Breast Cancer Res Treat DOI 10.1007/s10549-015-3287-5

CLINICAL TRIAL

Impact of body mass index on neoadjuvant treatment outcome: a pooled analysis of eight prospective neoadjuvant breast cancer trials Caterina Fontanella • Bianca Lederer • Stephan Gade • Mieke Vanoppen • Jens Uwe Blohmer • Serban Dan Costa • Carsten Denkert • Holger Eidtmann • Bernd Gerber • Claus Hanusch • Jo¨rn Hilfrich • Jens Huober • Andreas Schneeweiss Stefan Paepke • Christian Jackisch • Keyur Mehta • Valentina Nekljudova • Michael Untch • Patrick Neven • Gunter von Minckwitz • Sibylle Loibl

•

Received: 23 January 2015 / Accepted: 27 January 2015 Ó Springer Science+Business Media New York 2015

Abstract Obesity is associated with an increased risk of breast cancer (BC) and poorer outcome. We assessed the impact of body mass index (BMI) on pathological complete response (pCR), disease-free (DFS), and overall survival (OS), according to BC subtypes in patients with primary BC treated with neoadjuvant chemotherapy. 8,872 patients with primary BC from eight neoadjuvant trials were categorized according to BMI: underweight (\18.5 kg/m2), normal weight (18.5 to\25 kg/m2), overweight (25 to\30 kg/m2), obese (30 to \40 kg/m2), and very obese (C40 kg/m2). BC subtypes were defined as luminal-like (ER/PgR-positive and HER2-negative), HER2/luminal (ER/PgR-positive and HER2-positive), HER2-like (ER/PgR-negative and HER2positive), and triple-negative (TNBC; ER/PgR- and HER2-

negative). pCR rate was higher in normal weight patients compared with all other BMI groups (P = 0.003). Mean DFS and OS were shorter in obese (87.3 months, P = 0.014 and 94.9 months, P = 0.001, respectively) and very obese (66.6 months, P \ 0.001 and 75.3 months, P \ 0.001, respectively) compared with normal weight patients (91.5 and 98.8 months, respectively) which was confirmed by subpopulation treatment effect pattern plot analyses and was consistent in luminal-like and TNBC. No interaction was observed between BMI and pCR. Normal weight patients experienced less non-hematological adverse events (P = 0.002) and were more likely to receive full taxane doses (P \ 0.001) compared with all other BMI groups. In multivariable analysis, the dose of taxanes was predictive for pCR (P \ 0.001). Higher BMI was associated with lower

C. Fontanella
B. Lederer
S. Gade
K. Mehta
V. Nekljudova
G. von Minckwitz
S. Loibl (&) German Breast Group, Neu-Isenburg, Germany e-mail: [email protected]

C. Denkert Institute of Pathology, Charite´ Hospital, Berlin, Germany

C. Fontanella e-mail: [email protected] C. Fontanella Department of Medical Oncology, University Hospital of Udine, Piazzala Santa Maria della Misericordia 14, 33100 Udine, UD, Italy M. Vanoppen
P. Neven Department of Oncology, Catholic University of Leuven, Leuven, Belgium J. U. Blohmer Department of Gynaecology and Obstetrics, St. GertraudenHospital, Berlin, Germany S. D. Costa Department of Gynaecology and Obstetrics, University Hospital, Magdeburg, Germany

H. Eidtmann Department of Gynaecology and Obstetrics, University Hospital, Kiel, Germany B. Gerber Department of Gynaecology and Obstetrics, University Hospital, Rostock, Germany C. Hanusch Department of Gynaecology and Obstetrics, Rot-KreuzKlinikum, Munich, Germany J. Hilfrich Department of Gynaecology and Obstetrics, Eilenriede Klinik, Hannover, Germany J. Huober Department of Gynaecology and Obstetrics, University Hospital, Ulm, Germany

123

Breast Cancer Res Treat

pCR and a detrimental impact on survival. Normal weight patients had the best compliance to chemotherapy and received the highest taxane doses, which seems to be related with treatment outcomes. Keywords Breast cancer
Body mass index
Neoadjuvant treatment
Breast cancer subtypes

Introduction The worldwide prevalence of obesity, defined as a body mass index (BMI) C30 kg/m2, has reached epidemic proportions, with about one-third of the population being either overweight (BMI C 25 kg/m2) or obese [1]. Obesity is a well-documented risk factor contributing to a dramatic increase in morbidity and mortality [2–4]. A large amount of data show a negative impact of high BMI on cancer development and prognosis and a relationship between weight and risk of endometrial, ovarian, and breast cancer (BC) has recently been reported in a large population-based study [5]. Several factors have been proposed to explain this relationship, including high level of endogenous sex steroids, insulin resistance, and lowgrade chronic inflammation [6–8]. Over the last years, a growing body of evidence has suggested that high BMI should be considered a poor predictive and prognostic factor for patients with early BC treated with systemic therapy [9–15]. Moreover, it has also been assessed that the negative impact of BMI on BC longterm treatment outcome is independent of when BMI is ascertained [16]. Chemotherapy is traditionally being dosed on a patient’s estimated body surface area (BSA) and oncologists tend to cap BSA at 2.0 m2 as a threshold to calculate chemotherapy doses in obese patients for fear of overdosing [17]. This practice may also negatively influence the outcomes among overweight and obese patients. Moreover, a statis-

A. Schneeweiss National Center for Tumor Diseases, University Hospital, Heidelberg, Germany S. Paepke Department of Gynaecology and Obstetrics, University Hospital rechts der Isar, Munich, Germany C. Jackisch Department of Gynaecology and Obstetrics, Sana-Klinikum, Offenbach, Germany M. Untch Department of Gynaecology and Obstetrics, HELIOS Klinikum Berlin-Buch, Schwanebecker Chaussee 50, 13125 Berlin, Germany

123

tically significant association between obesity, diabetes mellitus, and BC recurrence or survival has been reported [18–20], suggesting that metabolic deregulation may play a major role in BC prognosis among obese patients. Neoadjuvant treatment is an important treatment option in all BC subtypes [21], and it has been demonstrated that achieving a pathological complete response (pCR) is predictive for longer survival, especially in patients with highly proliferative tumors [22]. Moreover, the neoadjuvant setting offers the unique possibility to evaluate in vivo chemosensitivity and to adapt the chemotherapy strategy in non-responders [23]. Therefore, assessing the prognostic and predictive impact of BMI in the neoadjuvant setting is of major interest. We aimed to evaluate the impact of BMI on pCR, disease-free survival (DFS), and overall survival (OS), according to BC subtypes in 8,872 patients treated with anthracycline-taxane-based neoadjuvant treatment.

