A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial.

BACKGROUND: This study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) using longitudinal MRI in breast cancer. METHODS: We included patients with node-positive breast cancer who received NAC following surgery from January 2012 to June 2022. We collected MRIs before and after NAC, and extracted radiomics features from the tumour, peritumour, and ALN regions. The Mann-Whitney U test, least absolute shrinkage and selection operator, and Boruta algorithm were used to select features. We utilised machine learning techniques to develop three single-modality models and a stacking model for predicting ALN response to NAC. RESULTS: This study consisted of a training cohort (n = 277), three external validation cohorts (n = 313, 164, and 318), and a prospective cohort (n = 81). Among the 1153 patients, 60.62% achieved ypN0. The stacking model achieved excellent AUCs of 0.926, 0.874, and 0.862 in the training, external validation, and prospective cohort, respectively. It also showed lower false-negative rates (FNRs) compared to radiologists, with rates of 14.40%, 20.85%, and 18.18% (radiologists: 40.80%, 50.49%, and 63.64%) in three cohorts. Additionally, there was a significant difference in disease-free survival between high-risk and low-risk groups (p < 0.05). CONCLUSIONS: The stacking model can accurately predict ALN status after NAC in breast cancer, showing a lower false-negative rate than radiologists. TRIAL REGISTRATION NUMBER: The clinical trial numbers were NCT03154749 and NCT04858529.

The features of male breast cancer in China: A real-world study.

BACKGROUND: Male breast cancer (MBC) is a rare disease. Although several large-scale studies have investigated MBC patients in other countries, the features of MBC patients in China have not been fully explored. This study aims to explore the features of Chinese MBC patients comprehensively. METHODS: We retrospectively collected data of MBC patients from 36 centers in China. Overall survival (OS) was evaluated by the Kaplan-Meier method, log-rank test, and Cox regression analyses. Multivariate Cox analyses were used to identify independent prognostic factors of the patients. RESULTS: In total, 1119 patients were included. The mean age at diagnosis was 60.9 years, and a significant extension over time was observed (P < 0.001). The majority of the patients (89.1 %) received mastectomy. Sentinel lymph node biopsy was performed in 7.8 % of the patients diagnosed in 2009 or earlier, and this percentage increased significantly to 38.8 % in 2020 or later (P < 0.001). The five-year OS rate for the population was 85.5 % [95 % confidence interval (CI), 82.8 %-88.4 %]. Multivariate Cox analysis identified taxane-based [T-based, hazard ratio (HR) = 0.32, 95 % CI, 0.13 to 0.78, P = 0.012] and anthracycline plus taxane-based (A + T-based, HR = 0.47, 95 % CI, 0.23 to 0.96, P = 0.037) regimens as independent protective factors for OS. However, the anthracycline-based regimen showed no significance in outcome (P = 0.175). CONCLUSION: As the most extensive MBC study in China, we described the characteristics, treatment and prognosis of Chinese MBC population comprehensively. T-based and A + T-based regimens were protective factors for OS in these patients. More research is required for this population.

Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden during Neoadjuvant Chemotherapy in Breast Cancer.

OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual cancer burden (RCB) scores during neoadjuvant chemotherapy (NAC) in breast cancer. SUMMARY BACKGROUND DATA: RCB III indicates drug resistance in breast cancer, and early detection methods are lacking. METHODS: This study enrolled 1048 patients with breast cancer from four institutions, who were all receiving NAC. Magnetic resonance images were collected at the pre- and mid-NAC stages, and radiomics and deep learning features were extracted. A multitask AI system was developed to classify patients into three groups (RCB 0-I, II, and III ) in the primary cohort (PC, n=335). Feature selection was conducted using the Mann-Whitney U- test, Spearman analysis, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Single-modality models were developed followed by model integration. The AI system was validated in three external validation cohorts. (EVCs, n=713). RESULTS: Among the patients, 442 (42.18%) were RCB 0-I, 462 (44.08%) were RCB II and 144 (13.74%) were RCB III. Model-I achieved an area under the curve (AUC) of 0.975 in the PC and 0.923 in the EVCs for differentiating RCB III from RCB 0-II. Model-II distinguished RCB 0-I from RCB II-III, with an AUC of 0.976 in the PC and 0.910 in the EVCs. Subgroup analysis confirmed that the AI system was consistent across different clinical T stages and molecular subtypes. CONCLUSIONS: The multitask AI system offers a noninvasive tool for the early prediction of RCB scores in breast cancer, supporting clinical decision-making during NAC.

