Purpose: To investigate the value of the metabolic tumor response assessed with 18F-fluorodeoxyglucose positron emission tomography (FDG-PET), compared with clinicobiologic markers to predict pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in women with triple-negative breast cancer (TNBC).

Experimental Design: Fifty consecutive women with TNBC and an indication for NAC were prospectively included. Different pretreatment clinical, biologic, and pathologic biomarkers, including SBR grade, the Ki-67 proliferation index, androgen receptor expression, EGF receptor (EGFR), and cytokeratin 5/6 staining, were assessed. Tumor glucose metabolism at baseline and its change after the first cycle of NAC (ΔSUVmax) were assessed using FDG-PET.

Results: The pCR rate was 42%. High Ki-67 proliferation index (P = 0.016), negative EGFR status (P = 0.042), and high ΔSUVmax (P = 0.002) were significantly associated with pCR. In multivariate logistic regression, both negative EGFR status (OR, 6.4; P = 0.043) and high ΔSUVmax (OR, 7.1; P = 0.014) were independent predictors of pCR. Using a threshold at −50%, tumor ΔSUVmax predicted pCR with a negative, a positive predictive value, and an accuracy of 79%, 70%, and 75%, respectively. Combining a low ΔSUVmax and positive EGFR status could predict non-pCR with an accuracy of 92%.

Conclusions: It is important to define the chemosensitivity of TNBC to NAC early. Combining EGFR status and the metabolic response assessed with FDG-PET can help the physician to early predict the probability of achieving pCR or not. Given these results, the interest of response-guided tailoring of the chemotherapy might be tested in multicenter trials. Clin Cancer Res; 21(24); 5460–8. ©2015 AACR.

This article is featured in Highlights of This Issue, p. 5407

Translational Relevance

The early identification of women with triple-negative breast cancers (TNBC) with low chemosensitivity to standard neoadjuvant chemotherapy (NAC) is an important issue. The aim is not only to predict the better outcome in women with responsive TNBC but also to avoid the use of an ineffective treatment in nonresponding women and to give them a better chance with an alternative therapy. The present study demonstrated the value of FDG-PET imaging compared with the usual clinicobiologic markers, such as the Ki-67 tumor proliferation index and EGFR tumor expression, in the early prediction of a pathologic complete response to NAC. Given these results, multicenter trials using PET-guided treatment strategies are now necessary to evaluate the benefit of early therapeutic changes in poorly responding women. Such a strategy should lead to enhanced personalized medicine.

Although neoadjuvant chemotherapy (NAC) does not improve survival when compared with adjuvant chemotherapy (1), it is increasingly used in operable breast cancer to downstage the breast tumor and to make breast-conserving surgery possible (2, 3).

Breast cancer includes several molecular entities that differ in their clinical behavior, biologic characteristics, and outcomes (4, 5). Triple-negative breast cancers (TNBC) account for roughly 15% of breast tumors (6, 7) and are defined by the absence of hormone receptor expression and no overexpression of HER2 (8). This subtype is characterized by its higher aggressiveness and poorer outcome compared with other subtypes (6) but also by its high responsiveness to NAC, called the “triple-negative paradox” (9, 10). Indeed, a pathologic complete response (pCR) is often reached at the end of NAC and is associated with a more favorable long-term outcome. In contrast, women who do not achieve pCR have a higher risk of relapse and reduced overall survival (10–12). This explains why pCR is often used as an important endpoint in the treatment of TNBC. One other important characteristic of TNBC is the diversity within this subgroup, as it includes distinct molecular subtypes. Despite a clear need for therapeutic options for women with TNBC tumors, their heterogeneity and the absence of high-frequency molecular alterations have limited the development of targeted therapies (7). It is therefore important to identify clinical, biologic, or imaging biomarkers that can predict early the therapeutic response. The aim is to avoid the use of an ineffective treatment in nonresponding women and to give them a better chance with an alternative therapy.

Fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG-PET/CT) is the gold standard for the in vivo evaluation of tumor glucose metabolism. Studies on the use of PET/CT to monitor early tumor response to NAC have shown promising results in predicting the final pCR whatever the tumor subtype (13–15). Few studies, however, have evaluated the relevance of metabolic responses to predict pCR in the TNBC subtype.

This prospective trial aimed to investigate the value of metabolic tumor parameters, assessed at baseline and after the first cycle of treatment with FDG-PET studies in the early prediction of pCR, and to compare these with pretreatment biologic markers.

Patients and study design

From November 2006 to March 2013, 240 women referred to our institution (Centre Georges-François Leclerc, Dijon, France) because of clinical stage II or III invasive breast cancer with an indication for NAC were consecutively and prospectively evaluated in this study. The clinical stage was first assessed using physical examination and conventional imaging procedures (mammogram and/or breast ultrasound, bone scan, abdominal ultrasound, chest X-rays). Only women with TNBC were included in the study. Patients with high glycemia (>9 mmol/L), unwilling to undergo the 2 PET exams or with unexpected metastasis on baseline FDG-PET were excluded. The institutional review board approved this prospective study, and the women gave their informed consent.

