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ORIGINAL REPORTS
May 07, 2012

Dual Effect of Metformin on Breast Cancer Proliferation in a Randomized Presurgical Trial

Publication: Journal of Clinical Oncology
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Abstract

Purpose

Metformin is associated with reduced breast cancer risk in observational studies in patients with diabetes, but clinical evidence for antitumor activity is unclear. The change in Ki-67 between pretreatment biopsy and post-treatment surgical specimen has prognostic value and may predict antitumor activity in breast cancer.

Patients and Methods

After tumor biopsy, we randomly allocated 200 nondiabetic women with operable breast cancer to either metformin 850 mg/twice per day (n = 100) or placebo (n = 100). The primary outcome measure was the difference between arms in Ki-67 after 4 weeks adjusted for baseline values.

Results

Overall, the metformin effect on Ki-67 change relative to placebo was not statistically significant, with a mean proportional increase of 4.0% (95% CI, −5.6% to 14.4%) 4 weeks apart. However, there was a different drug effect depending on insulin resistance (homeostasis model assessment [HOMA] index > 2.8, fasting glucose [mmol/L] × insulin [mU/L]/22.5; Pinteraction = .045), with a nonsignificant mean proportional decrease in Ki-67 of 10.5% (95% CI, −26.1% to 8.4%) in women with HOMA more than 2.8 and a nonsignificant increase of 11.1% (95% CI, −0.6% to 24.2%) with HOMA less than or equal to 2.8. A different effect of metformin according to HOMA index was noted also in luminal B tumors (Pinteraction = .05). Similar trends to drug effect modifications were observed according to body mass index (P = .143), waist/hip girth-ratio (P = .058), moderate alcohol consumption (P = .005), and C-reactive protein (P = .080).

Conclusion

Metformin before surgery did not significantly affect Ki-67 overall, but showed significantly different effects according to insulin resistance, particularly in luminal B tumors. Our findings warrant further studies of metformin in breast cancer with careful consideration to the metabolic characteristics of the study population.

Introduction

There is increasing evidence that hyperinsulinemia and insulin resistance worsen breast cancer prognosis,1 increase breast cancer risk,2 and partly explain the obesity–breast cancer risk association.3 Insulin may promote tumorigenesis via a direct effect on epithelial tissues or indirectly by affecting other modulators, such as insulin-like growth factors, sex hormones, and adipokines.4,5
Metformin is an oral biguanide that inhibits hepatic gluconeogenesis and sensitizes insulin action at peripheral level. It is the first-line treatment of type 2 diabetes6 and can substantially reduce diabetes incidence in obese women with glucose intolerance,7 with a good tolerability profile and low cost.8 Epidemiologic studies have shown a significant risk reduction in cancer incidence and mortality in patients with diabetes receiving metformin relative to other antidiabetic drugs, including positive results specifically in breast cancer.9,10 Preclinical studies suggest that metformin may have different mechanisms of tumor inhibition via insulin-dependent and -independent pathways,11 including activation of adenosine monophosphate kinase (AMPK) through liver kinase B112,13 and ataxia teleangiectasia-mutated gene kinase,14 with inhibition of cancer proliferation and apoptosis induction in breast cancer cell lines.15,16 These findings have prompted great interest in metformin as an anticancer agent,17 including an ongoing adjuvant trial in nondiabetic women with breast cancer.18
We assessed the antiproliferative activity of metformin in a window-of-opportunity trial in nondiabetic women with breast cancer who were candidates to surgery. The primary outcome measure was the response of Ki-67, which is used to select drugs in breast cancer because of its prognostic value and its postulated role in predicting drug efficacy.1921 Given the different effect of metformin on diabetes incidence depending on body mass index (BMI) or glucose intolerance,7 and the putative association between BMI and triple-negative disease,22,23 we also assessed whether metformin had a greater effect in women with insulin resistance, as assessed by the homeostasis model assessment (HOMA) index (fasting blood glucose [mmol/L] × insulin [mU/L]/22.5),24 and in estrogen receptor (ER)–negative tumors.

Patients and Methods

Patients

We conducted at the European Institute of Oncology, Milan, Italy, a randomized, phase II, double-blind, placebo-controlled trial in women with stage I to IIa breast cancer who were candidates for elective surgery and who received either metformin or placebo for 4 weeks before surgery. The study was conducted under the approval of the European Institute of Oncology institutional review board. Eligibility criteria were age ≥18 years; performance status = 0; palpable, histologically confirmed breast cancer candidate for surgery and no prior treatment; and signed informed consent. Exclusion criteria included prior invasive malignancy within 5 years, diabetes mellitus, creatinine more than 1.2 mg/dL, pregnancy, and participation in other trials. Regular alcohol consumption was categorized as 0, two or fewer, and more than two drinks per day consistent with the drinking pattern of Italian women in that age range.25

Treatment

Baseline core biopsies of tumor tissue and blood samples were obtained at study entry and before surgery for pre/post-treatment comparisons. Patients were randomly assigned to metformin 850-mg tablets or placebo once daily on days 1 to 3 to adapt to gastrointestinal symptoms, followed by two 850-mg tablets after dinner from days 4 through 28. Treatment was suspended 48 hours (24 hours at least) before anesthesia to avoid risk of lactic acidosis in compliance with US Food and Drug Administration prescribing indications.8,26 Toxicity was evaluated using National Cancer Institute Common Terminology Criteria of Adverse Events (NCI-CTCAE), version 3.0.