Short-term efficacy and safety of neoadjuvant pyrotinib plus taxanes for early HER2-positive breast cancer: a single-arm exploratory phase II trial.

Background: The effectiveness and safety of pyrotinib have been substantiated in human epidermal growth factor receptor 2 (HER2)-positive advanced breast cancer (BC). However, the role of pyrotinib as a single HER2 blockade in neoadjuvant setting among BC patients has not been studied. The objective of this study was to evaluate the efficacy and tolerability of pyrotinib plus taxanes as a novel neoadjuvant regimen in patients with HER2-positive early or locally advanced BC. Methods: In this single-arm exploratory phase II trial, patients with treatment-naïve HER2-positive BC (stage IIA-IIIC) received pyrotinib 400 mg once daily and taxanes [docetaxel 75 mg/m2 or nanoparticle albumin-bound (nab)-paclitaxel 260 mg/m2 every 3 weeks, or paclitaxel 80 mg/m2 weekly] for a total of four 21-day cycles before surgery. Efficacy assessment was based on pathological and clinical measurements. The primary endpoint of this study was the total pathological complete response (tpCR) rate. The secondary endpoints included breast pCR (bpCR) rate, investigator-assessed objective response rate (ORR) and adverse events (AEs) profiles. Results: From 1 September 2021 to 30 December 2022, a total of 31 patients were enrolled. One patient was withdrawn due to unbearable skin rash after the second cycle of neoadjuvant therapy. The majority of the intention-to-treat (ITT) population was premenopausal (54.8%), had large tumors (90.3%) and metastatic nodes (58.1%) at diagnosis and hormone-receptor positive tumors (64.5%). Most participants used nab-paclitaxel (74.2%) and received mastectomy (67.7%) after neoadjuvant treatment. The tpCR and bpCR rates were 48.4% [95% confidence interval (CI): 30.8-66%] and 51.6% (95% CI: 34-69.2%), respectively. Grade ≥3 treatment-related AEs were observed in 16.1% (5/31) of the ITT population, including diarrhea (n=2, 6.5%), hand and foot numbness (n=1, 3.2%), loss of appetite (n=1, 3.2%), and skin rash (n=1, 3.2%). AE related dose reduction or pyrotinib interruption was not required. Conclusions: In female patients with HER2-positive non-metastatic BC, neoadjuvant pyrotinib monotherapy plus taxanes appears to show promising clinical benefit and controllable AEs [Chinese Clinical Trial Registry (ChiCTR2100050870)]. The long-term efficacy and safety of this regime warrant further verification.

Comprehending the cuproptosis and cancer-immunity cycle network: delving into the immune landscape and its predictive role in breast cancer immunotherapy responses and clinical endpoints.

Background: The role of cuproptosis, a phenomenon associated with tumor metabolism and immunological identification, remains underexplored, particularly in relation to the cancer-immunity cycle (CIC) network. This study aims to rigorously examine the impact of the cuproptosis-CIC nexus on immune reactions and prognostic outcomes in patients with breast cancer (BC), striving to establish a comprehensive prognostic model. Methods: In the study, we segregated data obtained from TCGA, GEO, and ICGC using CICs retrieved from the TIP database. We constructed a genetic prognostic framework using the LASSO-Cox model, followed by its validation through Cox proportional hazards regression. This framework's validity was further confirmed with data from ICGC and GEO. Explorations of the tumor microenvironment were carried out through the application of ESTIMATE and CIBERSORT algorithms, as well as machine learning techniques, to identify potential treatment strategies. Single-cell sequencing methods were utilized to delineate the spatial distribution of key genes within the various cell types in the tumor milieu. To explore the critical role of the identified CICs, experiments were conducted focusing on cell survival and migration abilities. Results: In our research, we identified a set of 4 crucial cuproptosis-CICs that have a profound impact on patient longevity and their response to immunotherapy. By leveraging these identified CICs, we constructed a predictive model that efficiently estimates patient prognoses. Detailed analyses at the single-cell level showed that the significance of CICs. Experimental approaches, including CCK-8, Transwell, and wound healing assays, revealed that the protein HSPA9 restricts the growth and movement of breast cancer cells. Furthermore, our studies using immunofluorescence techniques demonstrated that suppressing HSPA9 leads to a notable increase in ceramide levels. Conclusion: This research outlines a network of cuproptosis-CICs and constructs a predictive nomogram. Our model holds great promise for healthcare professionals to personalize treatment approaches for individuals with breast cancer. The work provides insights into the complex relationship between the cuproptosis-CIC network and the cancer immune microenvironment, setting the stage for novel approaches to cancer immunotherapy. By focusing on the essential gene HSPA9 within the cancer-immunity cycle, this strategy has the potential to significantly improve the efficacy of treatments against breast cancer.

