Development and validation of an artificial intelligence model for predicting de novo distant bone metastasis in breast cancer: a dual-center study.

OBJECTIVE: Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. METHODS: Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. RESULTS: Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model's predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. CONCLUSION: This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.

Biomimetic Nanosystem Loading Aggregation-Induced Emission Luminogens and SO2 Prodrug for Inhibiting Insufficient Photothermal Therapy-Induced Breast Cancer Recurrence and Metastasis.

Photothermal therapy (PTT) holds considerable clinical promise. However, insufficient PTT-induced tumor recurrence and metastasis is an urgent practical problem that needs to be solved. Herein, a biomimetic mesoporous organosilicon nano-system called PSAB is designed to precisely deplete cancer stem cells (CSCs) and prevent tumor recurrence and metastasis after PTT. The PSAB system is made up of Aggregation-induced emission (AIE)-active photothermal agent, 2TT-oC26B, and SO2 prodrug, benzothiazole sulfinate (BTS), within mesoporous organosilicon nanoparticles (MON) enclosed by an exterior platelet membrane. PSAB effectively targets CSCs both in vitro and in vivo by P-selectin/CD44 interaction. The degradation of MON and subsequent release of BTS and AIE molecules are facilitated by intracellular glutathione (GSH). Subsequently, the acidic tumor environment triggers the SO2 gas therapy from BTS. This process leads to the depletion of GSH and CSCs elimination. After combining PSAB with photothermal therapy, there is no significant tumor recurrence or metastasis. These results indicate that SO2 gas therapy and AIE-mediated PTT act synergistically to offer a unique approach for preventing tumor recurrence and metastasis after PTT, thus holding significant promise for clinical applications in cancer PTT.

AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter.

Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study.

Objective: This study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients. Methods: Data from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models. Results: The LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase. Conclusion: This study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

Discovery of TNG908: A Selective, Brain Penetrant, MTA-Cooperative PRMT5 Inhibitor That Is Synthetically Lethal with MTAP-Deleted Cancers.

It has been shown that PRMT5 inhibition by small molecules can selectively kill cancer cells with homozygous deletion of the MTAP gene if the inhibitors can leverage the consequence of MTAP deletion, namely, accumulation of the MTAP substrate MTA. Herein, we describe the discovery of TNG908, a potent inhibitor that binds the PRMT5·MTA complex, leading to 15-fold-selective killing of MTAP-deleted (MTAP-null) cells compared to MTAPintact (MTAP WT) cells. TNG908 shows selective antitumor activity when dosed orally in mouse xenograft models, and its physicochemical properties are amenable for crossing the blood-brain barrier (BBB), supporting clinical study for the treatment of both CNS and non-CNS tumors with MTAP loss.

Ionic-liquid-modified MOFs incorporated in a mixed-matrix membrane by metal-site anchoring for gas separation.

Through metal-site anchoring, metal-organic frameworks (MOFs) were modified with ionic liquids (ILs) and used as a porous filler to prepare mixed-matrix membranes (MMMs). The targeted growth of the IL exposed more active sites and greatly enhanced CO2 transfer in the MMMs, which exhibited excellent gas separation performance and long durability.

Tailored Polypyrrole Nanofibers as Ion-to-Electron Transduction Membranes for Wearable K+ Sensors.

Conductive polymers are recognized as ideal candidates for the development of noninvasive and wearable sensors for real-time monitoring of potassium ions (K+) in sweat to ensure the health of life. However, the low ion-to-electron transduction efficiency and limited active surface area hamper the development of high-performance sensors for low-concentration K+ detection in the sweat. Herein, a wearable K+ sensor is developed by tailoring the nanostructure of polypyrrole (PPy), serving as an ion-to-electron transduction layer, for accurately and stably tracing the K+ fluctuation in human sweat. The PPy nanostructures can be tailored from nanospheres to nanofibers by controlling the supramolecular assembly process during PPy polymerization. Resultantly, the ion-to-electron transduction efficiency (17-fold increase in conductivity) and active surface area (1.3-fold enhancement) are significantly enhanced, accompanied by minimized water layer formation. The optimal PPy nanofibers-based K+ sensor achieved a high sensitivity of 62 mV decade-1, good selectivity, and solid stability. After being integrated with a temperature sensor, the manufactured wearable sensor realized accurate monitoring of K+ fluctuation in the human sweat.

