Breast Neoadjuvant Chemotherapy Research Articles

Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients

BackgroundGene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer.MethodFirst of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy.ResultsThe results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978–0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response.ConclusionThese results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.

Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study

ObjectivesDeveloping a deep learning radiomics model from longitudinal breast ultrasound and sonographer’s axillary ultrasound diagnosis for predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) in breast cancer.MethodsBreast cancer patients undergoing NAC followed by surgery were recruited from three centers between November 2016 and December 2022. We collected ultrasound images for extracting tumor-derived radiomics and deep learning features, selecting quantitative features through various methods. Two machine learning models based on random forest were developed using pre-NAC and post-NAC features. A support vector machine integrated these data into a fusion model, evaluated via the area under the curve (AUC), decision curve analysis, and calibration curves. We compared the fusion model’s performance against sonographer’s diagnosis from pre-NAC and post-NAC axillary ultrasonography, referencing histological outcomes from sentinel lymph node biopsy or axillary lymph node dissection.ResultsIn the validation cohort, the fusion model outperformed both pre-NAC (AUC: 0.899 vs. 0.786, p < 0.001) and post-NAC models (AUC: 0.899 vs. 0.853, p = 0.014), as well as the sonographer’s diagnosis of ALN status on pre-NAC and post-NAC axillary ultrasonography (AUC: 0.899 vs. 0.719, p < 0.001). Decision curve analysis revealed patient benefits from the fusion model across threshold probabilities from 0.02 to 0.98. The model also enhanced sonographer’s diagnostic ability, increasing accuracy from 71.9% to 79.2%.ConclusionThe deep learning radiomics model accurately predicted the ALN response to NAC in breast cancer. Furthermore, the model will assist sonographers to improve their diagnostic ability on ALN status before surgery.Clinical relevance statementOur AI model based on pre- and post-neoadjuvant chemotherapy ultrasound can accurately predict axillary lymph node metastasis and assist sonographer’s axillary diagnosis.Key PointsAxillary lymph node metastasis status affects the choice of surgical treatment, and currently relies on subjective ultrasound.Our AI model outperformed sonographer’s visual diagnosis on axillary ultrasound.Our deep learning radiomics model can improve sonographers’ diagnosis and might assist in surgical decision-making.

Conventional Tools for Predicting Satisfactory Response to Neoadjuvant Chemotherapy in HR+/HER2− Breast Cancer Patients

Aim: The aim of the study was to assess the role of Magee Equation 3 (MagEq3), IHC4 score, and HER2-low status in predicting “satisfactory response (SR)” to neoadjuvant chemotherapy (NAC) in HR+/HER2− breast cancer (BC) patients. Methods: In a retrospective study, female patients of any age with T_1–4, N_0–2, M₀ HR+/HER2− BC who received NAC and underwent adequate locoregional surgical treatment were included. Patients were grouped according to 2 outcomes: (a) overall response to NAC in breast and axilla by using residual cancer burden (RCB) criteria and (b) axillary downstaging after NAC by using N staging. 2 cohorts for overall response were overall SR (RCB 0–1) and no SR (RCB 2–3). On the other hand, for axillary downstaging, 2 cohorts constituted from axillary SR (ypN₀ and ypN_0i+) and no SR (ypN_mic-N3). MagEq3 and IHC4 scores were calculated from their pathological tumor slides in each patient. HER2 status was categorized as either “no” or “low.” In addition, patient age, family history, tumor histology, stage at admission, and Ki-67 status were compared between cohorts according to predefined outcomes. Results: In a total of 230 BC patients, 228 patients were included to compare according to their RCB levels. The mean age of patients with overall SR was significantly lower than those without. Patients with high Ki-67 expression, high (>30) MagEq3 score, high ICH4 quartile, and HER2-low status had significantly more overall SR. On the other hand, only patients with high Ki-67 expression had significantly more axillary SR. MagEq3 score levels, ICH4 quartiles, and HER2 status were similar between patients with axillary SR and not. Conclusion: MagEq3 and IHC4 tools seemed to be useful to predict those HR+/HER2− BC patients who are most likely to get benefit from NAC. But, only high Ki-67 expression level significantly predicted satisfactory axillary downstaging in HR+/HER2− BC patients.

Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy

BackgroundEarly changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor’s ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS).MethodsA total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components.ResultsWe identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002).ConclusionsThese results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis.

Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy

Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy

Predictive role of neostromal CD10 expression in breast cancer patients treated with neoadjuvant chemotherapy.

Background: The therapeutic strategy of invasive breast cancer is based on routine histopathological markers (estrogen-, progesterone receptor, HER2, Ki67) routinely evaluated in tumor cells. However, the assessment of cancer stroma could influence therapeutic strategies. Studies have shown that stromal expression of CD10, a zinc-dependent metalloproteinase, is associated with biological aggressiveness of the tumor. In the present retrospective study, we aimed to evaluate stromal CD10 expression and association between CD10 expression and response to neoadjuvant chemotherapy in invasive breast cancer. Methods: CD10 immunohistochemistry was performed on core biopsies taken before the neoadjuvant therapy. Stromal CD10 expression was determined and compared with well-known predictive and prognostic tissue markers as well as with the following groups defined according to the degree of tumor response: no regression, partial regression, and complete regression. Results: A total of 60 locally advanced invasive breast carcinomas of "no special type" were included. The proportion of CD10 positive tumors was significantly higher in the "no regression" group compared to "complete regression" group (p = 0.000). Stromal CD10 expression was found to be significantly associated with decrease in response to neoadjuvant chemotherapy. According to CD10 expression we did not find any difference in hormone receptor status, Ki67, tumor grade or neostromal area. Conclusion: Our data suggest that CD10 expression can serve as a predictive marker of the effect of neoadjuvant chemotherapy in breast cancer patients. Therefore, its inclusion into the routine assessment of biopsies to tailor tumor-specific therapeutic strategies merits consideration.

DNA methylation changes in response to neoadjuvant chemotherapy are associated with breast cancer survival

BackgroundLocally advanced breast cancer is a heterogeneous disease with respect to response to neoadjuvant chemotherapy (NACT) and survival. It is currently not possible to accurately predict who will benefit from the specific types of NACT. DNA methylation is an epigenetic mechanism known to play an important role in regulating gene expression and may serve as a biomarker for treatment response and survival. We investigated the potential role of DNA methylation as a prognostic marker for long-term survival (> 5 years) after NACT in breast cancer.MethodsDNA methylation profiles of pre-treatment (n = 55) and post-treatment (n = 75) biopsies from 83 women with locally advanced breast cancer were investigated using the Illumina HumanMethylation450 BeadChip. The patients received neoadjuvant treatment with epirubicin and/or paclitaxel. Linear mixed models were used to associate DNA methylation to treatment response and survival based on clinical response to NACT (partial response or stable disease) and 5-year survival, respectively. LASSO regression was performed to identify a risk score based on the statistically significant methylation sites and Kaplan–Meier curve analysis was used to estimate survival probabilities using ten years of survival follow-up data. The risk score developed in our discovery cohort was validated in an independent validation cohort consisting of paired pre-treatment and post-treatment biopsies from 85 women with locally advanced breast cancer. Patients included in the validation cohort were treated with either doxorubicin or 5-FU and mitomycin NACT.ResultsDNA methylation patterns changed from before to after NACT in 5-year survivors, while no significant changes were observed in non-survivors or related to treatment response. DNA methylation changes included an overall loss of methylation at CpG islands and gain of methylation in non-CpG islands, and these changes affected genes linked to transcription factor activity, cell adhesion and immune functions. A risk score was developed based on four methylation sites which successfully predicted long-term survival in our cohort (p = 0.0034) and in an independent validation cohort (p = 0.049).ConclusionOur results demonstrate that DNA methylation patterns in breast tumors change in response to NACT. These changes in DNA methylation show potential as prognostic biomarkers for breast cancer survival.

Treatment Response Prediction Using Ultrasound-Based Pre-, Post-Early, and Delta Radiomics in Neoadjuvant Chemotherapy in Breast Cancer.

