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Purpose: The GeparX study investigated whether denosumab as add-on treatment to nab-paclitaxel-based neoadjuvant chemotherapy (NACT) with two different schedules (125 mg/m² weekly vs. day 1, 8 every 22 days) may increase pathologic complete response (pCR) rate. The addition of denosumab to NACT did not improve pCR rates as recently published. In this study, we investigated whether receptor activator of nuclear factor-kappa B (RANK) expression, as part of the denosumab target pathway: (i) may retrospectively identify a subgroup of patients with additional clinical benefit of denosumab or (ii) may predict response to nab-paclitaxel NACT., Experimental Design: RANK protein was IHC-stained on pre-therapeutic core biopsies from patients of the GeparX study (n = 667) with the antibody RANK/Envision System HRP (DAB) and was analyzed for the percentage of membranous RANK tumor cell staining (>5% RANKhigh vs. ≤5% RANKlow)., Results: We could not identify any patient subgroup with differential response under denosumab add-on treatment in patients with RANKhigh expression [139/667, 20.8%; OR, 0.86; 95% confidence interval (CI), 0.44-1.68; P = 0.667] or RANKlow expression (528/667 (79.2%) OR, 1.10; 95% CI, 0.78-1.56; P = 0.589; Pinteraction = 0.528). However, the pCR rate was higher in the RANKhigh subgroup compared with RANKlow (50% vs. 39%; OR, 1.52; 95% CI, 1.04-2.21; P = 0.037). RANK expression constituted an independent predictor of response to NACT frequently in patients with luminal-like subtype (HR+/HER2-; OR, 2.98; 95% CI, 1.30-6.79; P = 0.010). No predictive value of RANK expression among the different nab-paclitaxel regimens was observed., Conclusion: We report RANK expression to be an independent predictive biomarker for response to NACT in patients with luminal-like breast cancer., (©2023 American Association for Cancer Research.)

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment.

Background: Risk assessment on the molecular level is important in predictive pathology to determine the risk of metastatic disease for ERpos, HER2neg breast cancer. The gene expression test EndoPredict (EP) was trained and validated for prediction of a 10-year risk of distant recurrence to support therapy decisions regarding endocrine therapy alone or in combination with chemotherapy. The EP test provides the 12-gene Molecular Score (MS) and the EPclin-Score (EPclin), which combines the molecular score with tumor size and nodal status. In this project we investigated the correlation of 12-gene MS and EPclin scores with classical pathological markers., Methods: EndoPredict-based gene expression profiling was performed prospectively in a total of 1652 patients between 2017 and 2020. We investigated tumor grading and Ki67 cut-offs of 20% for binary classification as well as 10% and 30% for three classes (low, intermediate, high), based on national and international guidelines., Results: 410 (24.8%) of 1652 patients were classified as 12-gene MS low risk and 626 (37.9%) as EPclin low risk. We found significant positive associations between 12-gene MS and grading (p < 0.001), EPclin and grading (p = 0.001), 12-gene MS and Ki67 (p < 0.001), and EPclin and Ki67 (p < 0.001). However, clinically relevant differences between EP test results, Ki67 and tumor grading were observed. For example, 118 (26.3%) of 449 patients with Ki67 > 20% were classified as low risk by EPclin. Same differences were seen comparing EP test results and tumor grading., Conclusion: In this study we could show that EP risk scores are distributed differentially among Ki67 expression groups, especially in Ki67 low and high tumors with a substantial proportion of patients with EPclin high risk results in Ki67 low tumors and vice versa. This suggests that classical pathological parameters and gene expression parameters are not interchangeable, but should be used in combination for risk assessment., (© 2021. The Author(s).)

Background: Current clinical guidelines suggest that breast cancers with low hormone receptor expression (LowHR) in 1-10% of tumor cells should be regarded as hormone receptor positive. However, clinical data show that these patients have worse outcome compared to patients with hormone receptor expression above 10%. We performed DNA methylation profiling on 23 LowHR breast cancer specimens, including 13 samples with HER2 amplification and compared our results with a reference breast cancer cohort from The Cancer Genome Atlas to clarify the status for this infrequent but important patient subgroup., Results: In unsupervised clustering and dimensionality reduction, breast cancers with low hormone receptor expression that lacked HER2 amplification usually clustered with triple negative breast cancer (TNBC) reference samples (8/10; "LowHR TNBC-like"). In contrast, most specimens with low hormone receptor expression and HER2 amplification grouped with hormone receptor positive cancers (11/13; "LowHR HRpos-like"). We observed highly similar DNA methylation patterns of LowHR TNBC-like samples and true TNBCs. Furthermore, the Ki67 proliferation index of LowHR TNBC-like samples and clinical outcome parameters were more similar to TNBCs and differed from LowHR HRpos-like cases., Conclusions: We here demonstrate that LowHR breast cancer comprises two epigenetically distinct groups. Our data strongly suggest that LowHR TNBC-like samples are molecularly, histologically and clinically closely related to TNBC, while LowHR HRpos-like specimens are closely related to hormone receptor positive tumors., (© 2021. The Author(s).)