RNADisease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction.

Numerous studies have shown that RNA plays an important role in the occurrence and development of diseases, and RNA-disease associations are not limited to noncoding RNAs in mammals but also exist for protein-coding RNAs. Furthermore, RNA-associated diseases are found across species including plants and nonmammals. To better analyze diseases at the RNA level and facilitate researchers in exploring the pathogenic mechanism of diseases, we decided to update and change MNDR v3.0 to RNADisease v4.0, a repository for RNA-disease association (http://www.rnadisease.org/ or http://www.rna-society.org/mndr/). Compared to the previous version, new features include: (i) expanded data sources and categories of species, RNA types, and diseases; (ii) the addition of a comprehensive analysis of RNAs from thousands of high-throughput sequencing data of cancer samples and normal samples; (iii) the addition of an RNA-disease enrichment tool and (iv) the addition of four RNA-disease prediction tools. In summary, RNADisease v4.0 provides a comprehensive and concise data resource of RNA-disease associations which contains a total of 3 428 058 RNA-disease entries covering 18 RNA types, 117 species and 4090 diseases to meet the needs of biological research and lay the foundation for future therapeutic applications of diseases.

VRK1 Is a Synthetic-Lethal Target in VRK2-Deficient Glioblastoma.

Synthetic lethality is a genetic interaction that results in cell death when two genetic deficiencies co-occur but not when either deficiency occurs alone, which can be co-opted for cancer therapeutics. Pairs of paralog genes are among the most straightforward potential synthetic-lethal interactions by virtue of their redundant functions. Here, we demonstrate a paralog-based synthetic lethality by targeting vaccinia-related kinase 1 (VRK1) in glioblastoma (GBM) deficient of VRK2, which is silenced by promoter methylation in approximately two thirds of GBM. Genetic knockdown of VRK1 in VRK2-null or VRK2-methylated cells resulted in decreased activity of the downstream substrate barrier to autointegration factor (BAF), a regulator of post-mitotic nuclear envelope formation. Reduced BAF activity following VRK1 knockdown caused nuclear lobulation, blebbing, and micronucleation, which subsequently resulted in G2-M arrest and DNA damage. The VRK1-VRK2 synthetic-lethal interaction was dependent on VRK1 kinase activity and was rescued by ectopic expression of VRK2. In VRK2-methylated GBM cell line-derived xenograft and patient-derived xenograft models, knockdown of VRK1 led to robust tumor growth inhibition. These results indicate that inhibiting VRK1 kinase activity could be a viable therapeutic strategy in VRK2-methylated GBM. SIGNIFICANCE: A paralog synthetic-lethal interaction between VRK1 and VRK2 sensitizes VRK2-methylated glioblastoma to perturbation of VRK1 kinase activity, supporting VRK1 as a drug discovery target in this disease.

Detection of Incidental Esophageal Cancers on Chest CT by Deep Learning.

OBJECTIVE: To develop a deep learning-based model using esophageal thickness to detect esophageal cancer from unenhanced chest CT images. METHODS: We retrospectively identified 141 patients with esophageal cancer and 273 patients negative for esophageal cancer (at the time of imaging) for model training. Unenhanced chest CT images were collected and used to build a convolutional neural network (CNN) model for diagnosing esophageal cancer. The CNN is a VB-Net segmentation network that segments the esophagus and automatically quantifies the thickness of the esophageal wall and detect positions of esophageal lesions. To validate this model, 52 false negatives and 48 normal cases were collected further as the second dataset. The average performance of three radiologists and that of the same radiologists aided by the model were compared. RESULTS: The sensitivity and specificity of the esophageal cancer detection model were 88.8% and 90.9%, respectively, for the validation dataset set. Of the 52 missed esophageal cancer cases and the 48 normal cases, the sensitivity, specificity, and accuracy of the deep learning esophageal cancer detection model were 69%, 61%, and 65%, respectively. The independent results of the radiologists had a sensitivity of 25%, 31%, and 27%; specificity of 78%, 75%, and 75%; and accuracy of 53%, 54%, and 53%. With the aid of the model, the results of the radiologists were improved to a sensitivity of 77%, 81%, and 75%; specificity of 75%, 74%, and 74%; and accuracy of 76%, 77%, and 75%, respectively. CONCLUSIONS: Deep learning-based model can effectively detect esophageal cancer in unenhanced chest CT scans to improve the incidental detection of esophageal cancer.

