bims-mesote Biomed News
on Mesothelioma
Issue of 2025–11–02
eight papers selected by
Laura Mannarino, Humanitas Research
  1. Risk factors, prognostic factors, and nomograms for distant metastasis in patients with malignant pleural mesothelioma: A population-based retrospective study.
  2. Dual HDAC and PI3K Inhibitor CUDC-907 Inhibits Growth of Pleural Mesothelioma: The Impact of Cisplatin Sensitivity and Myc Expression.
  3. Staging of Pleural Mesothelioma with the Ninth Edition TNM System: Problems Solved and Some That Remain.
  4. Erratum for: Radiologist's Guide to the Ninth Edition TNM Staging System for Quantitative and Qualitative Assessment of Pleural Mesothelioma.
  5. Malignant pleural mesothelioma classification and survival prediction with CT imaging using ResNet.
  6. Use of Patient-Derived Organoids for Pleural Mesothelioma 3D modelling.
  7. Pleural Mesothelioma: Ninth Edition of the TNM Staging Classification.
  8. Development of a Mesothelin-Binding Engineered Scaffold Protein as a Theranostic for Pleural Mesothelioma.
  1. J Int Med Res. 2025 Oct;53(10): 3000605251389356
    Risk factors, prognostic factors, and nomograms for distant metastasis in patients with malignant pleural mesothelioma: A population-based retrospective study.
    Quzhe Lu, Chao Liu, Meixiang Xu, Zhi Hu, Hui Yin, Jie Yang.
      ObjectivesMalignant pleural mesothelioma is a rare and aggressive thoracic tumor with a poor prognosis, wherein distant metastasis is associated with the lowest survival rates. It is imperative and emergent to construct nomograms based on risk factors and prognostic factors for distant metastasis in patients with malignant pleural mesothelioma.MethodsWe extracted data for the duration between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database and randomly categorized the patients into the training (70%) and validation (30%) cohorts. Risk factors for distant metastasis in patients with malignant pleural mesothelioma were identified using univariate and multivariate logistic regression analyses, and prognostic factors for patients with distant metastasis were determined using univariate and multivariate Cox regression analyses. Two nomograms were established based on the training cohort and evaluated using the validation cohort. The C-index, receiver operating characteristic curve, calibration curve, and decision curve analysis were used to assess the performance of the two nomograms.ResultsIn total, 2056 primary malignant pleural mesothelioma patients were included, and 341 patients were initially diagnosed with metastatic malignant pleural mesothelioma. Histology, laterality, grade, tumor stage, and node stage were independent risk factors for distant metastasis in patients with malignant pleural mesothelioma. Chemotherapy and metastasis to the lung, bone, and brain were independent prognostic factors for patients with malignant pleural mesothelioma and distant metastasis. The C-index values of the risk nomogram in the training and validation cohorts were 0.723 and 0.782, respectively. The C-index values of the prognostic nomogram in the training and validation cohorts were 0.678 and 0.712, respectively. The receiver operating characteristic curves, calibration curves, and decision curve analysis also demonstrated good predictive performance for the two nomograms in the training and validation cohorts.ConclusionsNomograms are useful and reliable tools for predicting distant metastatic risk in patients with malignant pleural mesothelioma and overall survival in patients with malignant pleural mesothelioma who had distant metastasis. These nomograms can provide strong references to clinicians to facilitate clinical decisions.
    Keywords:  Malignant pleural mesothelioma; distant metastasis; nomograms; overall survival; prognosis
    DOI:  https://doi.org/10.1177/03000605251389356
  2. Cells. 2025 Oct 15. pii: 1599. [Epub ahead of print]14(20):
    Dual HDAC and PI3K Inhibitor CUDC-907 Inhibits Growth of Pleural Mesothelioma: The Impact of Cisplatin Sensitivity and Myc Expression.
    Luca Hegedüs, Silvia Qaisieh, Christian Stülpnagel, Yavar Ganjeh Khor Dezfouli, Winny Tambo, Fabian Doerr, Natalie Baldes, Dirk Theegarten, Martin Schuler, Servet Bölükbas, Balazs Hegedüs.
      Objectives: Pleural mesothelioma (PM) is a rare cancer that often develops after a decades-long latency period and confers a grim prognosis. Novel, biomarker-based therapeutic modalities are expected to improve the outcome of patients with advanced PM. CUDC-907 (fimepinostat) is a dual inhibitor that affects both histone deacetylases and PI3K enzymes. Its antitumor activity was described in several cancer types, but it has not yet been explored in PM. Materials and Methods: The sensitivity of 22 PM cell lines-including 18 models established in our laboratory-to cisplatin and CUDC-907 was determined using a cell viability assay. BAP1, PTEN, and c-Myc expression, as well as MYC copy number variation, were measured. The effect of combination treatment with cisplatin was assessed with cell viability, cell cycle, and 3D spheroid formation assays. Results: Most PM cell lines were sensitive to CUDC-907 treatment, and the CUDC-907 response was significantly higher in cell lines with higher c-Myc expression due to MYC copy number gain or amplification. Importantly, all cisplatin-insensitive cell lines were sensitive to CUDC-907. Combination treatment with cisplatin synergistically decreased cell viability and induced G2/M arrest or cell death. We tested cisplatin-sensitive P31WT and cisplatin resistant P31cis isogeneic pair and found that in both 2D and 3D assays the cisplatin-resistant cells showed a higher sensitivity to CUDC-907 single treatment. Combining CUDC-907 with cisplatin further decreased cell growth even in cisplatin-resistant cells. Conclusions: The majority of PM cell models are sensitive to CUDC-907, which may be a potent therapeutic agent in PM.