Patients and methods Patient selection From June 1999 to June 2011, the German Breast Group (GBG) and Arbeitsgemeinschaft Gyna¨kologische Onkologie-Breast Group (AGO-B) conducted eight prospective clinical trials that explored systemic anthracycline-taxanebased neoadjuvant treatment in 8,872 patients with operable or non-operable primary BC. The study designs of GeparDuo [24], GeparTrio pilot [25], main study [26, 27], GeparQuattro [28, 29], GeparQuinto [30–33], AGO1 [34], Preoperative Epirubicin Paclitaxel Aranesp Study (PREPARE) [35], and Taxol Epirubicin Cyclophosphamide Herceptin Neoadjuvant (TECHNO) [36] have been reported previously. All patients provided written informed consent for study participation and data collection. All trials were approved by the relevant ethics committees. All patients were female and needed to have a primary BC measurable either by palpation, ultrasound, or mammography. Tumor size had to be C3 cm in AGO1, C1 cm according to ultrasound measurements in GeparQuattro and GeparQuinto, and C2 cm in all other trials. Locally advanced (cT4a–c) and inflammatory BCs were eligible except for GeparDuo. Diagnosis of invasive BC was histologically confirmed by core biopsy. In GeparQuattro and TECHNO, patients with human epidermal growth factor receptor-2 (HER2)-positive tumors received 1 year of trastuzumab treatment. In GeparQuinto, patients with HER2-positive disease received trastuzumab and lapatinib; patients with HER2-

Breast Cancer Res Treat

negative disease received bevacizumab, and non-responders after four initial cycles of epirubicin ? cyclophosphamide were randomized to paclitaxel ± everolimus. Patients with estrogen (ER)- and/or progesterone receptor (PgR)-positive tumors received adjuvant endocrine treatment for C5 years. In GeparDuo, GeparQuattro, and GeparQuinto, the BSA was capped at 2.0 m2 as a threshold to calculate chemotherapy doses. Adjuvant radiotherapy was given according to national guidelines. Only patients who received C1 cycle of neoadjuvant treatment were included in the analysis. Patients with primary metastatic disease, other prior malignancies, or prior treatment for invasive BC were excluded from all trials. The median follow-up of the 8,872 patients was 42.7 months (range 1.0–116.6). ER/PgR status was considered positive if C10 % of cells stained positive. HER2 status was assessed by immunohistochemistry (HER2 positivity if the score was 3) or fluorescent in situ hybridization (HER2 positivity if FISH ratio [2.2 [37]). BC subtypes were defined as follows: luminal-like (ER/PgR-positive and HER2-negative), HER2/luminal (ER/PgR-positive and HER2-positive), HER2-like (ER/PgR-negative and HER2-positive), and triple-negative (TNBC; ER/PgR- and HER2-negative). Histologic type, tumor grade, and ER, PgR, and HER2 status were assessed in the primary tumor core biopsy by local pathology and in 1,635 patients by central pathology. BMI was calculated as weight (in kg) divided by the squared height (in m) (kg/m2) and categorized into predefined groups as underweight (\18.5 kg/m2), normal weight (18.5 to \25 kg/m2), overweight (25 to \30 kg/m2), obese (30 to \39 kg/m2), and very obese (C40 kg/m2) [38]. Estimated BSA was calculated according to the Mosteller formula [39].

morbidities at the time of primary BC diagnosis, adverse events (AEs) during study treatment (according to National Cancer Institute Common Toxicity Criteria 3.0), and dose of chemotherapy administered. Univariable models (cross-tabulation and two-sided ChiSquare test) were fit to evaluate the predictive effect of BMI groups on categorical variables. Multivariable logistic regression analyses were used to calculate odds ratio (OR) and 95 % confidence intervals (CI). Variables considered in modeling probability of attaining a pCR after neoadjuvant treatment and survival analyses included BMI, BSA, BC subtypes, histological type, nuclear grade, clinical tumor and nodal stage, age (according to the St. Gallen 1998 Consensus, we categorized patients into \35 years and C35 years [41]), number of co-morbidities at the time of diagnosis, and dose of chemotherapy. Survival curves were plotted using the Kaplan–Meier product-limit method and compared between BMI groups using the log-rank test. Cox proportional hazards regression analyses were performed to calculate hazard ratios (HR) and 95 % CI for demographic and clinical characteristics and survival. A likelihood-ratio test was used to assess interaction between variables. All reported P values were two-sided, and P B 0.05 was considered statistically significant. IBM SPSS 20.0 (SPSS, Chicago, IL) was used to perform all analyses. Subpopulation treatment effect pattern plot (STEPP) sliding windows analyses [42] were performed using R 3.0.1. We used 95 % confidence level with Bonferroni correction to account for the number of subgroups.

Definition of endpoints

Overall, 1.5 % (134/8,872) of patients were underweight, 47.6 % (4,224/8,872) normal weight, 31.7 % (2,811/8,872) overweight, 17.8 % (1,575/8,872) obese, and 1.4 % (128/ 8,872) very obese. Moreover, 13.8 % (1,233/8,872) of patients had a BSA [ 2.0 m2. Median age was 49.7 years (range 21–81). According to BMI groups, median age was 44.1 years (23–72) in underweight, 47.4 (21–79) in normal weight, 51.6 (21–81) in overweight, 53.6 (25–79) in obese, and 52.3 (29–77) in very obese patients. Overweight (6.3 %, 176/2,793), obese (9.4 %, 147/1,558), and very obese patients (19.0 %, 24/126) had a significantly higher percentage of inflammatory BC compared with normal weight (3.3 %, 140/4,189) and underweight patients (2.2 %, 3/134; P \ 0.001), but no significant correlation with nodal stage at baseline was seen

pCR was defined as no invasive residual tumor in breast and nodes (ypT0/is ypN0). DFS was calculated from the time of randomization to the time of disease recurrence or metastasis, death irrespective of any cause or last follow-up [40]. OS was calculated from the date of randomization to the date of death irrespective of any cause or last follow-up. Statistics Baseline characteristics, histopathologic results at surgery, and follow-up were extracted from the original database for all 8,872 patients. For 4,061 patients enrolled in GeparQuattro and GeparQuinto, we also analyzed co-

Results Baseline characteristics

123

Breast Cancer Res Treat

(P = 0.542). The percentage of ductal cancers decreased with increasing BMI (P = 0.045) (Table 1). BMI was not associated with nuclear grade, ER, PgR, and HER2 status (Table 1). Distribution of subtypes did not differ significantly by BMI (P = 0.994), with approximately 48 % luminal-like, 16 % HER2/luminal, 13 % HER2-like, and 23 % TNBC in all BMI groups. Regarding the patients’ medical history, the percentage of patients with more than two co-morbidities at the time of primary BC diagnosis was 6.2 % (4/65) in underweight, 9.5 % (184/1,945) in normal weight, 12.7 % (164/1,290) in overweight, 20.5 % (144/704) in obese, and 31.6 % (18/ 57) in very obese patients (P \ 0.001). In particular, patients with a high BMI were more likely to be diagnosed with concomitant diabetes mellitus (P \ 0.001), hypertension (P \ 0.001), dyslipidemia (P = 0.002), hyperuricemia (P \ 0.001), history of thromboembolic events (P = 0.043), cardiac disorders (P = 0.023), and thyroid dysfunctions (P = 0.001) (Table 1). Delivery of chemotherapy and AEs in GeparQuattro and GeparQuinto A sequential administration of anthracycline–taxane-based regimen was planned in GeparQuattro and GeparQuinto (4,061 total patients). As stated above, the BSA was capped at 2.0 m2 as a threshold to calculate chemotherapy doses. The vast majority of patients with high BMI had a BSA [ 2.0 m2: 0.4 % (7/1,945) of normal weight, 9.5 % (122/1,290) of overweight, 56.4 % (397/704) of obese, and 91.2 % (52/57) of very obese patients (P \ 0.001). Patients with a high BMI were more likely to develop nonhematological AEs during study treatment (P = 0.002): underweight 44.6 % (29/65), normal weight 36.8 % (716/ 1,945), overweight 40.9 % (527/1,290), obese 45.0 % (317/ 704), and very obese 42.1 % (24/57); but they were less likely to develop hematological AEs (P = 0.016): underweight 93.8 % (61/65), normal weight 90.0 % (1,751/ 1,945), overweight 89.8 % (1,159/1,290), obese 88.4 % (622/704), and very obese 77.2 % (44/57). A significantly lower percentage of obese (61.8 %, 435/704) and very obese patients (63.2 %, 36/57) received the full dose of taxanes planned according to the protocol compared with the other BMI groups [70.8 % (46/65) of underweight, 71.1 % (1383/1945) of normal weight, and 66.0 % (851/1290) of overweight patients; P \ 0.001] (Table 2). No significant differences were observed in patients who received the full dose of epirubicin and cyclophosphamide among the five BMI groups (P = 0.221 and P = 0.614, respectively). In the multivariable logistic regression analysis, only BMI (P = 0.030), number of co-morbidities at the baseline (P \ 0.001), and the presence of non-hematological AEs