ObjectiveTo develop and validate a radiomics nomogram based on pre-treatment, early treatment ultrasound (US) radiomics features combined with clinical characteristics for early prediction of response to neoadjuvant chemotherapy (NAC) in breast cancer.MethodA total of 217 patients with histological results of breast cancer receiving four to eight cycles of NAC before surgery from January 2018 to December 2020 were enrolled. Patients from the study population were randomly separated into a training set (n = 152) and a validation set (n = 65) at a ratio of 7:3. A total of 788 radiomics features were extracted from each region of interest in the US image at pre-treatment baseline (radiomic signature, RS1), early treatment (after completion of two cycles of NAC, RS2) and delta radiomics (calculated between the pre-treatment and post-treatment features, Delta RS). The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection. The predictive nomogram was built based on the radiomics signature combined with clinicopathological risk factors. Discrimination, calibration, and prediction performance were further evaluated in the validation set.ResultsOf the 217 breast masses, 127 (58.5%) were responsive to NAC and 90 (41.5%) were non-responsive. Following feature selection, nine features in RS1, 11 features in RS2, and eight features in Delta RS remained. With multivariate analysis, the RS1, RS2, Delta RS, and Ki-67 expression were independently associated with breast NAC response. However, the performance of the Delta RS (AUC Delta RS = 0.743) was not higher than RS1 (AUC RS1 = 0.722, PDelta vs RS1 = 0.086) and RS2 (AUC RS2 = 0.811, PDelta vs RS2 = 0.173) with the Delong test. The nomogram incorporating RS1, RS2, and Ki-67 expression showed better predictive ability for NAC response with an area under the curve (AUC) of 0.866 in validation cohorts than either the single RS1 (AUC 0.725) or RS2 (AUC 0.793) or Ki-67 (AUC 0.643).ConclusionThe nomogram incorporating pre-treatment and early-treatment US radiomics features and Ki-67 expression showed good performance in terms of NAC response in breast cancer, thereby providing valuable information for individual treatment and timely adjustment of chemotherapy regimens.

Prediction of Tumor Shrinkage Pattern to Neoadjuvant Chemotherapy Using a Multiparametric MRI-Based Machine Learning Model in Patients With Breast Cancer.

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer.Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer.Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2–: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811).Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

Serial circulating tumor DNA identification associated with the efficacy and prognosis of neoadjuvant chemotherapy in breast cancer

Circulating tumor DNA (ctDNA) provides a promising noninvasive alternative to evaluate the efficacy of neoadjuvant chemotherapy (NCT) in breast cancer. Herein, we collected 63 tissue (aspiration biopsies and resected tissues) and 206 blood samples (baseline, during chemotherapy (Chemo), after chemotherapy (Post-Chemo), after operation (Post-Op), during follow-up) from 32 patients, and preformed targeted deep sequencing with a customed 1021-gene panel. As the results, TP53 (43.8%) and PIK3CA (40.6%) were the most common mutant genes in the primary tumors. At least one tumor-derived mutation was detected in the following number of blood samples: 21, baseline; 3, Chemo; 9, Post-Chemo; and 5, Post-Op. Four patients with pathologic complete response had no tissue mutation in Chemo and Post-Chemo blood. Compared to patients with mutation-positive Chemo or Post-Chemo blood, the counterparts showed a superior primary tumor decrease (median, 86.5% versus 54.6%) and lymph involvement (median, 1 versus 3.5). All five patients with mutation-positive Post-Op developed distant metastases during follow-up, and the sensitivity of detecting clinically relapsed patients was 71.4% (5/7). The median DFS was 9.8months for patients with mutation-positive Post-Op but not reached for the others (HR 23.53; 95% CI, 1.904-290.9; p < 0.0001). Our study shows that sequential monitoring of blood ctDNA was an effective method for evaluating NCT efficacy and patient recurrence. Integrating ctDNA profiling into the management of LABC patients might improve clinical outcome. This prospective study recruited LABC patients at Peking Union Medical College Hospital (ClinicalTrials.gov Identifier: NCT02797652).