Pathology and Immunohistochemistry Measures

Pathologic assessment included evaluation of histologic type, grade, peritumoral vascular invasion, ER, progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki-67, as previously described.27 Specifically, Ki-67 was assessed by immunohistochemistry (IHC) according to recent international recommendations28 using the Mib-1 monoclonal antibody (1:200 dilution; Dako, Glostrup, Denmark). Cut slides were stained using an automated Dako immunostainer. The percentage of cells showing definite nuclear immunoreactivity among 2,000 invasive neoplastic cells in randomly selected, high-power (×40) fields at the periphery of the tumor was recorded. Fluorescent in situ hybridization (PATHvision, Abbott, IL) was undertaken for tumors with a 2+ HER2 IHC score. Molecular tumor subtypes were classified by IHC in four categories according to 2011 St Gallen criteria29: luminal A, ER or PgR positive and Ki-67 less than 14%; luminal B, ER or PgR positive and either Ki-67 ≥ 14% with negative HER2 or HER2 positive regardless of Ki-67 level; HER2 positive and ER/PgR negative; and triple negative, all receptors negative. Subtypes defined by IHC are similar to but not identical to intrinsic subtypes and represent a convenient approximation.

Circulating Biomarkers

Morning fasting blood samples were collected between 8 and 10 am at baseline and at treatment completion. Serum aliquots for insulin and high-sensitive C-reactive protein (CRP) were measured on frozen samples stored at −80°C until assayed, whereas glucose and total cholesterol were measured on fresh samples. Insulin was measured with an electrochemiluminescence immunoassay by Cobas e411 (Roche Diagnostics, Mannheim, Germany). Serum concentrations of glucose, total cholesterol, and CRP were determined by COBAS INTEGRA 800 (Roche Diagnostics). The HOMA index more than 2.8 was the cutoff of insulin resistance based on a population study conducted in Italy.24

Sample Size and Statistical Analysis

Ki-67 was log-transformed (lnKi-67), and the normal distribution of residuals was graphically checked in all models. The effect of treatment was tested using the standard approach for studies of biomarker changes, that is, an analysis of covariance linear regression model with post-treatment lnKi-67 as response variable and baseline lnKi-67 and treatment arm as covariates.30 From a presurgical study,31 we assumed a mean ± standard deviation (SD) baseline lnKi-67 = 2.9 ± 0.65. With 100 women per arm, two-sided 5% α error and 10% drop-out, the study had 80% power to detect an absolute difference of 0.25 in post-treatment mean lnKi-67 between arms, equivalent to a 4% absolute reduction from a 18% pretreatment level. Because a 3% decrease in Ki-67 with tamoxifen was associated with a 15% reduction of recurrence,21 a 4% reduction of Ki-67 should correspond to a 20% reduction of recurrence after 5 years (hazard ratio = 0.80). Two subgroup analyses were anticipated in the protocol, one by HOMA index and one by ER status. The first analysis postulated a 20% greater reduction of Ki-67 by metformin in women with HOMA index more than 2.8 based on previous studies.7,9,10 The second analysis assumed a greater effect by metformin in ER-negative tumors,22,32 predicted to be 20% of all tumors. Because no correction for multiple tests of significance was included, the two subgroup analyses are to be considered exploratory.
The results are reported as median raw levels, mean ± SE changes, and proportional changes to be easily interpreted, with supplementary tables for log-transformed data. Subgroup analyses were conducted separately and the appropriate treatment × covariate interaction term was tested. A post hoc analysis of the metformin effect by HOMA index was also performed in luminal B tumors given the putative role of increased insulin and insulin-like growth factors tyrosine kinase signaling in determining the partial endocrine responsiveness of this tumor type.33,34 Because of the different effects of metformin according to obesity, insulin resistance, and moderate alcohol use observed in previous studies,7,3537 we included also in the linear regression models, one at a time, the interaction terms between treatment and BMI, waist/hip girth ratio, alcohol consumption, and CRP. Subpopulation treatment effect pattern plot methodology38 was used to explore and display graphically the treatment effect along the continuous scale of the effect modifiers, using overlapping patient subgroups. With the same methods, we explored the effects of metformin on insulin, glucose, CRP, and total cholesterol levels according to BMI using 27 kg/m2 as cutoff, which represents the 75th percentile of the Italian female population in that age range.39 All analyses were based on the intention-to-treat approach using STATA software, version 11 (StataCorp, College Station, TX). Two-tailed probabilities were reported at P = .05 significance level.