Organoid forming potential as complementary parameter for accurate evaluation of breast cancer neoadjuvant therapeutic efficacy.

BACKGROUND: 13-15% of breast cancer/BC patients diagnosed as pathological complete response/pCR after neoadjuvant systemic therapy/NST suffer from recurrence. This study aims to estimate the rationality of organoid forming potential/OFP for more accurate evaluation of NST efficacy. METHODS: OFPs of post-NST residual disease/RD were checked and compared with clinical approaches to estimate the recurrence risk. The phenotypes of organoids were classified via HE staining and ER, PR, HER2, Ki67 and CD133 immuno-labeling. The active growing organoids were subjected to drug sensitivity tests. RESULTS: Of 62 post-NST BC specimens, 24 were classified as OFP-I with long-term active organoid growth, 19 as OFP-II with stable organoid growth within 3 weeks, and 19 as OFP-III without organoid formation. Residual tumors were overall correlated with OFP grades (P < 0.001), while 3 of the 18 patients (16.67%) pathologically diagnosed as tumor-free (ypT0N0M0) showed tumor derived-organoid formation. The disease-free survival/DFS of OFP-I cases was worse than other two groups (Log-rank P < 0.05). Organoids of OFP-I/-II groups well maintained the biological features of their parental tumors and were resistant to the drugs used in NST. CONCLUSIONS: The OFP would be a complementary parameter to improve the evaluation accuracy of NST efficacy of breast cancers.

Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy: multicenter retrospective cohort study.

BACKGROUND: The high false negative rate (FNR) associated with sentinel lymph node biopsy often leads to unnecessary axillary lymph node dissection following neoadjuvant chemotherapy (NAC) in breast cancer. The authors aimed to develop a multifactor artificial intelligence (AI) model to aid in axillary lymph node surgery. MATERIALS AND METHODS: A total of 1038 patients were enrolled, comprising 234 patients in the primary cohort, 723 patients in three external validation cohorts, and 81 patients in the prospective cohort. For predicting axillary lymph node response to NAC, robust longitudinal radiomics features were extracted from pre-NAC and post-NAC magnetic resonance images. The U test, the least absolute shrinkage and selection operator, and the spearman analysis were used to select the most significant features. A machine learning stacking model was constructed to detect ALN metastasis after NAC. By integrating the significant predictors, we developed a multifactor AI-assisted surgery pipeline and compared its performance and false negative rate with that of sentinel lymph node biopsy alone. RESULTS: The machine learning stacking model achieved excellent performance in detecting ALN metastasis, with an area under the curve (AUC) of 0.958 in the primary cohort, 0.881 in the external validation cohorts, and 0.882 in the prospective cohort. Furthermore, the introduction of AI-assisted surgery reduced the FNRs from 14.88 (18/121) to 4.13% (5/121) in the primary cohort, from 16.55 (49/296) to 4.05% (12/296) in the external validation cohorts, and from 13.64 (3/22) to 4.55% (1/22) in the prospective cohort. Notably, when more than two SLNs were removed, the FNRs further decreased to 2.78% (2/72) in the primary cohort, 2.38% (4/168) in the external validation cohorts, and 0% (0/15) in the prospective cohort. CONCLUSION: Our study highlights the potential of AI-assisted surgery as a valuable tool for evaluating ALN response to NAC, leading to a reduction in unnecessary axillary lymph node dissection procedures.

Nuclear transport maintenance of USP22-AR by Importin-7 promotes breast cancer progression.

