and breast cancer risk in women without diabetes: a meta-analysis. Eur J Nutr 2013;52(5):1533–40. [40] Calip GS, Yu O, Hoskins KF, Boudreau DM. Associations between diabetes medication use and risk of second breast cancer events and mortality. Cancer Causes Control 2015;26(8):1065–77. [41] Barba M, Sperati F, Stranges S, Carlomagno C, Nasti G, Iaffaioli V, et al. Fasting glucose and treatment outcome in breast and colorectal cancer patients treated with targeted agents: results from a historic cohort. Ann Oncol 2012;23(7):1838–45. [42] Zelzer E, Levy Y, Kahana C, Shilo BZ, Rubinstein M, Cohen B. Insulin induces transcription of target genes through the hypoxia-inducible factor HIF-1alpha/ ARNT. EMBO J 1998;17(17):5085–94. [43] Pollak M. Insulin and insulin-like growth factor signalling in neoplasia. Nat Rev Cancer 2008;8(12):915–28. [44] Coppola D, Ferber A, Miura M, Sell C, D’Ambrosio C, Rubin R, et al. A functional insulin-like growth factor I receptor is required for the mitogenic and transforming activities of the epidermal growth factor receptor. Mol Cell Biol 1994;14(7):4588–95. [45] Roudabush FL, Pierce KL, Maudsley S, Khan KD, Luttrell LM. Transactivation of the EGF receptor mediates IGF-1-stimulated shc phosphorylation and ERK1/2 activation in COS-7 cells. J Biol Chem 2000;275(29): 22583–9. [46] Gallardo A, Lerma E, Escuin D, Tibau A, Munoz J, Ojeda B, et al. Increased signalling of EGFR and IGF1R, and deregulation of PTEN/PI3K/Akt pathway are related with trastuzumab resistance in HER2 breast carcinomas. Br J Cancer 2012;106(8):1367–73. [47] Nahta R. Deciphering the role of insulin-like growth factor-I receptor in trastuzumab resistance. Chemother Res Pract 2012;2012:648965. [48] Ye XM, Zhu HY, Bai WD, Wang T, Wang L, Chen Y, et al. Epigenetic silencing of miR-375 induces trastuzumab resistance in HER2-positive breast cancer by targeting IGF1R. BMC Cancer 2014;14:134.
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
RETRAM-4; No. of Pages 6 Current Research in Translational Medicine xxx (2016) xxx–xxx
Available online at
ScienceDirect www.sciencedirect.com
Original article
Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women A. Crispo a,*, M. Montella a, G. Buono b, M. Grimaldi a, M. D’Aiuto c, I. Capasso c, E. Esposito c, A. Amore d, F. Nocerino a, L.S.A. Augustin a,e, A. Giudice a, M. Di Bonito f, M. Giuliano b, V. Forestieri b, M. De Laurentiis c, M. Rinaldo c, G. Ciliberto g, S. De Placido b, G. Arpino b a
Unit of epidemiology, National Cancer Institute, G.-Pascale Foundation, Via Mariano Semmola 1, 80131 Naples, Italy Department of Clinical Medicine and Surgery, University of Naples Federico II, Via Sergio Pansini 5, 80131 Naples, Italy c Department of Breast Surgery, National Cancer Institute G. Pascale Foundation, Via Mariano Semmola 1, 80131 Naples, Italy d Department of Surgery, National Cancer Institute G. Pascale Foundation, Via Mariano Semmola 1, 80131 Naples, Italy e Clinical Nutrition and Risk Factor Modification Centre, St. Michael’s Hospital, 61 Queen St. East, Toronto, Canada f Division of Pathology, National Cancer Institute, G. Pascale Foundation, Via Mariano Semmola 1, 80131 Naples, Italy g Scientific Direction, National Cancer Institute, G. Pascale Foundation, Cappella dei Cangiani 1, 80131 Naples, Italy b
A R T I C L E I N F O
A B S T R A C T
Article history: Received 15 September 2015 Accepted 7 January 2016 Available online xxx
Breast cancer (BC) is the most common malignant tumor in women, obesity is associated with increased BC incidence and mortality and high levels of circulating insulin may negatively impact on cancer incidence. In the present study, we investigated whether the strength of several anthropometric and metabolic parameters varies between BC molecular subtypes. Eligible cases were 991 non-metastatic BC patients recruited between January 2009 and December 2013. Anthropometric, clinical and immunohistochemical features were measured. Multivariate logistic regression models were built to assess HER2 positive BC risk, comparing (a) triple positive (TP) with luminal A, luminal B and triple negative (TN) and (b) HER2-enriched group with luminal A, luminal B and TN. We stratified patients in pre- and post-menopause: significant differences emerged for luminal A in relation to age: they were more likely to be older compared to other groups. Among postmenopausal patients, the adjusted multivariate analysis showed that high BMI and high waist circumference were inversely correlated to TP subtype when compared to luminal B (OR = 0.48 and OR = 0.49, respectively). Conversely, HOMA-IR was a risk factor for TP when compared to luminal A and TN (OR = 2.47 and OR = 3.15, respectively). Our findings suggest a potential role of higher abdominal fat in the development of specific BC molecular subtypes in postmenopausal women. Moreover, they support a potential role of insulin resistance in the development of HER2 positive BC, although this role appears to be stronger when hormone receptors are co-expressed, suggesting a difference in the etiology of these two BC subtypes. ß 2016 Elsevier Masson SAS. All rights reserved.