    Keywords:  HDAC inhibitor; PI3K inhibitor; c-Myc; cisplatin; dual inhibitor; fimepinostat; pleural mesothelioma
    DOI:  https://doi.org/10.3390/cells14201599
  3. Radiology. 2025 Oct;317(1): e252343
    Staging of Pleural Mesothelioma with the Ninth Edition TNM System: Problems Solved and Some That Remain.
    Sharyn Katz, Leonid Roshkovan.
      
    DOI:  https://doi.org/10.1148/radiol.252343
  4. Radiology. 2025 Oct;317(1): e259017
    Erratum for: Radiologist's Guide to the Ninth Edition TNM Staging System for Quantitative and Qualitative Assessment of Pleural Mesothelioma.
    David J Murphy, Anna K Nowak, Thomas Frauenfelder, Michelle S Ginsberg, Hiroshi Kodama, Veronica Lenge de Rosen, Maria Mayoral, Christopher Straus, Giulia Benedetti, Valerie W Rusch, Ritu R Gill.
      
    DOI:  https://doi.org/10.1148/radiol.259017
  5. Eur Radiol. 2025 Oct 30.
    Malignant pleural mesothelioma classification and survival prediction with CT imaging using ResNet.
    Meng Zhou, Minghua Li, Qian Cao, Zhen Zhang, Leonard Wee, Andre Dekker, Ji Zhu, Haitao Jiang, Jiapeng Jiang, Xinyu Miao, Weimin Mao, Meng Yan, Hongyang Lu.
       OBJECTIVES: This study aims to achieve accurate differentiation of malignant pleural mesothelioma (MPM) from metastatic pleural disease (MPD) and to predict the overall survival of MPM.
    MATERIALS AND METHODS: This IRB-approved retrospective study included 385 subjects in total (85 patients with malignant mesothelioma and 290 with MPD secondary to lung adenocarcinoma). A ResNet-3D-18 model was trained on annotated pretreatment CT scans to distinguish MPM from MPD. Using chronological segregation, the training cohort included 70 histologically confirmed mesothelioma and 258 MPD cases, with an independent test cohort of 15 MPM and 32 MPD cases for validation. A multivariate logistic regression model served as the clinical benchmark for comparison. Deep learning features extracted from the trained ResNet model were then assessed for their prognostic utility in MPM patients using a random forest classifier. Model performance was evaluated at both lesion- and patient-levels, with metrics including the area under the ROC curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.
    RESULTS: The ResNet-3D-18 model demonstrated excellent discriminative performance in differentiating MPM from MPD, with mean AUCs of 0.972 (95% CI 0.947-0.990) and 0.840 (95% CI 0.757-0.929) in the training and independent test cohorts. Compared to the clinical model, the deep learning approach showed higher sensitivity (0.867 vs. 0.533) in the independent test dataset. For overall survival prediction in MPM patients, the random forest classifier achieved an AUC of 0.829 (95% CI 0.663-0.943) in 5-fold cross-validation.
    CONCLUSIONS: ResNet-3D-18 classification model has excellent abilities in differentiating MPM from MPD, and morphological distinctions between MPM and MPD also contain prognostic information.
    KEY POINTS: Question The rising global incidence of malignant pleural mesothelioma contrasts with persistent diagnostic challenges. Findings Deep learning-derived discriminative features simultaneously contain prognostic information. Clinical relevance This study bridges the gap between radiological findings and clinical decision-making in MPM, offering a reproducible tool for early diagnosis and personalized prognosis prediction based on CT imaging alone.
    Keywords:  Classification; Computed tomography; Deep learning; Mesothelioma; Prognosis
    DOI:  https://doi.org/10.1007/s00330-025-12094-y
  6. Am J Physiol Lung Cell Mol Physiol. 2025 Oct 29.
    Use of Patient-Derived Organoids for Pleural Mesothelioma 3D modelling.
    Luca Volpini, Federica Monaco, Elisabetta Casalone, Elton Jalis Herman, Rebecca Filomena, Sandra Manzotti, Olga Strogovets, Gianluca Moroncini, Matteo Mozzicafreddo, Antonella Poloni, Elena Marinelli Busilacchi, Francesca Gonnelli, Francesca Barbisan, Gaia Goteri, Lina Zuccatosta, Martina Bonifazi, Giuseppe Matullo, Jiri Neuzil, Lory Santarelli, Najib M Rahman, Kevin Blyth, Marco Tomasetti, Federico Mei.