Background: The development of anti-HER2 antibody-drug conjugates opens new therapeutic options for patients with breast cancer, including patients with low expression of HER2. To characterise this new breast cancer subtype, we have compared the clinical and molecular characteristics of HER2-low-positive and HER2-zero breast cancer, including response to neoadjuvant chemotherapy and prognosis., Methods: In this pooled analysis of individual patient data, we evaluated a cohort of 2310 patients with HER2-non-amplified primary breast cancer that were treated with neoadjuvant combination chemotherapy in four prospective neoadjuvant clinical trials (GeparSepto, NCT01583426; GeparOcto, NCT02125344; GeparX, NCT02682693; Gain-2 neoadjuvant, NCT01690702) between July 30, 2012, and March 20, 2019. Central HER2 testing was done prospectively before random assignment of participants in all trials. HER2-low-positive status was defined as immunohistochemistry (IHC) 1+ or IHC2+/in-situ hybridisation negative and HER2-zero was defined as IHC0, based on the American Society of Clinical Oncology/College of American Pathologists guidelines. Disease-free survival and overall survival data were available for 1694 patients (from all trials except GeparX) with a median follow-up of 46·6 months (IQR 35·0-52·3). Bivariable and multivariable logistic regression models and Cox-proportional hazards models were performed based on a predefined statistical analysis plan for analysis of the endpoints pathological complete response, disease-free survival, and overall survival., Findings: A total of 1098 (47·5%) of 2310 tumours were HER2-low-positive and 1212 (52·5%) were HER2-zero. 703 (64·0%) of 1098 patients with HER2-low-positive tumours were hormone receptor positive, compared with 445 (36·7%) of 1212 patients with HER2-zero tumours (p<0.0001). HER2-low-positive tumours had a significantly lower pathological complete response rate than HER2-zero tumours (321 [29·2%] of 1098 vs 473 [39·0%] of 1212, p=0·0002). Pathological complete response was also significantly lower in HER2-low-positive tumours versus HER2-zero tumours in the hormone receptor-positive subgroup (123 [17·5%] of 703 vs 105 [23·6%] of 445, p=0·024), but not in the hormone receptor-negative subgroup (198 [50·1%] of 395 vs 368 [48·0%] of 767, p=0·21). Patients with HER2-low-positive tumours had significantly longer survival than did patients with HER2-zero tumours (3-year disease-free survival: 83·4% [95% CI 80·5-85·9] vs 76·1% [72·9-79·0]; stratified log-rank test p=0·0084; 3-year overall survival: 91·6% [84·9-93·4] vs 85·8% [83·0-88·1]; stratified log-rank test p=0·0016). Survival differences were also seen in patients with hormone receptor-negative tumours (3-year disease-free survival: 84·5% [95% CI 79·5-88·3] vs 74·4% [70·2-78.0]; stratified log-rank test p=0·0076; 3-year overall survival: 90·2% [86·0-93·2] vs 84·3% [80·7-87·3], stratified log-rank test p=0·016), but not in patients with hormone receptor-positive tumours (3-year disease-free survival 82·8% [79·1-85·9] vs 79·3% [73·9-83·7]; stratified log-rank test p=0·39; 3-year overall survival 92·3% [89·6-94·4] vs 88·4% [83·8-91·8]; stratified log-rank test p=0·13)., Interpretation: Our results show that HER2-low-positive tumours can be identified as new subgroup of breast cancer by standardised IHC, distinct from HER2-zero tumours. HER2-low-positive tumours have a specific biology and show differences in response to therapy and prognosis, which is particularly relevant in therapy-resistant, hormone receptor-negative tumours. Our results provide a basis for a better understanding of the biology of breast cancer subtypes and the refinement of future diagnostic and therapeutic strategies., Funding: German Cancer Aid (Deutsche Krebshilfe)., Competing Interests: Declaration of interests CD reports grants from European Commission H2020, German Cancer Aid Translational Oncology, German Breast Group, during the conduct of the study; personal fees from Novartis, Roche, MSD Oncology, Daiichi Sankyo, AstraZeneca, Molecular Health, and Merck, and grants from Myriad, outside the submitted work. CD is the cofounder of Sividon diagnostics. CD has a patent VMScope digital pathology software with royalties paid, a patent WO2020109570A1-cancer immunotherapy pending, and a patent WO2015114146A1 and WO2010076322A1-therapy response issued. AS reports grants from Celgene, Roche, and AbbVie; personal fees from Roche, AstraZeneca, Celgene, Novartis, MSD, Tesaro, Lilly, and Roche, outside the submitted work. TL receives payment or honoraria from Tesaro, MSD, Roche, Novartis, Pfizer, Amgen, Clovis, and Lilly; receives support for travel costs from Roche, Pfizer, MSD, Celgene, and Clovis; reports participation on an Advisory Board from Roche, Tesaro, Amgen, MSD, Pfizer, Lilly, Myriad, Esai, and GSK, outside the submitted work. J-UB reports personal fees from AstraZeneca, Exact Sciences, Amgen, MSD Oncology, Lilly, Novartis, Sysmex, Roche, and Sonoscape, outside the submitted work. PW reports personal fees from Amgen, AstraZeneca, MSD, Novartis, Pfizer, PharmaMar, Roche, Teva, Eisai, Clovis, and Tesaro, outside the submitted work. HT reports consulting fees and travel costs from Pierre Fabre, Pfizer Pharma, Mundipharma, ClinSol, Novartis, Lilly, Grünenthal, Vifor, AstraZeneca, Mylan, and AMGEN; payment or honoraria from Vifor, Novartis, AstraZeneca, ClinSol, Mundipharma, Lilly, and Amgen; participation on an Advisory Board from Novartis, Bristol Myers Squibb, AstraZeneca, Molecular Health, Pfizer, Roche, Grünenthal, and Mylan; and is part of a leadership or fiduciary role in Arbeitskreis klinische Studien. ASCO, BNHO, DGHO, DKG, ESMO, Deutsche Gesellschaft für Senelogie. CJ receives payment or honoraria from Roche, AstraZeneca, Novartis, Lilly, Exact Sciences, and Pierre Fabre; recieves support for travel costs from Roche and AstraZeneca, reports participation on an Advisory Board from Roche, Lilly, and AstraZeneca. MR reports consulting fees from Roche, Novartis, Lilly, and Somatex; receives payment or honoraria from Novartis, Roche, Lilly, Somatex, and AstraZeneca; recieves support for travel costs from Novartis, Pfizer, and Celgene. EFS receives payment or honoraria from AstraZeneca, Pfizer, Celgene, Roche, and Amgen; support for travel costs from Amgen, AstraZeneca, Celgene, Clovis Oncology, Eisai, Erbe, Gedeon, Richter, Genomic Health, Jenapharm, Johnson Johnson, KLS Martin, Matramed, Medac, Mentor, Novartis, Pfizer, Pharma Mar, MSD, Roche, Samsung, Storz, Tewa, Vifor, Medconcept, and Thieme; is part of a leadership or fiduciary role of University of Saarland, Germany, society