Keywords: BMI Insulin resistance HOMA-I Breast cancer Molecular subtypes
1. Introduction Breast cancer (BC) is the most common malignant tumor in women, and it is second only to lung cancer as the most frequent cause of cancer related death in Europe [1]. Obesity is associated with increased BC incidence and mortality [2–4], with more advanced stage at the time of diagnosis, and with poorer prognosis [5]. It has been suggested that obesity may promote carcinogenesis both directly and indirectly [6]. The aromatase enzyme synthesizes estrogens
* Corresponding author. E-mail address: [email protected] (A. Crispo).
from circulating androgens in adipose tissue, hence directly stimulating cell proliferation in breast tissue while the suggested indirect effect is linked to chronic compensatory hyperinsulinemia as a consequence of visceral obesity. High levels of circulating insulin may result in aberrant mitogenic and anti-apoptotic effects [7–9] and may negatively impact on cancer incidence [8–11]. Glucose metabolism also seems to play an important role in breast carcinogenesis. Elevated baseline glucose levels have been associated with an increased risk of BC in several studies [12– 14]. Importantly, two distinct meta-analyses of case-control and prospective cohort studies on diabetes and cancer have shown a 1.2-fold increased risk of BC in postmenopausal diabetic patients [15,16].
http://dx.doi.org/10.1016/j.retram.2016.01.004 2452-3186/ß 2016 Elsevier Masson SAS. All rights reserved.
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
G Model
RETRAM-4; No. of Pages 6 2
A. Crispo et al. / Current Research in Translational Medicine xxx (2016) xxx–xxx
Recently, BC has been categorized into molecularly-defined subtypes with different clinical and biological characteristics [17]. The prognosis seems to differ according to subtype [18–20], and it has been suggested that the underlying etiology may also differ [21,22]. To date, a limited number of studies have evaluated the potential role of body weight, anthropometric characteristics and glucose metabolism on the risk of developing a specific molecular subtype of BC. In the present study, we investigated whether the strength of several anthropometric and metabolic parameters varies between BC subtypes. 2. Materials and methods 2.1. Study population and laboratory assays Eligible cases were 991 non-metastatic BC patients who were consecutively treated with mastectomy or breast-conserving surgery at the National Cancer Institute ‘‘G. Pascale Foundation’’ and Federico II University of Naples, (Southern Italy), between January 2009 and December 2013. For each patient, anthropometric features including weight in kilograms, height in meters, waist and hip circumference (WC and HC) in centimeters were measured, and venous blood was collected on study entry. Body mass index (BMI) (kg/m2) was calculated from weight and height and evaluated according to the World Health Organization classification ( 25 kg/m2 = underweight/normal; > 25 kg/m2 = overweight/obese). The circumferences of waist (measured 2 cm above the umbilicus) and hip (measured at the maximal protrusion) was measured at the time of interview. Waist to hip ratio (WHR) was calculated as the ratio between these measures. Fasting plasma glucose and insulin levels were assessed from blood samples according to the NCEP ATP III criteria [23]. Diabetes was considered an exclusion criterium and was determined from laboratory data when fasting plasma glucose was 126 mg/dl according to the American Diabetes Association guidelines [24]. We used HOMA-IR as a measure of insulin resistance computed according to the formula: [fasting serum insulin (mU/mL) fasting plasma glucose (mmol/L)/22.5] [25]. Patients were divided according to HOMA index into three categories: < 2.5; 2.5–5.4; and 5.5 [26]. The study was approved by Federico II Institutional Review Board (IRB approval # 743/15). Since data were extracted from a pre-existing computerized database, the IRB waved the need for patient informed consent for this study. Patient records and information were anonymized and de-identified prior to analysis. 2.2. Immunochemistry Data on tumor size (T), lymph node invasion and tumor grade (G) were collected for all patients. At each participating centers an experienced pathologist using light microscopy evaluated antigen expression. For each sample, at least five fields (inside the tumor and in the area exhibiting invasion) and > 500 cells were analyzed. Using a semi-quantitative scoring system, the intensity, extent and subcellular distribution of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2, also known as c-erb B2) and Ki-67, were evaluated. The cutoff used to distinguish ‘‘positive’’ from ‘‘negative’’ cases was 1% ER/PR positive tumor cells. Immunohistochemical analyses of HER2 expression describe the intensity and staining pattern of tumor cells evaluated using scores from 0 to 3+ in agreement with the Herceptest kit scoring guidelines. The Food and Drug Administration (FDA)-approved test, the HerceptestTM (DAKO), distinguishes between four categories: no staining or weak staining in fewer than 10% of the tumor cells (0); weak staining in part of the membrane in more than 10% of the tumor cells (1+); complete staining of the membrane with weak or moderate intensity in more than 10% of the neoplastic cells (2+); and strong staining in more than 10% (3+). Scores of 0 or 1+ were considered negative for HER2 expression, 2+ was uncertain, and 3+ was positive. Scores of 2+ undergo FISH analysis for HER2 gene amplification. The proliferative index Ki-67 was defined as the percentage of immune-reactive tumor cells out of the total number of cells. The percentage of positive cells per case was scored into 2 groups: ‘‘low’’: < 20% (low proliferative activity) and ‘‘high’’: 20% (high proliferative activity). 2.3. Molecular subtype classification BCs molecular subtypes were identified and categorized based on the 13th St Gallen International Breast Cancer Conference (2013) Expert Panel [18]. Briefly, cases were distinguished into:
luminal A: ER and PR positive, HER2 negative, Ki-67 ‘‘low’’ i.e., < 20%; luminal B-like (HER2 negative): ER positive, HER2 negative and with Ki-67 ‘high’ (i.e. 20%), and/or PR ‘negative or low’ (i.e., 20%); triple positive (TP): ER positive, HER2 over-expressed or amplified (HER2 positive), any Ki-67, any PR; HER2-enriched: HER2 over-expressed or amplified, ER and PR absent; triple negative (TN): ER and PR absent, HER2 negative.