A Novel Model Incorporating Tumor Stiffness, Blood Flow Characteristics, and Ki-67 Expression to Predict Responses After Neoadjuvant Chemotherapy in Breast Cancer.

ObjectiveTo investigate the ability of tumor stiffness, tumor blood flow, and Ki-67 expression alone or in combination in predicting the pathological response to neoadjuvant chemotherapy (NACT) in breast cancer.Patients and MethodsThis prospective cohort study included 145 breast cancer patients treated with NACT. Tumor stiffness (maximum stiffness (Emax), mean stiffness (Emean)), blood score (BS), and their relative changes, were evaluated before (t0), during (t1–t5), and at the end of NACT (t6) by shear-wave elastography and optical imaging. Ki-67 expression was quantitatively evaluated by immunohistochemistry using core biopsy specimens obtained before NACT. Pathological responses were evaluated by residual cancer burden. The ability of tumor stiffness, BS, Ki-67, and predRCB—which combined ΔEmean (t2) (the relative changes in Emean after the second NACT cycle), BS2 (BS after the second NACT cycle), and Ki-67—in predicting tumor responses was compared using receiver operating characteristic curves and the Z-test.ResultsTumor stiffness and BS decreased during NACT. ΔEmean (t2), BS2, and Ki-67 had better predictive performance than other indexes in identifying a favorable response (AUC = 0.82, 0.81, and 0.80) and resistance responses (AUC = 0.85, 0.79, and 0.84), with no significant differences between the three (p > 0.05). PredRCB had better predictive performance than any parameter alone for a favorable response (AUC = 0.90) and resistance (AUC = 0.93).ConclusionTumor stiffness, BS, and Ki-67 expression showed good and similar abilities for predicting the pathological response to NACT, and predRCB was a significantly better predictor than each index alone. These results may help design therapeutic strategies for breast cancer patients undergoing NACT.

Predictive Factors of Discordant Response to Neoadjuvant Chemotherapy in Breast and Axilla

Predictive Factors of Discordant Response to Neoadjuvant Chemotherapy in Breast and Axilla

The additive role of 1H-magnetic resonance spectroscopic imaging to ensure pathological complete response after neoadjuvant chemotherapy in breast cancer patients.

PurposeTo assess the role of 1H-magnetic resonance spectroscopy (1H-MRS) in the confirmation of pathological complete response after neoadjuvant chemotherapy in breast cancer.Material and methodsForty-seven cases (53.72 ± 8.53 years) were evaluated using magnetic resonance imaging (MRI) and 1H-MRS with choline (Cho) signal-to-noise ratio (SNR) measured followed by histopathology and ROC analyses.ResultsTwelve patients had complete response, and 35 patients had residual disease. Mean age was 53.72 ± 8.53 years. The mean tumour size before neoadjuvant chemotherapy (NAC) was 4.21 ± 0.99 cm and after NAC was 0.9 ± 0.44 cm.Positive total choline signal (tCho) was detected in all cases. The mean Cho SNR before NAC was 9.53 ± 1.7 and after NAC was 2.53 ± 1.3. The Cho SNR cut-off point differentiating between pathologic complete response (pCR) and the non pCR was 1.95. Dynamic MRI showed 83.3% sensitivity, 65.7% specificity, 45.5% positive predictive value, 92.0% negative predictive value, and 70.2% diagnostic accuracy. Combined evaluation done by using the dynamic MRI and 1H-MRS showed 91.5% diagnostic accuracy with 75.0% sensitivity, 97.1% specificity, 75% positive predictive value, and 91.9% negative predictive value. ROC curves of Cho SNR showed statistically significant differences between non pCR and pCR with AUC was 0.955, 82.9% sensitivity, 91.7% specificity, 96.7% positive predictive value, 64.7% negative predictive value, and 85.11% diagnostic accuracy.Conclusions1H-MRS improves the diagnostic accuracy in the prediction of the pCR after NAC.