Patients were treated with rule-based NAC: most underwent sequential NAC with anthracyclines [FEC100: fluorouracil (FU), 500 mg/m2; epirubicin, 100 mg/m2; cyclophosphamide, 500 mg/m2, 3 courses every 3 weeks] and taxanes (docetaxel, 100 mg/m2, 3 courses every 3 weeks). Some of those (n = 9) included at the beginning of the study underwent continuous NAC with 6 cycles of anthracyclines (FEC100) every 3 weeks.

About 1 month after the last course of NAC, the tumors were surgically removed and pCR was defined as no residual invasive cancer in the breast and nodes, although in situ breast residuals were allowed (ypT0/is ypN0; ref. 11). pCR was the endpoint of the study. This study followed the REMARK criteria (16).

Histopathologic analysis

Pretreatment core biopsies from the primary tumor were used to determine the histologic type and the tumor SBR grade (17) established by evaluating the architectural differentiation, nuclear polymorphism, and rate of mitosis. The immunohistochemical (IHC) analyses reported in this study were carried out in a single laboratory, and the slides were read by a single pathologist. The following molecular markers were examined: estrogen receptor (ER), progesterone receptor (PR), HER2 expression, Ki-67 proliferation index, androgen receptor (AR), EGF receptor (EGFR), and cytokeratin (CK) 5/6 tumor expression.

Tumor samples were quickly fixed on buffered formalin, embedded in paraffin, and cut into 4-μm-thick sections with a microtome. IHC was performed with an indirect immunoperoxidase method using antibodies directed against HER2 oncoprotein, ER and PR (HER2: prediluted rabbit monoclonal antibody 4B5; ER: prediluted rabbit monoclonal antibody SP1; PR: prediluted rabbit monoclonal antibody 1E2; Ventana), AR (monoclonal mouse anti-human androgen receptor, clone AR441, dilution 1/10e; Dako), Ki-67 (monoclonal mouse anti-human Ki67 antigen, clone MIB-1, dilution 1/50e; Dako), EGFR (mouse anti-epidermal growth factor receptor, clone 31G7, dilution 1/30e; Invitrogen), and CK 5/6 (mouse anti-cytokeratin 5 and 6, clone D5/16B4, dilution 1/25e; Invitrogen). All immunostaining was performed on an automated immunostainer (Ventana XT). ER and PR status were considered positive if the tumor showed at least 10% of positive cells (18). HER2 status was graded according to the HercepTest scoring system modified by ASCO/CAP recommendations (0, 1+, 2+, or 3+; ref. 19). Scores of 3+ were considered positive. In cases of 2+ scores, FISH was used to confirm HER2 amplification, using the dual color HER2 and CEN17 probes (ZytoLight, SPEC HER2/CEN17 Dual Color Prob Kit, Zytovision GmbH). HER2 amplification was defined according to ASCO/CAP criteria by a ratio of HER2/CEN17 > 2.2 (19). As for ER and PR tumor status, AR status was considered positive if the tumor showed at least 10% of positive cells. No cutoff is currently recommended for the definition of EGFR and CK5/6 positivity. In previous studies, tumors with no or faint membrane staining (≤5%) were considered negative for EGFR and CK5/6 (20, 21). The basal-like subtype was defined by positive EGFR and/or CK5/6 (22); other subtypes were considered non–basal-like TNBC. Finally, the cell proliferation index was studied, using Ki-67 tumor expression. As performed in 2 previous studies, a threshold for Ki-67 tumor expression of 50% was used to distinguish between tumors with a low (≤50%) and high (>50%) proliferation fraction (20, 21).

FDG-PET/CT procedures

A first FDG-PET/CT scan was done at baseline. Two different PET/CT imaging systems were used: a Gemini GXL PET/CT scanner from November 2006 to December 2010 and a Gemini TF PET/CT scanner from December 2010 to March 2013 (Philips Medical Systems). Patients were instructed to fast for at least 6 hours before the intravenous injection of 5 MBq/kg of 18F-FDG for Gemini GXL studies and 3 MBq/kg for Gemini TF studies. Patients were asked to rest. Sixty minutes after the injection, a whole-body PET/CT scan was done from the brain to the mid-thigh, with the patient supine. Finally, 90 minutes after the injection, a PET/CT scan restricted to the chest (2 bed positions) with patients in the prone position, both arms raised, was done. Emission data were all corrected for dead time, random, and scatter coincidences and attenuation before reconstruction with the RAMLA iterative method.

A second FDG-PET/CT scan with chest-restricted acquisition was done just before the second course of NAC and 90 minutes after the injection. For each patient, the same imaging system, FDG activity, and time from injection to acquisition were used for both studies.