123

(P \ 0.001) significantly predicted a reduction of the taxane dose during study treatment (Table 2). No significant associations were observed for age (P = 0.605) and hematological AEs (P = 0.086). pCR rate Overall, 21.3 % (1,890/8,872) of patients achieved a pCR following neoadjuvant treatment. The pCR rate in normal weight patients (22.7 %, 960/4,224) was significantly higher than all the other BMI groups [underweight 16.4 % (22/134), overweight 21.2 % (596/2,811), obese 18.3 % (289/1,575), and very obese 18.0 % (289/1,575); P = 0.003] (Table 3). The STEPP analysis results suggested a decrease in pCR rate in patients with increasing BMI (Fig. 1a). BMI was significantly associated with pCR rate also in luminal-like patients [underweight 4.2 % (2/48), normal weight 11.8 % (185/1,570), overweight 10.5 % (108/ 1,024), obese 7.7 % (43/562), and very obese 11.4 % (5/ 44); P = 0.047], but no significant association was observed in TNBC (P = 0.274), HER2/luminal (P = 0.745), and HER2-like (P = 0.389). There was no significant difference in the mastectomy rate after neoadjuvant treatment among BMI groups (P = 0.077). In a multivariable analysis performed in GeparQuattro and GeparQuinto patients, BMI did not predict for pCR (P = 0.434), but we observed that patients who received a reduced dose of taxanes achieved a lower pCR rate (OR 0.67, CI 0.56–0.80; P \ 0.001) (Table 3). DFS and OS DFS and OS were significantly associated with BMI in both STEPP analyses (Fig. 1b, c) and Kaplan–Meier analyses (Fig. 2). Mean DFS was significantly shorter in obese and very obese (87.3 months P = 0.014 and 66.6 months P \ 0.001, respectively) compared with normal weight patients (91.5 months). Accordingly, mean OS was shorter in obese and very obese (94.9 months P = 0.001 and 75.3 months P \ 0.001, respectively) compared with normal weight patients (98.8 months). In our cohort, BMI and pCR were independent prognostic factors (interaction tests were DFS P = 0.641 and OS P = 0.422). The detrimental impact of high BMI on survival was consistent also in luminal-like and TNBC. Obese and very obese patients with luminal-like tumors had a significantly lower mean DFS (91.4 months, P = 0.054 and 67.8 months, P = 0.003, respectively) as well as mean OS (97.9 months, P = 0.006 and 73.7 months, P \ 0.001, respectively) compared with normal weight patients (DFS

Breast Cancer Res Treat Table 1 Baseline characteristics by BMI groups (kg/m2) Factor

