Non-small cell lung cancer (NSCLC) patients with sensitive epidermal growth factor receptor (EGFR) mutations are successfully treated with EGFR tyrosine kinase inhibitors (EGFR-TKIs); however, resistance to treatment inevitably occurs. Given lipid metabolic reprogramming is widely known as a hallmark of cancer and intimately linked with EGFR-stimulated cancer growth. Activation of EGFR signal pathway increased monounsaturated fatty acids (MUFA) and lipid metabolism key enzyme Stearoyl-CoA Desaturase 1 (SCD1) expression. However the correlation between EGFR-TKI resistance and lipid metabolism remains to be determined. In this study the differences in lipid synthesis between paired TKI-sensitive and TKI-resistant patient tissues and NSCLC cell lines were explored. Oleic acid (OA, a kind of MUFA, the SCD1 enzymatic product) was used to simulate a high lipid metabolic environment and detected the affection on the cytotoxic effect of TKIs (Gefitinib and osimertinib) in cell lines with EGFR-activating mutations. (20S)-Protopanaxatriol (g-PPT), an aglycone of ginsenosides, has been reported to be an effective lipid metabolism inhibitor, was used to inhibit lipid metabolism. Additionally, synergism in cytotoxic effects and signal pathway activation were evaluated using CCK-8 assays, Western blotting, flow cytometry, Edu assays, plate clone formation assays and immunofluorescence. Furthermore, two xenograft mouse models were used to verify the in vitro results. Gefitinib-resistant cells have higher lipid droplet content and SCD1 expression than Gefitinib-sensitive cells in both NSCLC cell lines and patient tissues. Additionally oleic acid (OA, a kind of MUFA, the SCD1 enzymatic product) abrogates the cytotoxic effect of both Gefitinib and osimertinib in cell lines with EGFR-activating mutations. As a reported effective lipid metabolism inhibitor, g-PPT significantly inhibited the expression of SCD1 in lung adenocarcinoma cells, and then down-regulated the content of intracellular lipid droplets. Combined treatment with Gefitinib and g-PPT reverses the resistance to Gefitinib and inhibits the activation of p-EGFR and the downstream signaling pathways. Our findings uncover a link between lipid metabolic reprogramming and EGFR-TKI resistance, confirmed that combination target both EGFR and abnormal lipid metabolism maybe a promising therapy for EGFR-TKI resistance and highlighting the possibility of monitoring lipid accumulation in tumors for predicting drug resistance.

Objectives Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers. Methods This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps. Results The training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively. Conclusions By extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.

Objectives: To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image.Methods: This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).Results: In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686.Conclusion: The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

Background: Lung cancer is the leading cause of cancer-related death worldwide. The sex differences in the occurrence and fatality rates of non-small cell lung cancer (NSCLC), along with its association with estrogen dependence, suggest that estrogen receptors (ERs) contribute to the development of NSCLC. However, the influence of G protein-coupled estrogen receptor (GPER1) on NSCLC remains to be determined. Escape from ferroptosis is one of the hallmarks of tumor discovered in recent years. In this context, the present study evaluated whether GPER1 promotes NSCLC progression by preventing ferroptosis, and the underlying mechanism through which GPER1 protects against ferroptosis was also explored. Methods: The effects of GPER1 on the cytotoxicity of H₂O₂, the ferroptosis inducer RSL3, and Erastin were assessed using the CCK8 assay and plate cloning. Lipid peroxidation levels were measured based on the levels of MDA and BODIPY™581/591C11. GPER1 overexpression and knockdown were performed and G1 was used, and the expression of SCD1 and PI3K/AKT/mTOR signaling factors was measured. Immunofluorescence analysis and immunohistochemistry were performed on paired specimens to measure the correlation between the expression of GPER1 and SCD1 in NSCLC tissues. The effect of GPER1 on the cytotoxicity of cisplatin was measured in vitro using the CCK8 assay and in vivo using xenograft tumor models. Results: GPER1 and G1 alleviated the cytotoxicity of H₂O₂, reduced sensitivity to RSL3, and impaired lipid peroxidation in NSCLC tissues. In addition, GPER1 and G1 promoted the protein and mRNA expression of SCD1 and the activation of PI3K/AKT/mTOR signaling. GPER1 and SCD1 expression were elevated and positively correlated in NSCLC tissues, and high GPER1 expression predicted a poor prognosis. GPER1 knockdown enhanced the antitumor activity of cisplatin in vitro and in vivo. Conclusion: GPER1 prevents ferroptosis in NSCLC by promoting the activation of PI3K/AKT/mTOR signaling, thereby inducing SCD1 expression. Therefore, treatments targeting GPER1 combined with cisplatin would exhibit better antitumor effects. Graphical

G-protein-coupled estrogen receptor (GPER) was found to promote non-small cell lung cancer (NSCLC) by estrogen, indicating the potential necessity of inhibiting GPER by a selective antagonist. This study was performed to elucidate the function of GPER-selective inhibitor G15 in NSCLC development. Cytoplasmic GPER (cGPER) and nuclear GPER (nGPER) were detected by immunohistochemical analysis in NSCLC samples. The relation of GPER and estrogen receptor β (ERβ) expression and correlation between GPER, ERβ, and clinical factors were analyzed. The effects of activating GPER and function of G15 were analyzed in the proliferation of A549 and H1793 cell lines and development of urethane-induced adenocarcinoma. Overexpression of cGPER and nGPER was detected in 80.49% (120/150) and 52.00% (78/150) of the NSCLC samples. High expression of GPER was related with higher stages, poorer differentiation, and high expression of ERβ. The protein level of GPER in the A549 and H1793 cell lines was increased by treatment with E2, G1 (GPER agonist), or fulvestrant (Ful; ERβ antagonist) and decreased by G15. Administration with G15 reversed the E2- or G1-induced cell growth by inhibiting GPER. In urethane-induced adenocarcinoma mice, the number of tumor nodules and tumor index increased in the E2 or G1 group and decreased by treatment with G15. These findings demonstrate that using G15 to block GPER signaling may be considered as a new therapeutic target in NSCLC.

In non-small cell lung cancer (NSCLC), the heterogeneity promotes drug resistance, and the restricted expression of programmed death-ligand 1 (PD-L1) limits the immunotherapy benefits. Based on the mechanisms related to translation regulation and the association with PD-L1 of methyltransferase-like 3 (METTL3), the novel small-molecule inhibitor STM2457 is assumed to be useful for the treatment of NSCLC. We evaluated the efficacy of STM2457 in vivo and in vitro and confirmed the effects of its inhibition on disease progression. Next, we explored the effect of STM2457 on METTL3 and revealed its effects on the inhibition of catalytic activity and upregulation of METTL3 protein expression. Importantly, we described the genome-wide characteristics of multiple omics data acquired from RNA sequencing, ribosome profiling, and methylated RNA immunoprecipitation sequencing data under STM2457 treatment or METTL3 knockout. We also constructed a model for the regulation of the translation of METTL3 and PD-L1. Finally, we found PD-L1 upregulation by STM2457 in vivo and in vitro. In conclusion, STM2457 is a potential novel suppressor based on its inhibitory effect on tumor progression and may be able to overcome the heterogeneity based on its impact on the translatome. Furthermore, it can improve the immunotherapy outcomes based on PD-L1 upregulation in NSCLC.