2.4. Statistical analyses Mean and standard deviations were used for age (continuous data) while frequencies and percentage values were used for categorical data. Age differences were evaluated using the Student-t or One Way ANOVA test (post-hoc tests) according to the number (2 or more) of groups compared. We used Pearson’s Chi2 test of independence (2-tailed) to assess the relationship between BMI, WHR, WC and HOMA-IR and molecular subtypes either in pre- or post-menopause. Multivariate logistic regression models were then built to assess HER2 positive BC risk, comparing: TP with luminal A, luminal B and TN (Table 3); HER2-enriched group with luminal A, luminal B and TN (Table 4), by exclusively including those factors, which tested significant in the univariate analysis (data not shown). We considered P values less or equal to 0.05 as statistically significant. All statistical analyses were performed with the SPSS statistical software version 21 (SPSS Inc., Chicago IL, USA).
3. Results Clinical and tumor characteristics, according to BC subtypes, are reported in Table 1. In the overall study cohort, 43.2% of patients had luminal A, 28.3% luminal B-like, 15.9% HER2 positive, 11.5% TP, and 4.4% HER2-entriched tumors. Triple negative phenotype accounted for the 12.6% of the total. Tumor characteristics (tumor size, lymph node status, stage and grading) and menopausal status significantly differed according to BC subtypes (P = 0.001, P = 0.05, respectively). We also stratified patients in pre- and post-menopause (Table 2A and Table 2B, respectively). Among premenopausal patients (Table 2A) mean age was statistically significant (P = 0.01), in particular luminal A were less young compared to luminal B and TN (44.6 years, 42.3 years and 40.5 years, respectively). There was no statistically significant difference for BMI, WHR, WC and HOMA-IR, according to molecular subtypes in premenopausal women. Among postmenopausal patients age was significantly different among BC subtypes, particularly the youngest age was found among women with the HER2-enriched subtype (56.8 years) compared to all others (P = 0.002) (Table 2B). BMI showed statistically significant differences in distribution: TP, HER2-enriched and TN were more likely to be normal weight (BMI 25) compared to luminal A (43.1%, 46.7% and 44.1% vs. 33%, respectively P = 0.03). Also the WHR distribution was significantly different: TP and HER2-enriched subtypes were more likely to have WHR 80 cm compared to luminal A (42.3%, 58.6% vs. 29.5%, respectively P = 0.01). In relation to insulin resistance in postmenopausal women we found no statistically significant difference in distribution in HOMA-IR levels between molecular subtypes, although numerically, patients with the highest HOMA-IR (HOMA 5.5) were most likely TP (27.5%). The multivariate analysis was performed only in postmenopausal patients and included the following variables: BMI, WC and HOMA-IR (Table 3). High BMI and high WC were inversely associated with the TP subtype when compared to luminal B (OR = 0.48, 95% CI: 0.25–0.94; P = 0.03 and OR = 0.49, 95% CI: 0.25– 0.98; P = 0.05, respectively). Conversely, HOMA-IR was a risk factor for TP when compared to luminal A and TN (OR = 2.47, 95% CI: 1.04–5.89; P = 0.04 and OR = 3.15, 95% CI: 1.03–9.63, P = 0.04, respectively).