Results

From December 9, 2008, to May 3, 2011, 311 women were assessed for eligibility; 111 did not meet inclusion criteria or refused the study, and 200 were randomly assigned to either metformin or placebo for 4 weeks. The participant flow diagram is shown in Figure 1. Three patients receiving metformin and one receiving placebo were not assessed for the primary end point. Baseline patient and tumor characteristics, shown in Table 1, were evenly distributed between arms. Half of women were premenopausal, nearly 40% were overweight or obese, and 27% were insulin resistant. Half of tumors were greater than 25 mm, and more than 85% of women had ER-positive disease. Mean (± SD) treatment duration was 27.1 ± 1.9 versus 26.7 ± 4.4 days and mean (± SD) compliance by pill count was 96% ± 17% versus 96% ± 19% in the metformin and placebo arms, respectively. The median intervals elapsed from last drug intake to blood drawing for circulating biomarkers and to surgery were 36 hours (interquartile range [IQR], 14 to 43 hours) versus 36 hours (IQR, 19 to 43 hours) and 67 hours (IQR, 21 to 74 hours) versus 67 hours (IQR, 21 to 75 hours) in the metformin and placebo arms, respectively.
Fig 1. Participant flow diagram. AE, adverse event.
Table 1. Baseline Patient Characteristics and Tumor Characteristics at Biopsy (pretreatment) and Surgery (post-treatment) by Allocated Arm
Characteristic Metformin (n = 100) Placebo (n = 100) P
Baseline      
    Age, years     .2
        Mean 53 52  
        SD 10 10  
    Menopausal status     .8
        Premenopausal 49 51  
        Perimenopausal 7 5  
        Postmenopausal 44 44  
    Age at menarche, years     .1
        Mean 12 13  
        SD 1 2  
    Body mass index, kg/m2     .9
        <25 58 60  
        25-30 29 27  
        ≥30 13 13  
    HOMA index*     .9
        Mean 2.3 2.3  
        SD 1.6 1.3  
    Insulin resistance (HOMA >2.8) 27 27 1.0
    Metabolic syndrome     .8
        Yes 24 27  
        No 74 72  
        Unknown 2 1  
    Waist/hip girth ratio     .5
        Mean 0.84 0.85  
        SD 0.06 0.07  
    Smoking habits     .6
        Nonsmoker 68 62  
        Former smoker 15 20  
        Current smoker 17 18  
    Daily alcohol drinks     .9
        0 79 82  
        ≤2 19 16  
        >2 2 2  
Pretreatment      
    Tumor diameter at ultrasounds, mm     .6
        Median 18 19  
        IQR 8-35 8-36  
    ER expression     .7
        ≥1% 85 88  
        <1% 15 12  
    PgR expression     1.0
        ≥1% 71 71  
        <1% 29 29  
Post-treatment§      
    Treatment duration, days     .7
        Mean 27.1 26.7  
        SD 1.9 4.4  
    Pathologic tumor diameter, mm     .8
        Median 25 22  
        IQR 5-85 6-110  
    ER expression     .5
        ≥1% 82 87  
        <1% 15 12  
    PgR expression     .1
        ≥1% 67 78  
        <1% 30 21
    Nodal status     .5
        pN0 42 36  
        pN1 33 38  
        pN2 10 15  
        pN3 13 10  
    Mastectomy     .7
        Yes 42 39  
        No 56 60  
    Tumor type     .9
        Ductal 82 81  
        Lobular 10 10  
        Mixed 3 4  
        Other 3 4  
    Molecular subtype by IHC     .3
        Luminal A 23 29  
        Luminal B 62 59  
        HER2+ 7 2  
        TN 8 10  
    Grade     .9
        1 11 11  
        2 45 52  
        3 38 33  
        Unknown 4 3  
    Peritumoral vascular invasion     .3
        0 66 58  
        1 15 13  
        2 15 25  
        3 2 3  
    HER2 overexpression/amplification     .2
        Yes 14 8  
        No 83 91
Abbreviations: ER, estrogen receptor; HER2, human epidermal growth factor receptor 2; HOMA, homeostasis model assessment; IHC, immunohistochemistry; IQR, interquartile range; PgR, progesterone receptor; SD, standard deviation; TN, triple negative breast cancer.
*
HOMA formula: fasting blood glucose (mmol/L) × insulin (mU/L)/22.5.
As defined by the International Diabetes Federation.
One drink = 10 g of alcohol during meal.
§
Metformin arm, n = 98; placebo arm, n = 99.