Tumor glucose metabolism measurements.

A spheroidal VOI encompassing the primary tumor was manually drawn on the chest-restricted acquisitions obtained at 90 minutes after tracer injection to measure the standardized uptake value maximal index (SUVmax) at baseline (SUV1max) and after the first course of NAC (SUV2max). Measured SUVmax were systematically corrected for body surface area (BSA) and glycemia, as detailed in our previous studies (23).

The metabolic response to NAC was calculated as follows:

formula

Statistical analysis

WinSTAT software (Microsoft) and Systat software (Systat Inc.) were used for the statistical analyses. Data were described as numbers (percentages) or means and SD.

Associations between metabolic tumor parameters, clinicopathologic, molecular variables, and pCR achievement were assessed with the Mann–Whitney test.

Clinical, pathologic, molecular, and functional imaging parameters to predict pCR were identified in univariate and multivariate logistic regression. For ΔSUV and Ki-67 tumor expression, discrimination thresholds defined in previous studies were used: 50% for Ki-67 tumor expression (20, 21) and −50% for ΔSUV (24). Because no study was available concerning the cutoff for SUV2, its mean value was used.

Multivariate logistic regression with backward variable selection was done to identify predictive variables of independent statistical significance. To prevent colinearity, among the different PET parameters predictive of pCR in the univariate analysis, only the most predictive one (ΔSUVmax) was selected in the multivariate analysis. P < 0.05 was considered significant.

Patients' characteristics

Among the 240 women evaluated, 56 (23%) were identified as having TNBC (Table 1 and Fig. 1). Two of them were excluded because of technical problems during the baseline PET and 4 of them because NAC was no longer indicated after the first PET scan (obvious stage IV upstaging). In the remaining 50 patients included, 6 missed the second PET scan because of problems with the equipment or because they declined this second scan. The median age was 47 years (range, 26–70 years). Eighteen women (36%) were postmenopausal. The median primary tumor size, assessed with breast ultrasound and/or mammogram, was 4.0 cm (range, 1.5–7.0cm). According to axillary and subclavicular ultrasound scan, 37 of 50 women had a lymph node involvement. All of the tumors were invasive ductal carcinoma and 85% were the basal-like subtype. Mean Ki-67 tumor expression was 67% ± 18%. AR, EGFR and CK5/6 tumor expression were positive in 17% (8 of 46), 44% (20 of 46), and 74% (34 of 46) of tumors, respectively.

Figure 1.

Patient's flowchart.

Figure 1.

Patient's flowchart.

Close modal
Table 1.

Patient characteristics

Characteristicn (%)
Total patients 50 
Menopause 
 No 32 
 Yes 16 
 Unknown 
T-stage (tumor size obtained with breast ultrasound and/or mammogram) 
 T1 
 T2 34 
 T3 
 T4 
AJCC clinical stagea 
 IIA 13 
 IIB 21 
 IIIA 
 IIIB 
 IIIC 
Tumor grading 
 Grade I 
 Grade II 
 Grade III 39 
 Missing 
Architectural differentiation 
 Score I 
 Score II 
 Score III 38 
 Missing 
Nuclear pleomorphism 
 Score I 
 Score II 
 Score III 41 
 Missing 
Number of mitoses 
 Score I 
 Score II 
 Score III 33 
 Missing 
Ki-67 tumor expression (%) 
 Mean ± SD 67 ± 18 
 Median 70 
 Missing 
EGFR expression 
 Negative 26 
 Positive 20 
 Missing 
AR status 
 Negative 38 
 Positive (>10%) 
 Missing 
CK5/6 expression 
 Negative 12 
 Positive 34 
 Missing 
Phenotype 
 Basal-like 39 
 Non–basal-like 
 Missing 
Chemotherapy regimen 
 6 courses of FEC100 
 3 courses of FEC100 followed by 3 courses of docetaxel 41 
Surgery 
 Breast-conserving surgery 37 
 Mastectomy 13 
pCR (ypT0/is ypN0)b 
 Yes 21 
 No 29 
Characteristicn (%)
Total patients 50 
Menopause 
 No 32 
 Yes 16 
 Unknown 
T-stage (tumor size obtained with breast ultrasound and/or mammogram) 
 T1 
 T2 34 
 T3 
 T4 
AJCC clinical stagea 
 IIA 13 
 IIB 21 
 IIIA 
 IIIB 
 IIIC 
Tumor grading 
 Grade I 
 Grade II 
 Grade III 39 
 Missing 
Architectural differentiation 
 Score I 
 Score II 
 Score III 38 
 Missing 
Nuclear pleomorphism 
 Score I 
 Score II 
 Score III 41 
 Missing 
Number of mitoses 
 Score I 
 Score II 
 Score III 33 
 Missing 
Ki-67 tumor expression (%) 
 Mean ± SD 67 ± 18 
 Median 70 
 Missing 
EGFR expression 
 Negative 26 
 Positive 20 
 Missing 
AR status 
 Negative 38 
 Positive (>10%) 
 Missing 
CK5/6 expression 
 Negative 12 
 Positive 34 
 Missing 
Phenotype 
 Basal-like 39 
 Non–basal-like 
 Missing 
Chemotherapy regimen 
 6 courses of FEC100 
 3 courses of FEC100 followed by 3 courses of docetaxel 41 
Surgery 
 Breast-conserving surgery 37 
 Mastectomy 13 
pCR (ypT0/is ypN0)b 
 Yes 21 
 No 29 