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
G Model
RETRAM-4; No. of Pages 6 A. Crispo et al. / Current Research in Translational Medicine xxx (2016) xxx–xxx
3
Table 1 Tumor characteristics, anthropometrics and HOMA-IR according to breast cancer molecular subtypesa. HER2+
HER2–
Total patients in group n = 991 Age (years) at diagnosis (mean SD) Age (years) < 40 40–45 46–55 56–65 66–75 > 75 Tumor pathology Tumor size T1 T2 T3–T4 Lymph node status Negative Positive Pathological stage Stage 0 or I Stage II, III Grading I II III Menopausal status PrePostBMI (Kg/m2) 25 > 25 WHR 80 > 80 WC (cm) 88 > 88 HOMA_IR < 2.5 2.5–5.4 5.5
Triple negative n (%)
Luminal A n (%)
Luminal B n (%)
Triple positive n (%)
Her2-enriched n (%)
428 (43.2)
280 (28.3)
114 (11.5)
44 (4.4)
125 (12.6)
56.6 12.8
54.9 13
53.3 12.9
52.3 10.2
54.7 12.8
26 (6.1) 64 (15.0) 133 (31.1) 93 (21.7) 82 (19.2) 30 (7.0)
36 40 69 73 45 17
15 (13.2) 22 (19.3) 26 (22.8) 31 (27.2) 13 (11.4) 7 (6.1)
2 (4.5) 9 (20.5) 15 (34.1) 13 (29.5) 5 (11.4) –
18 (14.4) 12 (9.6) 26 (20.8) 44 (35.2) 19 (15.2) 6 (4.8)
(12.9) (14.3) (24.6) (26.1) (16.1) (6.1)
P*
0.08 .004
<.0001 285 (68.3) 111 (26.6) 21 (5.0)
144 (52.0) 121 (43.7) 12 (4.3)
56 (50.5) 48 (43.2) 7 (6.3)
15 (37.5) 21 (52.5) 4 (10.0)
61 (49.2) 56 (45.2) 7 (5.6)
255 (61.0) 163 (39.0)
127 (46.9) 144 (53.1)
51 (45.9) 60 (54.1)
20 (46.5) 23 (53.5)
73 (59.8) 49 (40.2)
203 (48.6) 215 (51.4)
79 (29.2) 192 (70.8)
35 (31.5) 76 (68.5)
10 (23.3) 33 (76.7)
40 (32.8) 82 (67.2)
52 (12.4) 269 (63.9) 100 (23.8)
3 (1.1) 82 (29.9) 189 (69.0)
2 (1.8) 32 (28.8) 77 (69.4)
1 (2.4) 12 (28.6) 29 (69.0)
1 (0.8) 17 (13.7) 106 (85.5)
157 (36.7) 271 (63.3)
113 (40.4) 167 (59.6)
56 (49.1) 58 (50.9)
14 (31.8) 30 (68.2)
42 (33.6) 83 (66.4)
185 (45.2) 225 (54.8)
113 (40.9) 163 (59.1)
59 (52.2) 54 (47.8)
23 (52.3) 21 (47.7)
57 (49.6) 58 (50.4)
173 (41.8) 241 (58.2)
107 (40.1) 160 (59.9)
46 (43.8) 59 (56.2)
27 (62.8) 16 (37.2)
47 (43.9) 60 (56.1)
193 (46.5) 222 (53.5)
106 (39.7) 161 (60.3)
56 (53.3) 49 (46.7)
20 (46.5) 23 (53.5)
53 (49.5) 54 (50.5)
154 (53.5) 105 (36.5) 29 (10.1)
121 (60.8) 56 (28.1) 22 (11.1)
48 (58.5) 20 (24.4) 14 (17.1)
16 (50) 12 (37.5) 4 (12.5)
51 (56.7) 32 (35.6) 7 (7.8)
.001
<.0001
<.0001
.05
.2
.08
.1
.3
a Molecular subtypes were defined as luminal A (hormone receptor positive (ER+, PR 20), low Ki-67 (< 20), HER2–); luminal B-like (hormone receptor positive (ER+, PR < 20), high Ki-67 (> 20), HER2–); human epidermal growth factor receptor 2 positive (triple positive: HER2+ hormone receptor positive; Her2-enriched: HER2+ hormone receptor negative/absent); triple negative (HER2–, hormone receptor negative/absent). * Chi2 test was used to compare the molecular subtypes and characteristics of the patients.
No significant associations were observed for HER2-enriched compared to all other subtypes in relation to BMI, WC and HOMAIR (Table 4). 4. Discussion Knowledge on clinical outcome of different molecular BC subtypes is progressively improving. However, little is known on the role of metabolism and anthropometric parameters on the activation of different pathways leading to these distinct BC subtypes. The present study confirms that age at diagnosis and clinical tumor characteristics differ according to specific BC molecular subtypes. Overall, women with HER2 positive BC were younger and had more advanced disease at diagnoses. Patients with luminal A were the eldest and were more likely to have a low stage, low grade BC at diagnosis. Among postmenopausal women, patients with HER2-enriched tumors were the youngest and patients with luminal A or B who tended to be older, not surprisingly, were more likely to have a higher BMI and higher WHR. Interestingly, TN postmenopausal patients, although not presenting with a large BMI at diagnoses, were more likely to have
a high WHR, confirming the finding of a peculiar body fat deposition in this subgroup. Women with TP subtype were less likely to have a high BMI compared to patients with luminal B, however they were more likely to have a higher HOMA-IR index when compared to patients with luminal A and TN subtypes. The positive relationship between BMI and luminal tumors among postmenopausal women is well characterized in the literature [27–35]. Indeed, in agreement with prior studies assessing the association between BMI and BC risk using ER/PR status [27–34], we also observed a positive association between BMI > 25 kg/m2 and risk of luminal BC among postmenopausal women. This association could be largely attributed to the positive relationship between BMI and endogenous estrogen levels since adipose tissue is the primary source of estrogen in postmenopausal women [36]. WHR is a well-known measure of central obesity in women. To our knowledge, at least one large study, the Carolina Breast Cancer (CBC) study [21], has investigated the relationship between anthropometric characteristics including WHR and risk of different BC molecular subtypes. In the CBC study, postmenopausal patients with a high WHR were more likely to develop TN rather than other BC subtypes [21]. Consistent with this finding, in our