The pre- versus post-treatment changes in Ki-67 by allocated arm and according to specific subgroups are shown in Table 2 and Appendix Table A1 (online only). Overall, metformin did not significantly change Ki-67 relative to placebo, with a mean proportional increase of 4.0% (95% CI, −5.6% to 14.4%) 4 weeks apart. However, the effect of metformin according to HOMA index was heterogeneous (Pinteraction = .045, not corrected for multiplicity). In women with HOMA index more than 2.8, Ki-67 at surgery was lower on metformin, with a mean proportional decrease of 10.5% (95% CI, −26.1% to 8.4%), whereas in women with HOMA less than or equal to 2.8, Ki-67 increased by 11.1% (95% CI, −0.6% to 24.2%). The effect of metformin was also significantly modified by HOMA index in the subgroup of 119 women with luminal B tumors (Pinteraction = .05). Although the overall Ki-67 proportional change was −1.75 (95% CI, −11.4 to 8.9, P = .7), it was 5.01 (95% CI, −7.2 to 18.8, P = .4) in women with HOMA index less than or equal to 2.8 (n = 86) and −16.0 (95% CI, −30.9 to 2.1, P = .08) in women with HOMA index more than 2.8 (n = 33). Similar effect modifications of the metformin effect on Ki-67 were noted according to BMI (Pinteraction = .143), waist/hip girth ratio (Pinteraction = .058), and CRP (Pinteraction = .08), with a trend to a decrease of Ki-67 in the upper quartile and a trend to an increase of Ki-67 in the lowest quartiles (Table 2 and Appendix Table A1). Likewise, women who consumed alcohol showed a significant decrease of Ki-67 on metformin, whereas nondrinkers exhibited a reversed trend (Pinteraction = .005). Figure 2 shows the subpopulation treatment effect pattern plot analyses of the Ki-67 change by HOMA index, BMI, and CRP. The plots show a reversal of metformin effect on Ki-67 for HOMA index values between 2 and 3, without any trend to a greater effect for higher HOMA values (Fig 2A, all assessable tumors; Fig 2B, luminal B tumors). Conversely, for BMI and CRP, a continuous trend was seen, suggesting a greater decrease of Ki-67 induced by metformin for high BMI and CRP values (Figs 2C and 2D). The effect of metformin on Ki-67 response was not significantly affected by the interval from treatment cessation to surgery (P = .4), ER/PgR status, molecular subtype, metabolic syndrome, and all variables listed in Table 1 (not shown).
Table 2. Effect of Metformin on Ki-67 (%) Using the Placebo Arm As Reference Overall and by Specific Subgroups
Effect Modifier Metformin Placebo Proportional Change* P
Pretreatment Post-Treatment Δ Pretreatment Post-Treatment Δ
Median IQR Median IQR Mean SE Median IQR Median IQR Mean SE Mean 95% CI
Per-protocol analyses                            
    Overall, n = 196 19 14-31 21 14-32 1.9 0.9 18 12-29 20 13-31 1.6 0.8 4.0 −5.6 to +14.4 .4
    HOMA index                              
        ≤2.8, n = 142 19 14-32 19 12-28 2.8 1.1 16 12-28 21 15-33 1.0 0.8 11.1 −0.6 to +24.2 .045
        >2.8, n = 53 24 15-34 21 15-30 −0.8 1.3 20 11-35 20 17-40 2.9 1.7 −10.5 −26.1 to +8.4
    Body mass index                              
        ≤ 27 kg/m2 (75th percentile), n = 148 19 15-30 21 14-30 2.5 1.0 17 12-28 18 12-30 1.5 0.9 8.2 −3.4 to +21.3 .143
        > 27 kg/m2, n = 48 21 14-36 21 16-33 −0.1 1.6 20 13-33 23 14-32 1.8 1.8 −8.0 −23.2 to +10.3
Exploratory analyses                              
    Waist/hip girth ratio                              
        ≤0.88 (75th percentile), n = 145 20 14-35 22 14-34 2.1 1.0 18 12-28 18 13-29 1.2 0.9 9.9 −1.8 to +23.2 .058
        >0.88, n = 51 18 15-26 20 15-25 0.9 1.9 19 11-35 22 15-34 2.5 1.3 −11.2 −26.3 to +6.9
    Alcohol consumption                              
        No, n = 157 19 15-29 21 15-31 1.7 0.9 18 12-29 18 12-29 0.4 0.7 10.3 −0.8 to +22.6 .005
        Yes, n = 39 22 14-33 21 14-34 2.3 2.4 20 14-25 26 18-35 6.9 2.2 −20.2 −35.6 to −0.9
    C-reactive protein                              
        ≤ 2.69 mg/L (75th percentile), n = 147 19 13-28 21 14-30 2.8 1.1 18 12-30 20 12-31 1.4 0.9 9.4 −2.7 to +23.1 .08
        > 2.69 mg/L, n = 49 20 15-36 21 15-33 −0.5 1.2 18 11-21 18 14-25 2.4 1.0 −11.3 −24.1 to +3.7
Abbreviations: HOMA, homeostasis model assessment; IQR, interquartile range; Δ, post-pre difference.
*
Analysis of covariance model, adjusted for baseline Ki-67; the proportional change is defined as 100 × exp (mean metformin post-treatment − mean placebo post-treatment)/(mean placebo post-treatment).
Data on HOMA index was available for 199 patients; HOMA formula: fasting blood glucose (mmol/L) × insulin (mU/L)/22.5.
P for interaction between treatment and the putative effect modifier.