Underweight

Normal weight

Overweight

Obese

Very obese

Total

BMI \18.5

BMI 18.5 to \25

BMI 25 to \30

BMI 30 to \40

BMI C40

N

N

N

N

N

N

%

%

%

%

P* value %

%

Age (years) \35

17

12.7

360

8.5

125

4.4

58

3.7

5

3.9

565

6.4

C35 117 Tumor stage

87.3

3,864

91.5

2,686

95.6

1,517

96.3

123

96.1

8,307

93.6

102

76.2

3,160

75.4

1,919

68.7

999

64.1

72

55.6

6,250

71.0

T3

T1–2

19

14.2

644

15.4

493

17.7

279

17.9

21

16.7

1,456

16.5

T4 a–c

10

7.5

245

5.8

205

7.3

133

8.5

11

8.7

604

6.9

3

2.2

140

3.3

176

6.3

147

9.4

24

19.0

490

5.6

T4 d

\0.001

\0.001

Lymph node stage LN 0

61

45.9

2,043

49.4

1,339

48.3

767

49.7

60

48.0

4,270

49.0

LN 1–3

65

48.9

1,892

45.7

1,261

45.5

682

44.2

55

44.0

3,955

45.4

LN 4–9

6

4.5

150

3.6

135

4.9

72

4.7

7

5.6

370

4.2

LN C10

1

0.8

54

1.3

37

1.3

22

1.4

3

2.4

117

1.3

0.542

Nuclear grade 1

4

3.1

160

4.0

96

3.6

51

3.4

2

1.6

313

3.7

2

75

57.7

2,217

55.1

1,456

54.6

803

54.2

69

56.6

4,620

54.8

3

51

39.2

1,648

40.9

1,114

41.8

627

42.3

51

41.8

3,491

41.4

Histological type Ductal 113

86.9

3,389

81.7

2,236

81.0

1,214

78.6

100

78.7

7,052

81.0

13

10.0

495

11.9

332

12.0

196

12.7

19

15.0

1,055

12.1

4

3.1

262

6.3

194

7.0

134

8.7

8

6.3

602

6.9

Positive

48

36.9

1,548

38.0

1,045

38.5

574

38.1

46

38.0

3,261

38.1

Negative

82

63.1

2,529

62.0

1,671

61.5

931

61.9

75

62.0

5,288

61.9

Positive

59

45.4

1,885

46.3

1,287

47.4

696

46.4

50

42.0

3,977

46.6

Negative

71

54.6

2,189

53.7

1,428

52.6

805

53.6

69

58.0

4,562

53.4

Positive

78

70.3

2,385

72.1

1,568

72.4

872

72.3

66

68.8

4,969

72.1

Negative

33

29.7

925

27.9

567

27.9

597

27.6

334

27.7

30

31.2

Lobular Other

0.869

0.045

ER status 0.992

PgR status 0.737

HER2 status 0.931

Number of co-morbidities** 0

38

28.4

1,086

25.7

583

20.7

234

14.9

17

13.3

1,958

22.1

1–2

23

17.2

675

16.0

543

19.3

326

20.7

22

17.2

1,589

17.9

73

54.5

2,463

58.3

1,685

59.9

1,015

64.4

89

69.5

5,325

60.0

No

65

100.0

1,927

99.1

1,256

97.4

652

92.6

52

91.2

3,952

97.3

Yes

0

0.0

18

0.9

34

2.6

52

7.4

5

8.8

109

2.7

No

61

100.0

1,724

88.6

1,007

78.1

424

60.2

31

54.4

3,247

80.0

Yes

4

6.3

221

11.4

283

21.9

280

39.8

26

45.6

814

20.0

No

63

96.9

1,906

98.0

1,251

97.0

674

95.7

52

91.2

3,946

97.2

Yes

2

3.1

39

2.0

39

3.0

30

4.3

5

8.8

115

2.8

[2 Diabetes**

\0.001

\0.001

Hypertension** \0.001

Dyslipidemia** 0.002

Hyperuricemia** No

65

100.0

1,944

99.9

1,286

99.7

695

98.7

56

98.2

4,046

99.6

Yes

0

0.0

1

0.1

4

0.3

9

1.3

1

1.8

15

0.4

\0.001

123

Breast Cancer Res Treat Table 1 continued Factor

Underweight

Normal weight

Overweight

Obese

Very obese

Total

BMI \18.5

BMI 18.5 to \25

BMI 25 to \30

BMI 30 to \40

BMI C40

N

N

N

N

%

N

N

1,281

99.3

693

99.3

56

98.2

4,031

99.3

0.7

11

0.7

1

1.8

30

0.7

97.8

673

95.6

55

96.5

3,951

97.3

2.2

31

4.4

2

3.5

110

2.7

%

%

%

P* value %

%

History of thromboembolic events** No

65

100.0

1,939

99.5

Yes

0

0.0

9

0.5

9

0.043

Cardiac disorders** No

65

100.0

1,896

97.5

Yes

0

0.0

49

2.5

1,262 28

0.023

Thyroid dysfunctions** No

61

93.8

1,684

86.6

1,071

83.0

583

82.8

43

75.4

3,442

84.8

Yes

4

6.2

261

13.4

219

17.0

121

17.2

14

24.6

619

15.2

0.001

* P value from Chi-Square test ** Only GeparQuattro and GeparQuinto patients (N = 4,061 patients) BMI body mass index; LN lymph nodes; ER estrogen receptor; PgR progesterone receptor; HER2 human epidermal growth factor receptor 2

Table 2 Univariable and multivariable logistic regression analyses for prediction of total taxane dose administered during study treatment in GeparQuattro and GeparQuinto trials (N = 4,061 patients) Factors*

Full dose of taxanes

Univariable analysis for full dose of taxanes prediction

Multivariable analysis for full dose of taxanes prediction

%

P value

OR

95 % CI

P value

OR

95 % CI

BMI 18.5 to \25

71.1

\0.001

1.0

–

–

1.0

–

BMI \18.5

70.8

0.98

0.57–1.69

0.953

1.02

0.58–1.77

0.957

BMI 25 to \30 BMI 30 to \40

66.0 61.8

0.79 0.66

0.68–0.92 0.55–0.79

0.002 \0.001

0.84 0.75

0.72–0.98 0.62–0.91

0.022 0.003

BMI C40

63.2

0.70

0.40–01.20

0.195

0.84

0.48–1.47

Body mass index

P value 0.030

Age (years)

–

0.537 0.605

\35

71.8

C35

67.4

0.151

1.0

–

0.81

0.61–1.08

1.0

–

1.16

0.94–1.44

–

1.0

–

0.151

0.93

0.69–1.24

–

1.0

–

0.161

1.21

0.97–1.50

Hematological AEs

– 0.605 0.086

None

64.6

Any grade

68.0

0.160

74.1

Any grade

57.9

0.086 \0.001

Non-hematological AEs None

–

\0.001

1.0

–

0.48

0.42–0.55

–

1.0

–

\0.001

0.49

0.43–0.56

– \0.001 \0.001

Number of co-morbidities \0.001

0

71.6

1.0

–

–

1.0

–

1–2

66.3

0.78

0.68–0.90

0.001

0.83

0.72–0.97

– 0.015

[2

57.0

0.53

0.43–0.64

\0.001

0.58

0.47–0.71

\0.001

OR odds ratio; CI confidence interval; BMI body mass index; AEs adverse events

96.8 months; OS 104.1 months; interaction tests between BMI and pCR for DFS P = 0.670 and OS P = 0.929). In TNBC, obese and very obese patients had a significantly lower mean DFS (68.0 months P = 0.043 and 42.3 months

123

P = 0.010, respectively) and mean OS (74.5 months P = 0.018 and 48.0 months P = 0.003, respectively) compared with normal weight patients (DFS 77.7 months; OS 85.3 months; interaction tests between BMI and pCR

Breast Cancer Res Treat Table 3 Univariable and multivariable logistic regression analyses of clinico-biological factors and odds ratios (OR) of pathological complete response (ypT0/is ypN0) in GeparQuattro and GeparQuinto patients (N = 4,061 patients) Factors*

pCR rate %

Univariable analysis for pCR prediction

Multivariable analysis for pCR prediction

P value

OR

95 % CI

P value

OR

95 % CI

0.003

1.0

–

–

1.0

–

Body mass index

P value 0.434

BMI 18.5 to \25

22.7

BMI \18.5 BMI 25 to \30

16.4 21.2

0.67 0.92

0.42–1.06 0.82–1.03

0.087 0.131

0.52 0.98

0.25–1.08 0.82–1.18

0.078 0.856

BMI 30 to \40

18.3

0.76

0.66–0.89

\0.001

0.91

0.73–1.15

0.441

BMI C40

18.0

0.75

0.47–1.18

0.206

1.14

0.58–2.23

Age (years)

