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FMS-like tyrosine kinase 3 (FLT3/CD135) regulates haematopoiesis and is frequently mutated as FLT3-internal tandem duplication (FLT3-ITD) in acute myeloid leukaemia (AML), associated with poor prognosis. Although FLT3 inhibitors show clinical benefits, resistance remains a challenge. This study hypothesises that antibody-drug conjugate (ADC) efficacy depends on distinct FLT3 trafficking mechanisms in FLT3-wt and FLT3-ITD cells. Confocal imaging showed that in THP-1 (FLT3-wt) cells, FLT3 mAb trafficked to lysosomes, while in MV4-11 (FLT3-ITD) cells, it accumulated in the Golgi. To evaluate the impact of this trafficking difference, we synthesised an anti-FLT3 mAb-MMAE, linked via a Val-Cit-PAB linker at the Fc N-glycan, which exhibited lower cytotoxicity in MV4-11 than THP-1 cells, indicating that the impaired lysosomal trafficking of FLT3-ITD limits drug release and reduces ADC potency. These findings highlight that effective lysosomal targeting is essential for ADC activity and suggest that optimising linker design or restoring lysosome trafficking may enhance FLT3-targeted ADC in AML.

To investigate the clinical characteristics and impact of SMARCA4 mutations in patients with non-small cell lung cancer (NSCLC). A total of 2,821 patients with NSCLC who underwent next-generation sequencing were retrospectively included. The frequency and types of SMARCA4 mutations and co-mutations were determined, and the clinical outcomes were assessed. SMARCA4 mutations were identified in 100 samples (3.54%), and 36% were missense mutations. The most frequent co-mutations were TP53 (67%) and EGFR (31%); 13% of SMARCA4 mutations occurred in samples carried EGFR and TP53 mutations. Notably, 63% SMARCA4 mutations did not present druggable driver mutations. SMARCA4 mutations were most prevalent in males and smokers. Patients with SMARCA4 mutant lung adenocarcinoma (LUAD) and EGFR mutations who received EGFR-tyrosine kinase inhibitors (EGFR-TKI) as first-line therapy had a lower objective response rate (ORR, 52.94%). In SMARCA4 mutation and EGFR wild-type (wt) NSCLC cohort who received first-line chemotherapy, age (hazard ratio [HR], 3.090; p = 0.026) and performance score (HR, 5.848; p = 0.045) were identified as independent predictors of progression-free survival (PFS). Conversely, brain metastasis was an independent predictor of superior overall survival (HR, 0.188; p = 0.011). The patients with EGFR wt and SMARCA4 mutant Stage IV LUAD who received chemotherapy plus anti-angiogenic therapy significantly improved median PFS compared to chemotherapy alone (p = 0.04). SMARCA4 mutations were predominantly males and smokers in NSCLC. SMARCA4 mutations conferred a poorer response for EGFR-mutant LUAD subgroups who received EGFR-TKIs. Additionally, chemotherapy plus anti-angiogenesis as first-line therapy may be more effective for Stage IV-SMARCA4 mutant LUAD with EGFR wt.

Histologic transformation from Waldenström macroglobulinemia (WM) to diffuse large B-cell lymphoma (DLBCL) is a rare but clinically challenging event. In this retrospective study, we analyzed 15 cases of histologic transformation among WM patients treated at the Department of Hematology, Jiangsu Province Hospital, between October 2015 and February 2025. The median age at transformation was 67 years, with a median time from initial WM diagnosis to transformation of 8 months (range: 0-177 months). Six patients (40%) received no WM-directed therapy before transformation. At transformation, 13 patients (86.7%) had stage IV disease. Extranodal involvement was frequent: 6 patients (40%) had ≥2 extranodal sites involved, with the most common sites being bone/bone marrow (each 33.3%), central nervous system (CNS, 20.0%), and nasopharynx/testis/gastrointestinal tract/peritoneum/skin (each 13.3%). Involvement of immune-privileged sites (CNS, testis) was observed in 5 patients (33.3%). Immunophenotyping revealed 13 cases (86.7%) as non-germinal center B-cell (non-GCB) DLBCL. Prognostic analysis showed a median overall survival (OS) of 26.0 months from transformation. Patients receiving Bruton's tyrosine kinase inhibitor (BTKi)-based regimens after transformation showed significantly prolonged OS (p = 0.007). Additionally, patients receiving BTKi-based therapy at any point showed a trend toward improved survival (p = 0.092). Although rare, histologic transformation from WM to DLBCL exhibits aggressive clinical behavior, frequent extranodal involvement, and poor prognosis. BTKi-based regimens may provide significant survival benefits in this patient population.