3D printed composite scaffolds with dual small molecule delivery for mandibular bone regeneration.

Functional reconstruction of craniomaxillofacial defects is challenging, especially for the patients who suffer from traumatic injury, cranioplasty, and oncologic surgery. Three-dimensional (3D) printing/bioprinting technologies provide a promising tool to fabricate bone tissue engineering constructs with complex architectures and bioactive components. In this study, we implemented multi-material 3D printing to fabricate 3D printed PCL/hydrogel composite scaffolds loaded with dual bioactive small molecules (i.e. resveratrol and strontium ranelate). The incorporated small molecules are expected to target several types of bone cells. We systematically studied the scaffold morphologies and small molecule release profiles. We then investigated the effects of the released small molecules from the drug loaded scaffolds on the behavior and differentiation of mesenchymal stem cells (MSCs), monocyte-derived osteoclasts, and endothelial cells. The 3D printed scaffolds, with and without small molecules, were further implanted into a rat model with a critical-sized mandibular bone defect. We found that the bone scaffolds containing the dual small molecules had combinational advantages in enhancing angiogenesis and inhibiting osteoclast activities, and they synergistically promoted MSC osteogenic differentiation. The dual drug loaded scaffolds also significantly promoted in vivo mandibular bone formation after 8 week implantation. This work presents a 3D printing strategy to fabricate engineered bone constructs, which can likely be used as off-the-shelf products to promote craniomaxillofacial regeneration.

Catalytic effect of transglutaminase mediated by myofibrillar protein crosslinking under microwave irradiation.

Microwave (MW) heating improved the activity of transglutaminase (TGase) by inducing conformational changes due to structural modification. However, when TGase and myofibrillar protein were heated, the solubility and degree of crosslinking were similar. Further, the gel properties of the mixed solution pre-gelled by MW heating were lower than that obtained with water bath (WB) pre-gelling. We compared the effects on myofibrillar proteins at the same heating rate, our results showed that MW promoted aggregation, as the particle distribution tended toward larger molecular size. The increase of random coil as investigated by circular dichroism (CD) indicated that WB induced the unfolding of myofibrillar protein. MW enhanced intermolecular forces by engendering more disulfide bonds, which hindered the catalysis by TGase. Finally, SDS-PAGE indicated that the myosin molecules had more head crosslinking during MW treatment. MW and WB cause different response behaviors of myofibrillar protein, thereby affecting the catalytic effect of TGase.

Down-regulation of lncRNA-ATB inhibits epithelial-mesenchymal transition of breast cancer cells by increasing miR-141-3p expression.

Long noncoding RNA activated by transforming growth factor-beta (lnc-ATB) is abnormally expressed in a number of tumor types. The aim of this study was to investigate the expression of lnc-ATB and miR-141-3p, and to determine whether lnc-ATB can regulate epithelial-mesenchymal transition (EMT) by miR-141-3p in breast cancer. Here, we found that lnc-ATB was highly expressed, whereas there was low expression of miR-141-3p in breast cancer tissues and cells. Knockdown of lnc-ATB in two breast cancer cell lines (MDA-MB-231 and BT549) significantly increased miR-141-3p expression. Down-regulation of lnc-ATB resulted in a morphological change of breast cancer cells from spindle-like to a round shape, and in a remarkable inhibition of cell migration and invasion, which were reversed by miR-141-3p inhibitor. Furthermore, we demonstrated that lnc-ATB knockdown decreased ZEB1, ZEB2, N-cadherin, and vimentin expression, and promoted E-cadherin expression, while miR-141-3p inhibitor could reverse those effects. Moreover, we proved that miR-141-3p directly bound to the 3' untranslated region (UTR) of ZEB1 and ZEB2 and negatively regulated ZEB1 and ZEB2 expression. Taken together, our results show that knockdown of lnc-ATB significantly inhibits the EMT process of breast cancer cells by increasing the expression of miR-141-3p, indicating that lnc-ATB might serve as a novel therapeutic target for breast cancer.