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
G Model
RETRAM-4; No. of Pages 6 A. Crispo et al. / Current Research in Translational Medicine xxx (2016) xxx–xxx
4
Table 2A Anthropometric characteristics and HOMA-IR in premenopausal women according to breast cancer molecular subtypesa. HER2+
HER2–
Total patients in group n = 382 Age (years) at diagnosis (mean SD) BMI (kg/m2) 25 > 25 WHR 80 > 80 WC (cm) 88 > 88 HOMA_IR < 2.5 2.5–5.4 5.5
Triple negative
P*
Luminal A
Luminal B
Triple positive
Her2-enriched
157 (41.1)
113 (29.6)
56 (14.7)
14 (3.7)
42 (11.0)
44.6a 4.9
42.3b 6.4
42.7ba 6.3
42.6ba 7.8
40.5b 8.0
0.01
99 (66.9) 49 (33.1)
68 (60.7) 44 (39.3)
34 (61.8) 21 (38.2)
9 (64.3) 5 (35.7)
23 (60.5) 15 (39.5)
0.8
56 (37.6) 93 (62.4)
29 (26.9) 79 (73.1)
14 (26.4) 39 (73.6)
3 (21.4) 11 (78.6)
15 (41.7) 21 (58.3)
0.1
107 (71.3) 43 (28.7)
60 (55.6) 48 (44.4)
33 (62.3) 20 (37.7)
8 (57.1) 6 (42.9)
26 (72.2) 10 (27.8)
75 (68.2) 29 (26.4) 6 (5.5)
59 (77.6) 16 (21.1) 1 (1.3)
30 (71.4) 9 (21.4) 3 (7.1)
5 (62.5) 3 (37.5) 0
23 (67.6) 10 (29.4) 1 (2.9)
0.08
0.7
BMI: body mass index; WHR: waist to hip ratio; WC: waist circumference; HP: hip circumference. a Molecular subtypes were defined as luminal A (hormone receptor positive (ER+, PR 20), low Ki-67 (< 20), HER2–); luminal B-like (hormone receptor positive (ER+, PR < 20), high Ki-67 (> 20), HER2–); human epidermal growth factor receptor 2 positive (triple positive: HER2+, hormone receptor positive; Her2-enriched: HER2+ hormone receptor negative/absent); triple negative (HER2–, hormone receptor negative/absent). * Chi2 test was used to compare molecular subtypes and characteristics of the patients for categorical variables and ANOVA post-hoc for continuous variables: values with different letters are significantly different from each other (P 0.05). Table 2B Anthropometric characteristics and HOMA-IR in postmenopausal women according to breast cancer molecular subtypesa. HER2–
Total patients in group n = 609 Age (years) at diagnosis (mean SD) BMI (kg/m2) 25 > 25 WHR 80 > 80 WC (cm) 88 > 88 HOMA_IR < 2.5 2.5–5.4 5.5
HER2+
Triple negative n (%)
Luminal A n (%)
Luminal B n (%)
Triple positive n (%)
Her2-enriched n (%)
271 (44.5)
167 (27.4)
58 (9.5)
30 (4.9)
a
a
a
b
P*
83 (13.6)
63.4 9.2
63.5 8.7
63.6 8.8
56.8 7.8
61.9a 7.8
86 (33.0) 175 (67.0)
45 (27.4) 119 (72.6)
25 (43.1) 33 (56.9)
14 (46.7) 16 (53.3)
34 (44.2) 43 (55.8)
78 (29.5) 186 (70.5)
50 (31.4) 109 (68.6)
22 (42.3) 30 (57.7)
17 (58.6) 12 (41.4)
21 (29.6) 50 (70.4)
86 (32.5) 179 (67.5)
46 (28.9) 113 (71.1)
23 (44.2) 29 (55.8)
12 (41.4) 17 (58.6)
27 (38.0) 44 (62.0)
79 (44.4) 76 (42.7) 23 (12.9)
62 (50.4) 40 (32.5) 21 (17.1)
18 (45.0) 11 (27.5) 11 (27.5)
11 (45.8) 9 (37.5) 4 (16.7)
28 (50.0) 22 (39.3) 6 (10.7)
0.002 0.03
0.01
0.2
0.3
BMI: body mass index; WHR: waist to hip ratio; WC: waist circumference; HP: hip circumference. a Molecular subtypes were defined as luminal A (hormone receptor positive (ER+, PR 20), low Ki-67 (< 20), HER2–); luminal B-like (hormone receptor positive (ER+, PR < 20), high Ki-67 (> 20), HER2–); human epidermal growth factor receptor 2 positive (triple positive: HER2+, hormone receptor positive; Her2-enriched: HER2+ hormone receptor negative/absent); triple negative (HER2–, hormone receptor negative/absent). * Chi2 test was used to compare molecular subtypes and characteristics of the patients for categorical variables and ANOVA post-hoc for continuous variables: values with different letters are significantly different from each other (P 0.05). Table 3 Multivariate logistic regression models testing variables associated with HER2 positivity in triple positive breast cancer among postmenopausal women. Factors
Multivariate analysis Triple positive(1) vs. luminal A a
OR (95% CI) BMI (kg/m2) 25 > 25 WC (cm) 88 > 88 HOMA_IR 5.5 > 5.5
Triple positive(2) vs. luminal B P
a
OR (95% CI)
0.1 1b 0.60 (0.32–1.10)
P
ORa (95% CI)
0.03 1b 0.48 (0.25–0.94)
0.08 1b 0.56 (0.29–1.07)
0.8
0.05
0.4 1b 0.70 (0.32–1.53)
0.1 1b 1.93 (0.80–4.65)
P
1b 0.92 (0.44–1.92)
1b 0.49 (0.25–0.98) 0.04
1b 2.47 (1.04–5.89)
Triple positive(3) vs. TN
0.04 1b 3.15 (1.03–9.63)
In the multivariate models the triple positive were compared with: luminal A(1); luminal B-like (HER2 negative)(2); triple negative (TN)(3). a Adjusted for those factors which were statistically significant in the univariate analysis. b Reference category.
