Breast Cancer In Clinical Practice Research Articles

The Japanese breast cancer society clinical practice guidelines for radiation treatment of breast cancer, 2022 edition.

The Breast Cancer Clinical Practice Guidelines, organized by the Japanese Breast Cancer Society (JBCS), were published in 2022. We present the English version of the Radiation Therapy (RT) section of the guidelines. The JBCS formed a task force to update the 2018 version of the JBCS Clinical Practice Guidelines. The Background Questions (BQs) contain the standard treatments for breast cancer in clinical practice, whereas the Clinical Questions (CQs) address daily clinical questions that remain controversial. Future Research Questions (FRQs) explore the subjects that are considered important issues, despite there being insufficient data for inclusion as CQs. The task force selected the 12 BQs, 8 CQs, and 6 FRQs for the RT section. For each CQ, systematic literature reviews and meta-analyses were conducted according to the Minds Manual for Guideline Development 2020, version 3.0. The recommendations, strength of recommendation, and strength of evidence for each CQ were determined based on systematic reviews and meta-analyses, and finalized by voting at the recommendation decision meeting.

Effectiveness of Chinese Herbal Medicine as a Complementary Treatment for Neutropenia Prevention and Immunity Modulation During Chemotherapy in Patients With Breast Cancer: Protocol for a Real-World Pragmatic Clinical Trial.

In recent years, advancements in cancer treatment have enabled cancer cell inhibition, leading to improved patient outcomes. However, the side effects of chemotherapy, especially leukopenia, impact patients' ability to tolerate their treatments and affect their quality of life. Traditional Chinese medicine is thought to provide complementary cancer treatment to improve the quality of life and prolong survival time among patients with cancer. This study aims to evaluate the effectiveness of Chinese herbal medicine (CHM) as a complementary treatment for neutropenia prevention and immunity modulation during chemotherapy in patients with breast cancer. We will conduct a real-world pragmatic clinical trial to evaluate the effectiveness of CHM as a supplementary therapy to prevent neutropenia in patients with breast cancer undergoing chemotherapy. Patients will be classified into CHM or non-CHM groups based on whether they received CHM during chemotherapy. Using generalized estimating equations or repeated measures ANOVA, we will assess differences in white blood cell counts, absolute neutrophil counts, immune cells, and programmed cell death protein 1 (PD-1) expression levels between the 2 groups. This study was approved by the research ethics committee of Hualien Tzu Chi Hospital (IRB 110-168-A). The enrollment process began in September 2021 and will stop in December 2024. A total of 140 patients will be recruited. Data cleaning and analysis are expected to finish in the middle of 2025. Traditional Chinese medicine is the most commonly used complementary medicine, and it has been reported to significantly alleviate chemotherapy-related side effects. This study's findings may contribute to developing effective interventions targeting chemotherapy-related neutropenia among patients with breast cancer in clinical practice. International Traditional Medicine Clinical Trial Registry ITMCTR2023000054; https://tinyurl.com/yc353hes. DERR1-10.2196/55662.

Personalised graded psychological intervention on negative emotion and quality of life in patients with breast cancer.

Psychological factors are a risk factor for the incidence of breast cancer and have a significant impact on patient prognosis. The present study aims to investigate the effects of personalised graded psychological intervention on negative emotion and quality of life in patients with breast cancer. A total of 200 patients with breast cancer were randomly divided into two groups: an experimental group (n= 100) and control group (n= 100). Both groups received routine nursing care. The experimental group received personalised graded psychological intervention care, and the control group received routine nursing measures. After 2 months of standard treatment, the patients' quality of life and negative emotions were evaluated using the self-rating depression scale (SDS), self-rating anxiety scale (SAS), social support rating scale (SSRS) and quality of life measurement scale (FACT-B) scoring criteria. There were no significant differences in the general data between the two groups (p> 0.05). Furthermore, there were no significant differences in the SDS, SAS, SSRS and FACT-B scores between the two groups before personalised graded psychological intervention (p> 0.05). After the intervention, the experimental group exhibited an improved nursing effect compared with the control group. The SDS and SAS scores were lower in the experimental group than in the control group (p< 0.05); after the intervention, the SDS and SAS scores were significantly lower in the experimental group than in the control group (p< 0.05). The SSRS and FACT-B scores were higher in the experimental group than in the control group (p< 0.05), and the experimental group's post-intervention SSRS and FACT-B scores were significantly higher than before the intervention (p< 0.05). The use of personalised graded psychological intervention for the nursing of patients with breast cancer in clinical practice can significantly reduce patients' negative emotions as well as improve positive emotions and quality of life; thus, this method can be popularised in the nursing process.

