Henan Cancer Hospital

Lung cancer is the leading cause of cancer-related death and has the second-highest incidence worldwide. For patients with advanced EGFR-mutated non-small cell lung cancer, EGFR tyrosine kinase inhibitors (EGFR-TKIs) are the preferred treatment option; however, acquired resistance to TKIs is inevitable. Gefitinib and osimertinib, the first-generation and third-generation EGFR-TKI, have shown promising results in patients with EGFR-mutated lung cancer in clinical treatment. Here, we identified that pyruvate dehydrogenase kinase 1 (PDK1) was up-regulated in gefitinib- and osimertinib-resistant cell lines, and PDK1 knockdown rendered cells more sensitive to TKI treatment. PDK1 expression levels were significantly increased in lung, colon, liver, and breast cancer tissues compared with those in normal tissues. Histone demethylase KDM3A was also induced in TKI-resistant cell lines, and demethylated histone H3 lysine 9 to facilitate PDK1 expression to regulate TKI resistance. Further study demonstrated that METTL16 promoted the m6A modification of PDK1 mRNA, and the m6A reader IGF2BP1 directly recognized and enhanced PDK1 mRNA stability. Interestingly, KDM3A also induced METTL16 expression. Moreover, PDK1 inhibitor JX06 rendered cancer cells more sensitive to gefitinib treatment in vivo, and JX06 and gefitinib combination treatments have a synergic effect to inhibit tumor growth. In conclusion, the KDM3A/METTL16/PDK1 axis plays an important role in cancer development and TKI resistance, which may offer new prognostic biomarkers and therapeutic targets for TKI resistance in the future.

Non-small-cell lung cancer (NSCLC) exhibits pronounced molecular heterogeneity, and current predictive models rarely incorporate mitochondrial quality-control programs such as mitophagy. We hypothesize that an artificial intelligence model based on mitophagy-related genes (MRGs) at single-cell resolution could improve prediction of survival and immunotherapy benefit. We analyzed single-cell RNA sequencing data from treatment-naïve NSCLC tumors to evaluate the activity of MRGs and identify genes exhibiting differential expression between cells with high versus low mitophagy levels. These differentially expressed genes were then cross-referenced with mitophagy gene sets to pinpoint candidate prognostic markers. Using LASSO regression combined with multiple machine learning classifiers, we constructed a risk model, which was validated in both internal and external cohorts, including a clinical immunotherapy trial. We further examined the relationship between the risk model, immune cell infiltration, and drug sensitivity in silico. The key MRGs were then experimentally validated in A549 cells using qRT-PCR, Western blotting, immunofluorescence, and functional assays for cell migration and wound healing. We quantified the mitochondrial autophagy activity of 18,167 single cells. Differential expression yielded 1,668 genes; intersection with the MRG list produced 39 candidates. A six-gene panel (FOS, CANX, EIF4G1, CALCOCO2, HSP90AB1, and PRKAR1A) emerged from LASSO. Gradient boosting machine (GBM) achieved the optimal cross-validated performance (testing set: AUC = 0.80, validation set: AUC = 0.72). SHAP analysis ranked PRKAR1A and CALCOCO2 as the top risk contributors. Patients classified into the high-MRG-score group exhibited consistently shorter overall survival (OS) across all datasets (HR = 3.66, 95% CI 1.72 − 7.81, P < 0.001). Low-MRG tumors displayed elevated immune and ESTIMATE scores with reduced tumor purity, and achieved a significantly higher objective response rate (35% vs 22%, P < 0.05) and prolonged OS (HR = 1.48, 95% CI 1.07 − 2.05, P = 0.017) with immune checkpoint blockade in the clinical-trial setting. Experimental results showed that knockdown of CALCOCO2 or overexpression of PRKAR1A significantly inhibited A549 cell proliferation and reduced mitochondrial membrane potential (P < 0.05), thereby affecting mitophagy. We developed an MRG-based model that reliably stratifies NSCLC patients according to prognosis and identifies those most likely to respond to immune checkpoint inhibitors, providing a framework for integrating tumor metabolic characteristics into personalized therapeutic decisions.