aClinical stage according to the seventh edition of the American Joint Committee on Cancer (AJCC) staging manual (tumor size assessed with ultrasound scan, lymph node involvement, and metastasis assessed both with ultrasound scan and with FDG-PET, metastasis status assessed with FDG-PET).

bpCR is defined as ypT0/is ypN0: no invasive residual in the breast and nodes; in situ breast residuals allowed.

Mean tumor SUV1max (±SD) was 13.4 ± 7.6. After the first cycle of NAC, mean tumor SUV2max was 6.9 ± 4.1 (Supplementary Fig. S1). Mean tumor ΔSUVmax was −43.7% ± 25.4%. Thirty-four patients of 50 had significant FDG uptake in the axillary lymph nodes, strongly suggesting lymph node involvement. Mean lymph node SUV1max was 7.1 ± 5.0. After the first cycle of NAC, mean lymph node SUV2max was 3.0 ± 3.5. Mean nodes ΔSUVmax was −50.1% ± 29.2%. Nine women exhibited a complete nodal metabolic response after the first cycle of treatment (qualitative disappearance of nodal uptake).

The NAC regimens are detailed in Table 1. Conservative surgery was performed in 74% (37 of 50) of the women. The pCR rate was 42% (21 of 50).

Association between clinical/histopathologic parameters and FDG-PET features

Higher tumor grading, tumor architectural dedifferentiation, and inflammatory breast cancer were associated with higher tumor SUV1max (P = 0.003, P = 0.03 and 0.04 respectively; Table 2).

Table 2.

Metabolic characteristics of the primary tumor according to the clinical, biologic, and pathologic status

Baseline SUVmaxΔSUVmax
nMean ± SDPanMean (%) ± SDPa
All patients 50 13.4 ± 7.6  44 −43.7 ± 25.4  
Inflammatory breast cancer   0.04   NS 
 No 46 13.0 ± 7.3  40 −44.7 ± 23.3  
 Yes 22.2 ± 7.7  −52.3 ± 32.6  
Tumor grading (SBR)   0.003   NS 
 Score II 7.3 ± 3.5  −33.7 ± 12.3  
 Score III 39 14.8 ± 7.5  34 −45.5 ± 27.0  
Architectural differentiation   0.03   NS 
 Score II 9.3 ± 3.0  −40.2 ± 22.3  
 Score III 38 14.7 ± 7.7  33 −44.4 ± 26.5  
Ki-67 expression   NS   0.020 
 ≤50% 11 11.7 ± 6.6  −23.4 ± 23.0  
 >50% 32 13.9 ± 6.4  29 −47.2 ± 25.3  
EGFR expression   NS   NS 
 Negative 26 13.7 ± 7.4  24 −46.8 ± 25.5  
 Positive 20 13.4 ± 8.6  16 −35.6 ± 24.4  
CK5/6 expression   0.06   NS 
 Negative 12 10.3 ± 6.9  12 −41.9 ± 19.6  
 Positive 34 14.7 ± 8.0  28 −42.5 ± 27.8  
AR status   0.09   NS 
 Negative (<10%) 38 14.3 ± 8.0  33 −44.5 ± 26.2  
 Positive 9.9 ± 6.6  −31.7 ± 18.6  
Phenotype   NS   NS 
 Basal-like 39 14.1 ± 7.7  33 −42.2 ± 26.3  
 Non–basal-like 10.5 ± 8.8  −42.8 ± 21.8  
Chemotherapy regimen   NS   NS 
 Continuous (FEC100) 13.9 ± 5.9  −54.7 ± 22.1  
 Sequential (FEC100 + taxotere) 41 13.3 ± 8.0  35 −40.8 ± 25.8  
Surgery   NS   0.03 
 Breast-conserving surgery 37 14.1 ± 7.8  33 −48.5 ± 23.0  
 Mastectomy 13 11.4 ± 7.0  11 −29.0 ± 27.8  
Baseline SUVmaxΔSUVmax
nMean ± SDPanMean (%) ± SDPa
All patients 50 13.4 ± 7.6  44 −43.7 ± 25.4  
Inflammatory breast cancer   0.04   NS 
 No 46 13.0 ± 7.3  40 −44.7 ± 23.3  
 Yes 22.2 ± 7.7  −52.3 ± 32.6  
Tumor grading (SBR)   0.003   NS 
 Score II 7.3 ± 3.5  −33.7 ± 12.3  
 Score III 39 14.8 ± 7.5  34 −45.5 ± 27.0  
Architectural differentiation   0.03   NS 
 Score II 9.3 ± 3.0  −40.2 ± 22.3  
 Score III 38 14.7 ± 7.7  33 −44.4 ± 26.5  
Ki-67 expression   NS   0.020 
 ≤50% 11 11.7 ± 6.6  −23.4 ± 23.0  
 >50% 32 13.9 ± 6.4  29 −47.2 ± 25.3  
EGFR expression   NS   NS 
 Negative 26 13.7 ± 7.4  24 −46.8 ± 25.5  
 Positive 20 13.4 ± 8.6  16 −35.6 ± 24.4  
CK5/6 expression   0.06   NS 
 Negative 12 10.3 ± 6.9  12 −41.9 ± 19.6  
 Positive 34 14.7 ± 8.0  28 −42.5 ± 27.8  
AR status   0.09   NS 
 Negative (<10%) 38 14.3 ± 8.0  33 −44.5 ± 26.2  
 Positive 9.9 ± 6.6  −31.7 ± 18.6  
Phenotype   NS   NS 
 Basal-like 39 14.1 ± 7.7  33 −42.2 ± 26.3  
 Non–basal-like 10.5 ± 8.8  −42.8 ± 21.8  
Chemotherapy regimen   NS   NS 
 Continuous (FEC100) 13.9 ± 5.9  −54.7 ± 22.1  
 Sequential (FEC100 + taxotere) 41 13.3 ± 8.0  35 −40.8 ± 25.8  
Surgery   NS   0.03 
 Breast-conserving surgery 37 14.1 ± 7.8  33 −48.5 ± 23.0  
 Mastectomy 13 11.4 ± 7.0  11 −29.0 ± 27.8  