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
G Model
RETRAM-4; No. of Pages 6 A. Crispo et al. / Current Research in Translational Medicine xxx (2016) xxx–xxx
5
Table 4 Multivariate logistic regression models testing variables associated with HER2 positivity in Her2-enriched breast cancer among postmenopausal women. Factors
Multivariate analysis HER2-enriched
(1)
a
OR (95% CI) BMI (kg/m2) 25 > 25 WC (cm) 88 > 88 HOMA_IR 5.5 >5.5
vs. luminal A
HER2-enriched P
(2)
OR (95% CI)
0.3 1b 0.65 (0.28–1.47)
P
ORa (95% CI)
0.2 1b 0.57 (0.24–1.37)
0.8 1b 0.92 (0.38–2.18)
P 0.8
1b 1.12 (0.43–2.91) 0.7
1b 0.83 (0.34–2.07) 0.3
1b 1.18 (0.83–6.26)
HER2-enriched(3) vs. TN
vs. luminal B
a
0.8 1b 1.14 (0.42–3.08)
0.8 1b 1.08 (0.30–3.89)
0.2 1b 2.50 (0.55–11.3)
In the multivariate models the triple positive were compared with: luminal A(1); luminal B-like (HER2 negative)(2); triple negative (TN)(3). a Adjusted for those factors which were statistically significant in the univariate analysis. b Reference category.
study, more than 70% of TN postmenopausal women had a WHR higher than 80, suggesting a potential role of higher abdominal fat in the development of specific BC molecular subtypes in postmenopausal women. Insulin, insulin-like growth factor-1 (IGF-1), hyperglycemia and type 2 diabetes are risk factors for breast cancer [9,37–39] and anti-hyperglycemic agents have been found protective in reducing BC recurrence [40]. Recent studies provide novel evidence for the predictive role of pretreatment elevated fasting blood glucose levels in the progression of HER2 positive BC [41]. An interplay between factors related to glucose metabolism and molecular targets such as HER2 status had been previously hypothesized [42] as well between the IGF-1 receptor (IGF-1R) and the insulin receptor substrate-1 (IRS-1) signaling in breast carcinogenesis [43]. Furthermore, suggestions of a tight interplay between IGF-IR and the epidermal growth factor receptor (EGFR) emerges from a number of studies. IGF-1R activation is crucial for the mitogenic and transforming activity of EGFR [44,45] and increased signaling from IGF-1R has been consistently described among the molecular mechanisms driving resistance to the humanized anti-HER2 antibody trastuzumab [46–48]. We have previously shown that insulin resistance may be associated to the development of more aggressive BC (e.g. HER2 positive) [34]. Given the bulk of existing evidence, in the present study, we suggest that a metabolic environment favoring higher insulin levels would be associated to greater risk of HER2 positive BC. In our patient population, especially in postmenopausal women, patients with TP tumor were more likely to have a higher HOMA score compared to patients with luminal A and TN BC. Another anthropometric parameter strongly associated to TP subtype was BMI. However, TP subtype was associated to lower BMI, which is difficult to explain in light of their higher HOMA-IR index. No evidence of such associations was found for HER2-enriched molecular subtype. HER2-enriched and TP subtypes are considered different entities with regard to prognoses and sensitivity to therapy. Our findings therefore may suggest a potential role for insulin resistance in the development of HER2 positive BC when hormone receptors are coexpressed, suggesting a difference in the etiology of these BC subtypes. Our study has some limitations. Given the retrospective design of the study we were unable to retrieve data on potentially relevant risk factors such as smoking status, medication use, family history or lifestyle habits. Importantly, we could not explore the role of several other potentially relevant markers of altered glucose metabolism such as IGF-1, IGF binding proteins and EGFR. However, our study also has several important strengths. First, for each of our patients, the standard assay of pretreatment fasting glucose and insulin levels were run by the central laboratory of the
pertinent participating institutions. Second, tumor histological characterization was available for all the study participants and data produced were highly reliable given the stringent quality control ongoing at the involved Institutions. Furthermore, an experienced medical assistant in close collaboration carried out data retrieval from the medical records with the medical oncologists who had prospectively followed the patients included in our analyses. Third, the large number of patients included in the present analyses strengthens the consistency of our results. 5. Conclusions In conclusion, the present study provides novel evidence of the predictive role of BMI and insulin resistance on the risk of developing specific subtypes of BC. These findings, if additional prospective and experimental studies corroborate our data, may have important clinical implications, especially in BC prevention. Ethic approval The study was approved by Federico II Institutional Review Board (IRB approval # 743/15). Since data were extracted from a pre-existing computerized database, the IRB waved the need for patient informed consent for this study. Disclosure of interest The authors declare that they have no competing interest. References [1] Ferlay J, Autier P, Boniol M, Heanue M, Colombet M, Boyle P. Estimates of the cancer incidence and mortality in Europe in 2006. Ann Oncol 2007;18(3): 581–92. [2] Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 2008;371(9612):569–78. [3] Basen-Engquist K, Chang M. Obesity and cancer risk: recent review and evidence. Curr Oncol Rep 2011;13(1):71–6. [4] Haakinson DJ, Leeds SG, Dueck AC, Gray RJ, Wasif N, Stucky CC, et al. The impact of obesity on breast cancer: a retrospective review. Ann Surg Oncol 2012;19(9):3012–8. [5] Crispo A, Barba M, D’Aiuto G, De Laurentiis M, Grimaldi M, Rinaldo M, et al. Molecular profiles of screen detected vs. symptomatic breast cancer and their impact on survival: results from a clinical series. BMC Cancer 2013;13:15. [6] Khandekar MJ, Cohen P, Spiegelman BM. Molecular mechanisms of cancer development in obesity. Nat Rev Cancer 2011;11(12):886–95. [7] Tran TT, Medline A, Bruce WR. Insulin promotion of colon tumors in rats. Cancer Epidemiol Biomarkers Prev 1996;5(12):1013–5. [8] Giovannucci E. Insulin and colon cancer. Cancer Causes Control 1995;6(2): 164–79. [9] Kaaks R, Lukanova A. Energy balance and cancer: the role of insulin and insulin-like growth factor-I. Proc Nutr Soc 2001;60(1):91–106.