Fig 2. Subpopulation treatment effect pattern plot of the adjusted difference (ie, the treatment effect estimate from the analysis of covariance model, adjusted for baseline Ki-67) between metformin and placebo by homeostasis model assessment (HOMA) index in (A) all patients and (B) luminal B tumors and by (C) body mass index (BMI) and (D) C-reactive protein levels. Positive change, metformin worse; negative change, metformin better. We used the “sliding window” approach with n1 = 45, n2 = 50.
The changes in insulin, glucose, CRP, and total cholesterol levels by allocated arm and according to BMI are shown in Table 3 and Appendix Table A2 (online only) for log-transformed data. Overall, metformin did not significantly modulate insulin and glucose levels, but there was a trend toward a significant effect modification by BMI (Pinteractions = .092 and = .015, respectively), with a reduction of both biomarkers in women with BMI more than 27. Moreover, an overall reduction of CRP and total cholesterol was noted under metformin. There was no significant influence of the interval from treatment cessation to blood drawing on any circulating biomarker, including insulin and glucose levels (P = .4 and P = .6, respectively).
Table 3. Effect of Metformin on Circulating Biomarkers, Overall and by BMI Subgroups
Biomarker Metformin Placebo Proportional Change* P
Pretreatment Post-Treatment Δ Pretreatment Post-Treatment Δ
Median IQR Median IQR Mean SE Median IQR Median IQR Mean SE Mean 95% CI
Insulin, mU/L                              
    Overall effect, n = 196 9.0 5.7-12.2 7.2 5.6-12.3 0.2 0.8 8.9 6.5-12.7 7.6 5.2-12.9 0.9 1.1 2.3 −10.6 to +17.1 .739
    Treatment × BMI interaction                              
        BMI ≤27 kg/m2 (75th percentile), n = 149 7.2 5.5-11.2 6.9 5.3-10.3 −0.2 0.6 8.5 6.3-11.0 6.5 4.9-9.5 −0.6 0.6 8.3 −4.6 to +22.9 .092
        BMI >27 kg/m2, n = 47 12.1 9.1-17.0 10.2 6.9-17.1 1.4 2.6 12.4 9.0-16.4 13.2 10.0-16.9 5.5 4.0 −13.8 −42.8 to +29.9
Glucose, mg/dL                              
    Overall effect, n = 194 88 83-93 87 81-94 0.3 1.2 91 86-98 91 83-96 0.4 1.0 −1.8 −4.7 to +1.3 .254
    Treatment × BMI interaction                              
        BMI ≤27 kg/m2 (75th percentile), n = 146 87 82-91 87 81-94 1.0 1.4 90 83-95 90 83-94 −0.4 0.9 0.04 −3.0 to +3.2 .015
        BMI >27 kg/m2, n = 48 91 85-99 87 81-96 −2.8 2.4 93 88-100 96 90-106 3.1 3.2 −6.8 −14.0 to +1.0
C-reactive protein, mg/L                              
    Overall effect, n = 196 1.40 0.66-3.00 1.04 0.47-2.07 −0.8 0.3 0.99 0.54-2.14 1.24 0.52-2.12 −0.4 0.4 −19.9 −34.3 to −2.2 .029
    Treatment × BMI interaction                              
        BMI ≤27 kg/m2 (75th percentile), n = 149 1.24 0.50-2.22 0.88 0.42-1.54 −0.6 0.2 0.77 0.38-1.33 0.91 0.42-1.55 −0.3 0.4 −18.7 −35.1 to +1.9 .7
        BMI >27 kg/m2, n = 47 2.63 1.09-3.72 1.90 1.01-3.85 −1.5 0.9 2.41 1.14-4.44 2.62 1.43-5.86 −0.8 1.3 −23.6 −51.4 to +20.1
Total cholesterol, mg/dL                              
    Overall effect, n = 197 212 187-242 204 179-227 −9.4 2.6 216 188-242 219 190-240 2.1 2.0 −5.3 −7.8 to −2.7 < .001
    Treatment × BMI interaction                              
        BMI ≤27 kg/m2 (75th percentile), n = 149 213 190-244 204 181-228 −10.4 3.0 217 188-244 216 190-239 −0.6 2.22 −4.5 −7.2 to −1.6 .191
        BMI >27 kg/m2, n = 48 208 186-235 210 170-226 −6.3 5.1 208 187-230 225 200-244 10.5 4.4 −7.9 −13.6 to −1.8
Abbreviations: BMI, body mass index; IQR, interquartile range; Δ, post-pre difference.
*
Analysis of covariance model, adjusted for baseline biomarker levels, age, and BMI; the proportional change is defined as 100 × exp (mean metformin post-treatment − mean placebo post-treatment) / (mean placebo post-treatment).
P for interaction between treatment and BMI.
Adverse events are listed in Table 4. Diarrhea (P = .05) and nausea (P < .05) were the most frequent adverse events observed in the metformin arm. There was one grade 3 event (abdominal pain) and one serious adverse event (dizziness, vomiting), both in the placebo arm.
Table 4. Number of Patients With Adverse Events by Allocated Arm
Adverse Event Metformin (n = 100) Placebo (n = 100)
Grade 1 Grade 2 Grade 1 Grade 2
Nausea* 31 2 13 2
Diarrhea* 45 4 9 3
Dyspepsia 8 1 7 1
Headache 9 2 7 1
Abdominal pain 11 2 5
Other 44 9 53 4
NOTE. Number of patients is equivalent to percentage.
*
P < .05, Fisher's exact test versus placebo.
One grade 3 adverse event in placebo arm.
One serious adverse event (dizziness, vomiting) in the placebo arm.