for German Gynecological Endoscopy. CH reports personal fees from Pfizer, Roche, Novartis, Lilly, AstraZeneca, Celgene, outside the submitted work. PAF reports personal fees from Novartis, Pfizer, Daiichi-Sankyo, AstraZeneca, Eisai, Merck Sharp & Dohme, Lilly, Pierre Fabre, Seattle Genetics, Roche, and Hexal, and grants from Biontech and Cepheid, during the conduct of the study. KL receives payment from Roche, Pfizer, MSD, Novartis, Lilly, and Genomic Health, support for travel costs from Roche, and reports participation on an Advisory Board from Roche, MSD, and Esai. JH reports research grants to institution from Celgene, Novartis, and Hexal; consulting fees from Lilly, Novartis, Roche, Pfizer, Hexal, AstraZeneca, MSD, Celgene, and Abbvie; receives payment from Lilly, Novartis, Roche, Pfizer, AstraZeneca, MSD, Celgene, Eisai, and Abbvie; recieves support for travel costs from Roche, Pfizer, Novartis, Celgene, and Daiichi. TR reports consulting fees from Pfizer, honoraria for presentations from AstraZeneca and receives payment from Pfizer, Roche, and Novartis. MS reports grants from AstraZeneca, BioNtech, Eisai, German Breast Group, Genentech, Novartis, Pantarhei Bioscience, Pfizer, Pierre Fabre, and Roche; consulting fees from AstraZeneca, Eisai, Lilly, Novartis, Pantarhei Bioscience, Pfizer, Pierre Fabre, Roche, and SeaGen; receives payment or honoraria from AstraZeneca, Novartis, Pfizer, Roche, and SeaGen; support for travel costs from Pfizer and Roche; has planned patents, issued or pending from EP 2951317 B1 and EP 2390370 B1; reports participation on an Advisory Board from AstraZeneca, Eisai, Lilly, Novartis, Pantarhei Bioscience, Pfizer, Pierre Fabre, Roche, and SeaGen; medical writing support from Roche. WJ reports grants and personal fees from Sanofi-Aventis, Novartis, Lilly, Pfizer, Roche, Chugai, AstraZeneca, MSD, and Daiichi Sankyo, during the conduct of the study. ES reports consulting fees from Roche and AstraZeneca; receives payment or honoraria from Roche, Pfizer, Novartis, and Astra Zeneca; support for travel costs from Roche, Pfizer, and Daiko. SSe reports reimbursement of travel costs from Novartis and personal fees from Roche, Mundipharma, and Amgen, outside the submitted work. MU reports personal fees and non-financial support from Abbvie, Amgen, AstraZeneca, Celgene, Daiichi Sankyo, Eisai, Lilly Int, MSD Merck, Mundipharma, Myriad Genetics, Odonate, Pfizer, Roche Pharma, Sanofi Aventis Deutschland, TEVA Pharmaceuticals, Novartis, and Clovis Oncology; personal fees from Bristol Myers Squibb, Lilly Deutschland, PUMA Biotechnology, Pierre Fabre, Seattle Genetics, outside the submitted work. SL reports grants or fundings from Amgen, AstraZeneca, Celgene, Novartis, Immunomedics, Pfizer, Roche, DSI Trevor, Vifor, Abbvie, Cepheid, Seagen, and VM Scope; receives payment or honoraria from AstraZeneca, Roche, Novartis, Pfizer, Prime/Medscape, Puma, and Samsung; has planned patents, issued or pending from EP14153692.0; reports participation on an Advisory Board from Amgen, AstraZeneca, DSI, Roche, Merck, Pfizer, BMS, SeaGen, Eirgenix, Celgene, Abbvie, Lilly, GlaxoSmithKline, and Pierre Fabre; is part of a leadership or fiduciary role from BIG. All other authors report no conflicts of interests., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Background: Different endogenous and exogenous mutational processes act over the evolutionary history of a malignant tumor, driven by abnormal DNA editing, mutagens or age-related DNA alterations, among others, to generate the specific mutational landscape of each individual tumor. The signatures of these mutational processes can be identified in large genomic datasets. We investigated the hypothesis that genomic patterns of mutational signatures are associated with the clinical behavior of breast cancer, in particular chemotherapy response and survival, with a particular focus on therapy-resistant disease., Patients and Methods: Whole exome sequencing was carried out in 405 pretherapeutic samples from the prospective neoadjuvant multicenter GeparSepto study. We analyzed 11 mutational signatures including biological processes such as APOBEC-mutagenesis, homologous recombination deficiency (HRD), mismatch repair deficiency and also age-related or tobacco-induced alterations., Results: Different subgroups of breast carcinomas were defined mainly by differences in HRD-related and APOBEC-related mutational signatures and significant differences between hormone-receptor (HR)-negative and HR-positive tumors as well as correlations with age, Ki-67 and immunological parameters were observed. We could identify mutational processes that were linked to increased pathological complete response rates to neoadjuvant chemotherapy with high significance. In univariate analyses for HR-positive tumors signatures, S3 (HRD, P < 0.001) and S13 (APOBEC, P = 0.001) as well as exonic mutation rate (P = 0.002) were significantly correlated with increased pathological complete response rates. The signatures S3 (HRD, P = 0.006) and S4 (tobacco, P = 0.011) were prognostic for reduced disease-free survival of patients with chemotherapy-resistant tumors., Conclusion: The results of this investigation suggest that the clinical behavior of a tumor, in particular, response to neoadjuvant chemotherapy and disease-free survival of therapy-resistant tumors, could be predicted by the composition of mutational signatures as an indicator of the individual genomic history of a tumor. After additional validations, mutational signatures might be used to identify tumors with an increased response rate to neoadjuvant chemotherapy and to define therapy-resistant subgroups for future therapeutic interventions., Competing Interests: Disclosure CD reports personal fees from Novartis, personal fees and travel support from Roche, personal fees from MSD Oncology, from Daiichi Sankyo, grants from Myriad Genetics and GBG, other from Sividon Diagnostics/Myriad, outside the submitted work. In addition, CD has a patent EP18209672 pending, a patent EP20150702464 pending and a patent Software (VMscope digital pathology) pending. MU reports personal fees and non-financial support from Abbvie, personal fees and non-financial support from Amgen GmbH, personal fees and non-financial support from AstraZeneca, personal fees from Bristol Myers Squibb (BMS), personal fees and non-financial support from Celgene GmbH, personal fees and non-financial support from Daiji Sankyo, personal fees and non-financial support from Eisai GmbH, personal fees from Lilly Deutschland, personal fees and non-financial support from Lilly Int., personal fees and non-financial