–

0.702 0.179

\35

32.6

C35

20.5

\0.001

1.0

–

0.53

0.44–0.64

1.0

–

–

1.0

–

\0.001

0.81

0.60–1.10

–

1.0

–

– 0.179 \0.001

Tumor stage T1–2

23.5

T3

\0.001

–

16.5

0.61

0.52–0.71

\0.001

0.58

0.45–0.74

\0.001

T4 a–c

6.8

0.50

0.40–0.64

\0.001

0.54

0.37–0.79

0.002

T4 d

5.7

0.81

0.65–1.01

0.066

0.61

0.45–0.83

Lymph node stage LN 0

23.1

LN 1–3 LN 4–9 LN C10

0.001

1.0

–

–

1.0

–

20.1

0.84

19.7 14.3

0.82 0.56

0.76–0.93

0.001

0.94

0.80–1.11

0.456

0.63–1.06 0.33–0.93

0.132 0.026

1.34 0.71

0.91–1.98 0.32–1.59

0.141 0.411

1.0

–

–

1.0

–

Histological type Ductal

–

0.059 22.8

\0.001

–

8.9

0.33

0.27–0.41

\0.001

0.64

0.44–0.93

0.019

25.5

1.16

0.96–1.41

0.124

1.04

0.77–1.42

0.785

1.0

–

–

1.0

–

Lobular Other

0.002 0.259

\0.001

Nuclear grade \0.001

1

7.0

2

14.4

2.23

1.43–3.47

\0.001

1.80

0.94–3.44

0.077

–

3

20.7

5.87

3.78–9.11

\0.001

2.81

1.46–5.40

0.002

1.0

–

–

1.0

–

0.20

0.17–0.23

\0.001

0.27

0.22–0.33

\0.001

Breast cancer subtype TNBC

37.0

Luminal-like

10.5

\0.001

– \0.001

HER2/luminal

25.4

0.58

0.49–0.69

\0.001

0.84

0.66–1.06

0.141

HER2-like

45.1

1.40

1.18–1.66

\0.001

2.19

1.72–2.78

\0.001

\0.001

1.0 0.54

– 0.41–0.73

– \0.001

1.0 0.61

– 0.28–1.34

\0.001

1.0

–

–

1.0

–

0.58

0.44–0.77

\0.001

0.93

0.43–2.01

Epirubicin dose Full dose Reduction

0.221 25.8 15.9

Cyclophosphamide dose Full dose

25.7

Reduction

16.7

– 0.221 0.845 – 0.845 \0.001

Taxanes dose Full dose

27.0

Reduction

20.0

\0.001

1.0

–

0.69

0.59–0.81

–

1.0

–

\0.001

0.67

0.56–0.80

– \0.001

123

Breast Cancer Res Treat Table 3 continued Factors*

pCR rate %

Univariable analysis for pCR prediction

Multivariable analysis for pCR prediction

P value

OR

95 % CI

P value

OR

95 % CI

\0.001

1.0

–

–

1.0

–

Number of co-morbidities 0

26.2

P value 0.324 –

1–2

24.0

0.89

0.76–1.04

0.136

0.94

0.79–1.11

0.463

[2

18.6

0.64

0.57–0.73

\0.001

0.82

0.63–1.07

0.140

* Adjusted for study in multivariable analysis OR odds ratio; CI confidence interval; BMI body mass index; LN lymph nodes; luminal-like, ER/PgR-positive and HER2-negative; HER2/luminal ER/PgR-positive and HER2-positive; HER2-like, ER/PgR-negative and HER2-positive; TNBC triple-negative, ER/PgR- and HER2negative; n.a. not applicable, 0.0 % of pathological complete response in very obese patients

Fig. 1 a STEPP analysis of BMI and pCR (ypT0/is ypN0). The median BMI within each subgroup is shown on the horizontal axis, and the vertical axis shows the corresponding pCR rate. b STEPP analysis of BMI and 5-year DFS. The median BMI within each subgroup is shown on the horizontal axis, and the vertical axis shows

the corresponding 5-year DFS percentage. c STEPP analysis of BMI and 5-year OS. The median BMI within each subgroup is shown on the horizontal axis, and the vertical axis shows the corresponding 5-year OS percentage

for DFS P = 0.877 and OS P = 0.978). In HER2/luminal tumors, significant differences were observed only in very obese patients compared with normal weight patients

(mean DFS 62.1 months vs. 93.5 months, P = 0.002 and mean OS 70.6 months versus 101.3 months, P = 0.002). There were no significant differences in HER2-like tumors.

123

Breast Cancer Res Treat

risk of both recurrence (HR 1.37, 95 % CI 1.16–1.61; P \ 0.001) and death (HR 1.53, 95 % CI 1.23–1.90; P \ 0.001) (Table 4).

Discussion

Fig. 2 a Kaplan–Meier curves of disease-free survival by body mass index groups. b Kaplan–Meier curves of overall survival by body mass index groups

In multivariable Cox regression analyses performed in GeparQuattro and GeparQuinto patients, very obese patients showed a higher risk of death compared with normal weight patients (HR 2.21, 95 % CI 1.27–3.86; P = 0.005); overweight patients seemed to have a lower risk of both recurrence (HR 0.78, 95 % CI 0.65-0.94; P = 0.008) and death (HR 0.76, 95 % CI 0.60–0.97; P = 0.029) compared with normal weight patients (Table 4). Patients who received a reduced dose of taxanes had a significantly higher

In this study, we demonstrated in more than 8,800 patients that increasing BMI results in decreasing pCR rates after neoadjuvant treatment and that BMI had a negative influence on DFS and OS independently from pCR. The subgroup analyses showed BMI to be related to pCR only in patients with luminal-like tumors. A detrimental effect of high BMI on survival was observed in luminal-like tumors and in TNBC, but not in HER2-positive tumors. As already known, the higher pCR rate in TNBC and HER2-positive compared with luminal-like tumors could be related to the higher chemosensitivity of these aggressive BC subgroups [21, 43] and/or to the introduction of anti-HER2 treatment [44, 45]. Particularly in HER2-positive BC, the addition of trastuzumab to a sequential anthracycline-taxane-based chemotherapy increased pCR rate from 17 to 40 %, and the effect can be further enlarged to 75 % with a dual HER2-receptor blockade plus chemotherapy [45]. In highly aggressive tumors, BMI seems to be a relatively weak negative predictor of pCR and survival, and its impact may not overcome the strong positive effect of neoadjuvant treatment. On the other hand, in luminal-like tumors, the impact of BMI seems to be important enough to affect the outcome after neoadjuvant treatment and the overall prognosis. Considering that the pCR rate in luminal-like tumors is generally low and their survival long, we can speculate that BMI is a major poor prognostic factor in hormone-receptor-positive tumors. A negative impact of high BMI has been described in hormone-receptor-positive BC patients treated with adjuvant endocrine treatment [46–48], in particular when an aromatase inhibitor was administered [49], suggesting a key role of the higher aromatase activity in patients with increased adipose tissue [50, 51]. Moreover, as shown above, our obese and very obese patients were more likely to be diagnosed with concomitant metabolic disorders such as diabetes mellitus. A significant cross-talk between the estrogen- and insulin-signaling pathways as well as a higher expression of proteins involved in the insulin growth factor pathway in hormone-receptor-positive tumors compared with hormone-receptor-negative has been extensively described [52, 53]. Goodwin and colleagues reported that patients with an increased insulin level at baseline seem to be at high risk of BC recurrence after primary treatment [54–56]. Finally, elevated C-peptide levels have been associated with an increased risk of cancer-related

123

Breast Cancer Res Treat Table 4 Univariable and multivariable Cox regression analyses of clinico-biological factors and risk of disease recurrence and death in GeparQuattro and GeparQuinto patients (N = 4,061 patients) Factors