Predictors of Pathological Complete Response in Women with Clinical Complete Response to Neoadjuvant Chemotherapy in Breast Carcinoma

Objective: There is insufficient information on predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast carcinoma that also presented clinical complete response (cCR) evaluated in breast, axilla and breast and axilla. Methods: This retrospective study included 310 women with breast carcinoma who received NAC from 1/1/13 to 12/31/15 with follow-up until 8/31/16. The factors analyzed to predict pCR and cCR were menopausal status, Ki67, estrogen receptor, histologic grade, molecular subtype, tumor size, axilla status, and stage. Results: The cCR/pCR rates were 53.2/16.5% (breast), 76.3/36.8% (axilla) and 50.6/13.9% (breast and axilla). Molecular subtype and HER2-positive were independent predictors to confirm pCR in women with cCR, mainly triple negative (TN) in breast (OR 22.81, 95% CI 7.13–72.96) and breast and axilla (OR 36.06, 95% CI 8.77–148.26), but not in axilla. Ki67 ≥50% expression was predictor of cCR in breast (OR 2.00, 95% CI 1.31–3.06) and breast and axilla (OR 1.67, 95% CI 1.10–1.45). Conclusion: TN subtype and HER2-positive were the main independent predictors of pCR in women who also had cCR to NAC in breast and breast and axilla, but none was predictor in axilla. The Ki67 ≥50% was the independent predictor of cCR in breast and breast and axilla.

Digital gene expression profiling analysis and its application in the identification of genes associated with improved response to neoadjuvant chemotherapy in breast cancer

BackgroundThis study aimed to screen sensitive biomarkers for the efficacy evaluation of neoadjuvant chemotherapy in breast cancer.MethodsIn this study, Illumina digital gene expression sequencing technology was applied and differentially expressed genes (DEGs) between patients presenting pathological complete response (pCR) and non-pathological complete response (NpCR) were identified. Further, gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then performed. The genes in significant enriched pathways were finally quantified by quantitative real-time PCR (qRT-PCR) to confirm that they were differentially expressed. Additionally, GSE23988 from Gene Expression Omnibus database was used as the validation dataset to confirm the DEGs.ResultsAfter removing the low-quality reads, 715 DEGs were finally detected. After mapping to KEGG pathways, 10 DEGs belonging to the ubiquitin proteasome pathway (HECTD3, PSMB10, UBD, UBE2C, and UBE2S) and cytokine–cytokine receptor interactions (CCL2, CCR1, CXCL10, CXCL11, and IL2RG) were selected for further analysis. These 10 genes were finally quantified by qRT-PCR to confirm that they were differentially expressed (the log2 fold changes of selected genes were − 5.34, 7.81, 6.88, 5.74, 3.11, 19.58, 8.73, 8.88, 7.42, and 34.61 for HECTD3, PSMB10, UBD, UBE2C, UBE2S, CCL2, CCR1, CXCL10, CXCL11, and IL2RG, respectively). Moreover, 53 common genes were confirmed by the validation dataset, including downregulated UBE2C and UBE2S.ConclusionOur results suggested that these 10 genes belonging to these two pathways might be useful as sensitive biomarkers for the efficacy evaluation of neoadjuvant chemotherapy in breast cancer.

Feasibility of Charcoal Tattooing of Cytology-Proven Metastatic Axillary Lymph Node at Diagnosis and Sentinel Lymph Node Biopsy after Neoadjuvant Chemotherapy in Breast Cancer Patients.