support from MSD Merck, personal fees and non-financial support from Mundipharma, personal fees and non-financial support from Myriad Genetics, personal fees and non-financial support from Odonate, personal fees and non-financial support from Pfizer GmbH, personal fees from PUMA Biotechnology, personal fees and non-financial support from Roche Pharma AG, personal fees and non-financial support from Sanofi Aventis Deutschland GmbH, personal fees and non-financial support from TEVA Pharmaceuticals Ind Ltd, personal fees and non-financial support from Novartis, personal fees from Pierre Fabre, personal fees and non-financial support from Clovis Oncology, outside the submitted work. SB reports other from NantOmics, LLC, during the conduct of the study; other from NantOmics, LLC, outside the submitted work; and Employee of NantOmics, LLC with equity interest. AS reports grants from Celgene, grants from Roche, grants from AbbVie, grants from Molecular Partner, personal fees from Roche, personal fees from AstraZeneca, personal fees from Celgene, personal fees from Roche, personal fees from Celgene, personal fees from Pfizer, personal fees from AstraZeneca, personal fees from Novartis, personal fees from MSD, personal fees from Tesaro, personal fees from Lilly, other from Roche, outside the submitted work. CJ reports personal fees from Roche, personal fees from Celgene, personal fees from Amgen, during the conduct of the study. TL reports non-financial support from Pharma Mar, non-financial support from Daiichi Sankyo, personal fees and non-financial support from MSD, personal fees from Amgen, personal fees and non-financial support from Pfizer, personal fees from Novartis, personal fees from Teva, personal fees from Tesaro, personal fees and non-financial support from Roche, personal fees and non-financial support from Clovis, non-financial support from Celgene, outside the submitted work. WDS reports grants from German Breast Group, during the conduct of the study; personal fees from AstraZeneca, outside the submitted work. VM reports grants and personal fees from Roche, during the conduct of the study; personal fees from Amgen, Astra Zeneca, Daiichi-Sankyo, Eisai, Pfizer, MSD, Novartis, Roche, Teva, Seattle Genetics and consultancy honoraria from Genomic Health, Hexal, Roche, Pierre Fabre, Amgen, ClinSol, Novartis, MSD, Daiichi-Sankyo, Eisai, Lilly, Tesaro, Nektar, personal fees from Genomic Health, Hexal, Roche, Pierre Fabre, Amgen, ClinSol, Novartis, MSD, Daiichi-Sankyo, Eisai, Lilly, Tesaro and Nektar, other from I Novartis, Roche, Seattle Genetics, Genentech, outside the submitted work. PSS is the CEO of NantOmics and reports non-financial support from NantOmics, outside the submitted work. MvM reports honoraria from Roche, Amgen, Genomic Health, Astra Zeneca, as well as travel support from Lilly and Novartis. PAF reports grants from Novartis, grants from BioNTech, personal fees from Novartis, personal fees from Roche, personal fees from Pfizer, personal fees from Celgene, personal fees from Daiichi-Sankyo, personal fees from AstraZeneca, personal fees from Merck Sharp & Dohme, personal fees from Eisai, personal fees from Puma, grants from Cepheid, personal fees from Lilly, personal fees from Seattle Genetics, during the conduct of the study. SR is the Chief Scientific Officer for NantOmics and reports non-financial support from NantOmics, outside the submitted work. SL reports grants and other from Celgene, grants and other from Roche, during the conduct of the study; grants and other from Abbvie, grants and other from Amgen, grants and other from AstraZeneca, grants and other from Novartis, grants and other from Pfizer, other from Seattle Genetics, other from PriME/Medscape, personal fees from Chugai, grants from Teva, grants from Vifor, grants and other from Daiichi-Sankyo, other from Lilly, other from Samsung, other from Eirgenix, other from BMS, other from Puma, other from MSD, grants from Immunomedics, outside the submitted work. In addition, SL has a patent EP14153692.0 pending. All other authors have declared no conflicts of interest., (Copyright © 2021 European Society for Medical Oncology. Published by Elsevier Ltd. All rights reserved.)

Background: The efficacy and toxicity of olaparib as combination therapy in early breast cancer (BC) patients with homologous recombinant deficiency (HRD) [score high and/or germline (g) or tumour (t) BRCA1/2 mutation] is not well described. GeparOLA (ClinicalTrials.gov, NCT02789332) investigated olaparib in combination with paclitaxel in HER2-negative early BC with HRD., Patients and Methods: Patients with untreated primary HER2-negative cT2-cT4a-d or cT1c with either cN+ or pNSLN+ or cT1c and triple-negative breast cancer (TNBC) or cT1c and Ki-67>20% BC with HRD were randomised either to paclitaxel (P) 80 mg/m 2 weekly plus olaparib (O) 100 mg twice daily for 12 weeks or P plus carboplatinum (Cb) area under the curve 2 weekly for 12 weeks, both followed by epirubicin/cyclophosphamide (EC). Stratification factors were hormone receptor (HR) status (HR+ versus HR-) and age (<40 versus ≥40 years). The primary endpoint was pathological complete response (pCR; ypT0/is ypN0). A two-sided one-group χ 2 -test was planned to exclude a pCR rate of ≤55% in the PO-EC arm. Secondary end points were other pCR definitions, breast conservation rate, clinical/imaging response, tolerability and safety., Results: A total of 107 patients were randomised between September 2016 and July 2018; 106 (PO N = 69; PCb N = 37) started treatment. Median age was 47.0 years (range 25.0-71.0); 36.2% had cT1, 61.0% cT2, 2.9% cT3, and 31.8% cN-positive tumours; grade 3 tumours: 86.8%; Ki-67>20%: 89.6%; TNBC: 72.6%; confirmed gBRCA1/2 mutation: 56.2%. The pCR rate with PO was 55.1% [90% confidence interval (CI) 44.5% to 65.3%] versus PCb 48.6% (90% CI 34.3% to 63.2%). Analysis for the stratified subgroups showed higher pCR rates with PO in the cohorts of patients <40 years and HR+ patients., Conclusion: GeparOLA could not exclude a pCR rate of ≤55% in the PO arm. PO was significantly better tolerated and the combination merits further evaluation., Competing Interests: Disclosure PAF reports grants from Novartis, BioNtech, personal fees from Novartis, Roche, Pfizer, Celgene, Daiichi Sankyo, AstraZeneca, MacroGenics, Eisai, Merck Sharp & Dohme, grants from Cepheid, personal fees from Lilly, during the conduct of the study. TL reports non-financial support from Pharma Mar, Daiichi Sankyo, Celgene; personal fees and non-financial support from MSD, Pfizer, Roche, Clovis; personal fees from Amgen, Novartis, Teva, Tesaro. AS reports grants from