An Updated Review on the Emerging Role of Indocyanine Green (ICG) as a Sentinel Lymph Node Tracer in Breast Cancer.

Sentinel lymph node biopsy (SLNB) has become the standard of care for clinically node-negative breast cancer and has recently been shown by clinical trials to be also feasible for clinically node-positive patients treated with primary systemic therapy. The dual technique using both radioisotope (RI) and blue dye (BD) as tracers for the identification of sentinel lymph nodes is considered the gold standard. However, allergic reactions to blue dye as well as logistics issues related to the use of radioactive agents, have led to research on new sentinel lymph node (SLN) tracers and to the development and introduction of novel techniques in the clinical practice. Indocyanine green (ICG) is a water-soluble dye with fluorescent properties in the near-infrared (NIR) spectrum. ICG has been shown to be safe and effective as a tracer during SLNB for breast cancer and accumulating evidence suggests that ICG is superior to BD and at least comparable to RI alone and to RI combined with BD. Thus, ICG was recently proposed as a reliable SLN tracer in some breast cancer clinical practice guidelines. Nevertheless, there is lack of consensus regarding the optimal role of ICG for SLN mapping. Specifically, it is yet to be determined whether ICG should be used in addition to BD and/or RI, or if ICG could potentially replace these long-established traditional SLN tracers. This article is an updated overview of somerecent studies that compared ICG with BD and/or RI regarding their accuracy and effectiveness during SLNB for breast cancer.

Artificial Intelligence for the Management of Breast Cancer: An Overview.

Breast cancer is a severe global health problem, and early detection, accurate diagnosis, and personalized treatment is the key to improving patient outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as promising breast cancer research and clinical practice tools in recent years. Various projects are underway in early detection, diagnosis, prognosis, drug discovery, advanced image analysis, precision medicine, predictive modeling, and personalized treatment planning using artificial intelligence and machine learning. These projects use different algorithms, including convolutional neural networks (CNNs), support vector machines (SVMs), decision trees, and deep learning methods, to analyze and improve different types of data, such as clinical, genomic, and imaging data for breast cancer management. The success of these projects has the potential to transform breast cancer care, and continued research and development in this area is likely to lead to more accurate and personalized breast cancer diagnosis, treatment, and outcomes.

Implementing shared decision-making interventions in breast cancer clinical practice: a scoping review

BackgroundShared decision-making (SDM) is a collaborative process whereby patients and clinicians jointly deliberate on the best treatment option that takes into account patients’ preferences and values. In breast cancer care, different treatment options have become available to patients in the last decade. Various interventions, including patient decision aids (PtDAs), have been designed to promote SDM in this disease area. This study aimed at investigating the factors that influence the successful adoption and implementation of SDM interventions in real-world healthcare delivery settings.MethodsA scoping review of scientific and grey literature was conducted for the period 2006–2021 to analyse the support for SDM interventions and their adoption in breast cancer clinical practice. The interpretation of findings was based on the Practical, Robust Implementation and Sustainability Model (PRISM) for integrating research findings into practice.ResultsOverall, 19 studies were included for data synthesis, with more than 70% published since 2017. The availability of SDM tools does not automatically translate into their actual use in clinical settings. Factors related to users’ co-creation, the clinical team’s attitude and knowledge, organisational support and regulatory provisions facilitate the adoption of SDM interventions. However, overlooking aspects such as the re-organisation of care pathways, patient characteristics, and assigning of resources (human, financial, and facilities) can hinder implementation efforts.ConclusionsCompared to the mounting evidence on the efficacy of SDM interventions, knowledge to support their sustained implementation in daily care is still limited, albeit results show an increasing interest in strategies that facilitate their uptake in breast cancer care over time. These findings highlight different strategies that can be used to embed SDM interventions in clinical practice. Future work should investigate which approaches are more effective in light of organisational conditions and external factors, including an evaluation of costs and healthcare system settings.

