Objectives: Anaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement status in lung adenocarcinomas by developing a machine learning model that combines PET/CT radiomic features and clinical characteristics.Methods: Five hundred twenty-six patients of lung adenocarcinoma with PET/CT scan examination were enrolled, including 109 positive and 417 negative patients for ALK rearrangements from February 2016 to March 2019. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images. The maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were further employed to select the most distinguishable radiomic features to construct predictive models. The mRMR is a feature selection method, which selects the features with high correlation to the pathological results (maximum correlation), meanwhile retain the features with minimum correlation between them (minimum redundancy). LASSO is a statistical formula whose main purpose is the feature selection and regularization of data model. LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the models, and the performance of different models was compared by the DeLong test.Results: A total of 22 radiomic features were extracted from PET/CT images for constructing the PET/CT radiomic model, and majority of these features used were based on CT features (20 out of 22), only 2 PET features were included (PET percentile 10 and PET difference entropy). Moreover, three clinical features associated with ALK mutation (age, burr and pleural effusion) were also employed to construct a combined model of PET/CT and clinical model. We found that this combined model PET/CT-clinical model has a significant advantage to predict the ALK mutation status in the training group (AUC = 0.87) and the testing group (AUC = 0.88) compared with the clinical model alone in the training group (AUC = 0.76) and the testing group (AUC = 0.74) respectively. However, there is no significant difference between the combined model and PET/CT radiomic model.Conclusions: This study demonstrated that PET/CT radiomics-based machine learning model has potential to be used as a non-invasive diagnostic method to help diagnose ALK mutation status for lung adenocarcinoma patients in the clinic.

Protein kinase B (AKT) hyperactivation and de novo lipogenesis are both common in tumor progression. Sterol regulatory element-binding protein 1 (SREBP1) is the master regulator for tumor lipid metabolism, and protein arginine methyltransferase 5 (PRMT5) is an enzyme that can catalyze symmetric dimethyl arginine (SDMA) modification of the mature form of SREBP1 (mSREBP1) to induce its hyperactivation. Here, we report that SDMA-modified mSREBP1 (mSREBP1-SDMA) was overexpressed and correlated with ser473-phosphorylated AKT (AKT-473P) expression and poor patient outcomes in human lung adenocarcinomas. Further, patients with AKT-473P and mSREBP1-SDMA co-expression showed the worst prognosis. Mechanistic investigation revealed that AKT activation upregulated SREBP1 at both the transcriptional and post-translational levels, whereas PRMT5 knockdown reversed AKT signaling-mediated mSREBP1 ubiquitin-proteasome pathway stabilization at the post-translational level. Meanwhile, AKT activation promoted nuclear PRMT5 to the cytoplasm without changing total PRMT5 expression, and the transported cytoplasmic PRMT5 (cPRMT5) induced by AKT activation showed a strong mSREBP1-binding ability. IHC assay demonstrated that AKT-473P and mSREBP1-SDMA were positively correlated with cPRMT5 in lung adenocarcinomas, and high cPRMT5 levels in tumors were associated with poor patient outcomes. Additionally, PRMT5 knockdown reversed AKT activation-induced lipid synthesis and growth advantage of lung adenocarcinoma cells both in vitro and in vivo. Finally, we defined an AKT/PRMT5/SREBP1 axis involved in de novo lipogenesis and the growth of lung cancer. Our data also support that cPRMT5 is a potential therapeutic target for hyperactive AKT-driven lung adenocarcinoma. Supporting Information

Rationale: Non-small cell lung cancer (NSCLC) is a predominant cause of cancer-related mortality, with its progression and treatment resistance significantly influenced by cancer stem cells (CSCs) and their complex intercellular communication mechanisms. Small extracellular vesicles (sEVs) have emerged as pivotal mediators of intercellular signaling, affecting tumor microenvironment modulation and therapeutic resistance. This study investigates the role of CSC-derived sEVs in transmitting stemness traits through the selective sorting of pyruvate kinase M2 phosphorylated at the Y105 site (pY105-PKM2), mediated by the adaptor protein IQGAP1, which supports CSC maintenance and drug resistance in NSCLC.

Purpose: To determine whether protein kinase C-iota (PKC-iota) is associated with glucose metabolism in non-small-cell lung cancer (NSCLC) and whether its regulatory effect on metabolic and biological changes observed in NSCLC can be mediated by glucose transporter 1 (GLUT1). Patients and methods: Forty-five NSCLC patients underwent combined ¹⁸F-fludeoxyglucose (¹⁸F-FDG) positron emission tomography and computed tomography (PET/CT) before surgery, and another eighty-one NSCLC patients were followed-up for 1–91 months after tumor resection. The rate of glucose metabolism in NSCLC was quantified by measuring the maximum standardized uptake value (SUVmax) by ¹⁸F-FDG PET/CT. PKC-iota and GLUT1 in NSCLC were detected by immunostaining. In vitro, PKC-iota was knocked down, whereas GLUT1 was silenced with or without PKC-iota overexpression to identify the role of PKC-iota in glycolysis. Spearman’s rank correlation coefficient was used in the correlation analysis. Kaplan-Meier analysis was used to assess survival duration. Results: There was a positive relationship between PKC-iota expression and SUVmax in NSCLC (r=0.649, P<0.001). PKC-iota expression also showed a positive relationship with GLUT1 in NSCLC tissues (r=0.686, P<0.001). Patients whose NSCLC tissues highly co-expressed PKC-iota and GLUT1 had worse prognosis compared with patients without high co-expression of PKC-iota and GLUT1. In vitro, PKC-iota silencing significantly decreased the expression of GLUT1 and inhibited glucose uptake and glycolysis; c-Myc silencing restrained PKC-iota-mediated GLUT1 elevation; GLUT1 knockdown remarkably suppressed PKC-iota-mediated glycolysis and cell growth. Conclusion: In NSCLC, the rate of glucose metabolism was positively correlated with PKC-iota expression. PKC-iota increased glucose accumulation and glycolysis by upregulating c-Myc/GLUT1 signaling and is thus involved in tumor progression.