Coriandrum sativum L. (coriander) is a medicinal herb with diverse pharmacological properties, but its molecular mechanism in clear cell renal cell carcinoma (ccRCC) remains unclear. This study aimed to systematically investigate the underlying mechanisms of coriander in ccRCC by multi-omics analysis. Active compounds were screened using Traditional Chinese Medicine Systems Pharmacology (TCMSP) and predicted targets identified via SwissTargetPrediction (STP) and Similarity ensemble approach (SEA). Transcriptomic data from GSE53757 were analysed with WGCNA and intersected with coriander targets. Key genes were selected using LASSO, SVM, and random forest models. NEK6 was further analysed for clinical relevance, methylation, immune association, single-cell expression, molecular docking and molecular dynamics simulation. Fourteen coriander compounds were identified, yielding 22 potential ccRCC-related targets. NEK6 and PYGL were consistently selected by all machine learning algorithms. NEK6 was overexpressed in ccRCC and associated with better prognosis, promoter hypomethylation, and lower mutation rates. NEK6 expression correlated with immune infiltration, particularly macrophages, and was enriched in tumour and myeloid cells at the single-cell level. Molecular docking and molecular dynamics simulation revealed strong and stable binding of luteolin, quercetin, and chryseriol to NEK6. NEK6 may function as a prognostic and immune-regulatory biomarker in ccRCC. Coriander flavonoids could target NEK6 to modulate the immune microenvironment, providing new insight into plant-based therapeutic strategies for ccRCC.

The benefit of adding anti-PD-1 antibodies to definitive chemoradiotherapy (dCRT) in elderly patients with esophageal squamous cell carcinoma (ESCC) remains uncertain. This study evaluated its efficacy and safety versus dCRT alone. We retrospectively analyzed the patients aged ≥ 70 years with ESCC treated at three academic centers from 2009 to 2023. All patients received first-line dCRT and the study group additionally received anti-PD-1 antibodies (IO group). Propensity score matching (PSM) was applied to balance baseline factors. A total of 241 patients were enrolled, including 130 in the IO group and 111 in the dCRT group. After 1:1 PSM (110 patients per group), no significant differences in overall survival (OS) or progression-free survival (PFS) were observed. The median OS was 34.5 vs 33.7 months (HR = 0.86, 95%CI: 0.58-1.28, p = 0.467) and median PFS was 29.8 vs 17.8 months (HR = 0.79, 95%CI: 0.55-1.13, p = 0.194). Multivariate Cox analysis identified high nutritional risk as an independent predictor of worse OS (p = 0.014), while both advanced TNM stage (p = 0.030) and high nutritional risk (p = 0.016) were independently associated with shorter PFS. Subgroup analyses suggested that patients with good performance, better nutritional status or lower comorbidity burden may benefit from combination therapy. Grade 3-4 adverse events were comparable between two groups. Adding anti-PD-1 antibodies to dCRT did not result in a significant improvement in OS or PFS in the ESCC patients aged ≥ 70 years; however exploratory findings indicate a potential PFS signal in selected patients with favorable baseline conditions, which requires confirmation in prospective studies.

Nasopharyngeal carcinoma (NPC) poses significant diagnostic challenges due to the anatomical complexity of the nasopharynx and reliance on endoscopic visual interpretation, often leading to delayed detection and unnecessary biopsies. Although artificial intelligence (AI) algorithms have shown promise in enhancing endoscopic cancer diagnosis, their real-world impact on clinician diagnostic performance remains insufficiently characterized. In this prospective, multi-reader study, 47 clinicians, including experts, residents, and trainees, interpreted 200 nasoendoscopic images from 100 patients with histopathologically confirmed NPC or benign lesions. Each participant completed two diagnostic sessions: an unassisted evaluation and an AI-assisted assessment using a real-time lesion-annotating model (NPC-SDNet), with a 4-week washout period between sessions. Without AI support, the overall diagnostic accuracy was 73.6% (95% CI: 70.1-77.0%), with a sensitivity of 76.1% (95% CI: 70.4-81.4%) and a specificity of 69.9% (95% CI: 63.8-76.0%). AI assistance significantly improved accuracy to 85.6% (95% CI: 83.1-87.6%, p < 0.001), sensitivity to 90.1% (95% CI: 86.6- 92.9%, p < 0.001), and specificity to 79.1% (95% CI: 75.6-82.7%, p < 0.001). Subgroup analysis revealed the greatest improvements among trainees (64.8% vs 83.5%, p < 0.001) and residents (77.2% vs 84.9%, p = 0.003). Moreover, AI integration substantially reduced median image interpretation time from 1411.7 to 818.5 s (p < 0.001). AI-assisted nasoendoscopic evaluation significantly enhances diagnostic accuracy, efficiency and interobserver consistency, particularly among less-experienced clinicians. These findings support the clinical integration of real-time AI tools to augment NPC recognition and standardize diagnostic performance across varying expertise levels.