WTP8.12 Measurement of a panel of biological markers in breast cancer, a comparison between core needle biopsy and surgical excision samples

Abstract Aims ER, PR and HER2 are routinely measured in breast cancer in clinical practice. Numerous other biomarkers exist which are only utilised in a research setting, but may have a potential role in breast cancer. The majority of research in breast cancer is conducted in surgical excision (SE) specimens due to the availability of tissue; however, it is important markers can be measured in core needle biopsy (CNB) samples in patients undergoing neoadjuvant or non-operative treatment. Methods A panel of 15 biomarkers was measured in 202 CNB and paired SE samples in a series of older women with primary breast cancer using immunohistochemistry and H-scores: ER, PR, EGFR, HER2, HER3, HER4, VEGF, p53, BRCA1, LKB1, Ki67, BCL2, CK5/6, CK 7/8, MUC1. Concordance rates were calculated between positive/negative cut-offs for each marker measured in the two samples. Results The percentage concordance rates between positive/negative cut-offs were &amp;gt;60% for ER, PR, EGFR, HER2, VEGF, p53, Ki67, BCL2, CK 7/8, MUC1 and &amp;lt;45% for HER3, HER4, CK 5/6, BRCA1 and LKB1. In general, cases were more likely to be positive in the SE sample. This could suggest SE result is an overestimation of the true value and CNB is more accurate, or at least not likely to overestimate. Conclusions The biomarkers most frequently studied in breast cancer (ER, PR, EGFR, HER2, VEGF, p53, Ki67) did show good concordance between SE and CNB samples. This may suggest that for markers less frequently studied, there is disagreement with interpretation of staining, leading to greater variation.

Asymmetrically split DNAzyme-based colorimetric and electrochemical dual-modal biosensor for detection of breast cancer exosomal surface proteins

Exosomal surface proteins are potentially useful for breast cancer diagnosis and awareness of risk. However, some detection techniques involving complex operations and expensive instrumentation are limited to advance to clinical applications. To solve this problem, we develop a dual-modal sensor combining naked-eye detection and electrochemical assay of exosomal surface proteins from breast cancer. Most of existing sensors rely on aptamers recognizing exosomes and generating amplified signals at the same time, which require well-designed aptamer probes to avoid difficulties in identifying exosomes. In our work, aptamers not bound by the exosomes can serve as complete templates to induce formation of G quadruplexes. The peroxidase activity of the G-quadruplex/hemin DNAzyme catalyze substrates can generate both color and electrochemical signals. The developed dual-modal sensor offers a remarkable capability to differentiate nonmetastatic, metastatic breast cancer patients, and healthy individuals through the analysis of exosomal surface proteins. The sensor's distinctive features, including its universality, simplicity, and cost-effectiveness, position it as a promising diagnostic tool in breast cancer research and clinical practice.

Alcohol consumption and breast cancer prognosis after breast cancer diagnosis: a systematic review and meta‑analysis of the Japanese Breast Cancer Society Clinical Practice Guideline, 2022 edition

Alcohol consumption is internationally recognized as one of the compelling risk factors for breast cancer, but it does not necessarily correlate with the prognosis of breast cancer patients. Alcohol consumption in breast cancer patients was addressed in the 2022 Breast Cancer Clinical Practice Guidelines. A systematic review and meta-analysis of epidemiological studies on alcohol consumption and breast cancer recurrence, breast cancer-related mortality, all-cause mortality, and cardiovascular disease mortality in breast cancer patients was performed. The PubMed, Cochrane Library, and Ichushi-Web databases were searched for relevant publications reporting cohort or case–control studies published until March 2021. A total of 33 studies (32 cohort studies and 1 case–control study) met the eligibility criteria; 4638 cases of recurrence, 12,209 cases of breast cancer-specific mortality, and 21,945 cases of all-cause mortality were observed. With regard to breast cancer recurrence, 7 studies assessed pre-diagnosis alcohol consumption (relative risk (RR) 1.02, 95% confidence interval (95% CI) 0.77–1.37, p = 0.88) and 3 studies assessed post-diagnosis alcohol consumption (RR 0.96, 95% CI 0.85–1.10, p = 0.57), and no significant increase or decrease in risk was observed. With regard to breast cancer-related mortality, 19 studies assessed pre-diagnosis alcohol consumption (RR 1.02, 95% CI 0.93–1.11, p = 0.69), 9 studies assessed post-diagnosis alcohol consumption (RR 0.96, 95% CI 0.77–1.19, p = 0.70), and no significant increase or decrease in risk was observed. With regard to all-cause mortality, 18 studies assessed pre-diagnosis alcohol consumption (RR 0.90, 95% CI 0.82–0.99, p = 0.02), 8 studies assessed post-diagnosis alcohol consumption (RR 0.88, 95% CI 0.74–1.02, p = 0.08), and pre-diagnosis alcohol consumption was associated with a significantly decreased risk. With regard to cardiovascular disease mortality and alcohol consumption, 2 studies assessed it, and the RRwas 0.47 (95% CI 0.28–0.79, p = 0.005), showing that alcohol consumption was associated with a significantly decreased risk. The limitations of this study are that drinking status was mainly based on a questionnaire survey, which is somewhat inaccurate and has many confounding factors, and the cut-off value for the maximum alcohol intake in many studies was low, and it is possible that the actual intake was only an appropriate amount. In many countries, a standard drinking amount is set, and wise decisions are required.

