Predicting Recurrence in Pancreatic Ductal Adenocarcinoma after Radical Surgery Using an AX-Unet Pancreas Segmentation Model and Dynamic Nomogram.

This study aims to investigate the reliability of radiomic features extracted from contrast-enhanced computer tomography (CT) by AX-Unet, a pancreas segmentation model, to analyse the recurrence of pancreatic ductal adenocarcinoma (PDAC) after radical surgery. In this study, we trained an AX-Unet model to extract the radiomic features from preoperative contrast-enhanced CT images on a training set of 205 PDAC patients. Then we evaluated the segmentation ability of AX-Unet and the relationship between radiomic features and clinical characteristics on an independent testing set of 64 patients with clear prognoses. The lasso regression analysis was used to screen for variables of interest affecting patients' post-operative recurrence, and the Cox proportional risk model regression analysis was used to screen for risk factors and create a nomogram prediction model. The proposed model achieved an accuracy of 85.9% for pancreas segmentation, meeting the requirements of most clinical applications. Radiomic features were found to be significantly correlated with clinical characteristics such as lymph node metastasis, resectability status, and abnormally elevated serum carbohydrate antigen 19-9 (CA 19-9) levels. Specifically, variance and entropy were associated with the recurrence rate (p < 0.05). The AUC for the nomogram predicting whether the patient recurred after surgery was 0.92 (95% CI: 0.78-0.99) and the C index was 0.62 (95% CI: 0.48-0.78). The AX-Unet pancreas segmentation model shows promise in analysing recurrence risk factors after radical surgery for PDAC. Additionally, our findings suggest that a dynamic nomogram model based on AX-Unet can provide pancreatic oncologists with more accurate prognostic assessments for their patients.

ALKBH5-Mediated m6A Demethylation of GLUT4 mRNA Promotes Glycolysis and Resistance to HER2-Targeted Therapy in Breast Cancer.

Resistance to HER2-targeted therapy represents a significant challenge for the successful treatment of patients with breast cancer with HER2-positive tumors. Through a global mass spectrometry-based proteomics approach, we discovered that the expression of the N6-methyladenosine (m6A) demethylase ALKBH5 was significantly upregulated in HER2-targeted therapy-resistant breast cancer cells. Elevated expression of ALKBH5 was sufficient to confer resistance to HER2-targeted therapy, and specific knockdown of ALKBH5 rescued the efficacy of trastuzumab and lapatinib in resistant breast cancer cells. Mechanistically, ALKBH5 promoted m6A demethylation of GLUT4 mRNA and increased GLUT4 mRNA stability in a YTHDF2-dependent manner, resulting in enhanced glycolysis in resistant breast cancer cells. In breast cancer tissues obtained from patients with poor response to HER2-targeted therapy, increased expression of ALKBH5 or GLUT4 was observed and was significantly associated with poor prognosis in the patients. Moreover, suppression of GLUT4 via genetic knockdown or pharmacologic targeting with a specific inhibitor profoundly restored the response of resistant breast cancer cells to trastuzumab and lapatinib, both in vitro and in vivo. In conclusion, ALKBH5-mediated m6A demethylation of GLUT4 mRNA promotes resistance to HER2-targeted therapy, and targeting the ALKBH5/GLUT4 axis has therapeutic potential for treating patients with breast cancer refractory to HER2-targeted therapies. SIGNIFICANCE: GLUT4 upregulation by ALKBH5-mediated m6A demethylation induces glycolysis and resistance to HER2-targeted therapy and represents a potential therapeutic target for treating HER2-positive breast cancer.

P21-activated kinase 2-mediated ß-catenin signaling promotes cancer stemness and osimertinib resistance in EGFR-mutant non-small-cell lung cancer.