Discussion

Therapeutic targeting of aberrant tumor metabolism has gained attention because the abnormally high proliferative activity of malignant cells requires high levels of nutrients to meet the increased demands for energy consumption and protein biosynthesis.40 Abnormalities of host glucose and insulin metabolism are critical in promoting breast cancer,41 and preclinical15 and epidemiologic evidence9,10 indicates that metformin may have therapeutic activity against breast cancer through indirect (insulin mediated) and direct mechanisms. We assessed in a proof-of-principle trial whether metformin decreased breast cancer proliferation in nondiabetic women using short-term Ki-67 response as surrogate biomarker.1921
Overall, metformin did not significantly decrease breast cancer proliferation, thus failing to support our primary hypothesis. However, in a planned subgroup analysis, the effect of metformin on Ki-67 was modified by insulin resistance status (HOMA index), with a nonsignificant 10.5% decrease of breast cancer proliferation in women with insulin resistance and a nonsignificant 11% increase in women without insulin resistance. The nominal statistical significance of the interaction test (P = .045) must be considered with caution because correction for multiple testing would bring it to nonsignificant levels. Yet, the qualitative interaction between metformin and insulin resistance may have important clinical implications and warrants confirmation in independent studies.
In additional unplanned analyses, similar modifications of metformin effects on Ki-67 by HOMA index were noted in women with luminal B tumors and in women who were overweight or obese, had abdominal obesity, had moderate alcohol intake, and had elevated CRP levels. In contrast to our secondary hypothesis, metformin did not differentially affect cancer proliferation according to ER status, although the statistical power of this analysis was less than 60% due to the lower than expected number of ER-negative tumors. Finally, a heterogeneous effect of metformin was noted on circulating insulin and glucose according to BMI, further suggesting a host-dependent therapeutic effect. Importantly, these findings are in line with the preventive effect of metformin on diabetes incidence,7 which was restricted to obese or glucose-intolerant women, with a greater benefit for increasing BMI and glucose levels.
The trend to a decrease in breast cancer proliferation in women with high HOMA index suggests that indirect, insulin- and glucose-mediated effects are the main mechanism of anticancer effect of metformin in human breast cancer and attenuate the role of direct effects on specific pathways of the AMPK–phospatidylinositol 3-kinase–mammalian target of rapamycin cascade,18 possibly because of the much higher concentrations used in vitro15,32 and in vivo16,42 relative to humans.8 An insulin-mediated mechanism is also in line with the vast majority of epidemiologic studies in patients with diabetes,9 in whom metformin is the first-line treatment and is particularly effective in reducing major diabetes complications in overweight and obese patients.7,35 In its initial phase, which accounts for a large fraction of the Western population, type 2 diabetes is characterized by insulin resistance and is associated with the highest increase in breast cancer risk,43,44 presumably as a result of the proliferative effects of elevated insulin, glucose, insulin-like growth factors, adipokines, sex hormones, and low sex hormone–binding globulin levels.5,45 Thus the public health implications of a 10% decrease in Ki-67 on breast cancer risk in insulin-resistant women, coupled with the remarkable reduction of diabetes onset in women with glucose intolerance or obesity,7 could be substantial and supportive of preventive studies in women with these characteristics.1 This is especially important in the United States, where the prevalence of obesity and insulin resistance is at least twice as much as our study population, where only 13% had a BMI more than 30 and approximately a quarter were insulin resistant. Likewise, the heterogeneous effect in luminal B cancers, although derived from a post hoc analysis, has potential therapeutic implications given the high frequency of this partially endocrine resistant tumor type.
The trend to an increased proliferation in women without insulin resistance, albeit nonsignificant, suggests a complex biologic effect of metformin in women without metabolic alterations. Animal studies have shown that metformin may have tumor suppressive effects where a metabolic phenotype of high caloric intake, metabolic syndrome, and diabetes exists, but no effect or even a growth promoting effect under normal energy intake.16,46 In humans, metformin has shown differential effects according to grade of obesity and glucose intolerance, exercise, diet, alcohol use, and lipid levels, supporting the notion of a drug that regulates metabolic homeostasis depending on energy balance.7,13,16,3537 Further mechanistic studies are necessary to understand these heterogeneous findings. Because regular and moderate alcohol use is a significant risk factor for breast cancer,47 presumably through increased estrogen levels, the observation that metformin prevented the increased cancer proliferation in moderate alcohol users is noteworthy. One mechanism could be the decrease of testosterone levels observed in our study (unpublished data), similarly to what is observed in women with polycystic ovary syndrome.48 Interestingly, a synergistic interaction between metformin and moderate alcohol use in lowering diabetes incidence was noted in the Diabetes Prevention Program trial,37 suggesting the existence of common biologic pathways on energy regulation.
A presurgical study49 recently conducted in Scotland in approximately 50 patients with breast cancer showed a significant decrease of Ki-67 under metformin, with modulation of different target genes. There was no evidence of specific subgroup effects, so a direct comparison with our study is difficult. However, the higher insulin levels compared with our study and the likely higher BMI levels of the Scottish population supports the notion that metformin lowers breast cancer proliferation in insulin resistant or overweight women.
Adverse events were in line with the known toxicity profile of metformin, with a higher incidence of gastrointestinal symptoms, mostly low-grade diarrhea and nausea of transient duration.8 Notably, compliance was extremely high, and there were no cases of hypoglycemia in this nondiabetic population.
The strengths of our study include use of an international reference laboratory for breast cancer tissue biomarkers, a large cohort representing a wide range of host and tumor characteristics that enabled subgroup analyses, and a sufficient power for the predicted interaction with HOMA index. A potential limitation, which was dictated by safety issues, is the interval from last drug intake to surgery (median, 67 hours; IQR, 21 to 74 hours), which is longer than the blood half-life of metformin (approximately 18 hours8). However, there was no significant association between any biomarker changes and the interval from treatment cessation, and metformin can reach higher tissue:blood concentrations,50 which should not decrease its biologic effects up to several days from drug cessation. If anything, however, the wash-out time diluted our findings toward the null hypothesis without affecting overall conclusions.
In conclusion, our results suggest a heterogeneous effect of metformin on breast cancer proliferation depending on insulin resistance and other factors reflecting altered energy balance, with a trend to a decreased proliferation in women with elevated HOMA index and an opposite trend in women with normal insulin sensitivity. Although our observation is consistent with the current epidemiologic evidence derived from diabetic women, further clinical studies are necessary to confirm these findings in the general population.