The translocation of biological macromolecules between cytoplasm and nucleus is of great significance to maintain various life processes in both normal and cancer cells. Disturbance of transport function likely leads to an unbalanced state between tumor suppressors and tumor-promoting factors. In this study, based on the unbiased analysis of protein expression differences with a mass spectrometer between human breast malignant tumors and benign hyperplastic tissues, we identified that Importin-7, a nuclear transport factor, is highly expressed in breast cancer (BC) and predicts poor outcomes. Further studies showed that Importin-7 promotes cell cycle progression and proliferation. Mechanistically, through co-immunoprecipitation, immunofluorescence, and nuclear-cytoplasmic protein separation experiments, we discovered that AR and USP22 can bind to Importin-7 as cargoes to promote BC progression. In addition, this study provides a rationale for a therapeutic strategy to restream the malignant progression of AR-positive BC by inhibiting the high expression state of Importin-7. Moreover, the knockdown of Importin-7 increased the responsiveness of BC cells to the AR signaling inhibitor, enzalutamide, suggesting that targeting Importin-7 may be a potential therapeutic strategy.

Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study.

Background: Accurate identification of pCR to neoadjuvant chemotherapy (NAC) is essential for determining appropriate surgery strategy and guiding resection extent in breast cancer. However, a non-invasive tool to predict pCR accurately is lacking. Our study aims to develop ensemble learning models using longitudinal multiparametric MRI to predict pCR in breast cancer. Methods: From July 2015 to December 2021, we collected pre-NAC and post-NAC multiparametric MRI sequences per patient. We then extracted 14,676 radiomics and 4096 deep learning features and calculated additional delta-value features. In the primary cohort (n = 409), the inter-class correlation coefficient test, U-test, Boruta and the least absolute shrinkage and selection operator regression were used to select the most significant features for each subtype of breast cancer. Five machine learning classifiers were then developed to predict pCR accurately for each subtype. The ensemble learning strategy was used to integrate the single-modality models. The diagnostic performances of models were evaluated in the three external cohorts (n = 343, 170 and 340, respectively). Findings: A total of 1262 patients with breast cancer from four centers were enrolled in this study, and pCR rates were 10.6% (52/491), 54.3% (323/595) and 37.5% (66/176) in HR+/HER2-, HER2+ and TNBC subtype, respectively. Finally, 20, 15 and 13 features were selected to construct the machine learning models in HR+/HER2-, HER2+ and TNBC subtypes, respectively. The multi-Layer Perception (MLP) yields the best diagnostic performances in all subtypes. For the three subtypes, the stacking model integrating pre-, post- and delta-models yielded the highest AUCs of 0.959, 0.974 and 0.958 in the primary cohort, and AUCs of 0.882-0.908, 0.896-0.929 and 0.837-0.901 in the external validation cohorts, respectively. The stacking model had accuracies of 85.0%-88.9%, sensitivities of 80.0%-86.3%, and specificities of 87.4%-91.5% in the external validation cohorts. Interpretation: Our study established a novel tool to predict the responses of breast cancer to NAC and achieve excellent performance. The models could help to determine post-NAC surgery strategy for breast cancer. Funding: This study is supported by grants from the National Natural Science Foundation of China (82171898, 82103093), the Deng Feng project of high-level hospital construction (DFJHBF202109), the Guangdong Basic and Applied Basic Research Foundation (grant number, 2020A1515010346, 2022A1515012277), the Science and Technology Planning Project of Guangzhou City (202002030236), the Beijing Medical Award Foundation (YXJL-2020-0941-0758), and the Beijing Science and Technology Innovation Medical Development Foundation (KC2022-ZZ-0091-5). Funding sources were not involved in the study design, data collection, analysis and interpretation, writing of the report, or decision to submit the article for publication.

Tumor-Targeting Extracellular Vesicles Loaded with siS100A4 for Suppressing Postoperative Breast Cancer Metastasis.

Introduction: S100A4 promotes the establishment of tumor microenvironment for malignant cancer cells, and knockdown of S100A4 can inhibit tumorigenesis. However, there is no efficient way to target S100A4 in metastatic tumor tissues. Here, we investigated the role of siS100A4-loaded iRGD-modified extracellular vesicles (siS100A4-iRGD-EVs) in postoperative breast cancer metastasis. Methods: siS100A4-iRGD-EVs nanoparticles were engineered and analyzed using TEM and DLS. siRNA protection, cellular uptake, and cytotoxicity of EV nanoparticles were examined in vitro. Postoperative lung metastasis mouse model was created to investigate the tissue distribution and anti-metastasis roles of nanoparticles in vivo. Results: siS100A4-iRGD-EVs protected siRNA from RNase degradation, enhanced the cellular uptake and compatibility in vitro. Strikingly, iRGD-modified EVs significantly increased tumor organotropism and siRNA accumulation in lung PMNs compared to siS100A4-EVs in vivo. Moreover, siS100A4-iRGD-EVs treatment remarkedly attenuated lung metastases from breast cancer and increased survival rate of mice through suppressing S100A4 expression in lung. Conclusions: siS100A4-iRGD-EVs nanoparticles show more potent anti-metastasis effect in postoperative breast cancer metastasis mouse model. Supplementary Information: The online version contains supplementary material available at 10.1007/s12195-022-00757-5.