Patients with peripheral T cell lymphoma (PTCL) who achieved tumor response with first-line standard therapy were at high risk of disease relapse. We explored golidocitinib (150 mg once daily) as maintenance therapy for this group of patients (JACKPOT26, NCT06511869). This study included two cohorts: patients achieving a complete response (Cohort 1 (CR), N = 30) and a partial response (Cohort 2 (PR), N = 18) during induction stage. All enrolled patients were transplant ineligible or did not have a transplant plan. All dosed patients were included in the efficacy and safety analysis. In Cohort 1, the 24-month disease free survival (DFS) rate was 74.2% with golidocitinib treatment. In nodal subtypes (AITL, NOS, ALK- ALCL), the 24-month DFS rate was 62.7%. In Cohort 2, median progression free survival (PFS) was 17.4 months, and 24-month PFS rate was 48.6%. Nine out of 18 patients with initial PR achieved complete response, leading to a complete response rate of 50.0%, and median duration of response of 23.9 months. The most common ≥grade 3 treatment-related treatment-emergent adverse events (TRAEs) were hematological adverse events in nature, including neutrophil count decreased (47.9%), white blood cell count decreased (31.3%), lymphocyte count decreased (14.6%) and leukopenia (12.5%). The majority of these TRAEs were reversible and clinically manageable. TRAEs leading to treatment interruption and discontinuation occurred in 60.4% and 10% of patients, respectively. No TRAEs leading to fatal outcomes were reported. This study suggests the potential of golidocitinib as maintenance therapy for patients with PTCL.

Metastasis remains the leading cause of cancer-related mortality. The concept of the pre-metastatic niche (PMN) has provided a new framework for understanding how tumors establish favorable conditions in distant organs before metastatic colonization. This review delineates the cellular and molecular hallmarks of PMN, including immune suppression, vascular/lymphatic remodeling, metabolic reprogramming, and stromal reorganization, and traces their spatiotemporal evolution from initiation to colonization. It further examines the origins of metastatic lesions, with a focus on circulating tumor cells (CTCs) and stromal stem-like cells, highlights the pivotal role of extracellular vesicles (EVs) in mediating intercellular communication, metabolic reprogramming, and therapeutic applications. Deciphering the immune and stromal determinants of PMN formation offers key mechanistic insight into organ-specific metastasis. Consequently, this review explores translational strategies targeting the PMN, such as biomarker development, spatiotemporal profiling aided by artificial intelligence (AI), and immune, metabolic, or EV-based interventions. Deciphering PMN biology is therefore poised to open new avenues for the early interception and treatment of metastasis.

Immunotherapy targeting the programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) has significantly improved outcomes for various cancers, yet the emergence of resistance remains a major challenge. This review focuses on the dual role of endogenous glucocorticoids (GCs) and their receptor (GR) within the tumor microenvironment (TME), systematically elucidating the mechanisms by which they regulate responses to PD-1/PD-L1 therapy via the hypothalamic-pituitary-adrenal (HPA) axis, local synthesis and metabolism, and heterogeneity of GR signaling. We elaborate on the regulatory effects of the GC/GR signaling pathway on immune cells (such as cluster of differentiation 8 positive [CD8 + ] T cells, regulatory T cells [Tregs], dendritic cells, natural killer [NK] cells, and macrophages), and highlight the impact of GCs on PD-L1 expression, cytokine secretion, and the immunosuppressive microenvironment. This review also explores the controversial role of exogenous GCs in managing immune-related adverse events (irAEs) and their potential impact on therapeutic efficacy. The development of tissue-selective GR modulators holds promise for balancing immunosuppression while enhancing anti-tumor activity. A deeper understanding of the role of endogenous GCs in cancer immunotherapy is crucial for optimizing therapeutic strategies, overcoming resistance, and finally achieving precision in cancer immunotherapy.

Abstract Leptomeningeal metastasis (LM) is a devastating complication of lung adenocarcinoma, the current diagnosis of which relies on invasive cerebrospinal fluid cytology or costly MRI, both of which are limited in sensitivity and accessibility. To enable early, noninvasive LM risk prediction, we developed a 2.5D delta-radiomics deep learning framework based on routinely acquired contrast-enhanced CT scans. The proposed architecture integrates multi-instance learning to aggregate slice-level features into patient-level representations while preserving spatial context and controlling computational complexity. Delta features, derived by comparing pre-LM and post-LM scans, quantified subtle longitudinal changes within intratumoral and peritumoral regions. Multiple classifiers were evaluated, with XGBoost selected for its optimal performance, and Grad-CAM visualization was employed to assess spatial attention, revealing biologically plausible regions that drove model predictions. A nomogram was constructed to facilitate individualized clinical application, and decision curve analysis (DCA) demonstrated a consistent net benefit across a broad range of decision thresholds. In the test set, the delta-based multi-instance learning signature (MILDelta) achieved an AUC of 0.871, outperforming models trained solely on pre-LM (AUC 0.574) or post-LM (AUC 0.661) images, whereas a combined model incorporating imaging signatures and clinical variables, including EGFR mutation status, yielded the highest predictive accuracy, with an AUC of 0.910. This interpretable delta-informed model leverages standard contrast-enhanced CT to achieve individualized, noninvasive LM risk stratification with demonstrated generalizability and clinical utility, offering a scalable tool for timely intervention in precision oncology workflows.