Abstract 6695: Utility of circulating tumor DNA in breast cancer clinical research and practice: systematic review and meta-analysis

Abstract Background: Circulating tumor DNA (ctDNA) has been increasingly used in the clinical care of patients with cancer. This systematic review summarizes current data on the utility of ctDNA in breast cancer clinical research and practice and provides insights for integrating ctDNA into drug development and patient treatment. Methods: The Embase database was used to identify original clinical research articles of ctDNA in breast cancer published from 2015 to 2020. ctDNA clinical utilities were reviewed and summarized, focusing on 3 areas: early diagnosis, prognostication, and monitoring disease progression/treatment response. Associations of ctDNA with disease-free survival (DFS) and overall survival (OS) were estimated in the embedded meta-analysis. Pooled estimates were calculated using fixed-effect or random-effect models depending on study heterogeneity. Results: After screening and review, 72 articles were included. ctDNA studies have increased in recent years, with 59.7% of included articles published in 2019 or 2020. Overall, 61.1% of the studies were in patients with advanced stage/metastatic cancer; 45.8% were restricted to patients with specific cancer subtypes. ctDNA was minimally used in diagnosis; it was predominantly used in clinical outcome prediction with differences by stage. In patients with early-stage disease, the meta-analyses found that overall ctDNA detection/positivity was associated with shorter DFS (n=697; HR, 5.37; 95% CI, 2.94-9.80) and OS (n=238; HR, 4.60; 95% CI, 2.56-8.26). Specific gene mutations in ctDNA were mainly assessed in patients with advanced/metastatic disease, with mutations of ERBB, ESR1, and TP53 linked to shorter DFS in the meta-analyses (P&amp;lt;0.05). Moreover, current studies provided evidence that ctDNA reflects the dynamics of breast tumors in the patient treatment journey, acting as a biomarker to monitor tumor evolution and drug resistance. Conclusions: Recent studies demonstrate ctDNA application in breast cancer prognostication/monitoring, suggesting an important role in patient stratification, molecular relapse identification, and personalized treatment. Large, multicenter clinical trials and real-world longitudinal data sets are needed to validate findings and to address challenges related to ctDNA to improve clinical utility and to integrate ctDNA testing into clinical practice and drug development. Citation Format: Xuezheng Sun, Nicholas Ballew, Linda Kalilani, Kelesitse Phiri, Kelly Bell, Alexander Slowly, Magdalena Zajac, Erin Hofstatter, Angela Silvestro, Zebin Wang, Jeanne Schilder. Utility of circulating tumor DNA in breast cancer clinical research and practice: systematic review and meta-analysis. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6695.

Role of RUNX2 in breast cancer development and drug resistance (Review).

Breast cancer is the most common malignancy and ranks second among the causes of tumor-associated death in females. The recurrence and drug resistance of breast cancer are intractable due to the presence of breast cancer stem cells (BCSCs), which are adequate to initiate tumor formation and refractory to conventional remedies. Runt-related transcription factor 2 (RUNX2), a pivotal transcription factor in mammary gland and bone development, has also been related to metastatic cancer and BCSCs. State-of-the-art research has indicated the retention of RUNX2 expression in a more invasive subtype of breast cancer, and in particular, triple-negative breast cancer development and drug resistance are associated with estrogen receptor signaling pathways. The present review mainly focused on the latest updates on RUNX2 in BCSCs and their roles in breast cancer progression and drug resistance, providing insight that may aid the development of RUNX2-based diagnostics and treatments for breast cancer in clinical practice.

