Neoadjuvant Pertuzumab plus Trastuzumab in Combination with Docetaxel and Carboplatin in Patients with HER2 Positive Breast Cancer: Real-World Data from a National Institute of Oncology in Poland.

Neoadjuvant systemic therapy has now become the the standard in early breast cancer management. Chemotherapy in combination with trastuzumab +/- pertuzumab targeted therapy can improve rates of pathologic complete response (pCR) in patients with HER2-positive breast cancer. Achieving a pCR is considered a good prognostic factor, in particular in patients with more aggressive breast cancer subtypes such as TNBC or HER2 positive cancers. Furthermore, most studies demonstrate that chemotherapy in combination with trastuzumab and pertuzumab is well tolerated. The retrospective analysis presented here concentrates on neoadjuvant therapy with the TCbH-P regimen, with a particular emphasis on patients over 60 years of age. We analysed the factors affecting the achievement of pCR and presented adverse effects of the applied therapies, which opened a discussion about optimizing the therapy of older patients with HER-2 positive breast cancer.

Pathologic Complete Response and Its Impact on Breast Cancer Recurrence and Patient’s Survival after Neoadjuvant Therapy: A Comprehensive Meta-Analysis

Objective. Earlier research has illustrated prognostic significance of pathologic complete response (pCR) in neoadjuvant therapy (NAT) for breast cancer, whereas correlation between treatment after achieving pCR and survival improvement remains underexplored. We attempted to measure the relation between pCR achieved after NAT and breast cancer recurrence or patient’s survival. Methods. We searched PubMed, EMBASE, Web of Science, and The Cochrane Library databases to find relevant articles from their inception to November 2020. According to eligibility criteria, studies were selected and basic data were extracted. The primary endpoint was the correlation between pCR achieved after NAT and event-free survival (EFS) or overall survival (OS). The results were obtained by directly extracting specific information from the literature or estimating individual data by survival curves on DigitizeIt software, presented with HR and 95% CI. All data were processed on Stata 14.0 software. Results. Among 4338 articles, there were 25 eligible articles involving 8767 patients. The EFS of patients achieved pCR after NAT improved obviously ( HR = 0.27 ; 95% CI, 0.24-0.31), especially in triple negative ( HR = 0.17 ; 95% CI, 0.12-0.24) and HER2 positive ( HR = 0.24 ; 95% CI, 0.20-0.30) breast cancer patients. As such, pCR after NAT was implicated in significantly increased OS ( HR = 0.32 ; 95% CI, 0.27–0.37). Conclusion. Achieving pCR after NAT was notably related to the improvement of EFS and OS, especially for patients with triple-negative and HER2-positive breast cancer. pCR can be a surrogate indicator for outcome of breast cancer patients after NAT, as well as a predictor of treatment efficacy after NAT. Besides, well-designed studies are still warranted for confirmation.

Multivariable Models Based on Baseline Imaging Features and Clinicopathological Characteristics to Predict Breast Pathologic Response after Neoadjuvant Chemotherapy in Patients with Breast Cancer

Introduction Currently, the accurate evaluation and prediction of response to neoadjuvant chemotherapy (NAC) remains a great challenge. We developed several multivariate models based on baseline imaging features and clinicopathological characteristics to predict the breast pathologic complete response (pCR). Methods We retrospectively collected clinicopathological and imaging data of patients who received NAC and subsequent surgery for breast cancer at our hospital from 2014 June till 2020 September. We used mammography, ultrasound and magnetic resonance imaging (MRI) to investigate the breast tumors at baseline. Results A total of 308 patients were included and 111 patients achieved pCR. The HER2 status and Ki-67 index were significant factors for pCR on univariate analysis and in all multivariate models. Among the prediction models in this study, the ultrasound-MRI model performed the best, producing an area under curve of 0.801 (95%CI=0.749-0.852), a sensitivity of 0.797 and a specificity of 0.676. Conclusion Among the multivariable models constructed in this study, the ultrasound plus MRI model performed the best in predicting the probability of pCR after NAC. Further validation is required before it is generalized.