Celgene, Roche, AbbVie, Molecular Partners, expert testimony from Roche and AstraZeneca; travel expenses from Celgene, Roche and Pfizer, honoraria from Roche, Celgene, Pfizer, Novartis, AstraZeneca, MSD, Tesaro and Lilly, outside the submitted work. CJ reports personal fees from AstraZeneca and Roche during the conduct of the study. JH reports personal fees and travel expenses from Pfizer, Roche, AstraZeneca; personal fees from Lilly, Celgene, MSD, AbbVie, Eisai; grants from Hexal, Celgene; grants and personal fees from Novartis, travel expenses from Daiichi Sankyo, outside the submitted work. WDS reports grants from German Breast Group, during the conduct of the study; personal fees from AstraZeneca, outside the submitted work. SaS reports other from AstraZeneca during the conduct of the study; personal fees and advisory boards from Amgen, Hexal, Roche, Mundipharma; travel expenses from Novartis, outside the submitted work. CD reports personal fees from Novartis, Roche, MSD Oncology, Daiichi Sankyo, grants from Myriad Genetics, other from Sividon Diagnostics/Myriad outside the submitted work and has a patent EP18209672 pending, a patent EP20150702464 pending, and a patent software (VMscope digital pathology) pending. CH reports personal fees from Roche, Celgene, Pfizer, Lilly, AstraZeneca, Novartis outside the submitted work. KR reports personal fees from AstraZeneca, Tesaro, Pfizer outside the submitted work. RS reports grants from Cologne Furtune during the conduct of the study. J-UB reports personal fees from Amgen, AstraZeneca, MSD, Novartis, Pfizer, Roche, SonoScape, outside the submitted work. MU reports personal fees and non-financial support to the institute from AbbVie, Amgen GmbH, AstraZeneca, Celgene GmbH, Daiichi Sankyo, Eisai GmbH, MSD, Mundipharma, Myriad Genetics, Odonate, Pfizer GmbH, Roche, Sanofi Aventis Deutschland GmbH; Teva Pharmaceuticals Ind Ltd, Novartis, Clovis Oncology; personal fees and others from BMS, Lilly; personal fees from Puma Biotechnology, Pierre Fabre outside the submitted work. SL reports grants and honoraria from AstraZeneca during the conduct of the study; grants and honoraria from AbbVie, Amgen, Celgene, Novartis, Pfizer, Roche, other from Seattle Genetics, PriME/ Medscape; personal fees and lecture honoraria from Chugai, grants from Teva, Vifor, Immunomedics grants and honoraria from Daiichi Sankyo, honoraria from Lilly, Samsung, Eirgenix, BMS, Puma, MSD, outside the submitted work and has a patent EP14153692.0 pending. All other authors declare no conflict of interest., (Copyright © 2020 European Society for Medical Oncology. Published by Elsevier Ltd. All rights reserved.)

Background: Clinical application of cancer immunotherapy requires a better understanding of tumor immunogenicity and the tumor microenvironment. HLA class I molecules present antigens to CD8 + cytotoxic cells. Their loss or downregulation is frequently found in tumors resulting in reduced T cell responses and worse prognosis., Methods: We evaluated HLA class I heavy chain expression by immunohistochemistry in 863 biopsies (GeparTrio trial). Patients received neoadjuvant chemotherapy and adjuvant endocrine treatment if tumors were hormone receptor-positive (HR+). In parallel, the expression of HLA-A was analyzed using a microarray cohort of 320 breast cancer patients from the MD Anderson Cancer Center. We evaluated its association with clinical outcome, tumor-infiltrating lymphocytes (TILs), and immune cell metagenes., Results: In HR+/HER2- breast cancer, HLA class I heavy chain expression was associated with increased TILs and better response to chemotherapy (7% vs. 14% pCR rate, P = 0.029), but worse disease-free survival (hazard ratio (HR) 1.6 (1.1-2.4); P = 0.024). The effect was significant in a multivariate model adjusted for clinical and pathological variables (HR 1.7 (1.1-2.6); P = 0.016) and was confirmed by analysis of HLA-A in a microarray cohort. HLA-A was correlated to most immune cell metagenes. There was no association with response or survival in triple-negative or HER2+ disease., Conclusions: The study confirms the negative prognostic role of lymphocytes in HR+ breast cancer and points at a complex interaction between chemotherapy, endocrine treatment, and tumor immunogenicity. The results point at a subtype-specific and potentially treatment-specific role of tumor-immunological processes in breast cancer with different implications in triple-negative and hormone receptor-positive disease.

Purpose: The GeparSepto trial demonstrated that weekly nanoparticle albumin-bound (NAB)-paclitaxel significantly improves the pathologic complete remission rate compared with weekly solvent-based (sb) paclitaxel followed by epirubicin plus cyclophosphamide as neoadjuvant treatment in patients with primary breast cancer (BC). Here, we report data on long-term outcomes., Methods: Patients with histologically confirmed primary BC were randomly assigned in a 1:1 ratio to 12 times weekly NAB-paclitaxel 150 mg/m 2 (after study amendment, 125 mg/m 2 ) or weekly sb-paclitaxel 80 mg/m 2 followed in both arms by four times epirubicin 90 mg/m 2 plus cyclophosphamide 600 mg/m 2 every 3 weeks. Patients with human epidermal growth factor receptor 2 (HER2)-positive BC received dual antibody treatment with trastuzumab (8 mg/kg loading dose followed by 6 mg/kg every 3 weeks) and pertuzumab (840 mg loading dose followed by 420 mg every 3 weeks) concurrently to chemotherapy and continued for 1 year., Results: A total of 1,206 patients started treatment, 606 with NAB-paclitaxel and 600 with sb-paclitaxel. After a median follow-up of 49.6 months (range, 0.5 to 64.0 months), 243 invasive disease-free survival (iDFS) events were reported (143 in the sb-paclitaxel and 100 in the NAB-paclitaxel arm). At 4 years, overall patients treated with NAB-paclitaxel had a significantly better iDFS compared with sb-paclitaxel (84.0% v 76.3%; hazard ratio, 0.66; 95% CI, 0.51 to 0.86; P = .002), whereas overall survival did not significantly differ between the two treatment arms (89.7% v 87.2%, respectively; hazard ratio, 0.82; 95% CI, 0.59 to 1.16; P = .260). Long-term follow-up of the treatment-related peripheral sensory neuropathy (PSN) showed a significant decrease of the median time to resolve PSN after NAB-paclitaxel 125 mg/m 2 compared with NAB-paclitaxel 150 mg/m 2 ., Conclusion: The significantly higher pathologic complete response rate with NAB-paclitaxel translated into a significantly improved iDFS in patients with early BC as compared with sb-paclitaxel. PSN improved much faster under NAB-paclitaxel 125 mg/m 2 compared with NAB-paclitaxel 150 mg/m 2 .