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004
G Model
RETRAM-4; No. of Pages 6 6
A. Crispo et al. / Current Research in Translational Medicine xxx (2016) xxx–xxx
[10] Vigneri P, Frasca F, Sciacca L, Pandini G, Vigneri R. Diabetes and cancer. Endocr Relat Cancer 2009;16(4):1103–23. [11] Capasso I, Esposito E, Pentimalli F, Montella M, Crispo A, Maurea N, et al. Homeostasis model assessment to detect insulin resistance and identify patients at high risk of breast cancer development: National Cancer Institute of Naples experience. J Exp Clin Cancer Res 2013;32:14. [12] Jee SH, Ohrr H, Sull JW, Yun JE, Ji M, Samet JM. Fasting serum glucose level and cancer risk in Korean men and women. JAMA 2005;293(2):194–202. [13] Rapp K, Schroeder J, Klenk J, Ulmer H, Concin H, Diem G, et al. Fasting blood glucose and cancer risk in a cohort of more than 140,000 adults in Austria. Diabetologia 2006;49(5):945–52. [14] Stattin P, Bjor O, Ferrari P, Lukanova A, Lenner P, Lindahl B, et al. Prospective study of hyperglycemia and cancer risk. Diabetes Care 2007;30(3):561–7. [15] Larsson SC, Mantzoros CS, Wolk A. Diabetes mellitus and risk of breast cancer: a meta-analysis. Int J Cancer 2007;121(4):856–62. [16] Larsson SC, Orsini N, Wolk A. Diabetes mellitus and risk of colorectal cancer: a meta-analysis. J Natl Cancer Inst 2005;97(22):1679–87. [17] Prat A, Parker JS, Karginova O, Fan C, Livasy C, Herschkowitz JI, et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res 2010;12(5):R68. [18] Goldhirsch A, Winer EP, Coates AS, Gelber RD, Piccart-Gebhart M, Thurlimann B, et al. Personalizing the treatment of women with early breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2013. Ann Oncol 2013;24(9):2206–23. [19] Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, et al. Molecular portraits of human breast tumours. Nature 2000;406(6797):747–52. [20] Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001;98(19):10869–74. [21] Millikan RC, Newman B, Tse CK, Moorman PG, Conway K, Dressler LG, et al. Epidemiology of basal-like breast cancer. Breast Cancer Res Treat 2008;109(1):123–39. [22] Tamimi RM, Colditz GA, Hazra A, Baer HJ, Hankinson SE, Rosner B, et al. Traditional breast cancer risk factors in relation to molecular subtypes of breast cancer. Breast Cancer Res Treat 2012;131(1):159–67. [23] National Cholesterol Education Program Expert Panel on Detection E, Treatment of High Blood Cholesterol in A. Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) final report. Circulation 2002;106(25):3143–421. [24] Goodwin PJ, Ennis M, Pritchard KI, Trudeau ME, Koo J, Madarnas Y, et al. Fasting insulin and outcome in early-stage breast cancer: results of a prospective cohort study. J Clin Oncol 2002;20(1):42–51. [25] Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28(7):412–9. [26] Gayoso-Diz P, Otero-Gonzalez A, Rodriguez-Alvarez MX, Gude F, Garcia F, De Francisco A, et al. Insulin resistance (HOMA-IR) cut-off values and the metabolic syndrome in a general adult population: effect of gender and age: EPIRCE cross-sectional study. BMC Endocr Disord 2013;13:47. [27] Colditz GA, Rosner BA, Chen WY, Holmes MD, Hankinson SE. Risk factors for breast cancer according to estrogen and progesterone receptor status. J Natl Cancer Inst 2004;96(3):218–28. [28] Enger SM, Ross RK, Paganini-Hill A, Carpenter CL, Bernstein L. Body size, physical activity, and breast cancer hormone receptor status: results from two case-control studies. Cancer Epidemiol Biomarkers Prev 2000;9(7):681–7. [29] Potter JD, Cerhan JR, Sellers TA, McGovern PG, Drinkard C, Kushi LR, et al. Progesterone and estrogen receptors and mammary neoplasia in the Iowa Women’s Health Study: how many kinds of breast cancer are there? Cancer Epidemiol Biomarkers Prev 1995;4(4):319–26.