A Clinical–Radiomics Model for Predicting Axillary Pathologic Complete Response in Breast Cancer With Axillary Lymph Node Metastases

PurposeTo develop a clinical–radiomics model based on radiomics features extracted from MRI and clinicopathologic factors for predicting the axillary pathologic complete response (apCR) in breast cancer (BC) patients with axillary lymph node (ALN) metastases.Materials and MethodsThe MR images and clinicopathologic data of 248 eligible invasive BC patients at the Peking University First Hospital from January 2013 to December 2020 were included in this study. All patients received neoadjuvant chemotherapy (NAC), and the presence of ALN metastases was confirmed through cytology pre-NAC. The data from January 2013 to December 2018 were randomly divided into the training and validation sets in a ratio of 7:3, and the data from January 2019 to December 2020 served as the independent testing set. The following three types of prediction models were investigated in this study. 1) A clinical model: the model was built by independently predicting clinicopathologic factors through logistic regression. 2) Radiomics models: we used an automatic segmentation model based on deep learning to segment the axillary areas, visible ALNs, and breast tumors on post-NAC dynamic contrast-enhanced MRI. Radiomics features were then extracted from the region of interest (ROI). Radiomics models were built based on different ROIs or their combination. 3) A clinical–radiomics model: it was built by integrating radiomics signature and independent predictive clinical factors by logistic regression. All models were assessed using a receiver operating characteristic curve analysis and by calculating the area under the curve (AUC).ResultsThe clinical model yielded AUC values of 0.759, 0.787, and 0.771 in the training, validation, and testing sets, respectively. The radiomics model based on the combination of MRI features of breast tumors and visible ALNs yielded the best AUC values of 0.894, 0.811, and 0.806 in the training, validation, and testing sets, respectively. The clinical–radiomics model yielded AUC values of 0.924, 0.851, and 0.878 in the training, validation, and testing sets, respectively, for predicting apCR.ConclusionWe developed a clinical–radiomics model by integrating radiomics signature and clinical factors to predict apCR in BC patients with ALN metastases post-NAC. It may help the clinicians to screen out apCR patients to avoid lymph node dissection.

Role and evaluation of pathologic response in early breast cancer specimens after neoadjuvant therapy: consensus statement

Pathologic evaluation of early breast cancer after neoadjuvant therapy is essential to provide prognostic information based on tumor response to treatment (pathologic complete response [pCR] or non-pCR) and to inform therapy decisions after surgery. To harmonize the pathologist’s handling of surgical specimens after neoadjuvant therapy, a panel of experts in breast cancer convened to developed a consensus on six main topics: (1) definition of pCR, (2) required clinical information, (3) gross examination and sampling, (4) microscopic examination, (5) evaluation of lymph node status, and (6) staging of residual breast tumor. The resulting consensus statements reported in this document highlight the role of an accurate evaluation of tumor response and define the minimum requirements to standardize the assessment of breast cancer specimens after neoadjuvant therapy.

Racial disparity in distant recurrence-free survival in localized breast cancer patients: A pooled analysis of NSABP trials

Background: Black race is associated with worse outcome in patients with breast cancer. We evaluated distant relapse-free survival (DRFS) between Black and White women with localized breast cancer who participated in NCI-sponsored clinical trials. Methods: We analyzed pooled data from eight National Surgical Adjuvant Breast and Bowel Project (NSABP) trials including 9,702 women with localized breast cancer treated with adjuvant chemotherapy (AC, n=7,485) or neoadjuvant chemotherapy (NAC, n=2,217), who self-reported as Black (n=1,070) or White (n=8,632). The association between race and DRFS was analyzed using log-rank tests and multivariate Cox regression. Results: After adjustment for covariates including age, tumor size, nodal status, body mass index and taxane use, and treatment (AC vs. NAC), Black race was associated with an inferior DRFS in ER-positive (HR 1.24 [95% CI 1.05-1.46], p=0.01), but not in ER-negative disease (HR 0.97 [95% CI 0.83-1.14], p=0.73), and significant interaction between race and ER status was observed (p=0.03). There was no racial disparity in DRFS among patients with pathologic complete response (pCR) (Log-rank p =0.8). For patients without pCR, black race was associated with worse DRFS in ER-positive (HR 1.67 [95% CI 1.14-2.45], p=0.01), but not in ER-negative disease (HR 0.91 [95% CI 0.65-1.28], p=0.59). Conclusion: Black race was associated with significantly inferior DRFS in ER-positive localized breast cancer treated with AC or NAC, but not in ER-negative disease. In the NAC group, racial disparity was also observed in patients with residual ER-positive breast cancer at surgery, but not in those who had a pathologic complete response.