Osimertinib (AZD9291) is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI), used for treating patients with advanced non-small-cell lung cancer (NSCLC) harboring EGFR-activating mutations or the resistant T790M mutation. However, acquired resistance to osimertinib is inevitable in EGFR-mutant NSCLC. By employing a global mass spectrometry-based phosphoproteomics approach, we identified that the activated p21-activated kinase 2 (PAK2)/ß-catenin axis acts as a driver of osimertinib resistance. We found that PAK2 directly phosphorylates ß-catenin and increases the nuclear localization of ß-catenin, leading to the increased expression and transcriptional activity of ß-catenin, which in turn enhances cancer stem-like properties and osimertinib resistance. Moreover, we revealed that HER3 as an upstream regulator of PAK2, drives the activation of PAK2/ß-catenin pathways in osimertinib-resistant cells. The clinical relevance of these findings was further confirmed by examining tissue specimens from patients with EGFR-mutant NSCLC. The results demonstrated that the levels of HER3, phospho-PAK2 (p-PAK2) and ß-catenin in the tissues from patients with EGFR-mutant NSCLC, that had relapsed after treatment with osimertinib, were elevated compared to those of the corresponding untreated tissues. Additionally, the high levels of HER3, p-PAK2 and ß-catenin correlated with shorter progression-free survival (PFS) in patients with EGFR-TKI-treated NSCLC. We additionally observed that the suppression of PAK2 via knockdown or pharmacological targeting with PAK inhibitors markedly restored the response of osimertinib-resistant NSCLC cells to osimertinib both in vitro and in vivo. In conclusion, these results indicated that the PAK2-mediated activation of ß-catenin is important for osimertinib resistance and targeting the HER3/PAK2/ß-catenin pathway has potential therapeutic value in NSCLCs with acquired resistance to osimertinib.

AX-Unet: A Deep Learning Framework for Image Segmentation to Assist Pancreatic Tumor Diagnosis.

Image segmentation plays an essential role in medical imaging analysis such as tumor boundary extraction. Recently, deep learning techniques have dramatically improved performance for image segmentation. However, an important factor preventing deep neural networks from going further is the information loss during the information propagation process. In this article, we present AX-Unet, a deep learning framework incorporating a modified atrous spatial pyramid pooling module to learn the location information and to extract multi-level contextual information to reduce information loss during downsampling. We also introduce a special group convolution operation on the feature map at each level to achieve information decoupling between channels. In addition, we propose an explicit boundary-aware loss function to tackle the blurry boundary problem. We evaluate our model on two public Pancreas-CT datasets, NIH Pancreas-CT dataset, and the pancreas part in medical segmentation decathlon (MSD) medical dataset. The experimental results validate that our model can outperform the state-of-the-art methods in pancreas CT image segmentation. By comparing the extracted feature output of our model, we find that the pancreatic region of normal people and patients with pancreatic tumors shows significant differences. This could provide a promising and reliable way to assist physicians for the screening of pancreatic tumors.

Targeting nicotinamide N-methyltransferase overcomes resistance to EGFR-TKI in non-small cell lung cancer cells.

Activating mutations of epidermal growth factor receptor (EGFR) contributes to the progression of non-small cell lung cancer (NSCLC). EGFR tyrosine kinase inhibitor (TKI)-targeted therapy has become the standard treatment for NSCLC patients with EGFR-mutations. However, acquired resistance to these agents remains a major obstacle for managing NSCLC. Here, we investigated a novel strategy to overcome EGFR TKI resistance by targeting the nicotinamide N-methyltransferase (NNMT). Using iTRAQ-based quantitative proteomics analysis, we identified that NNMT was significantly increased in EGFR-TKI-resistant NSCLC cells. Moreover, we found that NNMT expression was increased in EGFR-TKI-resistant NSCLC tissue samples, and higher levels were correlated with shorter progression-free survival in EGFR-TKI-treated NSCLC patients. Knockdown of NNMT rendered EGFR-TKI-resistant cells more sensitive to EGFR-TKI, whereas overexpression of NNMT in EGFR-TKI-sensitive cells resulted in EGFR-TKI resistance. Mechanically, upregulation of NNMT increased c-myc expression via SIRT1-mediated c-myc deacetylation, which in turn promoted glycolysis and EGFR-TKI resistance. Furthermore, we demonstrated that the combination of NNMT inhibitor and EGFR-TKI strikingly suppressed the growth of EGFR-TKI-resistant NSCLC cells both in vitro and in vivo. In conclusion, our research indicated that NNMT overexpression is important for acquired resistance to EGFR-TKI and that targeting NNMT might be a potential therapeutic strategy to overcome resistance to EGFR TKI.