Aberrant activation of YAP-TEAD4 drives tumorigenesis, progression, and chemoresistance. Disrupting their interaction serves as an alternative anticancer strategy, with peptides better adapting to the large, flat interaction interface. In this study, the peptides 1-4 were screened from the peptide database via pharmacophore modelling, molecular docking, and interaction analysis. Subsequently, affinity experiments showed that among the peptides 1-4, peptide-4 possessed the lowest Kd values (Kd = 5.08 ± 0.42 nM) measured by MST and exhibited the binding affinity for TEAD4. MD simulations further demonstrated that peptide-4 stably bound to the TEAD4. MTT assays showed that peptide-4 suppressed AML-193 cell viability with an IC50 of 0.65 ± 0.04 μM. RT-qPCR assays demonstrated that Peptide-4 significantly downregulated the mRNA expression levels of CTGF and CYR61. In conclusion, the data demonstrated that the peptide-4 may serve as a promising candidate to disrupt the YAP-TEAD4 interaction and enhance biological activity in AML-related cellular models.

KRAS-G12D mutations are common drivers of pancreatic and colorectal cancers, yet effective targeted therapies remain limited. This study describes the design, synthesis, and biological evaluation of two novel KRAS-G12D inhibitors, GD-2 and GD-4. Both compounds exhibited strong antiproliferative activity in AGS and ASPC1 cancer cell lines, with IC₅。 values ranging from 0.2 to 1.8 µM. The protein binding assay also demonstrated high affinity for KRAS-G12D, with dissociation constants (Kd) of 146 nM for GD-2 and 3.18 nM for GD-4. Mechanistic investigations revealed that both compounds significantly reduced downstream, as evidenced by a clear decrease in phospho-ERK expression. Additionally, molecular dynamics simulations confirmed stable binding interactions within the KRAS-G12D pocket. Collectively, these findings identify GD-2 and GD-4 as promising therapeutic candidates for KRAS-G12D-driven cancers.

Microscopy-based cancer cell classification traditionally relies on cell-based morphological features, while subcellular organelle organization remains underutilized. Existing machine learning methods often require manual preprocessing and handcrafted feature extraction, limiting scalability and introducing user bias. This study proposes an automated, interpretable, and organelle-focused deep learning framework for classifying breast cancer cell lines from high-resolution fluorescence microscopy images. We developed an end-to-end framework that incorporates patch-based sampling, sparsity filtering, and a channel-wise intermediate fusion strategy to independently extract and integrate organelle-specific features. Model interpretability was assessed using Grad-CAM visualizations and single-organelle classifier analyses. The framework was evaluated on fluorescence microscopy images from six breast cancer cell lines using 5-fold cross-validation. The proposed framework achieved a classification accuracy of 97.1 ± 1.1 %, performing comparably to or exceeding conventional handcrafted feature-based approaches while eliminating the need for manual segmentation and 3D rendering steps. Interpretability and classifier analyses revealed inter-organelle dependencies and mitochondria as the most informative contributors to classification decisions. Organelle morphology and spatial organization provide strong discriminative signals for cancer cell classification. The proposed framework offers a scalable, automated, and interpretable deep learning solution that advances microscopy-based phenotyping and supports broader applications in computational pathology and cellular informatics.

Radiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing radiotherapy. Accurate risk stratification is crucial for individualized management. But predictive models integrating multimodal data are lacking. This study aimed to develop a novel machine learning-based nomogram integrating clinical, dosimetric, and inflammatory predictors for RP risk assessment in this high-risk population. This retrospective study of 424 ILD patients collected clinical, dosimetric, and inflammatory data. Machine learning algorithms created composite dosimetric (D score) and inflammatory (Inflamm score) scores. A multivariable logistic regression nomogram was built incorporating these scores with clinical risk factors. Model performance was assessed using area under the curve (AUC), calibration curve, and decision curve analysis (DCA). RP occurred in 200 (47%) patients. Independent risk factors included higher performance status, Charlson comorbidity index (CCI), usual interstitial pneumonia (UIP) pattern, immunotherapy, concurrent chemoradiotherapy, more radiation sessions, and lower lung volume. The D score and Inflamm score were both independent predictors. The integrated nomogram (AUC = 0.929) showed excellent discrimination, significantly outperforming the clinical model (AUC = 0.86), D score (AUC = 0.758) (both p < 0.001), and Inflamm score (AUC = 0.910, p = 0.168). Calibration curve and DCA confirmed its strong calibration ability and clinical utility to identify high-risk patients early. The integrated nomogram combining clinical, dosimetric, and inflammatory predictors enables accurate, individualized RP risk assessment in lung cancer patients with ILD. It can guide adjustments to individualized radiotherapy plans or preventive interventions, supporting better patient selection, treatment decisions, and proactive follow-up.