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Fluid-Derived Organoids from Pleural Effusion and Ascites: Emerging Models for Drug Resistance and Personalized Oncology

J Cancer. 2026 Mar 4;17(3):614-625. doi: 10.7150/jca.127511. eCollection 2026.

ABSTRACT

Malignant pleural effusion (MPE) and malignant ascites (MA) are common complications in advanced-stage cancers, often signifying disease progression and resistance to treatment. Compared to tissue biopsies or surgical specimens, materials derived from effusions offer advantages such as minimal invasiveness, ease of accessibility, and the feasibility of repeated collection during therapeutic interventions. Organoids generated from tumor cells in effusions, termed fluid-derived organoids (FDOs), have demonstrated the ability to maintain genetic heterogeneity and accurately replicate patient-specific tumor phenotypes. These characteristics position FDOs as promising models for investigating drug resistance mechanisms and informing personalized oncology strategies. In the context of lung cancer, organoids derived from pleural effusions have been employed to study acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors and immunotherapy. Similarly, in ovarian and gastrointestinal cancers, organoids derived from ascites have proven to be valuable platforms for examining chemotherapy resistance and conducting drug sensitivity testing. FDOs have shown significant potential for translational applications by effectively correlating ex vivo drug responses with clinical outcomes, thus facilitating real-time monitoring of resistance evolution. However, several challenges remain, such as achieving culture standardization, maintaining the integrity of tumor microenvironment components, and integrating with multi-omics approaches. This review provides a comprehensive overview of recent advancements in the use of pleural effusion- and ascites-derived organoids for drug resistance research, underscores their applications in personalized oncology, and explores future research directions.

PMID:41869438 | PMC:PMC13003542 | DOI:10.7150/jca.127511

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Fluid-Derived Organoids from Pleural Effusion and Ascites: Emerging Models for Drug Resistance and Personalized Oncology

J Cancer. 2026 Mar 4;17(3):614-625. doi: 10.7150/jca.127511. eCollection 2026.

ABSTRACT

Malignant pleural effusion (MPE) and malignant ascites (MA) are common complications in advanced-stage cancers, often signifying disease progression and resistance to treatment. Compared to tissue biopsies or surgical specimens, materials derived from effusions offer advantages such as minimal invasiveness, ease of accessibility, and the feasibility of repeated collection during therapeutic interventions. Organoids generated from tumor cells in effusions, termed fluid-derived organoids (FDOs), have demonstrated the ability to maintain genetic heterogeneity and accurately replicate patient-specific tumor phenotypes. These characteristics position FDOs as promising models for investigating drug resistance mechanisms and informing personalized oncology strategies. In the context of lung cancer, organoids derived from pleural effusions have been employed to study acquired resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors and immunotherapy. Similarly, in ovarian and gastrointestinal cancers, organoids derived from ascites have proven to be valuable platforms for examining chemotherapy resistance and conducting drug sensitivity testing. FDOs have shown significant potential for translational applications by effectively correlating ex vivo drug responses with clinical outcomes, thus facilitating real-time monitoring of resistance evolution. However, several challenges remain, such as achieving culture standardization, maintaining the integrity of tumor microenvironment components, and integrating with multi-omics approaches. This review provides a comprehensive overview of recent advancements in the use of pleural effusion- and ascites-derived organoids for drug resistance research, underscores their applications in personalized oncology, and explores future research directions.

PMID:41869438 | PMC:PMC13003542 | DOI:10.7150/jca.127511

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Multi-Omics and Single-Cell Mendelian Randomization Reveal a Potential Role of VNN2 in Lung Adenocarcinoma in Resting Natural Killer Cells

World J Oncol. 2026 Mar 5;17(2):247-255. doi: 10.14740/wjon2689. eCollection 2026 Apr.

ABSTRACT

BACKGROUND: We aimed to evaluate the potential association between genetically predicted vanin-2 (VNN2) expression and lung adenocarcinoma (LUAD) risk, and to explore the immune cell subtype that may underlie this relationship.