Multigene signatures for early breast cancer in clinical practice: A report of the Lombardy genomic assays for breast cancer working group.

The increasing understanding of breast cancer biology has provided the basis for the development of multigene signatures aimed to improve the capability of clinicians to assess patients' prognostication and risk stratification. Incorporating these tools in clinical practice has profoundly impacted on the decision-making process for the adjuvant therapy of patients with ER+/HER2- early breast cancer and the results from prospective adjuvant trials have strengthened the clinical utility of multigene signatures in this setting. In July 2019, Lombardy was the first Region in Italy to reimburse genomic testing for patients with ER+/HER2- early breast cancer. Three years later, a group of investigators from six referral Cancer Centers in Lombardy convened to debate the use of multigene signatures in clinical practice and share their own experience with the tests after reimbursement. Here, we reviewed relevant data on the role of multigene signatures in tailoring adjuvant chemotherapy for patients with ER+/HER2- early breast cancer and discussed about the optimal use of these assays in current clinical practice. As the treatment landscape of early breast cancer evolves and novel questions about the possible additional applications of multigene assays arise, we also provide our viewpoint on the potential implementation of the assays in the evolving scenario ER+/HER2- early breast cancer treatment.

Abstract P2-16-04: MNX1 promotes tumor sensitivity to HER2-targeted therapy in HER2-positive breast cancer

Abstract Background: Female breast cancer has been the world’s leading cancer incidence among women. HER2-positive breast cancer accounts for about 15%~20% of all breast cancer, with a high degree of malignancy and easy metastasis and recurrence. With the development of medical technology and the continuous innovation of HER2 targeting drugs, patients with HER2-positive breast cancer have more treatment options and their prognosis had been greatly improved. However, there is still a lack of biomarkers for HER2-positive early breast cancer in clinical practice. Methods: This study prospectively collected single-center (Fudan University Shanghai Cancer Center, FUSCC) preoperative core needle biopsy samples of breast cancer patients from July 2017 to July 2018 who received neoadjuvant paclitaxel, carboplatin plus with trastuzumab (PCH). The patients were divided into pCR and non-PCR groups, and 81 patients were enrolled. The differential expression of genes was screened and validated by RNA-seq. The gene expression data for GSE181574 (this database has a total of 105 cases of breast cancer tissue samples were collected by core needle biopsy before neoadjuvant treatment. 52 cases underwent Ado-trastuzumab emtansine plus pertuzumab, 9 cases of paclitaxel plus trastuzumab, and 44 cases of paclitaxel plus trastuzumab and pertuzumab), GSE52707 (SK-BR-3 Lapatinib resistance cell group vs control group), and GSE15043 (BT474 Herceptin-resistant cell group vs control group), were downloaded from the Gene Expression Omnibus (GEO) database. Cell cloning formation, proliferation assay, and drug sensitivity experiments were conducted in MNX1 ectopic and knockdown cell lines. Co-IP assay, RNA-Seq, and ChIP-Seq analysis were used to explore the downstream pathways that MNX1 might be involved. Results: High-throughput sequencing results of core needle biopsy samples from 81 HER2-positive breast cancer patients were divided into pCR and non-pCR groups. Using DEseq2 packet analysis to screen differentially expressed genes, P-value &amp;lt; 0.05, | log2FoldChange | &amp;gt; 1 as the filter, there were 620 up-regulated genes and 715 down-regulated genes in the pCR group. The same method was used for the analysis of the GSE181574 data set. Combined the two datasets used P values and log2FoldChange (P-value &amp;lt; 0.05, | log2FoldChange | &amp;gt; 1.2) as selection criteria, we found the MNX1 and PNMT expressions were significantly higher in the pCR group. Subsequently, differential analysis of GSE52707 and GSE15043 datasets showed that MNX1 and PNMT expression levels were reduced in drug-resistant cell lines. Considering the P-value and | log2FoldChange |, we ultimately selected motor neuron and pancreatic homeobox 1(MNX1) gene as the target factors for follow-up studies. In the cloning formation and proliferation assay, overexpression of MNX1 inhibits proliferation and clonal formation of HER2-positive breast cancer cells. In the drug sensitivity experiments overexpression of MNX1 enhances the sensitivity of HER2-positive breast cancer cells to tyrosine kinase inhibitors (TKI) such as lapatinib and pyrotinib. MNX1 knockdown reduces the sensitivity of HER2-positive breast cancer cells to TKI. Through RNA-seq and ChIP-seq, we found that CD-M6PR might be a downstream target gene regulated by MNX1. Verification by Dual-Luciferase Reporter Assay MNX1 was found to positively regulate CD-M6PR transcription. By Co-IP assay, it was found that MNX1 interacted with EEF1D, and EEF1D stabilized MNX1. MNX1 and CD-M6PR affect the proliferation and drug sensitivity of breast cancer cells by down-regulating the PI3K-AKT-mTOR pathway. Conclusion: Overexpression of MNX1 increases the sensitivity of HER-2 positive cells to TKI. MNX1 binds to EEF1D and is stabilized by EEF1D. MNX1 positively regulates M6PR and affects the proliferation of breast cancer cells and the sensitivity of breast cancer cells to TKI through the PI3K-AKT-mTOR pathway. Citation Format: Wei-Ru Chi, Bingqiu Xiu, Min Xiong, Pei Li, Qi Zhang, Lun Li, Jianjing Hou, Xujie Zhou, Yuting Sang, Ming Chen, Liyi Zhang, Jingyan Xue, Yayun Chi, Jiong Wu. MNX1 promotes tumor sensitivity to HER2-targeted therapy in HER2-positive breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P2-16-04.