Univariable analysis for prognostic impact on DFS

Univariable analysis for prognostic impact on OS

Multivariable analysis for prognostic impact on DFS

Multivariable analysis for prognostic impact on OS

HR

95 % CI

HR

95 % CI

HR

HR

95 % CI

BMI 18.5 to \25

1.0

–

–

1.0

–

–

1.0

–

BMI \18.5

1.19

0.83–1.71

0.194

1.45

BMI 25 to \30

1.03

0.93–1.15

0.433

BMI 30 to \40

1.17

1.03–1.33

0.014

BMI C40

1.93

1.42–2.64

0.002

\35

1.0

–

C35

0.89

0.74–1.07

Body mass index

P value

0.001

–

0.95–2.20

0.019

1.09

0.65–1.83

0.752

1.55

0.86–2.80

0.147

1.06

0.93–1.21

0.737

0.78

0.65–0.94

0.008

0.76

0.60–0.97

0.029

1.31

1.12–1.52

0.015

0.97

0.78–1.19

0.748

1.11

0.85–1.45

0.459

2.12

1.46–3.06 \0.001

1.43

0.86–239

0.168

2.21

1.26–3.86

0.005

–

1.0

–

–

1.0

–

0.390

0.99

0.72–1.36

0.937

0.82

0.55–1.23

0.390

–

1.0

–

0.217

0.91

0.72–1.14

0.937

\0.001

1.0

–

1.29–1.65 \0.001

1.65

1.42–1.91 \0.001

1.42

1.14–1.75 \0.001

1.61

1.22–2.10

0.001

T4 a–c

1.86

1.59–2.19 \0.001

2.25

1.86–2.72 \0.001

1.57

1.18–2.09 \0.001

1.83

1.27–2.65

0.001

3.40

2.92–3.95 \0.001 \0.001

4.11

3.45–4.91 \0.001 \0.001

2.97

2.38–3.72 \0.001 \0.001

4.00

3.00–5.22 \0.001 \0.001

1.85

–

–

1.68–2.05 \0.001

1.0 2.08

–

–

1.0

1.0

–

–

–

0.343 \0.001

1.0

1.0

–

\0.001

1.46

LN 1–3

–

0.343 –

T3

LN 0

1.0

–

T1–2

T4 d Lymph node stage

–

0.028

P value

1.0

\0.001 –

P value

–

0.217

Tumor stage

95 % CI

\0.001

0.004

Age (years)

P value

–

1.0

–

–

–

1.83–2.36 \0.001

1.62

1.37–1.92 \0.001

1.77

1.41–2.23 \0.001

LN 4–9

2.64

2.17–3.21 \0.001

3.27

2.60–4.12 \0.001

1.47

1.04–2.07

0.029

1.55

0.99–2.42

0.055

LN C10

5.89

4.54–7.64 \0.001

7.21

5.36–9.70 \0.001

2.55

1.48–4.40

0.001

3.09

1.60–5.95

0.001

–

1.0

–

–

1.0

–

0.110

1.02

0.76–1.36

0.921

1.13

0.77–1.65

0.545

0.379

0.94

0.67–1.31

0.707

0.75

0.47–1.20

0.234

Histological type

0.051

Ductal

1.0

–

Lobular

0.90

0.77–1.04

Other

1.00

0.83–1.21

1.0

–

0.018

0.90

0.75–1.07

0.441

0.99

0.79–1.25

\0.001

Nuclear grade 1

1.0

–

2

1.56

1.14–2.13

3

2.35

1.72–3.20 \0.001

1.0 0.49

1.0

–

1.75

1.16–2.63

2.91

1.93–4.38 \0.001

1.0 0.42

Negative

1.0

Positive

0.52

–

–

0.47–0.57 \0.001

HER2 status 1.0

–

Positive

1.01

0.90–1.15

0.47

–

No

1.0

Yes

0.49

–

1.0

–

0.821

0.87

0.74–1.02

0.35–0.50 \0.001

Epirubicin dose 1.0

–

Reduction

1.48

1.17–1.89

Cyclophosphamide dose

1.0 0.38

–

1.0

–

Reduction

1.48

1.15–1.89

–

1.0 0.54

048

–

–

1.0

–

1.53

1.13–2.08

–

1.0

–

1.57

1.15–2.14

2.34

0.95–5.76

–

0.081

0.69

–

0.57–0.83 \0.001

1.0 0.46

0.25

–

–

0.20–0.32 \0.001

\0.001 1.0 0.46

–

–

1.0

–

1.25

0.61–2.58

–

\0.001 1.0 0.56

–

–

1.0

–

1.13

0.54–2.36

–

–

0.44–0.73 \0.001 \0.001

1.0 0.17

–

–

0.12–0.25 \0.001 0.898

1.0

–

0.94

0.33–2.64

0.748

0.005

–

0.34–0.61 \0.001

0.544

0.007

0.066

– – 0.35–0.62 \0.001

\0.001 1.0

– 0.248 \0.001

\0.001 –

0.005

0.002

– 0.70–4.15

0.38–0.60 \0.001

0.007

0.001

1.0 1.70

\0.001 1.0

1.0

–

– 0.282

– – 0.43–0.67 \0.001

–

0.007

\0.001

–

0.29–0.46 \0.001

0.002

Full dose

1.02–3.48 \0.001

\0.001

0.001

Full dose

1.88

0.081

–

–

– 0.76–2.56

0.41–0.53 \0.001

\0.001

pCRr

1.0 1.40

\0.001 1.0

0.821

Negative

– 0.008

– – 0.38–0.48 \0.001

0.394

0.001

\0.001

\0.001

PgR status

123

– 0.005

– – 0.44–0.54 \0.001

0.925

\0.001

\0.001

ER status Negative Positive

0.207

–

– 0.898 0.413

1.0

–

1.55

0.54–4.39

– 0.412

Breast Cancer Res Treat Table 4 continued Factors

Univariable analysis for prognostic impact on DFS

Univariable analysis for prognostic impact on OS

Multivariable analysis for prognostic impact on DFS

Multivariable analysis for prognostic impact on OS

HR

95 % CI

HR

95 % CI

HR

HR

95 % CI

Full dose

1.0

–

1.0

–

Reduction

1.53

P value \0.001

Taxane dose

–

1.31–1.79 \0.001

P value

95 % CI

\0.001 1.75

–

1.43–2.14 \0.001

P value \0.001

P value \0.001

1.0

–

–

1.0

–

–

1.37

1.16–1.61

–

1.53

1.23–1.90

–

* Adjusted for study and number of co-morbidities in multivariable analysis DFS disease-free survival; OS overall survival; HR hazard ratio; CI confidence interval; BMI body mass index; LN lymph nodes; ER estrogen receptor; PgR progesterone receptor; HER2 human epidermal growth factor receptor 2

death, specifically in hormone-receptor-positive tumors [57]. Overall, these data offer plausible biological reasons for the lower pCR rate and the shorter survival observed in the luminal-like patients with high BMI. We also evaluated if the lower pCR rate could be linked with lower chemotherapy doses. As previously assessed by Griggs and colleagues, systemic chemotherapy doses not calculated on the patient’s real estimated BSA and the practice of intentionally reducing the chemotherapy dose in patients with high BMI may negatively affect treatment outcome [17, 58, 59]. Consistently, a recent pooled analysis of two French randomized studies showed no impact of BMI on BC prognosis when chemotherapy dose was calculated according to BSA and delivered at the appropriate dose intensity [60]. In our GeparQuattro and GeparQuinto cohort, 91.2 % of very obese and 56.4 % of obese patients had a BSA C 2.0 m2, and therefore, they received a relatively lower chemotherapy dose compared with their counterparts with a BSA \ 2.0 m2. To note, even if the vast majority of our patients with a BMI C 30 kg/m2 received a capped chemotherapy dose, they were more likely to develop non-hematological AEs during the study treatment and to experience a reduction in the delivered taxane dose compared with patients with BMI \ 30 kg/m2. Considering that in the adjuvant setting, the addition of taxanes to an anthracycline-based strategy reduced the risk of both recurrence and death by 17 % [61] and in the neoadjuvant setting led to an absolute increase in complete response rate by 6.7–15.5 % [62]; the significantly lower taxane dose administered to obese and very obese patients in our cohort could further explain the poorer outcome observed in these patients compared with normal weight patients. This assumption was also sustained by the results of the multivariable analysis that confirmed the strong relationship between taxane dose and the probability to achieve pCR. Further, the significantly lower incidence of non-hematological AEs in normal weight patients compared with the other BMI groups may suggest that a BMI outside of normal range could help to identify patients with a reduced tolerability of chemotherapy.