Purpose: Next-generation sequencing (NGS) can be used for comprehensive investigation of molecular events in breast cancer. We evaluated the relevance of genomic alterations for response to neoadjuvant chemotherapy (NACT) in the GeparSepto trial., Experimental Design: Eight hundred fifty-one pretherapeutic formalin-fixed paraffin-embedded (FFPE) core biopsies from GeparSepto study were sequenced. The panel included 16 genes for mutational ( AKT1, BRAF, CDH1, EGFR, ERBB2, ESR1, FBXW7, FGFR2, HRAS, KRAS, NRAS, SF3B1, TP53, HNF1A, PIK3CA , and PTEN ) and 8 genes for copy-number alteration analysis ( CCND1, ERBB2, FGFR1, PAK1, PIK3CA, TOP2A, TP53 , and ZNF703 )., Results: The most common genomic alterations were mutations of TP53 (38.4%) and PIK3CA (21.5%), and 8 different amplifications ( TOP2A 34.9%; ERBB2 30.6%; ZNF703 30.1%; TP53 21.9%; PIK3CA 24.1%; CCND1 17.7%; PAK1 14.9%; FGFR 12.6%). All other alterations had a prevalence of less than 5%. The genetic heterogeneity in different breast cancer subtypes [lum/HER2neg vs. HER2pos vs. triple-negative breast cancer (TNBC)] was significantly linked to differences in NACT response. A significantly reduced pathologic complete response rate was observed in PIK3CA -mutated breast cancer [ PIK3CA mut: 23.0% vs. wild-type (wt) 38.8%, P < 0.0001] in particular in the HER2pos subcohort [multivariate OR = 0.43 (95% CI, 0.24-0.79), P = 0.006]. An increased response to nab-paclitaxel was observed only in PIK3CA wt breast cancer, with univariate significance for the complete cohort ( P = 0.009) and the TNBC ( P = 0.013) and multivariate significance in the HER2pos subcohort (test for interaction P = 0.0074)., Conclusions: High genetic heterogeneity was observed in different breast cancer subtypes. Our study shows that FFPE-based NGS can be used to identify markers of therapy resistance in clinical study cohorts. PIK3CA mutations could be a major mediator of therapy resistance in breast cancer., (©2019 American Association for Cancer Research.)

Background: Tumour-infiltrating lymphocytes (TILs) are predictive for response to neoadjuvant chemotherapy in triple-negative breast cancer (TNBC) and HER2-positive breast cancer, but their role in luminal breast cancer and the effect of TILs on prognosis in all subtypes is less clear. Here, we assessed the relevance of TILs for chemotherapy response and prognosis in patients with TNBC, HER2-positive breast cancer, and luminal-HER2-negative breast cancer., Methods: Patients with primary breast cancer who were treated with neoadjuvant combination chemotherapy were included from six randomised trials done by the German Breast Cancer Group. Pretherapeutic core biopsies from 3771 patients included in these studies were assessed for the number of stromal TILs by standardised methods according to the guidelines of the International TIL working group. TILs were analysed both as a continuous parameter and in three predefined groups of low (0-10% immune cells in stromal tissue within the tumour), intermediate (11-59%), and high TILs (≥60%). We used these data in univariable and multivariable statistical models to assess the association between TIL concentration and pathological complete response in all patients, and between the amount of TILs and disease-free survival and overall survival in 2560 patients from five of the six clinical trial cohorts., Findings: In the luminal-HER2-negative breast cancer subtype, a pathological complete response (pCR) was achieved in 45 (6%) of 759 patients with low TILs, 48 (11%) of 435 with intermediate TILs, and 49 (28%) of 172 with high TILs. In the HER2-positive subtype, pCR was observed in 194 (32%) of 605 patients with low TILs, 198 (39%) of 512 with intermediate TILs, and 127 (48%) of 262 with high TILs. Finally, in the TNBC subtype, pCR was achieved in 80 (31%) of 260 patients with low TILs, 117 (31%) of 373 with intermediate TILs, and 136 (50%) of 273 with high TILs (p<0·0001 for each subtype, χ 2 test for trend). In the univariable analysis, a 10% increase in TILs was associated with longer disease-free survival in TNBC (hazard ratio [HR] 0·93 [95% CI 0·87-0·98], p=0·011) and HER2-positive breast cancer (0·94 [0·89-0·99], p=0·017), but not in luminal-HER2-negative tumours (1·02 [0·96-1·09], p=0·46). The increase in TILs was also associated with longer overall survival in TNBC (0·92 [0·86-0·99], p=0·032), but had no association in HER2-positive breast cancer (0·94 [0·86-1·02], p=0·11), and was associated with shorter overall survival in luminal-HER2-negative tumours (1·10 [1·02-1·19], p=0·011)., Interpretation: Increased TIL concentration predicted response to neoadjuvant chemotherapy in all molecular subtypes assessed, and was also associated with a survival benefit in HER2-positive breast cancer and TNBC. By contrast, increased TILs were an adverse prognostic factor for survival in luminal-HER2-negative breast cancer, suggesting a different biology of the immunological infiltrate in this subtype. Our data support the hypothesis that breast cancer is immunogenic and might be targetable by immune-modulating therapies. In light of the results in luminal breast cancer, further research investigating the interaction of the immune system with different types of endocrine therapy is warranted., Funding: Deutsche Krebshilfe and European Commission., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: In metastatic breast cancer, nab-paclitaxel has been shown to significantly increase progression-free survival compared with solvent-based paclitaxel. The GeparSepto (GBG 69) trial assessed whether weekly nab-paclitaxel could increase the proportion of patients achieving pathological complete response compared with weekly solvent-based paclitaxel, both followed by epirubicin plus cyclophosphamide as neoadjuvant treatment., Method: In a phase 3 randomised trial, we enrolled patients with previously untreated unilateral or bilateral primary invasive breast cancer and randomly assigned them in a 1:1 ratio using dynamic allocation and Pocock minimisation by breast cancer subtype, Ki67 and SPARC expression. Patients were treated for 12 weeks with either intravenous nab-paclitaxel 150 mg/m(2) (after study amendment, 125 mg/m(2)) on days 1, 8, and 15 for four 3-week cycles, or solvent-based intravenous paclitaxel 80 mg/m(2) on days 1, 8, and 15 for four 3-week cycles. Taxane treatment was followed in both groups by intravenous epirubicin 90 mg/m(2) plus intravenous cyclophosphamide 600 