NOTE: Age (cutoff, 50 years), menopausal status, tumor size (cutoff, 5 cm), nuclear pleomorphism, number of mitoses, phenotype (basal vs. nonbasal), CA15.3, ACE, and CA-125 values were not significantly correlated with tumor metabolic characteristics. Bold numerals correspond to statistically significant P values (<0.05).

Abbreviation: NS, not significant (P < 0.1).

aMann–Whitney test.

Tumors with no AR expression trended to have higher SUV1max, but this difference was not significant (SUV1max = 14.3 ± 8.0 in AR-negative tumors vs. 9.9 ± 6.6 in AR-positive tumors, P = 0.09).

Using a threshold of 50%, a higher Ki-67 proliferation index was not associated with baseline tumor SUVmax but was associated with a greater metabolic response (ΔSUVmax = −47.2% ± 25.3% in high-proliferation tumors vs. −23.4% ± 23.0% in low-proliferation tumors, P = 0.02).

Association between pCR and tumor clinicopathologic, biologic, and imaging biomarkers

Using the Mann–Whitney test, pCR was significantly associated with higher Ki-67 tumor expression (P = 0.016) and higher ΔSUVmax (P = 0.0004; Tables 3 and 4, Fig. 2). Mean ΔSUVmax was −58.7% ± 19.5% in women achieving pCR and −32.2% ± 23.6% in those without pCR. Mean Ki-67 expression was 77% ± 13% in women achieving pCR and 62% ± 21% in those without pCR.

Figure 2.

Distribution of tumor metabolic response (ΔSUVmax) for prediction of pCR. With a threshold at −50%, sensitivity is 74% (14/19), specificity is 76% (19/25), positive predictive value is 70% (14/20), negative predictive value is 79% (19/24), and accuracy is 75% (33/44). The line corresponds to the threshold of ΔSUVmax = −50%.

Figure 2.

Distribution of tumor metabolic response (ΔSUVmax) for prediction of pCR. With a threshold at −50%, sensitivity is 74% (14/19), specificity is 76% (19/25), positive predictive value is 70% (14/20), negative predictive value is 79% (19/24), and accuracy is 75% (33/44). The line corresponds to the threshold of ΔSUVmax = −50%.

Close modal
Table 3.

Tumor biologic and imaging characteristics according to achievement of pCR

NMean ± SDPa
SUV1max 
 pCR 21 14.3 ± 6.1 NS 
 No pCR 29 12.8 ± 8.6  
SUV2max 
 pCR 19 5.6 ± 3.0 0.09 
 No pCR 25 7.9 ± 4.6  
ΔSUVmax (%) 
 pCR 19 −58.7 ± 19.5 0.0004 
 No pCR 25 −32.2 ± 23.6  
Ki-67 expression (%) 
 pCR 18 77 ± 13 0.016 
 No pCR 25 62 ± 21  
NMean ± SDPa
SUV1max 
 pCR 21 14.3 ± 6.1 NS 
 No pCR 29 12.8 ± 8.6  
SUV2max 
 pCR 19 5.6 ± 3.0 0.09 
 No pCR 25 7.9 ± 4.6  
ΔSUVmax (%) 
 pCR 19 −58.7 ± 19.5 0.0004 
 No pCR 25 −32.2 ± 23.6  
Ki-67 expression (%) 
 pCR 18 77 ± 13 0.016 
 No pCR 25 62 ± 21  

NOTE: CA15.3, ACE, and CA-125 values were not significantly correlated with pCR. Bold numerals correspond to statistically significant P values (<0.05).

aMann–Whitney test.