METHODS: We integrated whole-blood expression quantitative trait loci (eQTL) data from eQTLGen, plasma protein quantitative trait loci (pQTL) data from deCODE, and LUAD genome-wide association study (GWAS) data from European-ancestry cohorts, together with differential expression analysis using GEPIA2, to identify candidate genes for subsequent single-cell eQTL (sc-eQTL) Mendelian randomization (MR) analysis. For the sc-eQTL analysis, VNN2-associated eQTLs from 14 immune cell types profiled in the OneK1K single-cell eQTL resource were tested for associations with LUAD risk.

RESULTS: Bulk-level MR analysis showed that genetically predicted increases in VNN2 expression and protein levels were significantly associated with a reduced risk of LUAD (eQTL-MR: odds ratio (OR) = 0.964, 95% confidence interval (95% CI), 0.934-0.995; P = 0.024; pQTL-MR: OR = 0.946, 95% CI, 0.921-0.970; P = 2.87 Γ— 10-5). Transcriptomic analyses confirmed significant downregulation of VNN2 in LUAD tumors compared with normal lung tissues. sc-eQTL MR identified the strongest association in resting natural killer (rNK) cells (OR = 0.896, 95% CI, 0.829-0.967; P = 0.005).

CONCLUSIONS: Multi-omics and sc-eQTL MR analyses indicated that genetically predicted increases in VNN2 expression were associated with a reduced risk of LUAD, with the most pronounced effect observed in rNK cells. These findings suggest a potential cell type-specific role of VNN2 in LUAD susceptibility and warrant further studies to validate its biological relevance and clinical implications.

PMID:41822323 | PMC:PMC12978397 | DOI:10.14740/wjon2689

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Efficient endometrial carcinoma screening via cross-modal synthesis and gradient distillation

arXiv:2602.19822v1 Announce Type: cross Abstract: Early detection of myometrial invasion is critical for the staging and life-saving management of endometrial carcinoma (EC), a prevalent global malignancy. Transvaginal ultrasound serves as the primary, accessible screening modality in resource-constrained primary care settings; however, its diagnostic reliability is severely hindered by low tissue contrast, high operator dependence, and a pronounced scarcity of positive pathological samples. Existing artificial intelligence solutions struggle to overcome this severe class imbalance and the subtle imaging features of invasion, particularly under the strict computational limits of primary care clinics. Here we present an automated, highly efficient two-stage deep learning framework that resolves both data and computational bottlenecks in EC screening. To mitigate pathological data scarcity, we develop a structure-guided cross-modal generation network that synthesizes diverse, high-fidelity ultrasound images from unpaired magnetic resonance imaging (MRI) data, strictly preserving clinically essential anatomical junctions. Furthermore, we introduce a lightweight screening network utilizing gradient distillation, which transfers discriminative knowledge from a high-capacity teacher model to dynamically guide sparse attention towards task-critical regions. Evaluated on a large, multicenter cohort of 7,951 participants, our model achieves a sensitivity of 99.5\%, a specificity of 97.2\%, and an area under the curve of 0.987 at a minimal computational cost (0.289 GFLOPs), substantially outperforming the average diagnostic accuracy of expert sonographers. Our approach demonstrates that combining cross-modal synthetic augmentation with knowledge-driven efficient modeling can democratize expert-level, real-time cancer screening for resource-constrained primary care settings.
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ParaCook: On Time-Efficient Planning for Multi-Agent Systems

arXiv:2510.11608v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit strong reasoning abilities for planning long-horizon, real-world tasks, yet existing agent benchmarks focus on task completion while neglecting time efficiency in parallel and asynchronous operations. To address this, we present ParaCook, a benchmark for time-efficient collaborative planning. Inspired by the Overcooked game, ParaCook provides an environment for various challenging interaction planning of multi-agent systems that are instantiated as cooking tasks, with a simplified action space to isolate the core challenge of strategic parallel planning. Through a comprehensive evaluation of state-of-the-art LLMs, we find that current approaches achieve suboptimal plans, which struggle with parallel actions or coordination. Our analysis also reveals LLMs' potential on abstract tasks where they can focus on high-level parallel optimization. ParaCook provides a scalable evaluation framework with adjustable complexity, establishing a foundation for developing and assessing time efficiency-aware multi-agent planning. The code and data are available at https://github.com/zsq259/ParaCook.
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