Acknowledgment

We thank Serena Mora and Chiara Bresciani for the data management assistance and Brandy Heckman for her thoughtful scientific advice.
See accompanying editorial on page 2573 and article on page 2698; listen to the podcast by Dr Ligibel at www.jco.org/podcasts
Clinical trial information can be found for the following: ISRCTN16493703.
The summary of supplementary materials could not be included.

Supplementary Material

File (protocol_selected_sections.pdf)

Authors' Disclosures of Potential Conflicts of Interest

The author(s) indicated no potential conflicts of interest.

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Appendix

Table A1. Effect of Metformin on Log-Transformed Ki-67 (%) Using the Placebo Arm As Reference Overall and by Specific Subgroups
Effect Modifier Metformin Placebo Adjusted Difference,* Metformin v Placebo P
Pretreatment Post-Treatment Pretreatment Post-Treatment
Mean lnKi-67 SE Mean lnKi-67 SE Mean lnKi-67 SE Mean lnKi-67 SE Mean lnKi-67 95% CI
Per-protocol analyses                      
    Overall, n = 196 2.991 0.067 3.082 0.061 2.909 0.065 2.977 0.065 0.039 −0.057 to 0.135 .4
    HOMA index                      
        ≤ 2.8, n = 142 2.947 0.080 3.079 0.072 2.873 0.072 2.914 0.074 0.105 −0.006 to 0.216 .045
        > 2.8, n = 53 3.139 0.122 3.130 0.130 2.996 0.138 3.129 0.129 −0.111 −0.303 to 0.080
    Body mass index                      
        ≤27 kg/m2 (75th percentile), n = 148 2.956 0.081 3.076 0.072 2.886 0.073 2.940 0.076 0.079 −0.035 to 0.193 .143
        >27 kg/m2, n = 48 3.095 0.116 3.098 0.111 2.980 0.141 3.093 0.124 −0.083 −0.265 to 0.098
Exploratory analyses            
    Waist/hip girth ratio          
        ≤0.88 (75th percentile), n = 145 2.999 0.083 3.111 0.073 2.906 0.074 2.941 0.077 0.095 −0.019 to 0.208 .058
        > 0.88, n = 51 2.961 0.085 2.976 0.098 2.917 0.132 3.060 0.121 −0.119 −0.305 to 0.067
    Alcohol consumption          
        No, n = 157 2.970 0.079 3.075 0.068 2.909 0.073 2.927 0.073 0.098 −0.008 to 0.204 .005
        Yes, n = 39 3.065 0.127 3.107 0.135 2.911 0.137 3.202 0.130 −0.225 −0.441 to 0.009
    C-reactive protein          
        ≤2.69 mg/L (75th percentile), n = 147 2.949 0.083 3.083 0.073 2.928 0.075 2.976 0.076 0.090 −0.028 to 0.208 .08
        >2.69 mg/L, n = 49 3.094 0.113 3.079 0.109 2.838 0.127 2.981 0.119 −0.120 −0.276 to 0.036
Abbreviation: HOMA, homeostasis model assessment.
*
Analysis of covariance model, adjusted for baseline Ki-67.
Data on HOMA index was available for 199 patients; HOMA formula: fasting blood glucose (mmol/L) × insulin (mU/L)/22.5.
P for interaction between treatment and the putative effect modifier.
Table A2. Effect of Metformin on Log-Transformed Circulating Biomarker Levels, Overall and by BMI Subgroups
Biomarker Metformin Placebo Adjusted Difference,* Metformin v Placebo P
Pretreatment Post-Treatment Pretreatment Post-Treatment
Mean ln SE Mean ln SE Mean ln SE Mean ln SE Mean ln 95% CI
Insulin, mU/L                      
    Overall effect, n = 196 2.145 0.057 2.113 0.060 2.194 0.050 2.124 0.067 0.02 −0.112 to 0.158 .739
    Treatment × BMI interaction                      
        BMI ≤27 kg/m2 (75th percentile), n = 149 2.047 0.063 2.018 0.061 2.103 0.055 1.967 0.062 0.079 −0.047 to 0.206 .092
        BMI >27 kg/m2, n = 47 2.463 0.112 2.419 0.145 2.480 0.092 2.613 0.162 −0.149 −0.559 to 0.261
Blood glucose, mg/dL                      
    Overall effect, n = 194 4.486 0.011 4.482 0.013 4.511 0.013 4.513 0.014 −0.018 −0.048 to 0.013 .254
    Treatment × BMI interaction                      
        BMI ≤27 kg/m2 (75th percentile), n = 146 4.471 0.012 4.478 0.015 4.491 0.013 4.486 0.013 0.001 −0.031 to 0.031 .015
        BMI >27 kg/m2, n = 48 4.533 0.022 4.496 0.032 4.570 0.030 4.597 0.036 −0.071 −0.151 to 0.010
C-reactive protein, mg/L                      
    Overall effect, n = 196 0.276 0.116 0.012 0.105 0.101 0.117 0.143 0.108 −0.222 −0.421 to 0.023 .029
    Treatment × BMI interaction                      
        BMI ≤27 kg/m2 (75th percentile), n = 149 0.078 0.128 −0.182 0.116 −0.188 0.122 −0.133 0.114 −0.207 −0.433 to 0.019 .7
        BMI >27 kg/m2, n = 47 0.893 0.199 0.636 0.188 0.970 0.210 1.000 0.183 −0.269 −0.722 to 0.183
Total cholesterol, mg/dL                      
    Overall effect, n = 197 5.362 0.017 5.320 0.016 5.363 0.016 5.373 0.016 −0.054 −0.081 to −0.027 < .001
    Treatment × BMI interaction                      
        BMI ≤27 kg/m2 (75th percentile), n = 149 5.366 0.021 5.321 0.018 5.368 0.019 5.366 0.018 −0.046 −0.075 to −0.016 .191
        BMI >27 kg/m2, n = 48 5.350 0.032 5.316 0.037 5.348 0.030 5.394 0.033 −0.082 −0.147 to −0.018
Abbreviation: BMI, body mass index.
*
Analysis of covariance model, adjusted for baseline biomarker levels, age, and BMI.
P for interaction between treatment and BMI.