Diagnostic value of endoscopic appearance during ductoscopy in patients with pathological nipple discharge.

BACKGROUND: To explore the features of ductoscopic appearance that may be diagnostic in patients with pathologic nipple discharge (PND) and to discuss the diagnostic criteria for intraductal tumors. METHODS: We reviewed 247 patients with PND but without a palpable mass who were evaluated using either surgical biopsy or excision. Data concerning patient age, duration of discharge, discharge color, and the details of endoscopic appearance were analyzed according to the pathological results. RESULTS: The postoperative diagnosis in 61 patients (24.70%) was a nonmass lesion, and 186 patients (76.52%) had an intraductal tumor. Among those with intraductal lesions, 10 patients (4.05%) had a malignant tumor, including 4 (1.62%) with ductal carcinoma in situ and 6 (2.43%) with invasive ductal carcinoma. On univariate analysis, patients of older age with spontaneous and bloody discharge were more likely to suffer from intraductal lesions. On logistic regression analysis, bloody nipple discharge, morphology, and a broad lesion base revealed by ductoscopy showed a statistically significant correlation with malignancy (p = 0.001, p < 0.001, p = 0.022, respectively). CONCLUSIONS: Both clinical features and endoscopic appearance are significant for the precise diagnosis of an intraductal lesion seen on ductoscopy. The endoscopic features of bloody discharge, morphology, and a broad lesion base are independent risk factors for malignancy and represent new criteria for the diagnosis of patients with PND.

An ultrasensitive assay format for detecting ULK1 inhibition by monitoring the phosphorylation status of Atg13.

A new technology from Quanterix called SiMoA (single molecule array) which employs a fully automated system capable of ultrasensitive sandwich based ELISA detection was explored. Our studies focused upon the inhibition of the autophagy initiating kinase ULK1 by measuring the both total Atg13 and the phosphorylation of Atg13(pSer(318)) from control and following compound treatment in either overexpressing or wild type tissue culture samples. The results show linear protein concentration dependence over two orders of magnitude and provide an assay window of 8- to 100-fold signal to background for inhibition of phosphorylation for both wild type and overexpressed samples, respectively. Moreover, overexpressed samples displayed 17-fold pSer(318)-Atg13 above wild type levels of with no apparent differences in compound potency. Lastly, the inhibition of ULK1 from mouse derived wild type xenografts also demonstrated loss of pSer(318)-Atg13 upon ULK1 inhibitor treatment that compared favorably to Western blot. These results show that the SiMoA technology can detect quantitatively low levels of endogenous biomarkers with the ability to detect the loss of pSer(318)-Atg13 upon ULK1 inhibition.

Circulating microRNA-451 as a predictor of resistance to neoadjuvant chemotherapy in breast cancer.

OBJECTIVE: We aimed to explore the potential application of circulating microRNA-451 (miR-451) in serum in predicting the resistance to neoadjuvant chemotherapy (NACT) in breast cancer (BC). METHODS: Eighty-two BC patients who underwent NACT were recruited in our study, including 41 NACT-sensitive patients (NACT-sensitive group) and 41 NACT-resistant patients (NACT-resistant group). Additionally, 60 healthy subjects were selected as normal controls. Epirubicin-resistant MCF-7 BC cell line (MCF-7/EPI) and docetaxel-resistant MCF-7 BC cell line (MCF-7/DOC) were cultured in our study. MTT assay was applied to calculate the survival rates of MCF-7 cells, MCF-7/DOC cells and MCF-7/EPI cells. The expression levels of miR-451 in normal controls, NACT-sensitive group, NACT-resistant group, MCF-7 cells, MCF-7/DOC cells and MCF-7/EPI cells were measured by qRT-PCR method. RESULTS: The proliferation rates of both the MCF-7/DOC and MCF-7/EPI cells were significantly restrained at the drug concentration of 10 ng/ml, 50 ng/ml, 100 ng/ml and 200 ng/ml. However, the proliferation rates of MCF-7/DOC and MCF-7/EPI cells both increased significantly at the drug concentration of 500ng/ml. Furthermore, the IC50 of MCF-7/DOC cells was 23.603 ng/ml and the IC50 of MCF-7/EPI cells was 3.209 ng/ml. The relative expression of miR-451 was significantly lower in both the NACT-resistant group and the NACT-sensitive group than the normal control group. We also found that the relative expression level of miR-451 was significantly lower in the NACT-resistant group than that in the NACT-sensitive group. The expression of miR-451 in the MCF-7/EPI and the MCF-7/DOC cell lines was significantly lower than that in the MCF-7 cell lines. CONCLUSION: We supported the view that the relative expression level of miR-451 was lower in the NACT-resistant BC patients, suggesting the circulating miR-451 may have a functional significance in predicting the resistance to NACT in BC patients. We laid a foundation for further research on the resistance to NACT in BC treatment.