      Defining pre-clinical models is of utmost importance for pleural mesothelioma (PM) to improve prognosis and predict therapeutic response. Using cells isolated from pleural fluid (PF) and diagnostic pleural biopsy (PB), we generated PM patient-derived organoids (PM-PDOs) and reactive-mesothelial (RM)-patient derived organoids (RM-PDO) aiming at assessing the proportion of successful cultures both from PF and PB. We also compared the architectural and immune-histochemical features of PM-PDOs to those of parental tissues and evaluated the PM-PDOs response to chemo-immunotherapy. We obtained 11 successful PM-PDOs from 15 PF/PB (73.3%). The rate of success was higher in epithelioid PM (88.8%) compared to biphasic PM (40.0%) (p=0.175), and when using PF (60.0%) compared to PB (20.0%) (p=0.001). We also obtained 3 RM effective cultures from 6 asbestos-exposed patients (50%) with non-specific pleuritis. Transcriptome analysis identified gene expression profile in PM-PDOs, which differentiate from RM-PDOs. PM-PDOs successfully maintained the histological architecture and molecular markers of their parental tumour tissues. The macrophagic component (CD68+ and CD163+) was an important component in RM-PDOs and was present in all three PM histotypes. Epithelioid PM-PDOs showed resistance to both Cis/PeMtx and pembrolizumab plus peripheral blood mononuclear cells (PBMCs), while both biphasic and sarcomatoid subtypes were sensitive to immunotherapy. Notably, immunotherapy induced an upregulation of PD-L1 expression and activated the STAT3/NF-κB signaling pathway, suggesting a mechanism of immune evasion. PF offers a valuable source of cancer and stromal cells to generate PDO, reinforcing its clinical utility for patients who cannot undergo invasive procedures.
    Keywords:  Immunotherapy; PD-L1; Patient-Derived Organoids; Pleural Mesothelioma; Reactive Mesothelium
    DOI:  https://doi.org/10.1152/ajplung.00078.2025
  7. Radiol Cardiothorac Imaging. 2025 Oct;7(5): e250213
    Pleural Mesothelioma: Ninth Edition of the TNM Staging Classification.
    Chad D Strange, Jitesh Ahuja, Rishi Agrawal, Melissa C Price, Mylene T Truong, Ritu R Gill.
      The ninth edition of the TNM staging classification of pleural mesothelioma is an update in the TNM staging, refining the tumor descriptors with the first-time use of a size criteria for tumor evaluation in addition to invasion of adjacent structures. There are no changes to the N and M categories. These modifications aim to improve staging accuracy and guide clinical decision-making. Keywords: Thorax, Pleura, Neoplasms-Primary, Staging © RSNA, 2025.
    Keywords:  Neoplasms-Primary; Pleura; Staging; Thorax
    DOI:  https://doi.org/10.1148/ryct.250213
  8. Bioconjug Chem. 2025 Oct 27.
    Development of a Mesothelin-Binding Engineered Scaffold Protein as a Theranostic for Pleural Mesothelioma.
    Roberto Silvestri, Margherita Piccardi, Alessia Laurenza, Filomena Rea, Allison R Sirois, Martina Lari, Francesco Bartoli, Giovanni Signore, Lorena Tedeschi, Elisabetta Ferraro, Paola Anna Erba, Roberto Giovannoni, Stefano Landi, Federica Gemignani, Sarah J Moore.
      Mesothelin (MSLN) is a tumor biomarker expressed at high levels on the surface of numerous cancers with extremely limited expression in healthy tissues. MSLN-targeting agents developed for diagnosis and therapy could have a significant impact on the management of MSLN-expressing cancers. Pleural mesothelioma (PM) is a deadly cancer that arises from mesothelial cells lining the pleura and is predominantly linked to asbestos exposure. There are currently no effective treatments, and diagnosis occurs in late stages of disease due to the lack of clinical symptoms in the early stages. Recent efforts to diagnose and treat PM have focused on identifying and targeting relevant biomarkers, including MSLN. We engineered proteins based on the nonantibody fibronectin type III (Fn3) protein scaffold that bind MSLN with high affinity and specificity, using yeast-surface display and directed evolution. Previous work with Fn3 scaffold proteins has demonstrated tissue distribution desirable for applications in molecular imaging and targeted radiotherapy, which may overcome limitations encountered thus far with antibody-based approaches to treat PM. The MSLN-targeting Fn3 was further developed for bioconjugation with the 1,4,7,10-tetraazacyclododecane,1-(glutaric acid)-4,7,10-triacetic acid (DOTAGA) radiometal chelator. MSLN-binding Fn3 specifically binds to the MSLN-expressing PM lines, colocalizes with MSLN, and internalizes upon binding. Fn3-DOTAGA was further coupled with cold metal gallium-69, and the resulting conjugate maintained binding with high affinity to MSLN-expressing PM cells. MSLN-binding Fn3-DOTAGA-69Ga is a promising molecule with diagnostic and therapeutic relevance, toward applications in molecular imaging and targeted radiotherapy.
    DOI:  https://doi.org/10.1021/acs.bioconjchem.5c00425
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