Single-cell transcriptomics reveal metastatic CLDN4+ cancer cells underlying the recurrence of malignant pleural effusion in patients with advanced non-small-cell lung cancer.

BACKGROUND: Recurrent malignant pleural effusion (MPE) resulting from non-small-cell lung cancer (NSCLC) is easily refractory to conventional therapeutics and lacks predictive markers. The cellular or genetic signatures of recurrent MPE still remain largely uncertain. METHODS: 16 NSCLC patients with pleural effusions were recruited, followed by corresponding treatments based on primary tumours. Non-recurrent or recurrent MPE was determined after 3-6 weeks of treatments. The status of MPE was verified by computer tomography (CT) and cytopathology, and the baseline pleural fluids were collected for single-cell RNA sequencing (scRNA-seq). Samples were then integrated and profiled. Cellular communications and trajectories were inferred by bioinformatic algorithms. Comparative analysis was conducted and the results were further validated by quantitative polymerase chain reaction (qPCR) in a larger MPE cohort from the authors' centre (n = 64). RESULTS: The scRNA-seq revealed that 33 590 cells were annotated as 7 major cell types and further characterized into 14 cell clusters precisely. The cell cluster C1, classified as Epithelial Cell Adhesion Molecule (EpCAM)+ metastatic cancer cell and correlated with activation of tight junction and adherence junction, was significantly enriched in the recurrent MPE group, in which Claudin-4 (CLDN4) was identified. The subset cell cluster C3 of C1, which was enriched in recurrent MPE and demonstrated a phenotype of ameboidal-type cell migration, also showed a markedly higher expression of CLDN4. Meanwhile, the expression of CLDN4 was positively correlated with E74 Like ETS Transcription Factor 3 (ELF3), EpCAM and Tumour Associated Calcium Signal Transducer 2 (TACSTD2), independent of driver-gene status. CLDN4 was also found to be associated with the expression of Hypoxia Inducible Factor 1 Subunit Alpha (HIF1A) and Vascular Endothelial Growth Factor A (VEGFA), and the cell cluster C1 was the major mediator in cellular communication of VEGFA signalling. In the extensive MPE cohort, a notably increased expression of CLDN4 in cells from pleural effusion among patients diagnosed with recurrent MPE was observed, compared with the non-recurrent group, which was also associated with a trend towards worse overall survival (OS). CONCLUSIONS: CLDN4 could be considered as a predictive marker of recurrent MPE among patients with advanced NSCLC. Further validation for its clinical value in cohorts with larger sample size and in-depth mechanism studies on its biological function are warranted. TRIAL REGISTRATION: Not applicable.

A prior-information-based automatic segmentation method for the clinical target volume in adaptive radiotherapy of cervical cancer.

OBJECTIVE: Adaptive planning to accommodate anatomic changes during treatment often requires repeated segmentation. In this study, prior patient-specific data was integrateda into a registration-guided multi-channel multi-path (Rg-MCMP) segmentation framework to improve the accuracy of repeated clinical target volume (CTV) segmentation. METHODS: This study was based on CT image datasets for a total of 90 cervical cancer patients who received two courses of radiotherapy. A total of 15 patients were selected randomly as the test set. In the Rg-MCMP segmentation framework, the first-course CT images (CT1) were registered to second-course CT images (CT2) to yield aligned CT images (aCT1), and the CTV in the first course (CTV1) was propagated to yield aligned CTV contours (aCTV1). Then, aCT1, aCTV1, and CT2 were combined as the inputs for 3D U-Net consisting of a channel-based multi-path feature extraction network. The performance of the Rg-MCMP segmentation framework was evaluated and compared with the single-channel single-path model (SCSP), the standalone registration methods, and the registration-guided multi-channel single-path (Rg-MCSP) model. The Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average surface distance (ASD) were used as the metrics. RESULTS: The average DSC of CTV for the deformable image DIR-MCMP model was found to be 0.892, greater than that of the standalone DIR (0.856), SCSP (0.837), and DIR-MCSP (0.877), which were improvements of 4.2%, 6.6%, and 1.7%, respectively. Similarly, the rigid body DIR-MCMP model yielded an average DSC of 0.875, which exceeded standalone RB (0.787), SCSP (0.837), and registration-guided multi-channel single-path (0.848), which were improvements of 11.2%, 4.5%, and 3.2%, respectively. These improvements in DSC were statistically significant (p < 0.05). CONCLUSION: The proposed Rg-MCMP framework achieved excellent accuracy in CTV segmentation as part of the adaptive radiotherapy workflow.