Single-cell and spatial transcriptome analyses revealed cell heterogeneity and immune environment alternations in metastatic axillary lymph nodes in breast cancer.

BACKGROUND: Tumor heterogeneity plays essential roles in developing cancer therapies, including therapies for breast cancer (BC). In addition, it is also very important to understand the relationships between tumor microenvironments and the systematic immune environment. METHODS: Here, we performed single-cell, VDJ sequencing and spatial transcriptome analyses on tumor and adjacent normal tissue as well as axillar lymph nodes (LNs) and peripheral blood mononuclear cells (PBMCs) from 8 BC patients. RESULTS: We found that myeloid cells exhibited environment-dependent plasticity, where a group of macrophages with both M1 and M2 signatures possessed high tumor specificity spatially and was associated with worse patient survival. Cytotoxic T cells in tumor sites evolved in a separate path from those in the circulatory system. T cell receptor (TCR) repertoires in metastatic LNs showed significant higher consistency with TCRs in tumor than those in nonmetastatic LNs and PBMCs, suggesting the existence of common neo-antigens across metastatic LNs and primary tumor cites. In addition, the immune environment in metastatic LNs had transformed into a tumor-like status, where pro-inflammatory macrophages and exhausted T cells were upregulated, accompanied by a decrease in B cells and neutrophils. Finally, cell interactions showed that cancer-associated fibroblasts (CAFs) contributed most to shaping the immune-suppressive microenvironment, while CD8+ cells were the most signal-responsive cells. CONCLUSIONS: This study revealed the cell structures of both micro- and macroenvironments, revealed how different cells diverged in related contexts as well as their prognostic capacities, and displayed a landscape of cell interactions with spatial information.

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study.

INTRODUCTION: Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs. MATERIALS AND METHODS: We retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs. RESULTS: The WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80). CONCLUSION: Our study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.

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 June 2014 till September 2020. 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 HER-2 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 plus MRI model performed 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 best in predicting the probability of pCR after NAC. Further validation is required before it is generalized.

Dynamic changes in intrinsic subtype, immunity status, and risk score before and after neoadjuvant chemo- and HER2-targeted therapy without pCR in HER2-positive breast cancers: A cross-sectional analysis.

Very few studies have been done in HER2 positive patients without complete pathological response (pCR) after combined neoadjuvant chemo- and HER2-target therapy to investigate changes in intrinsic subtype, risk of recurrence (ROR) score, and immunity status before and after treatment. Patients with nonmetastatic HER2-positive breast cancer failed to achieve pCR after neoadjuvant chemotherapy plus trastuzumab were included in current study. We examined the distribution of PAM50 subtypes, ROR score and immunity score in 25 paired baseline and surgical samples. The Miller-Payne grading system was used to evaluate the efficacy of the neoadjuvant therapy. It was observed that the distribution of intrinsic subtype, ROR category and immunity subgroup varied according to hormone receptor (HR) status. HER2-enriched and basal-like subtypes, median-high ROR categories and immunity-weak subgroup were dominant in baseline tumors. Compared to baseline samples, conversion of intrinsic subtype, ROR categories and immunity subgroups were found in 15 (60.0%), 13(52.0%), and 11(44.0%) surgical samples, respectively. The PAM50 subtype, ROR category, and immunity subgroup were concordant between baseline and surgical samples where nonluminal subtypes, median-high ROR categories and i-weak subgroup were still common. In conclusion, the HER2-positive breast cancer is highly heterogeneous with a distribution of 72-gene expression varying according to HR co-expression. The dynamics of the 72-gene expression pre- and posttreatment may become novel biomarker for guiding adjuvant therapy and hence warrant further investigation.

circ_0041732 Promotes Breast Cancer Progression.