Table 4.

Univariate and multivariate logistic analysis of significant predictive factors for pCR

PatientsUnivariate analysisMultivariate analysis
Overall (n)pCR (%)ORPOR(95% CI)P
SUV2max 
 ≥6.9 18 3 (17)     
 <6.9 26 16 (61) 5.6 0.013    
 Missing      
ΔSUVmax 
 >−50% 24 5 (21%)    
 ≤−50% 20 14 (70%) 8.8 0.002 7.09 (1.48–33.3) 0.014 
 Missing      
Number of mitosis 
 Score I and II 12 3 (25)     
 Score III 33 18 (54) 3.6 0.089    
Missing      
Ki-67 expression 
 ≤50% 11 2 (18%)     
 >50% 32 16 (50%) 4.5 0.079    
Missing      
EGFR 
 Positive 20 4 (20)    
 Negative 26 13 (50) 4.0 0.042 6.45 (1.05–40.0) 0.043 
 Missing      
PatientsUnivariate analysisMultivariate analysis
Overall (n)pCR (%)ORPOR(95% CI)P
SUV2max 
 ≥6.9 18 3 (17)     
 <6.9 26 16 (61) 5.6 0.013    
 Missing      
ΔSUVmax 
 >−50% 24 5 (21%)    
 ≤−50% 20 14 (70%) 8.8 0.002 7.09 (1.48–33.3) 0.014 
 Missing      
Number of mitosis 
 Score I and II 12 3 (25)     
 Score III 33 18 (54) 3.6 0.089    
Missing      
Ki-67 expression 
 ≤50% 11 2 (18%)     
 >50% 32 16 (50%) 4.5 0.079    
Missing      
EGFR 
 Positive 20 4 (20)    
 Negative 26 13 (50) 4.0 0.042 6.45 (1.05–40.0) 0.043 
 Missing      

NOTE: Age (cutoff, 50 years), menopausal status, pregnancy history, UICC staging (2 vs. 3), SBR grade, architectural differentiation, nuclear pleomorphism, AR status, CA15.3 (cutoff, 30 kU/L), CA-125 (cutoff, 35 kU/L), ACE (cutoff, 0.8 μg/L), SUV1max, tumor size, tumor inflammation, CK, tumor phenotype (basal and non-basal) were not significant predictive factors of pCR. Among the significant imaging parameters in univariate analysis, only the most predictive one was used for multivariate analysis. Bold numerals correspond to statistically significant P values (<0.05).

In univariate logistic analysis, negative EGFR status (P = 0.042), high metabolic response (cutoff = −50%; P = 0.002), and low tumor SUV2max (cutoff = 6.9; P = 0.013) correlated with pCR (Table 4). Using 50% tumor expression as a cutoff, Ki-67 tended to be, but was not, significantly correlated with pCR (P = 0.079).

The most accurate biomarker to predict a pCR was tumor ΔSUVmax: with the cutoff at −50% previously defined by Groheux and colleagues (24), the negative predictive value (NPV), positive predictive value (PPV), sensitivity, specificity, and accuracy of a high ΔSUVmax to predict pCR were 79%, 70%, 74%, 76%, and 75%, respectively (Fig. 2).

In multivariate analysis, ΔSUVmax was the strongest independent predictor of pCR: High tumor ΔSUVmax had a high OR of 7.1 [95% confidence interval (CI), 1.48-33.3; P = 0.014]. EGFR tumor status was also an independent predictor of pCR (OR, 6.4; 95% CI, 1.05-40.0; P = 0.043).

The patient's age, menopausal status, tumor size, UICC staging, lymph node involvement, tumor inflammation, tumor histologic grading, CA15.3 values, ACE values, and tumor baseline metabolism showed no correlation with the pCR rate.

Combining a low metabolic response (ΔSUVmax ≤ −50%) with positive EGFR status provided the opportunity to predict non-pCR with an accuracy of 92% (11 women with non-pCR of a total of 12 women). Conversely, the association of a good metabolic response and negative tumor EGFR status predicted pCR with an accuracy of 77% (10 of 13 women).

Concerning the axillary tumor response, none of the 9 women with a complete lymph node response on interim PET had residual axillary disease at the end of NAC.