mg/m(2) on day 1 for four 3-week cycles. Patients with HER2-positive tumours received concurrent trastuzumab 6 mg/kg (loading dose 8 mg/kg) and pertuzumab 420 mg (loading dose 840 mg) on day 1 of every 3-week cycle. Trastuzumab and pertuzumab were given every 3 weeks concomitantly with chemotherapy for all cycles. This report is the final analysis of the primary endpoint, pathological complete response (ypT0 ypN0), analysed for all patients who started treatment (modified intention to treat). We used a closed test procedure to test for non-inferiority, with the nab-paclitaxel group calculated as non-inferior to the solvent-based paclitaxel group if the lower 95% CI for the OR was above 0·858 (OR equivalent to pathological complete response [33%] minus a 10% non-inferiority margin [3·3%]; 29·7%). We planned to test for superiority only in case of a positive non-inferiority test, using an α of 0·05. Safety was assessed in all patients who received study drug. The trial is registered with ClinicalTrials.gov, number NCT01583426., Findings: Between July 30, 2012, and Dec 23, 2013, we randomly assigned 1229 women, of whom 1206 started treatment (606 with nab-paclitaxel and 600 with solvent-based paclitaxel). The nab-paclitaxel dose was reduced after enrolment of 464 participants to 125 mg/m(2) due to increased treatment discontinuation and sensory neuropathy in this group. Pathological complete response occurred more frequently in the nab-paclitaxel group (233 [38%, 95% CI 35-42] patients) than in the solvent-based paclitaxel group (174 [29%, 25-33] patients; OR 1·53, 95% CI 1·20-1·95; unadjusted p=0·00065). The incidence of grade 3-4 anaemia (13 [2%] of 605 patients in the nab-paclitaxel group vs four [1%] of patients in the solvent-based paclitaxel group; p=0·048) and peripheral sensory neuropathy grade 3-4 (63 [10%] patients receiving any nab-paclitaxel dose; 31 [8%] of patients starting with 125 mg/m(2) and 32 [15%] of patients starting with 150 mg/m(2); vs 16 [3%] in the solvent-based paclitaxel group, p<0·001) was significantly higher for nab-paclitaxel than for solvent-based paclitaxel. Overall, 283 (23%) patients were noted to have at least one serious adverse event (based on study drug received), 156 (26%) in the nab-paclitaxel group and 127 (21%) in the solvent-based paclitaxel group (p=0·057). There were three deaths (during epirubicin plus cyclophosphamide treatment) in the nab-paclitaxel group (due to sepsis, diarrhoea, and accident unrelated to the trial) versus one in the solvent-based paclitaxel group (during paclitaxel treatment; cardiac failure)., Interpretation: Substituting solvent-based paclitaxel with nab-paclitaxel significantly increases the proportion of patients achieving a pathological complete response rate after anthracycline-based chemotherapy. These results might lead to an exchange of the preferred taxane, solvent-based paclitaxel, for nab-paclitaxel in therapy for primary breast cancer., Funding: Celgene, Roche., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Purpose: Scoring proliferation through Ki67 immunohistochemistry is an important component in predicting therapy response to chemotherapy in patients with breast cancer. However, recent studies have cast doubt on the reliability of "visual" Ki67 scoring in the multicenter setting, particularly in the lower, yet clinically important, proliferation range. Therefore, an accurate and standardized Ki67 scoring is pivotal both in routine diagnostics and larger multicenter studies., Experimental Design: We validated a novel fully automated Ki67 scoring approach that relies on only minimal a priori knowledge on cell properties and requires no training data for calibration. We applied our approach to 1,082 breast cancer samples from the neoadjuvant GeparTrio trial and compared the performance of automated and manual Ki67 scoring., Results: The three groups of autoKi67 as defined by low (≤ 15%), medium (15.1%-35%), and high (>35%) automated scores showed pCR rates of 5.8%, 16.9%, and 29.5%, respectively. AutoKi67 was significantly linked to prognosis with overall and progression-free survival P values P(OS) < 0.0001 and P(PFS) < 0.0002, compared with P(OS) < 0.0005 and P(PFS) < 0.0001 for manual Ki67 scoring. Moreover, automated Ki67 scoring was an independent prognosticator in the multivariate analysis with P(OS) = 0.002, P(PFS) = 0.009 (autoKi67) versus P(OS) = 0.007, PPFS = 0.004 (manual Ki67)., Conclusions: The computer-assisted Ki67 scoring approach presented here offers a standardized means of tumor cell proliferation assessment in breast cancer that correlated with clinical endpoints and is deployable in routine diagnostics. It may thus help to solve recently reported reliability concerns in Ki67 diagnostics., (©2014 American Association for Cancer Research.)

RANK ligand (RANKL) is crucial for the development of mouse mammary glands during pregnancy. RANKL functions as a major paracrine effector of the mitogenic action of progesterone in mammary epithelium via its receptor RANK and has a role in expansion and regenerative potential of mammary stem cells. Pharmacologic inhibition of RANKL attenuates the development of mammary carcinoma and inhibits metastatic progression in multiple mouse models. Primary breast carcinoma samples from the neoadjuvant GeparTrio study were analyzed to correlate the expression of human RANK and RANKL with pathological complete response (pCR), disease-free (DFS), and overall (OS) survival. Pre-treatment FFPE core biopsies (n = 601) were analyzed for percentage and intensity of immunohistochemical RANK and RANKL expression. Antibodies against human RANK (N-1H8; Amgen) and human RANKL (M366; Amgen) were used. RANK protein was expressed in 160 (27 %) patients. Increased RANK expression was observed in 14.5 % of patients and correlated with high tumor grade (p < 0.023) and negative hormone receptor (HR) status (p < 0.001). Patients with high RANK expression showed a higher pCR rate (23.0 % vs. 12.6 %, p = 0.010), shorter DFS (p = 0.038), and OS (p = 0.011). However, prognostic and predictive information was not an independent parameter. Only 6 % of samples expressed RANKL, which was not correlated with any clinical features. Higher RANK expression in the primary tumor is associated with a higher sensitivity to chemotherapy, but also a higher risk of relapse and death. Our study provides a basis for further exploration of the antitumor activity of clinical antibodies against RANKL.