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Journal of Clinical Oncology
Pages: 2593 - 2600
PubMed: 22564993

History

Published online: May 07, 2012
Published in print: July 20, 2012

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Bernardo Bonanni
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Matteo Puntoni
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Massimiliano Cazzaniga
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Giancarlo Pruneri
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Davide Serrano
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Aliana Guerrieri-Gonzaga
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Alessandra Gennari
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Maria Stella Trabacca
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Viviana Galimberti
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Paolo Veronesi
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Harriet Johansson
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Valentina Aristarco
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Fabio Bassi
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Alberto Luini
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Matteo Lazzeroni
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Clara Varricchio
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Giuseppe Viale
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Paolo Bruzzi
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.
Andrea DeCensi [email protected]
Bernardo Bonanni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, and Andrea DeCensi, European Institute of Oncology; Giancarlo Pruneri, Paolo Veronesi, and Giuseppe Viale, University of Milan, Milan; Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, and Andrea DeCensi, E.O. Ospedali Galliera; and Paolo Bruzzi, National Cancer Research Institute, Genoa, Italy.

Notes

Corresponding author: Andrea DeCensi, MD, Division of Medical Oncology, E.O. Ospedali Galliera, Mura delle Cappuccine 14, 16128 Genoa, Italy; e-mail: [email protected].

Author Contributions

Conception and design: Bernardo Bonanni, Matteo Puntoni, Alessandra Gennari, Maria Stella Trabacca, Andrea DeCensi
Financial support: Bernardo Bonanni, Aliana Guerrieri-Gonzaga, Andrea DeCensi
Administrative support: Aliana Guerrieri-Gonzaga
Provision of study materials or patients: Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale
Collection and assembly of data: Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale
Data analysis and interpretation: Bernardo Bonanni, Matteo Puntoni, Aliana Guerrieri-Gonzaga, Paolo Bruzzi, Andrea DeCensi
Manuscript writing: All authors
Final approval of manuscript: All authors

Disclosures

Authors' disclosures of potential conflicts of interest and author contributions are found at the end of this article.

Funding Information

Supported by grants from the Italian League Against Cancer and the Italian Health Ministry and a contract from the Italian Foundation for Cancer Research. Metformin and placebo were donated by Laboratori Guidotti, Pisa, Italy.

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Bernardo Bonanni, Matteo Puntoni, Massimiliano Cazzaniga, Giancarlo Pruneri, Davide Serrano, Aliana Guerrieri-Gonzaga, Alessandra Gennari, Maria Stella Trabacca, Viviana Galimberti, Paolo Veronesi, Harriet Johansson, Valentina Aristarco, Fabio Bassi, Alberto Luini, Matteo Lazzeroni, Clara Varricchio, Giuseppe Viale, Paolo Bruzzi, Andrea DeCensi
Journal of Clinical Oncology 2012 30:21, 2593-2600

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