Among the various breast cancer subtypes, the TNBC subtype remains a challenge as it has a poor prognosis and as no specific targeted therapy is currently available (6, 7). Indeed, TNBC is associated with higher risk of distant recurrence and death, especially within the first 3 years after diagnosis (8, 25). It is thus extremely important to identify the clinicobiologic, molecular, or imaging biomarkers that can early predict which TNBC tumors will respond to NAC. The ultimate goal is to better tailor neoadjuvant treatment in poorly responding TNBC.

Biologic biomarkers of tumor response

The present prospective study was undertaken to determine which biomarkers could predict a pCR in TNBCs. No pretherapy clinical factors, including UICC staging, are able to predict pCR. Among the biologic markers, negative EGFR status correlated with the achievement of pCR (Table 4), which is consistent with previous studies (20, 21).

As the biology of TNBC is better understood today, it is clear that TNBC does not directly correspond to a single molecular entity but rather to a heterogeneous subgroup that includes various genomic entities (26, 27). The basal-like subtype, defined by gene expression analysis, is the most frequent one. Using IHC, the basal-like subtype can be approached as CK5/6-positive and/or EGFR-positive (22, 28). We found that a negative EGFR status, mainly observed in non–basal-like tumors, was independently associated with a higher pCR rate (50% in EGFR-negative tumors vs. 20% in EGFR-positive tumors). This result is consistent with 2 previous reports, which showed a higher pCR rate in EGFR-negative and non–basal-like tumors, defined by IHC (20, 21) but not with the results of Rouzier and colleagues, who reported that the basal-like subtype, defined using gene expression profiling, was more sensitive to NAC than were normal-like cancers (29). The reason may be that the normal-like subtype, classified according to the gene expression profile, included 60% of ER-positive tumors and thus did not match the non–basal-like phenotype based on IHC. Moreover, Lehmann and colleagues reported a 7-subtype molecular classification of TNBC, in which they distinguished between 2 basal-like subtypes: BL1 and BL2 (30). Masuda and colleagues recently demonstrated that among the 7 TNBC subtypes, the BL1 subtype had the highest chemosensitivity (pCR rate = 52%) whereas the BL2 subtype had the lowest one (pCR rate = 0%; ref. 31). Thus, clinically speaking, basal tumors cannot be considered a single good-response entity.

Classification based on IHC is easier to use in clinical practice and less expensive than gene expression. Nevertheless, one limit is the absence of consensus regarding the optimal threshold to define EGFR, CK5/6-positive tumors or the Ki-67 optimal cutoff.

pCR achievement was also associated with higher Ki-67 tumor expression (P = 0.016, Table 3) but, in contrast with previous studies (20, 21, 32), Ki-67 expression could not significantly predict pCR in the logistic regression analysis (P = 0.08 with a 50% tumor expression threshold).

Our results found that IHC can help define a TNBC subgroup with a higher likelihood of achieving pCR, but IHC is still not accurate enough to reliably identify, at baseline, patients with no benefit to a conventional NAC regimen. Other early biomarkers of tumor response are needed to better tailor the treatment.

Tumor glucose metabolism as a biomarker of response

One originality of the present study was to include FDG-PET (Fig. 3). Tumor metabolic behavior was assessed at baseline and after the first cycle of NAC. Different metabolic parameters were evaluated. Among them, the tumor metabolic response assessed using the percentage decrease in SUVmax on interim PET offered the greatest accuracy in predicting pCR: a decrease in tumor FDG uptake greater than 50% predicted pCR with a PPV of 70% and an NPV of 79%. In multivariate analysis, the metabolic response was an independent predictor of pCR. Few studies have evaluated the predictive value of FDG-PET in TNBC treated with NAC. Most of these found a good predictive value of PET in this subtype, after both 1 and 2 cycles of NAC (24, 33, 34). In the study of Groheux and colleagues, a cutoff of −50% of ΔSUVmax offered the best accuracy in predicting pCR, whereas a −42% cutoff was optimal to predict relapse (24). Although our own optimal cutoff was found at −60%, the 50% threshold previously defined was used in the present study to validate it on our independent dataset. Compared with Groheux's results, we found a lower accuracy of ΔSUVmax to predict pCR (75% vs. 80%), a lower NPV (79% vs. 96%) but higher PPV (70% vs. 59%). One of the reasons may be the different timing of the interim PET examination, performed after 2 cycles in Groheux's study (24). In keeping with the previous study by Groheux and colleagues, we found that measuring the response in axillary lymph nodes provided no predictive benefit over metabolic assessment of the breast tumor alone.

Figure 3.

Assessment of tumor metabolic response with baseline and interim 18F-FDG-PET exams. CT (A and C) and fusion (B and D) imaging at baseline and after the first cycle of neoadjuvant chemotherapy of a woman with a good-responding triple-negative tumor in the left breast (ΔSUV = −79%). This woman achieved a pCR at the end of NAC.

Figure 3.