Background: The proliferation marker Ki67 has been suggested as a promising cancer biomarker. As Ki67 needs an exact quantification, this marker is a prototype of a new generation of tissue-based biomarkers. In this study, we have systematically evaluated different cut points for Ki67 using three different clinical end points in a large neoadjuvant study cohort., Patients and Methods: We have evaluated pretherapeutic Ki67 levels by immunohistochemistry in 1166 breast cancer core biopsies from the neoadjuvant GeparTrio trial. We used the standardized cutoff-finder algorithm for three end points [response to neoadjuvant chemotherapy (pCR), disease-free (DFS) and overall-survival (OS)]. The analyses were stratified for hormone receptor (HR) and HER2 status by molecular subtype radar diagrams (MSRDs)., Results: A wide range of Ki67 cut points between 3%-94% (for pCR), 6%-46% (for DFS) and 4%-58% (for OS) were significant. The three groups of Ki67 ≤ 15% versus 15.1%-35% versus >35% had pCR-rates of 4.2%, 12.8%, and 29.0% (P < 0.0005), this effect was also present in six of eight molecular subtypes. In MSRD, Ki67 was significantly linked to prognosis in uni- and multivariate analysis in the complete cohort and in HR-positive, but not triple-negative tumors., Conclusions: Ki67 is a significant predictive and prognostic marker over a wide range of cut points suggesting that data-derived cut point optimization might not be possible. Ki67 could be used as a continuous marker; in addition, the scientific community could define standardized cut points for Ki67. Our analysis explains the variability observed for Ki67 cut points in previous studies; however, this should not be seen as weakness, but as strength of this marker. MSRDs are an easy new approach for visualization of biomarker effects on outcome across molecular subtypes in breast cancer. The experience with Ki67 could provide important information regarding the development and implementation of other quantitative biomarkers.

Purpose: The value of Ki67 measured on residual disease after neoadjuvant chemotherapy is not sufficiently described., Experimental Design: Participants of the GeparTrio study with primary breast cancer randomly received neoadjuvant response-guided [8 cycles TAC (docetaxel/doxorubicin/cyclophosphamide) in responding and TAC-NX (vinorelbine/capecitabine) in nonresponding patients] or conventional (6 cycles TAC) chemotherapy according to interim response assessment. Ki-67 levels were centrally measured immunohistochemically after neoadjuvant treatment if tumor tissue was available. Here, we analyze 1,151 patients having a pathologic complete response (pCR; n, 484), or residual disease with low (0-15%), intermediate (15.1-35%), or high (35.1-100%) posttreatment Ki67 levels in 488, 77, and 102 patients, respectively., Results: Patients with high posttreatment Ki67 levels showed higher risk for disease relapse (P < 0.0001) and death (P < 0.0001) compared with patients with low or intermediate Ki67 levels. Patients with low Ki67 levels showed a comparable outcome to patients with a pCR (P = 0.211 for disease-free and P = 0.779 for overall survival). Posttreatment Ki67 levels provided more prognostic information than pretreatment Ki67 levels or changes of Ki67 from pre- to posttreatment. Information on pCR plus posttreatment Ki67 levels surmount the prognostic information of pCR alone in hormone-receptor-positive disease [hazard ratios (HR), 1.82-5.88] but not in hormone-receptor-negative disease (HR: 0.61-1.73). Patients with conventional and response-guided treatment did not show a different distribution of posttreatment Ki67 (P = 0.965)., Conclusions: Posttreatment Ki67 levels provide prognostic information for patients with hormone-receptor-positive breast cancer and residual disease after neoadjuvant chemotherapy. Levels were not prognostic for outcome after response-guided chemotherapy. High posttreatment Ki67 indicates the need for innovative postneoadjuvant treatments., (©2013 AACR.)

Gene or protein expression data are usually represented by metric or at least ordinal variables. In order to translate a continuous variable into a clinical decision, it is necessary to determine a cutoff point and to stratify patients into two groups each requiring a different kind of treatment. Currently, there is no standard method or standard software for biomarker cutoff determination. Therefore, we developed Cutoff Finder, a bundle of optimization and visualization methods for cutoff determination that is accessible online. While one of the methods for cutoff optimization is based solely on the distribution of the marker under investigation, other methods optimize the correlation of the dichotomization with respect to an outcome or survival variable. We illustrate the functionality of Cutoff Finder by the analysis of the gene expression of estrogen receptor (ER) and progesterone receptor (PgR) in breast cancer tissues. This distribution of these important markers is analyzed and correlated with immunohistologically determined ER status and distant metastasis free survival. Cutoff Finder is expected to fill a relevant gap in the available biometric software repertoire and will enable faster optimization of new diagnostic biomarkers. The tool can be accessed at http://molpath.charite.de/cutoff.

Purpose: Core needle biopsies (CNBs) are widely used to determine human epidermal growth factor receptor 2 (HER2) status in breast cancer. Recent publications reported up to 20% false-positive results on CNBs if immunohistochemistry (IHC) is compared with fluorescent in situ hybridization (FISH). To clarify, if confirmation of IHC positivity by FISH is generally required, we analyzed the reliability of IHC positivity on CNBs versus surgical specimens in a multi-institutional study., Patients and Methods: Five pathologic laboratories contributed to this study by performing IHC on 500 CNBs and the corresponding surgical specimens overall. If IHC revealed score 2+ or 3+, HER2 status was confirmed by FISH in a central laboratory. We compared evaluation according to US Food and Drug Administration-approved scoring criteria and recently published American Society of Clinical Oncology (ASCO)-College of American Pathologists (CAP) guidelines., Results: CNBs scored 3+ revealed five false-positive results if scoring followed the US Food and Drug Administration criteria (five of 40; 12.5%) and two false-positives in terms of the ASCO-CAP criteria (two of 33; 6.1%). IHC was false negative in one CNB only. By contrast, IHC on surgical specimens revealed five false-negative results, but only one false-positive result (one of 35; 2.9%) if scored following US Food and Drug Administration-approved criteria. With the aid of the ASCO-CAP criteria, false-positive IHC results were obtained in only one of the five participating institutions., Conclusion: IHC 3+ scores on CNBs proved to be reliable in four of the five participating institutions if scoring followed the ASCO-CAP criteria. Therefore, accurate determination of HER2 status in breast cancer is possible on CNB using the common strategy to screen all cases by IHC and retest only 2+ scores by FISH. Prerequisites are quality assurance and the application of the new ASCO-CAP criteria.