Binary classification of multi-magnification histopathological breast cancer images using late fusion and transfer learning

PurposeHistopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.Design/methodology/approachThree pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.FindingsThis study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.Originality/valueThe best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.

Efficacy Evaluation of Bevacizumab Combined with Capecitabine in the Treatment of HER2-Negative Metastatic Breast Cancer: A Meta-Analysis.

This study aims to evaluate the efficacy of bevacizumab combined with capecitabine in treating HER2-negative metastatic breast cancer through meta-analysis. We searched literature from databases, including PubMed, Web of Science, Wiley Online Library, Ovid, CNKI, and Wanfang databases, for randomized controlled trials (RCTs) of bevacizumab combined with capecitabine (experimental group) and other treatments (control group) for HER2-negative metastatic breast cancer. Retrieved articles were published from the establishment of the database to August 9, 2022. The main outcome indicators were disease progression rate (RDP), disease progression-free survival (PFS), 1-year survival rate (OSR), the occurrence of serious adverse events (SAEs), and objective remission rate (ORR). The risk of bias was assessed according to the Cochrane systematic evaluation tool. Then, the meta-analysis was carried out using Stata16.0 software, and subgroup analysis was carried out based on various intervention methods in the control group. 8 RCTs were finally included in this study, including 2470 patients with HER2-negative metastatic breast cancer. The results of meta-analysis showed that bevacizumab combined with capecitabine had no significant advantage over the control group in terms of RDP, but the results of subgroup analysis were consistent and significant (subgroup 1 (bevacizumab or chemotherapy): DR = -0.03, 95% CI (-0.14, 0.09), P = 0.01; subgroup 2 (bevacizumab plus paclitaxel therapy): DR = -0.03, 95% CI (-0.14, 0.09), P = 0.03). Furthermore, there was no statistical difference in terms of PFS of the experimental group (MD = 9.24, 95% CI (7.88, 32.67), P = 0.05). However, the subgroup analysis showed that the combination of bevacizumab and capecitabine demonstrated a more significant significance than bevacizumab or chemotherapy alone (subgroup 1: MD = 10.11, 95% CI (7.88, 12.34), P = 0.00). Compared with the control group, the experimental group had significant differences in OSR (DR = 0.07, 95% CI (-0.01, 0.15), P = 0.00) and ORR (DR = 0.07, 95% CI (-0.01, 0.15), P = 0.00). In terms of safety, the incidence of serious adverse events in the experimental group did not show a statistically significant difference (MD = 0.01, 95% CI (-0.21, 0.19), P = 0.82). When subgroup analyses were performed, the bevacizumab plus capecitabine regimen was associated with an increased incidence of serious adverse events compared with the drug alone (subgroup 1: MD = 0.02, 95% CI (-0.16, 0.20), P = 0.00) but a reduction in serious adverse events compared with the bevacizumab plus paclitaxel regimen (subgroup 2: DR = -0.01, 95% CI (-0.21, 0.19), P = 0.00). The combination therapy of bevacizumab and capecitabine can significantly improve the RDP and OSR of patients compared with the control group. The PFS and ORR of the experimental group are significantly higher than those of bevacizumab or chemotherapy alone. Still, no statistical difference was observed for these outcome indicators between two combined treatments of bevacizumab with capecitabine or paclitaxel. Although this combined treatment scheme may increase the incidence of serious adverse events compared with that of bevacizumab or chemotherapy alone, the incidence of adverse events was decreased compared with bevacizumab combined with paclitaxel. Therefore, the chemotherapy regimen for HER2-negative metastatic breast cancer in clinical practice can be selected according to the actual situation of the patients.