Assessment of tumor metabolic response with baseline and interim 18F-FDG-PET exams. CT (A and C) and fusion (B and D) imaging at baseline and after the first cycle of neoadjuvant chemotherapy of a woman with a good-responding triple-negative tumor in the left breast (ΔSUV = −79%). This woman achieved a pCR at the end of NAC.

Close modal

As most studies that have previously assessed PET-based tumor response in breast cancer (24, 34), and in accordance with the recommendations of the European Organization of Research and Treatment of cancer (EORTC; ref. 35), the relative change in tumor SUVmax was measured to assess tumor response. Because SUVmax and SUVpeak have both demonstrated lower interobserver variability than other quantitative PET parameters (36), SUVpeak could also has been used, as suggested by Wahl and colleagues (37). Indeed, SUVpeak is less sensitive than SUVmax to the image noise and voxel size (38). However, the region of interest (ROIpeak) used to measure SUVpeak is not uniquely defined (39, 40). This inconsistent definition of ROIpeak results in substantial variation of individual PET response using SUVpeak, particularly in heterogeneous tumor (40).

Inflammatory breast cancer (IBC) is usually distinguished from non-IBC by specific morphologic, phenotypic, biologic properties, and a poorer outcome (41). We found higher baseline tumor glucidic metabolism (SUV1max) in IBC than in non-IBC. It may be explained by 2 reasons: glucose metabolism is increased in inflammatory tissue and inflammatory breast tumors are usually highly proliferative. We did not assess significant differences of metabolic response between IBC and non-IBC. Tumor inflammation may affect the breast tumor metabolic behavior and response to treatment but larger studies are needed to clarify this point.

Among all the clinical, biologic, and imaging biomarkers evaluated, 2 of them were independent predictors of tumor response. Combining these biomarkers is an interesting approach to define probability groups for pCR and to develop accurate predictive model (nomogram). In the present study, combining a poor metabolic response with positive EGFR status predicted non-pCR with a high value of 92%.

Limitations of the study

One limitation of our study is the use of different therapeutic protocols and the frequent switch to docetaxel after the first 3 cycles of FEC100. Several phase III trials have demonstrated that this systematic switch at mid-course improves the pCR rate (42, 43): one meta-analysis found an absolute difference of 2.4% (P = 0.013; ref. 44). The survival benefit of this switch is more controversial (42, 43). In the present study and others (33), the final pathologic response to the sequential regimen strongly depended on the tumor response assessed after the first cycle of FEC or EC and was independent of any further change in the chemotherapy regimen. This suggests that the systematic switch to docetaxel has little benefit.

In the neoadjuvant setting, pCR is a strong prognostic marker, particularly in the TNBC subtype (10–12). Nevertheless, it is still not clear whether an increased pCR rate, due to a novel treatment, translates into improved survival (12). This is a crucial issue for the design of future PET-guided therapeutic trials to evaluate the benefit of an early therapeutic switch in nonresponding women (45).

The early identification of TNBC tumors with low chemosensitivity to NAC is an important issue. Negative tumor EGFR status, mostly observed in non–basal-like TNBC, is an independent predictor of pCR after NAC. However, the benefit of routinely identifying basal-like and non–basal-like cancers is unclear and the distinction is not currently used for treatment decision making. The metabolic response after the first cycle of NAC can more accurately predict pCR. Moreover, the tumor metabolic response and EGFR status can be combined to improve the early identification of women unlikely to achieve pCR at the end of NAC. Given these results, multicenter trials using PET-guided treatment strategies are now necessary to evaluate the benefit of early therapeutic changes in poorly responding women. Such a strategy should lead to enhanced personalized medicine. Further studies are also needed to evaluate the link between the metabolic behavior of TNBC and the expression of different gene signatures. These will lead to better understanding of the biology of poorly responding TNBC.

P. Fumoleau is a consultant/advisory board member for Novartis and Roche. No potential conflicts of interest were disclosed by the other authors.

Conception and design: O. Humbert, C. Charon-Barra, A. Berriolo-Riedinger, I. Desmoulins, S. Kanoun, P. Fumoleau, A. Cochet, F. Brunotte

Development of methodology: O. Humbert, C. Charon-Barra, A. Berriolo-Riedinger, P. Fumoleau, A. Cochet, F. Brunotte

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): O. Humbert, C. Charon-Barra, A. Berriolo-Riedinger, P. Fumoleau, A. Cochet

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): O. Humbert, J.-M. Riedinger, C. Charon-Barra, S. Kanoun, P. Fumoleau, F. Brunotte

Writing, review, and/or revision of the manuscript: O. Humbert, C. Charon-Barra, A. Berriolo-Riedinger, I. Desmoulins, V. Lorgis, S. Kanoun, C. Coutant, P. Fumoleau, F. Brunotte

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): O. Humbert

Study supervision: A. Berriolo-Riedinger, I. Desmoulins, P. Fumoleau, A. Cochet, F. Brunotte

This study is part of the PharmImage project. The authors thank Bastable for proofreading the text.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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