PurposeSentinel lymph node biopsy (SLNB) can be performed when node-positive disease is converted to node-negative status after neoadjuvant chemotherapy (NCT). Tattooing nodes might improve accuracy but supportive data are limited. This study aimed to investigate the feasibility of charcoal tattooing metastatic axillary lymph node (ALN) at presentation followed by SLNB after NCT in breast cancers.Materials and MethodsTwenty patientswith cytology-proven node metastases prospectively underwent charcoal tattooing at diagnosis. SLNB using dual tracers and axillary surgery after NCT were then performed. The detection rate of tattooed node and diagnostic performance of SLNB were analyzed.ResultsAll patients underwent charcoal tattooingwithout significant morbidity. Sentinel and tattooed nodes could be detected during surgery after NCT. Nodal pathologic complete response was achieved in 10 patients. Overall sensitivity, false-negative rate (FNR), negative predictive value, and accuracy of hot/blue SLNB were 80.0%, 20.0%, 83.3%, and 90.0%, respectively. Retrieving more nodes and favorable nodal response were associated with improved performance. The best accuracy was observed when excised tattooed node was calculated together (FNR, 0.0%). Cold/non-blue tattooed nodes of five patients were removed during non-sentinel axillary surgery but clinicopathological parameters did not differ compared to patients with hot/blue tattooed node detected during SLNB, suggesting the importance of the tattooing procedure itself to improve performance.ConclusionCharcoal tattooing of cytology-confirmed metastatic ALN at presentation is technically feasible and does not limit SLNB after NCT. The tattooing procedure without additional preoperative localization is advantageous for improving the diagnostic performance of SLNB in this setting.

Ki-67 Expression by Immunohistochemistry and Quantitative Real-Time Polymerase Chain Reaction as Predictor of Clinical Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer.

Background Chemotherapy has become a standard of treatment in managing breast cancer. To achieve proper treatment for the right patients, the predictive marker is needed. Ki-67 is a biomarker of proliferation for solid tumor. Studies mentioned association of Ki-67 expression with chemotherapy response. The study aims are to evaluate whether Ki-67 expression detected by immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR) may predict clinical response to neoadjuvant chemotherapy in breast cancer. Methods This study utilized a longitudinal study. IHC and qRT-PCR methods were used for detection of Ki-67 expression. Chemotherapy response was calculated using RECIST. Data were analyzed with Chi-square and Wilcoxon's test. Results There were 48 subjects in this study. Analysis of Ki-67 expression with chemotherapy response has a significant correlation with p = 0.025 (<0.05), OR: 1.69, confidence interval (95% CI) 1.022–2.810. Analysis of Ki-67 mRNA expression with chemotherapy response has a significant correlation p = 0.002 (<0.05), OR: 6.85, confidence interval (95% CI) 1.064–44.193. Detection of Ki-67 expression using IHC and qRT-PCR has similar results, p = 0.012 (<0.05). Conclusion These results suggest that Ki-67 expression detected by both IHC and qRT-PCR is considered to be a predictor of clinical response to neoadjuvant chemotherapy in locally advanced breast cancer.

Investigating the prediction value of multiparametric magnetic resonance imaging at 3\xa0T in response to neoadjuvant chemotherapy in breast cancer

ObjectiveTo explore the predictive value of parameters derived from diffusion-weighted imaging (DWI) and contrast-enhanced (CE)-MRI at different time-points during neoadjuvant chemotherapy (NACT) in breast cancer.MethodsInstitutional review board approval and written, informed consent from 42 breast cancer patients were obtained. The patients were investigated before and at three different time-points during neoadjuvant chemotherapy (NACT) using tumour diameter and volume from CE-MRI and ADC values obtained from drawn 2D and segmented 3D regions of interest. Prediction of pathologic complete response (pCR) was evaluated using the area under the curve (AUC) of receiver operating characteristic analysis.ResultsThere was no significant difference between pathologic complete response and non-pCR in baseline size measures (p > 0.39). Diameter change was significantly different in pCR (p < 0.02) before the mid-therapy point. The best predictor was lesion diameter change observed before mid-therapy (AUC = 0.93). Segmented volume was not able to differentiate between pCR and non-pCR at any time-point. The ADC values from 3D-ROI were not significantly different from 2D data (p = 0.06). The best AUC (0.79) for pCR prediction using DWI was median ADC measured before mid-therapy of NACT.ConclusionsThe results of this study should be considered in NACT monitoring planning, especially in MRI protocol designing and time point selection.Key Points• Mid-therapy diameter changes are the best predictors of pCR in neoadjuvant chemotherapy.• Volumetric measures are not strictly superior in therapy monitoring to lesion diameter.• Size measures perform as a better predictor than ADC values.