Breast cancer is quite a prevalent cancer worldwide, and it is the leading cause of cancer-related deaths among female populations worldwide. Increasingly more efforts have been made in exploration of circular RNA functions in various malignancies. In this study, the primary target was to verify the putative influences of circ_0041732 on breast cancer progression and the corresponding regulatory mechanism. In addition to measurement of RNAs and proteins, functional assays were done to examine the changes in cell proliferation and cell cycle, and the potential association among genes was investigated by mechanism assays. According to experimental results, significant upregulation of circ_0041732 was confirmed in breast cancer tissues and cell lines. E2F4 was proved to transcriptionally modulate circ_0041732. Moreover, circ_0041732 was validated to accelerate breast cancer cell proliferation and impede G2-M arrest and cell apoptosis, and the oncogenic role of circ_0041732 in breast cancer was further verified via in vivo experiments. circ_0041732 could sponge miR-541-3p to enhance expression levels of RelA and GLI4, thus activating NFκB and Hedgehog pathways and affecting breast cancer cell proliferation, cell cycle, and apoptosis. In all, E2F4-mediated circ_0041732 could activate RelA/NFκB and GLI4/Hedgehog signaling pathways via modulation on miR-541-3p/RelA/GLI4 to promote breast cancer progression. IMPLICATIONS: E2F4-mediated circ_0041732 upregulation resulted in the activation of NFκB and Hedgehog pathways via sponging miR-541-3p and enhancing expression levels of RelA and GLI4, thus affecting breast cancer cell proliferation, cell cycle, and cell apoptosis.

A comparison of Chinese multicenter breast cancer database and SEER database.

There are different characteristics of BC in developing countries and developed countries. We intended to study the factors which influence the survival and prognosis of BC between southern China and the United States. (a) To study the two groups BC patients in southern China from 2001 to 2016 and SEER database from 1975 to 2016. (b) To register, collect and analyze the clinicopathological features and treatment information. Our study found that there are significant differences in tumor size, positive lymph node status and KI-67 between southern China and SEER cohort (P < 0.000). The positive lymph node status may be one of the causes of difference of morbidity and mortality of BC patients in China. Furthermore, the differences in treatment methods may also account for the differences between China and seer databases.

Research on DCE-MRI Images Based on Deep Transfer Learning in Breast Cancer Adjuvant Curative Effect Prediction.

Breast cancer is a serious threat to women's physical and mental health. In recent years, its incidence has been on the rise and it has become the top female malignant tumor in China. At present, adjuvant chemotherapy for breast cancer has become the standard mode of breast cancer treatment, but the response results usually need to be completed after the implementation of adjuvant chemotherapy, and the optimization of the treatment plan and the implementation of breast-conserving therapy need to be based on accurate estimation of the pathological response. Therefore, to predict the efficacy of adjuvant chemotherapy for breast cancer patients is to find a predictive method that is conducive to individualized choice of chemotherapy regimens. This article introduces the research of DCE-MRI images based on deep transfer learning in breast cancer adjuvant curative effect prediction. Deep transfer learning algorithms are used to process images, and then, the features of breast cancer after adjuvant chemotherapy are collected through image feature collection. Predictions are made, and the research results show that the accuracy of the prediction reaches 70%.

Clinical Evidence of Chemotherapy or Endocrine Therapy Maintenance in Patients with Metastatic Breast Cancer: Meta-Analysis of Randomized Clinical Trials and Propensity Score Matching of Multicenter Cohort Study.

PURPOSE: This study aims to comprehensively evaluate the clinical efficacy of chemotherapy or endocrine therapy maintenance in metastatic breast cancer (MBC) patients. MATERIALS AND METHODS: The meta-analysis of randomized clinical trials (RCTs) and propensity score matching of multicenter cohort study evaluated MBC patients who underwent first-line chemotherapy or endocrine therapy maintenance. This study is registered with PROSPERO: CRD42017071858 and ClinicalTrials.gov: NCT04258163. RESULTS: A total of 2,867 patients from 15 RCTs and 760 patients from multicenter cohort were included. The results from meta-analysis showed that chemotherapy maintenance improved progression-free survival (PFS) (hazard ratio [HR], 0.63; 95% confidence interval [CI], 0.54 to 0.73; p < 0.001; moderate-quality evidence) and overall survival (OS) (HR, 0.87; 95% CI 0.78 to 0.97; p=0.016; high-quality evidence) than observation. In the cohort study, for hormone receptor-positive MBC patients, chemotherapy maintenance improved PFS (HR, 0.67; 95% CI, 0.52 to 0.85; p < 0.001) and OS (HR, 0.55; 95% CI 0.42 to 0.73; p < 0.001) compared with observation, and endocrine therapy maintenance also improved PFS (HR, 0.65; 95% CI, 0.53 to 0.80; p < 0.001) and OS (HR, 0.55; 95% CI, 0.44 to 0.69; p < 0.001). There were no differences between chemotherapy and endocrine therapy maintenance in PFS and OS (all p > 0.05). Regardless of the continuum or switch maintenance therapy, showed prolonged survival in MBC patients who were response to first-line treatment. CONCLUSION: This study provided evidences for survival benefits of chemotherapy and endocrine therapy maintenance in MBC patients, and there was no difference efficacy between chemotherapy and endocrine therapy maintenance for hormone receptor-positive patients.