[30] Cotterchio M, Kreiger N, Theis B, Sloan M, Bahl S. Hormonal factors and the risk of breast cancer according to estrogen- and progesterone-receptor subgroup. Cancer Epidemiol Biomarkers Prev 2003;12(10):1053–60. [31] Huang WY, Newman B, Millikan RC, Schell MJ, Hulka BS, Moorman PG. Hormone-related factors and risk of breast cancer in relation to estrogen receptor and progesterone receptor status. Am J Epidemiol 2000;151(7): 703–14. [32] Rosenberg LU, Einarsdottir K, Friman EI, Wedren S, Dickman PW, Hall P, et al. Risk factors for hormone receptor-defined breast cancer in postmenopausal women. Cancer Epidemiol Biomarkers Prev 2006;15(12):2482–8. [33] Suzuki R, Rylander-Rudqvist T, Ye W, Saji S, Wolk A. Body weight and postmenopausal breast cancer risk defined by estrogen and progesterone receptor status among Swedish women: a prospective cohort study. Int J Cancer 2006;119(7):1683–9. [34] Capasso I, Esposito E, de Laurentiis M, Maurea N, Cavalcanti E, Botti G, et al. Metabolic syndrome-breast cancer link varies by intrinsic molecular subtype. Diabetol Metabol Syndr 2014;6(1):105. [35] Phipps AI, Malone KE, Porter PL, Daling JR, Li CI. Body size and risk of luminal, HER2-overexpressing, and triple-negative breast cancer in postmenopausal women. Cancer Epidemiol Biomarkers Prev 2008;17(8):2078–86. [36] Wang X, Simpson ER, Brown KA. Aromatase overexpression in dysfunctional adipose tissue links obesity to postmenopausal breast cancer. J Steroid Biochem Mol Biol 2015. [37] Bosetti C, Rosato V, Polesel J, Levi F, Talamini R, Montella M, et al. Diabetes mellitus and cancer risk in a network of case-control studies. Nutr Cancer 2012;64(5):643–51. [38] Campbell PT, Newton CC, Patel AV, Jacobs EJ, Gapstur SM. Diabetes and causespecific mortality in a prospective cohort of one million U.S. adults. Diabetes Care 2012;35(9):1835–44. [39] Boyle P, Koechlin A, Pizot C, Boniol M, Robertson C, Mullie P, et al. Blood glucose concentrations and breast cancer risk in women without diabetes: a meta-analysis. Eur J Nutr 2013;52(5):1533–40. [40] Calip GS, Yu O, Hoskins KF, Boudreau DM. Associations between diabetes medication use and risk of second breast cancer events and mortality. Cancer Causes Control 2015;26(8):1065–77. [41] Barba M, Sperati F, Stranges S, Carlomagno C, Nasti G, Iaffaioli V, et al. Fasting glucose and treatment outcome in breast and colorectal cancer patients treated with targeted agents: results from a historic cohort. Ann Oncol 2012;23(7):1838–45. [42] Zelzer E, Levy Y, Kahana C, Shilo BZ, Rubinstein M, Cohen B. Insulin induces transcription of target genes through the hypoxia-inducible factor HIF-1alpha/ ARNT. EMBO J 1998;17(17):5085–94. [43] Pollak M. Insulin and insulin-like growth factor signalling in neoplasia. Nat Rev Cancer 2008;8(12):915–28. [44] Coppola D, Ferber A, Miura M, Sell C, D’Ambrosio C, Rubin R, et al. A functional insulin-like growth factor I receptor is required for the mitogenic and transforming activities of the epidermal growth factor receptor. Mol Cell Biol 1994;14(7):4588–95. [45] Roudabush FL, Pierce KL, Maudsley S, Khan KD, Luttrell LM. Transactivation of the EGF receptor mediates IGF-1-stimulated shc phosphorylation and ERK1/2 activation in COS-7 cells. J Biol Chem 2000;275(29): 22583–9. [46] Gallardo A, Lerma E, Escuin D, Tibau A, Munoz J, Ojeda B, et al. Increased signalling of EGFR and IGF1R, and deregulation of PTEN/PI3K/Akt pathway are related with trastuzumab resistance in HER2 breast carcinomas. Br J Cancer 2012;106(8):1367–73. [47] Nahta R. Deciphering the role of insulin-like growth factor-I receptor in trastuzumab resistance. Chemother Res Pract 2012;2012:648965. [48] Ye XM, Zhu HY, Bai WD, Wang T, Wang L, Chen Y, et al. Epigenetic silencing of miR-375 induces trastuzumab resistance in HER2-positive breast cancer by targeting IGF1R. BMC Cancer 2014;14:134.
Please cite this article in press as: Crispo A, et al. Body weight and risk of molecular breast cancer subtypes among postmenopausal Mediterranean women. Curr Res Transl Med (2016), http://dx.doi.org/10.1016/j.retram.2016.01.004