The value of tumor infiltrating lymphocytes (TILs) for predicting response to neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis.

BackgroundWe carried out a systematic review and meta-analysis to evaluate the predictive roles of tumor infiltrating lymphocytes (TILs) in response to neoadjuvant chemotherapy (NAC) in breast cancer.MethodA PubMed and Web of Science literature search was designed. Random or fixed effect models were adopted to estimate the summary odds ratio (OR). Heterogeneity and sensitivity analyses were performed to explore heterogeneity among studies and to assess the effects of study quality. Publication bias was evaluated using a funnel plot, Egger's test and Begg's test. We included studies where the predictive significance of TILs, and/or TILs subset on the pathologic complete response (pCR) were determined in NAC of breast cancer.ResultsA total of 13 published studies (including 3251 patients) were eligible. In pooled analysis, the detection of higher TILs numbers in pre-treatment biopsy was correlated with better pCR to NAC (OR = 3.93, 95% CI, 3.26–4.73). Moreover, TILs predicted higher pCR rates in triple negative (OR = 2.49, 95% CI: 1.61–3.83), HER2 positive (OR = 5.05, 95% CI: 2.86–8.92) breast cancer, but not in estrogen receptor (ER) positive (OR = 6.21, 95%CI: 0.86–45.15) patients. In multivariate analysis, TILs were still an independent marker for high pCR rate (OR = 1.41, 95% CI: 1.19–1.66). For TILs subset, higher levels of CD8+ and FOXP3+ T-lymphocytes in pre-treatment biopsy respectively predicted better pathological response to NAC (OR = 6.44, 95% CI: 2.52–16.46; OR = 2.94, 95% CI: 1.05–8.26). Only FOXP3+ lymphocytes in post-NAC breast tissue were a predictive marker for low pCR rate in univariate (OR = 0.41, 95% CI: 0.21–0.80) and multivariate (OR = 0.36, 95% CI: 0.13–0.95) analysis.ConclusionHigher TILs levels in pre-treatment biopsy indicated higher pCR rates for NAC. TILs subset played different roles in predicting response to NAC.

A Randomized Phase II Trial of Capecitabine Plus Vinorelbine Followed by Docetaxel Versus Adriamycin Plus Cyclophosphamide Followed by Docetaxel as Neoadjuvant Chemotherapy for Breast Cancer.

PurposeGiven the promising activity of capecitabine and vinorelbine in metastatic breast cancer, this randomized phase II trial evaluated the efficacy and safety of this combination as neoadjuvant chemotherapy in breast cancer.Materials and MethodsPatients with operable breast cancer (n=75) were randomly assigned to receive either four cycles of adriamycin 60 mg/m2 plus cyclophosphamide 600 mg/m2 every 3 weeks followed by four cycles of docetaxel 75 mg/m2 every 3 weeks (AC-D) or four cycles of capecitabine 2,000 mg/m2 (day 1-14) plus vinorelbine 25 mg/m2 (days 1 and 8) every 3 weeks followed by four cycles of docetaxel 75 mg/m2 (CV-D). The primary endpoint was pathologic complete response (pCR) in the primary breast (ypT0/is).ResultsMost patients (84%) had locally advanced (n=41) or inflammatory breast cancer (n=22). pCR rates in the primary breast were 15% (95% confidence interval [CI], 7% to 30%) and 11% (95% CI, 4% to 26%) in the AC-D and CV-D groups, respectively. The overall response rates and 5-year progression-free survival rates in the AC-D and CV-D groups were 62% and 64%, and 51.3% (95% CI, 34.6% to 68.0%) and 30.2% (95% CI, 13.3% to 47.1%), respectively. Although both regimens were well tolerated, CV-D showed less frequent grade 3-4 neutropenia and vomiting than AC-D, whereas manageable diarrhea and hand-foot syndrome were more common in the CV-D group.ConclusionCV-D is a feasible and active non-anthracycline–based neoadjuvant chemotherapy regimen for breast cancer.