MicroRNA-148a inhibits migration of breast cancer cells by targeting MMP-13.

Breast cancer is a threat to the health of women, and metastasis of breast cancer cells plays an important role in the deterioration of breast cancer. MicroRNAs play a critical role in the tumorigenesis and development of breast cancer. MicroRNA-148a (miR-148a) is associated with the growth and metastasis of tumor cells. In the present study, we investigated the role of miR-148a in migration of breast cancer cells as well as the underlying mechanism. MiR-148a was found to inhibit the proliferation and migration of breast cancer cells. To further explore the mechanism through which miR-148a plays its antitumor role, matrix metalloproteinase-13 (MMP-13) was identified as a target of miR-148a by western blot and luciferase reporter assay. Moreover, silence of MMP-13 mimicked the effect of miR-148a, whereas overexpression of MMP-13 rescued the impaired migration caused by miR-148a. Our study demonstrates that miR-148a inhibits the migration of breast cancer cells by targeting MMP-13 and also lays theoretical foundation for further exploration for the function of miR-148a.

Influence of MiR-451 on Drug Resistances of Paclitaxel-Resistant Breast Cancer Cell Line.

BACKGROUND: This study aimed to investigate the potential influence of microRNA-451 (miR-451) in drug resistances of the Paclitaxel-resistant breast cancer cell line by transfecting miR-451 mimics and miR-451 inhibitors to MCE-7, MCF-7/EPI, and MCF-7/DOC. MATERIAL AND METHODS: Real-time quantitative PCR (qRT-PCR) was performed for detecting whether transfected miR-451 mimics and miR-451 inhibitors could regulate the expression of miR-451 effectively. The apoptosis of the 3 cell lines was measured by applying Annexin V-APC/PI staining. Western blot was used for the detection of the protein expression of Bcl-2 and Caspase 3 after the transfection of miR-451 mimics /inhibitors. Bioinformatics analysis demonstrated that Bcl-2 protein is a potential target gene for miR-451. RESULTS: In comparison to the control group, after transfection with miR-451 mimics, there was a significant increase in miR-451 expression in MCF-7, MCF-7/EPI, and MCF-7/DOC. Cells in the three cell lines had increased apoptosis, Bcl-2 protein expression decreased significantly, and Caspase protein expression increased obviously. After the transfection with miR-451 inhibitors, miR-451 expression was significantly decreased and apoptosis in the 3 cell lines had no significant decrease compared with the control group. CONCLUSIONS: Increased miR-451 expression may negatively regulate Bcl-2 mRNA and protein expression, followed by affecting the protein expression of caspase 3, and accelerate the apoptosis in breast cancer, indicating that miR-451 might influence the drug resistances of the Paclitaxel-resistant breast cancer cell line.

Partial axillary lymph node dissection inferior to the intercostobrachial nerves complements sentinel node biopsy in patients with clinically node-negative breast cancer.