The major strength of this study is the large number of BC patients treated with currently available neoadjuvant treatment (anthracycline–taxane-based chemotherapy, antiHER2 target drugs, and endocrine therapy according to national guidelines). An important limitation is the use of OS instead of BC-specific mortality as time-to-event endpoint, especially in the light of a recent pooled analysis showing that even obese without concomitant metabolic disorders were at increased risk for all-cause mortality compared with normal weight individuals [63]. In conclusion, in a cohort of patients receiving neoadjuvant treatment, a BMI C 30 kg/m2 has a negative impact on pCR, DFS, and OS, particularly when luminal-like tumors are diagnosed. Moreover, several biological factors may play a role in obese patients’ prognosis which could not be tested in this analysis. Among the tested variables, a reduction in taxane dose showed a negative impact on pCR rate.

Conflict of interest Jens-Uwe Blohmer: Consultant/Advisory role by ROCHE. Claus Hanusch: Consultant/Advisory role by NOVARTIS, AMGEN. All other authors have declared no conflicts of interest.

References 1. Flegal KM, Carroll MD, Kit BK, Ogden CL (2012) Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999–2010. JAMA 307:491–497 2. Mokdad AH, Ford ES, Bowman BA et al (2003) Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 289:76–79 3. Hossain P, Kawar B, El Nahas M (2007) Obesity and diabetes in the developing world–a growing challenge. N Engl J Med 356:213–215 4. Flegal KM, Kit BK, Orpana H, Graubard BI (2013) Association of all-cause mortality with overweight and obesity using standard body mass index categories: a systematic review and meta-analysis. JAMA 309:71–82 5. Pfeiffer RM, Park Y, Kreimer AR et al (2013) Risk prediction for breast, endometrial, and ovarian cancer in white women aged 50 y or older: derivation and validation from population-based cohort studies. PLoS Med 10:e1001492

123

Breast Cancer Res Treat 6. Harvey AE, Lashinger LM, Hursting SD (2011) The growing challenge of obesity and cancer: an inflammatory issue. Ann NY Acad Sci 1229:45–52 7. Hursting SD, Dunlap SM (2012) Obesity, metabolic dysregulation, and cancer: a growing concern and an inflammatory (and microenvironmental) issue. Ann NY Acad Sci 1271:82–87 8. Shoelson SE, Lee J, Goldfine AB (2006) Inflammation and insulin resistance. J Clin Invest 116:1793–1801 9. Gnant M, Pfeiler G, Sto¨ger H et al (2013) The predictive impact of body mass index on the efficacy of extended adjuvant endocrine treatment with anastrozole in postmenopausal patients with breast cancer: an analysis of the randomised ABCSG-6a trial. Br J Cancer 109:589–596 10. Litton JK, Gonzalez-Angulo AM, Warneke CL et al (2008) Relationship between obesity and pathologic response to neoadjuvant chemotherapy among women with operable breast cancer. J Clin Oncol 26:4072–4077 11. Del Fabbro E, Parsons H, Warneke CL et al (2012) The relationship between body composition and response to neoadjuvant chemotherapy in women with operable breast cancer. Oncologist 17:1240–1245 12. Chen S, Chen CM, Zhou Y et al (2012) Obesity or overweight is associated with worse pathological response to neoadjuvant chemotherapy among Chinese women with breast cancer. PLoS ONE 7:e41380 13. Berclaz G, Li S, Price KN et al (2004) Body mass index as a prognostic feature in operable breast cancer: the International Breast Cancer Study Group experience. Ann Oncol 15:875–884 14. Pajares B, Polla´n M, Martı´n M et al (2013) Obesity and survival in operable breast cancer patients treated with adjuvant anthracyclines and taxanes according to pathological subtypes: a pooled analysis. Breast Cancer Res 15:R105 15. Tait S, Pacheco JM, Gao F et al (2014) Body mass index, diabetes, and triple-negative breast cancer prognosis. Breast Cancer Res Treat 146:189–197 16. Chan DS, Vieira AR, Aune D et al (2014) Body mass index and survival in women with breast cancer–systematic literature review and meta-analysis of 82 follow-up studies. Ann Oncol 25:1901–1914 17. Griggs JJ, Mangu PB, Anderson H et al (2012) Appropriate chemotherapy dosing for obese adult patients with cancer: American Society of Clinical Oncology clinical practice guideline. J Clin Oncol 30:1553–1561 18. Peairs KS, Barone BB, Snyder CF et al (2011) Diabetes mellitus and breast cancer outcomes: a systematic review and meta-analysis. J Clin Oncol 29:40–46 19. Jiralerspong S, Palla SL, Giordano SH et al (2009) Metformin and pathologic complete responses to neoadjuvant chemotherapy in diabetic patients with breast cancer. J Clin Oncol 27:3297–3302 20. Jiralerspong S, Kim ES, Dong W et al (2013) Obesity, diabetes, and survival outcomes in a large cohort of early-stage breast cancer patients. Ann Oncol 24:2506–2514 21. von Minckwitz G, Fontanella C (2013) Selecting the neoadjuvant treatment by molecular subtype: how to maximize the benefit? Breast 22(Suppl 2):S149–S151 22. von Minckwitz G, Untch M, Blohmer JU et al (2012) Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol 30:1796–1804 23. von Minckwitz G, Blohmer JU, Costa SD et al (2013) Responseguided neoadjuvant chemotherapy for breast cancer. J Clin Oncol 31:3623–3630 24. von Minckwitz G, Raab G, Caputo A et al (2005) Doxorubicin with cyclophosphamide followed by docetaxel every 21 days compared with doxorubicin and docetaxel every 14 days as preoperative

123

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

37.

38.

39. 40.