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer.

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.

The Evolution of Ki-67 and Breast Carcinoma: Past Observations, Present Directions, and Future Considerations.

The 1983 discovery of a mouse monoclonal antibody-the Ki-67 antibody-that recognized a nuclear antigen present only in proliferating cells represented a seminal discovery for the pathologic assessment of cellular proliferation in breast cancer and other solid tumors. Cellular proliferation is a central determinant of prognosis and response to cytotoxic chemotherapy in patients with breast cancer, and since the discovery of the Ki-67 antibody, Ki-67 has evolved as an important biomarker with both prognostic and predictive potential in breast cancer. Although there is universal recognition among the international guideline recommendations of the value of Ki-67 in breast cancer, recommendations for the actual use of Ki-67 assays in the prognostic and predictive evaluation of breast cancer remain mixed, primarily due to the lack of assay standardization and inconsistent inter-observer and inter-laboratory reproducibility. The treatment of high-risk ER-positive/human epidermal growth factor receptor-2 (HER2) negative breast cancer with the recently FDA-approved drug abemaciclib relies on a quantitative assessment of Ki-67 expression in the treatment decision algorithm. This further reinforces the urgent need for standardization of Ki-67 antibody selection and staining interpretation, which will hopefully lead to multidisciplinary consensus on the use of Ki-67 as a prognostic and predictive marker in breast cancer. The goals of this review are to highlight the historical evolution of Ki-67 in breast cancer, summarize the present literature on Ki-67 in breast cancer, and discuss the evolving literature on the use of Ki-67 as a companion diagnostic biomarker in breast cancer, with consideration for the necessary changes required across pathology practices to help increase the reliability and widespread adoption of Ki-67 as a prognostic and predictive marker for breast cancer in clinical practice.

High Thermosensitivity of MDA-MB-231 Cells as a Prerequisite for Thermoradiosensitization of Triple-negative Breast Cancer in Clinical Practice

High Thermosensitivity of MDA-MB-231 Cells as a Prerequisite for Thermoradiosensitization of Triple-negative Breast Cancer in Clinical Practice

Applying Deep Learning for Breast Cancer Detection in Radiology.

Recent advances in deep learning have enhanced medical imaging research. Breast cancer is the most prevalent cancer among women, and many applications have been developed to improve its early detection. The purpose of this review is to examine how various deep learning methods can be applied to breast cancer screening workflows. We summarize deep learning methods, data availability and different screening methods for breast cancer including mammography, thermography, ultrasound and magnetic resonance imaging. In this review, we will explore deep learning in diagnostic breast imaging and describe the literature review. As a conclusion, we discuss some of the limitations and opportunities of integrating artificial intelligence into breast cancer clinical practice.

BMI-1 promotes breast cancer proliferation and metastasis through different mechanisms in different subtypes.

Breast cancer is among the most common malignant cancers in women. B-cell-specific Moloney murine leukemia virus integration site 1 (BMI-1) is a transcriptional repressor that has been shown to be involved in tumorigenesis, the cell cycle, and stem cell maintenance. In our study, increased expression of BMI-1 was found in both human triple negative breast cancer and luminal A-type breast cancer tissues compared with adjacent tissues. We also found that knockdown of BMI-1 significantly suppressed cell proliferation and migration in vitro and in vivo. Further mechanistic research demonstrated that BMI-1 directly bound to the promoter region of CDKN2D/BRCA1 and inhibited its transcription in MCF-7/MDA-MB-231. More importantly, we discovered that knockdown of CDKN2D/BRCA1 could promote cell proliferation and migration after repression by PTC-209. Our results reveal that BMI-1 transcriptionally suppressed BRCA1 in TNBC cell lines whereas, in luminal A cell lines, CDKN2D was the target gene. This provides a reference for the precise treatment of different types of breast cancer in clinical practice.