G3BP1 interacts with YWHAZ to regulate chemoresistance and predict adjuvant chemotherapy benefit in gastric cancer

BACKGROUND A large proportion of gastric cancer patients are susceptible to chemoresistance, while the underlying mechanism remains obscure. Stress granules (SGs) play a self-defence role for tumour cells in inhibiting chemotherapy-induced apoptosis. As an SG assembly effector, G3BP1 (Ras-GTPase-activating protein SH3 domain-binding protein) has been reported to be overexpressed in gastric cancer; thus, here we aim to explore its potent roles in gastric cancer chemoresistance.METHODS Kaplan–Meier analysis was used to compare survival rates in gastric cancer patients with different G3BP1 expression. The influence of G3BP1 on gastric cancer cell chemoresistance and apoptosis were evaluated by in vitro and in vivo approaches. The interaction between G3BP1 and YWHAZ was assessed by immunohistochemistry, immunoprecipitation and immunofluorescence.RESULTS G3BP1 was associated with the poor outcome of gastric cancer patients who received adjuvant chemotherapy. G3BP1 knockdown significantly increased the sensitivity of gastric cancer cells to chemotherapy drugs. Mechanically, cell apoptosis and pro-apoptotic-associated molecules were significantly elevated upon G3BP1 depletion. Gene co-expression network analyses identified YWHAZ as the critical interlayer of G3BP1; as a result, G3BP1 interacted with YWHAZ to sequester Bax into the cytoplasm. Clinically, G3BP1^highYWHAZ^high gastric cancer patients displayed the worst outcome compared with other patients after chemotherapy.CONCLUSIONS The expression of G3BP1 and YWHAZ could predict the adjuvant chemotherapy benefit in gastric cancer patients.Subject terms: Gastric cancer, Cell death     

Non-Mass Breast Lesions on Ultrasound: Feature Exploration and Multimode Ultrasonic Diagnosis

The aim of this study was to analyze the features of non-mass breast lesions (NMLs) on B-mode ultrasound (US), color Doppler US, strain elastography (SE) and contrast-enhanced ultrasound (CEUS) and to develop a multimode ultrasonic method for NML differentiation. Seventy-one NMLs were included in this retrospective study. Binary logistic regression was used to identify the independent risk factors. Pathology results were used as the standard criterion. Microcalcification on US, high stiffness on SE and hyper-enhanced intensity on CEUS were identified as features correlated with malignancy. A multimode method to evaluate NMLs based on the logistic regression was developed. The sensitivity and specificity for US, US?+?Doppler, US?+?SE, US?+?CEUS and the multimode method were 100% and 29%, 92.5% and 41.9%, 97.5% and 58.1%, 90.0% and 58.1% and 95.0% and 77.4%, respectively. The accuracy of these methods was 69.0%, 70.4%, 80.2%, 76.1% and 87.3%, respectively. The multimode ultrasonic method is simple and exhibited high diagnostic performance, which might be helpful for predicting the potential malignancy of NMLs.     

Discovery of structure-based small molecular inhibitor of αB-crystallin against basal-like/triple-negative breast cancer development in vitro and in vivo

αB-crystallin (CRYAB) is present at a high frequency in poor prognosis basal-like breast tumours, which are largely absent of oestrogen, progesterone receptors and HER2 known as triple-negative breast cancer (TNBC). CRYAB functions as a molecular chaperone to bind to and correct intracellular misfolded/unfolded proteins such as vascular endothelial growth factor (VEGF), preventing non-specific protein aggregations under the influence of the tumour microenvironment stress and/or anti-cancer treatments including bevacizumab therapy. Directly targeting CRYAB can sensitize tumour cells to chemotherapeutic agents and decrease tumour aggressiveness. However, growing evidence shows that CRYAB is a critical adaptive response element after ischemic heart disease and stroke, implying that directly targeting CRYAB might cause serious unwanted side effects. Here, we used structure-based molecular docking of CRYAB and identified a potent small molecular inhibitor, NCI-41356, which can strongly block the interaction between CRYAB and VEGF₁₆₅ without affecting CRYAB levels. The disruption of the interaction between CRYAB and VEGF₁₆₅ elicits in vitro anti-tumour cell proliferation and invasive effects through the down-regulation of VEGF signalling in the breast cancer cells. The observed in vitro anti-tumour angiogenesis of endothelial cells might be attributed to the down-regulation of paracrine VEGF signalling in the breast cancer cells after treatment with NCI-41356. Intraperitoneal injection of NCI-41356 greatly inhibits the tumour growth and vasculature development in in vivo human breast cancer xenograft models. Our findings provide ‘proof-of-concept’ for the development of highly specific structure-based alternative targeted therapy for the prevention and/or treatment of TNBC.     

Deep learning applied to two-dimensional color Doppler flow imaging ultrasound images significantly improves diagnostic performance in the classification of breast masses: a multicenter study

BackgroundThe current deep learning diagnosis of breast masses is mainly reflected by the diagnosis of benign and malignant lesions. In China, breast masses are divided into four categories according to the treatment method: inflammatory masses, adenosis, benign tumors, and malignant tumors. These categorizations are important for guiding clinical treatment. In this study, we aimed to develop a convolutional neural network (CNN) for classification of these four breast mass types using ultrasound (US) images.MethodsTaking breast biopsy or pathological examinations as the reference standard, CNNs were used to establish models for the four-way classification of 3623 breast cancer patients from 13 centers. The patients were randomly divided into training and test groups (n = 1810 vs. n = 1813). Separate models were created for two-dimensional (2D) images only, 2D and color Doppler flow imaging (2D-CDFI), and 2D-CDFI and pulsed wave Doppler (2D-CDFI-PW) images. The performance of these three models was compared using sensitivity, specificity, area under receiver operating characteristic curve (AUC), positive (PPV) and negative predictive values (NPV), positive (LR+) and negative likelihood ratios (LR−), and the performance of the 2D model was further compared between masses of different sizes with above statistical indicators, between images from different hospitals with AUC, and with the performance of 37 radiologists.ResultsThe accuracies of the 2D, 2D-CDFI, and 2D-CDFI-PW models on the test set were 87.9%, 89.2%, and 88.7%, respectively. The AUCs for classification of benign tumors, malignant tumors, inflammatory masses, and adenosis were 0.90, 0.91, 0.90, and 0.89, respectively (95% confidence intervals [CIs], 0.87–0.91, 0.89–0.92, 0.87–0.91, and 0.86–0.90). The 2D-CDFI model showed better accuracy (89.2%) on the test set than the 2D (87.9%) and 2D-CDFI-PW (88.7%) models. The 2D model showed accuracy of 81.7% on breast masses ≤1 cm and 82.3% on breast masses >1 cm; there was a significant difference between the two groups (P < 0.001). The accuracy of the CNN classifications for the test set (89.2%) was significantly higher than that of all the radiologists (30%).ConclusionsThe CNN may have high accuracy for classification of US images of breast masses and perform significantly better than human radiologists.Trial registrationChictr.org, ChiCTR1900021375; http://www.chictr.org.cn/showproj.aspx?proj=33139.