treatment in operable breast cancer: the GEPARDUO study of the German Breast Group. J Clin Oncol 23:2676–2685 von Minckwitz G, Blohmer JU, Raab G et al (2005) In vivo chemosensitivity-adapted preoperative chemotherapy in patients with early-stage breast cancer: the GEPARTRIO pilot study. Ann Oncol 16:56–63 von Minckwitz G, Ku¨mmel S, Vogel P et al (2008) Intensified neoadjuvant chemotherapy in early-responding breast cancer: phase III randomized GeparTrio study. J Natl Cancer Inst 100:552–562 von Minckwitz G, Ku¨mmel S, Vogel P et al (2008) Neoadjuvant vinorelbine-capecitabine versus docetaxel-doxorubicin-cyclophosphamide in early nonresponsive breast cancer: phase III randomized GeparTrio trial. J Natl Cancer Inst 100:542–551 Untch M, Rezai M, Loibl S et al (2010) Neoadjuvant treatment with trastuzumab in HER2-positive breast cancer: results from the GeparQuattro study. J Clin Oncol 28:2024–2031 von Minckwitz G, Darb-Esfahani S, Loibl S et al (2012) Responsiveness of adjacent ductal carcinoma in situ and changes in HER2 status after neoadjuvant chemotherapy/trastuzumab treatment in early breast cancer–results from the GeparQuattro study (GBG 40). Breast Cancer Res Treat 132:863–870 von Minckwitz G, Eidtmann H, Loibl S et al (2011) Integrating bevacizumab, everolimus, and lapatinib into current neoadjuvant chemotherapy regimen for primary breast cancer. Safety results of the GeparQuinto trial. Ann Oncol 22:301–306 Untch M, Loibl S, Bischoff J et al (2012) Lapatinib versus trastuzumab in combination with neoadjuvant anthracycline-taxane-based chemotherapy (GeparQuinto, GBG 44): a randomised phase 3 trial. Lancet Oncol 13:135–144 von Minckwitz G, Eidtmann H, Rezai M et al (2012) Neoadjuvant chemotherapy and bevacizumab for HER2-negative breast cancer. N Engl J Med 366:299–309 Huober J, Fasching PA, Hanusch C et al (2013) Neoadjuvant chemotherapy with paclitaxel and everolimus in breast cancer patients with non-responsive tumours to epirubicin/cyclophosphamide (EC) ± bevacizumab—results of the randomised GeparQuinto study (GBG 44). Eur J Cancer 49:2284–2293 Untch M, Mo¨bus V, Kuhn W et al (2009) Intensive dose-dense compared with conventionally scheduled preoperative chemotherapy for high-risk primary breast cancer. J Clin Oncol 27:2938–2945 Untch M, Fasching PA, Konecny GE et al (2011) PREPARE trial: a randomized phase III trial comparing preoperative, dosedense, dose-intensified chemotherapy with epirubicin, paclitaxel and CMF versus a standard-dosed epirubicin/cyclophosphamide followed by paclitaxel ± darbepoetin alfa in primary breast cancer–results at the time of surgery. Ann Oncol 22:1988–1998 Untch M, Fasching PA, Konecny GE et al (2011) Pathologic complete response after neoadjuvant chemotherapy plus trastuzumab predicts favorable survival in human epidermal growth factor receptor 2-overexpressing breast cancer: results from the TECHNO trial of the AGO and GBG study groups. J Clin Oncol 29:3351–3357 Goldhirsch A, Wood WC, Gelber RD et al (2007) 10th St. Gallen conference. Progress and promise: highlights of the international expert consensus on the primary therapy of early breast cancer 2007. Ann Oncol 18:1133–1144 World Health Organization (2014) BMI classification. http:// apps.who.int/bmi/index.jsp?introPage=intro_3.html. Accessed 17 December 2014 Mosteller RD (1987) Simplified calculation of body-surface area. N Engl J Med 317:1098 Hudis CA, Barlow WE, Costantino JP et al (2007) Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. J Clin Oncol 25:2127–2132

Breast Cancer Res Treat 41. Goldhirsch A, Glick JH, Gelber RD, Senn HJ (1998) International Consensus Panel on the treatment of primary breast cancer. V: update 1998. Recent results. Cancer Res 152:481–497 42. Bonetti M, Gelber RD (2000) A graphical method to assess treatment-covariate interactions using the Cox model on subsets of the data. Stat Med 19:2595–2609 43. Houssami N, Macaskill P, von Minckwitz G et al (2012) Metaanalysis of the association of breast cancer subtype and pathologic complete response to neoadjuvant chemotherapy. Eur J Cancer 48:3342–3354 44. Dent S, Oyan B, Honig A et al (2013) HER2-targeted therapy in breast cancer: a systematic review of neoadjuvant trials. Cancer Treat Rev 39:622–631 45. von Minckwitz G, Loibl S, Untch M (2012) What is the current standard of care for anti-HER2 neoadjuvant therapy in breast cancer? Oncology (Williston Park) 26:20–26 46. Sparano JA, Wang M, Zhao F et al (2012) Obesity at diagnosis is associated with inferior outcomes in hormone receptor-positive operable breast cancer. Cancer 118:5937–5946 47. Sestak I, Distler W, Forbes JF et al (2010) Effect of body mass index on recurrences in tamoxifen and anastrozole treated women: an exploratory analysis from the ATAC trial. J Clin Oncol 28:3411–3415 48. Pan H, Gray RG (on behalf of the Early Breast Cancer Trialists’ Collaborative Group) (2014) Effect of obesity in premenopausal ER? early breast cancer: EBCTCG data on 80,000 patients in 70 trials. J Clin Oncol 32:5 (suppl; abstr 503) 49. Pfeiler G, Ko¨nigsberg R, Fesl C et al (2011) Impact of body mass index on the efficacy of endocrine therapy in premenopausal patients with breast cancer: an analysis of the prospective ABCSG-12 trial. J Clin Oncol 29:2653–2659 50. Simpson ER, Brown KA (2013) Minireview: obesity and breast cancer: a tale of inflammation and dysregulated metabolism. Mol Endocrinol 27:715–725 51. Pfeiler G, Ko¨nigsberg R, Hadji P et al (2013) Impact of body mass index on estradiol depletion by aromatase inhibitors in postmenopausal women with early breast cancer. Br J Cancer 109:1522–1527 52. Becker MA, Ibrahim YH, Cui X et al (2011) The IGF pathway regulates ERa through a S6K1-dependent mechanism in breast cancer cells. Mol Endocrinol 25:516–528

53. Tinoco G, Warsch S, Glu¨ck S et al (2013) Treating breast cancer in the 21st century: emerging biological therapies. J Cancer 4:117–132 54. Goodwin PJ, Ennis M, Pritchard KI et al (2002) Fasting insulin and outcome in early-stage breast cancer: results of a prospective cohort study. J Clin Oncol 20:42–51 55. Goodwin PJ, Ennis M, Bahl M et al (2009) High insulin levels in newly diagnosed breast cancer patients reflect underlying insulin resistance and are associated with components of the insulin resistance syndrome. Breast Cancer Res Treat 114:517–525 56. Goodwin PJ, Ennis M, Pritchard KI et al (2012) Insulin- and obesity-related variables in early-stage breast cancer: correlations and time course of prognostic associations. J Clin Oncol 30:164–171 57. Irwin ML, Duggan C, Wang CY et al (2011) Fasting C-peptide levels and death resulting from all causes and breast cancer: the health, eating, activity, and lifestyle study. J Clin Oncol 29:47–53 58. Griggs JJ, Culakova E, Sorbero ME et al (2007) Effect of patient socioeconomic status and body mass index on the quality of breast cancer adjuvant chemotherapy. J Clin Oncol 25:277–284 59. Griggs JJ, Sorbero ME, Lyman GH (2005) Undertreatment of obese women receiving breast cancer chemotherapy. Arch Intern Med 165:1267–1273 60. Ladoire S, Dalban C, Roche´ H et al (2014) Effect of obesity on disease-free and overall survival in node-positive breast cancer patients in a large French population: a pooled analysis of two randomised trials. Eur J Cancer 50:506–516 61. Qin YY, Li H, Guo XJ et al (2011) Adjuvant chemotherapy, with or without taxanes, in early or operable breast cancer: a metaanalysis of 19 randomized trials with 30698 patients. PLoS ONE 6:e26946 62. Cuppone F, Bria E, Carlini P et al (2008) Taxanes as primary chemotherapy for early breast cancer: meta-analysis of randomized trials. Cancer 113:238–246 63. Kramer CK, Zinman B, Retnakaran R (2013) Are metabolically healthy overweight and obesity benign conditions? A systematic review and meta-analysis. Ann Intern Med 159:758–769

123