Circulating T-Cell Repertoires Correlate With the Tumor Response in Patients With Breast Cancer Receiving Neoadjuvant Chemotherapy.

PURPOSE: Neoadjuvant chemotherapy (NAC) has been widely used in patients with breast cancer to minish tumor burden and increase resection rate of cancer. T-cell repertoire has been believed to be able to monitor antitumor immune responses. This study aimed to explore the dynamic change of T-cell repertoire and its clinical value in evaluating the tumor response in patients with breast cancer receiving NAC. MATERIALS AND METHODS: Ninety-four patients who underwent NAC before surgery were recruited, and peripheral blood samples were collected at multiple time points during NAC. High-throughput T-cell receptor (TCR)-ß sequencing was used to characterize the T-cell repertoire of every sample and analyzed the changes in circulating T-cell repertoire during NAC. RESULTS: We found that the diversity of TCR repertoires was associated with age and clinical stage of the patients with breast cancer. The distribution of Vß and Jß genes in TCR repertoires was skewed in patients with human epidermal growth factor receptor 2-positive (HER2+) breast cancer. Vß20.1 and Vß30 expression levels before NAC correlate with tumor response after all cycles of NAC in HER2- and HER2+ patients, respectively. Some CDR3 motifs that correlated with clinical response in either HER2+ or HER2- patients were identified. Besides, TCR repertoire evolved during NAC and the diversity of TCR repertoire decreased more after two cycles of NAC in patients with good tumor response after all cycles of NAC (P = .0061). CONCLUSION: Our results demonstrated that TCR repertoire correlated with the characteristics of the tumor, such as the expression status of HER2. Moreover, some characteristics of TCR repertoires that correlated with clinical response were identified and they might provide useful information to tailor therapeutic regimens at the early cycle of NAC.

Plasma-Derived Extracellular Vesicles Circular RNAs Serve as Biomarkers for Breast Cancer Diagnosis.

Breast cancer is the second cause of cancer-associated death among women and seriously endangers women's health. Therefore, early identification of breast cancer would be beneficial to women's health. At present, circular RNA (circRNA) not only exists in the extracellular vesicles (EVs) in plasma, but also presents distinct patterns under different physiological and pathological conditions. Therefore, we assume that circRNA could be used for early diagnosis of breast cancer. Here, we developed classifiers for breast cancer diagnosis that relied on 259 samples, including 144 breast cancer patients and 115 controls. In the discovery stage, we compared the genome-wide long RNA profiles of EVs in patients with breast cancer (n=14) and benign breast (n=6). To further verify its potential in early diagnosis of breast cancer, we prospectively collected plasma samples from 259 individuals before treatment, including 144 breast cancer patients and 115 controls. Finally, we developed and verified the predictive classifies based on their circRNA expression profiles of plasma EVs by using multiple machine learning models. By comparing their circRNA profiles, we found 439 circRNAs with significantly different levels between cancer patients and controls. Considering the cost and practicability of the test, we selected 20 candidate circRNAs with elevated levels and detected their levels by quantitative real-time polymerase chain reaction. In the training cohort, we found that BCExoC, a nine-circRNA combined classifier with SVM model, achieved the largest AUC of 0.83 [95% CI 0.77-0.88]. In the validation cohort, the predictive efficacy of the classifier achieved 0.80 [0.71-0.89]. Our work reveals the application prospect of circRNAs in plasma EVs as non-invasive liquid biopsies in the diagnosis and management of breast cancer.