BACKGROUND: The practice of breast cancer diagnosis and treatment in China varies to that in western developed countries. With the unavailability of radioactive tracer technique for sentinel lymph nodes biopsy (SLNB), using blue dye alone has been the only option in China. Also, the diagnosis of breast malignant tumor in most Chinese centres heavily relies on intraoperative instant frozen histology which is normally followed by sentinel lymph nodes mapping, SLNB and the potential breast and axillary operations in one consecutive session. This practice appears to cause a high false negative rate (FNR) for SLNB. The present study aimed to investigate the impact of the current practice in China on the accuracy of SLNB, and whether partial axillary lymph node dissection (PALND), dissection of lymph nodes inferior to the intercostobrachial nerve (ICBN), was a good complementary procedure following SLNB using blue dye. METHODS: 289 patients with clinically node-negative breast cancer were identified and recruited. Tumorectomy, intraoperative instant frozen histological diagnosis, SLNB using methylene blue dye, and PALND or complete axillary node dissection (ALND) were performed in one consecutive operative session. The choice of SLNB only, SLNB followed by PALND or by ALND was based on the pre-determined protocol and preoperative choice by the patient. Clinical parameters were analyzed and survival analysis was performed. RESULTS: 37% patients with clinically negative nodes were found nodes positive. 59 patients with positive SLN underwent ALND, including 47 patients with up to two positive nodes which were all located inferior to the ICBN. 9 patients had failed SLNB and underwent PALND. Among them, 3 (33.3%) patients were found to have one metastatic node. 149 patients showed negative SLNB but chose PALND. Among them, 30 (20.1%), 14 (9.4) and 1 (0.7%) patients were found to have one, two and three metastatic node(s), respectively. PALND detected 48 (30.4%) patients who had either failed SLNB or negative SLNB to have additional positive nodes. All the patients with up to two positive nodes had their nodes located inferior to the ICBN. The FNR of SLNB was 43%. The accuracy rate was 58%. The follow-up ranged 12-33 months. The incidence of lymphedema for SLNB, PALND, and ALND was 0%, 0%, and 25.4%, respectively (P < 0.005). The disease-free survivals for SLNB, PALND, and ALND groups were 95.8%, 96.8%, and 94.9%, respectively (p > 0.05). CONCLUSIONS: Under the circumstances of current practice in China, PALND is a good complementary procedure following SLNB in clinically node-negative breast cancer.

Neoadjuvant chemotherapy of breast cancer with pirarubicin versus epirubicin in combination with cyclophosphamide and docetaxel.

Breast cancers (BC) are treated with surgery, radiotherapy, and chemotherapy. Neoadjuvant chemotherapy (NACT) is an emerging treatment option in many cancers and is given before primary therapy to shrink tumor size. The efficacy of NACT in varied settings of BC, such as inoperable tumors, borderline resectable tumors, and breast-conserving surgery, has been debated extensively in literature, and the results remain unclear and depended on a wide variety of factors such as cancer type, disease extent, and the specific combination of chemotherapy drugs. This study was performed to examine the efficacy, toxicity, and tolerability of pirarubicin (THP) and epirubicin (EPI) in combination with docetaxel and cyclophosphamide in a NACT setting for BC. A total of 48 patients with stage II or III breast cancers were randomly divided into two groups: THP group and EPI group. The patients in THP group received 2-4 cycles of neoadjuvant chemotherapy with DTC regimen (docetaxel, THP, cyclophosphamide), while patients in the EPI group received 2-4 cycles of DEC regimen (docetaxel, EPI, cyclophosphamide) before surgery. The incidence of adverse reactions and the efficacy of the treatment regimen were compared between the two groups. Prognostic evaluation indexes were estimated by Kaplan-Meier survival analysis, including the 5-year disease-free survival (DFS) and overall survival (OS). The overall response rate in THP group was 83.3 %, and the EPI group showed a response rate of 79.2 %, with no statistically significant difference in response rate between the two groups. The incidence of cardiac toxicity, myelosuppression, nausea, and vomiting in the THP group was significantly lower than the EPI group (all P < 0.05). The incidence of hepatic toxicity, alopecia, and diarrhea in the THP group was also lower than the EPI group, but these differences were not statistically significant. The 5-year DFS and OS in THP versus EPI groups were 80 versus 76 % (DFS) and 86 versus 81 % (OS), respectively. Our study found that NACT with DTC regimen and DEC regimen were both very effective in treatment of BC. However, THP-based combination therapy was associated with significantly lower incidence of cardiac toxicity, myelosuppression, nausea, and vomiting.