-
American Journal of Respiratory Cell... Jan 2024
Topics: Humans; Pleurisy; Fibrosis; Mechanistic Target of Rapamycin Complex 2
PubMed: 37788451
DOI: 10.1165/rcmb.2023-0327ED -
Nature Communications Nov 2023Malignant pleural mesothelioma (MPM) is an aggressive tumor with a poor prognosis. As the available therapeutic options show a lack of efficacy, novel therapeutic...
Malignant pleural mesothelioma (MPM) is an aggressive tumor with a poor prognosis. As the available therapeutic options show a lack of efficacy, novel therapeutic strategies are urgently needed. Given its T-cell infiltration, we hypothesized that MPM is a suitable target for therapeutic cancer vaccination. To date, research on mesothelioma has focused on the identification of molecular signatures to better classify and characterize the disease, and little is known about therapeutic targets that engage cytotoxic (CD8+) T cells. In this study we investigate the immunopeptidomic antigen-presented landscape of MPM in both murine (AB12 cell line) and human cell lines (H28, MSTO-211H, H2452, and JL1), as well as in patients' primary tumors. Applying state-of-the-art immuno-affinity purification methodologies, we identify MHC I-restricted peptides presented on the surface of malignant cells. We characterize in vitro the immunogenicity profile of the eluted peptides using T cells from human healthy donors and cancer patients. Furthermore, we use the most promising peptides to formulate an oncolytic virus-based precision immunotherapy (PeptiCRAd) and test its efficacy in a mouse model of mesothelioma in female mice. Overall, we demonstrate that the use of immunopeptidomic analysis in combination with oncolytic immunotherapy represents a feasible and effective strategy to tackle untreatable tumors.
Topics: Humans; Female; Animals; Mice; Mesothelioma, Malignant; Pleural Neoplasms; Mesothelioma; Immunotherapy; Peptides; Cell Line, Tumor; Lung Neoplasms
PubMed: 37923723
DOI: 10.1038/s41467-023-42668-7 -
Chest Jul 2023Previous studies have inconsistently reported associations between refractory ceramic fibers (RCFs) or mineral wool fibers (MWFs) and the presence of pleural plaques....
BACKGROUND
Previous studies have inconsistently reported associations between refractory ceramic fibers (RCFs) or mineral wool fibers (MWFs) and the presence of pleural plaques. All these studies were based on chest radiographs, known to be associated with a poor sensitivity for the diagnosis of pleural plaques.
RESEARCH QUESTION
Does the risk of pleural plaques increase with cumulative exposure to RCFs, MWFs, and silica? If the risk does increase, do these dose-response relationships depend on the co-exposure to asbestos or, conversely, are the dose-response relationships for asbestos modified by co-exposure to RCFs, MWFs, and silica?
STUDY DESIGN AND METHODS
Volunteer workers were invited to participate in a CT scan screening program for asbestos-related diseases in France. Asbestos exposure was assessed by industrial hygienists, and exposure to RCFs, MWFs, and silica was determined by using job-exposure matrices. A cumulative exposure index (CEI) was then calculated for each subject and separately for each of the four mineral particle exposures. All available CT scans were submitted to randomized double reading by a panel of radiologists.
RESULTS
In this cohort of 5,457 subjects, significant dose-response relationships were determined after adjustment for asbestos exposure between CEI to RCF or MWF and the risk of PPs (ORs of 1.29 [95% CI, 1.00-1.67] and 1.84 [95% CI, 1.49-2.27] for the highest CEI quartile, respectively). Significant interactions were found between asbestos on one hand and MWF or RCF on the other.
INTERPRETATION
This study suggests the existence of a significant association between exposure to RCFs and MWFs and the presence of pleural plaques in a large population previously exposed to asbestos and screened by using CT scans.
Topics: Humans; Occupational Exposure; Asbestos; Pleural Diseases; Silicon Dioxide
PubMed: 36773934
DOI: 10.1016/j.chest.2023.02.004 -
The Korean Journal of Internal Medicine Jan 2024Post-tuberculosis lung disease (PTLD) is emerging as a significant area of global interest. As the number of patients surviving tuberculosis (TB) increases, the... (Review)
Review
Post-tuberculosis lung disease (PTLD) is emerging as a significant area of global interest. As the number of patients surviving tuberculosis (TB) increases, the subsequent long-term repercussions have drawn increased attention due to their profound clinical and socioeconomic impacts. A primary obstacle to its comprehensive study has been its marked heterogeneity. The disease presents a spectrum of clinical manifestations which encompass tracheobronchial stenosis, bronchiectasis, granulomas with fibrosis, cavitation with associated aspergillosis, chronic pleural diseases, and small airway diseases-all persistent consequences of PTLD. The spectrum of symptoms a patient may experience varies based on the severity of the initial infection and the efficacy of the treatment received. As a result, the long-term management of PTLD necessitates a detailed and specific approach, addressing each manifestation individually-a tailored strategy. In the immediate aftermath (0-12 months after anti-TB chemotherapy), there should be an emphasis on monitoring for relapse, tracheobronchial stenosis, and smoking cessation. Subsequent management should focus on addressing hemoptysis, managing infection including aspergillosis, and TB-associated chronic obstructive pulmonary disease or restrictive lung function. There remains a vast expanse of knowledge to be discovered in PTLD. This review emphasizes the pressing need for comprehensive, consolidated guidelines for management of patients with PTLD.
Topics: Humans; Tuberculosis, Pulmonary; Constriction, Pathologic; Lung Diseases; Chronic Disease; Tuberculosis; Aspergillosis
PubMed: 38225822
DOI: 10.3904/kjim.2023.395 -
Thoracic Cancer May 2024Tumor recurrence remains the main barrier to survival after surgery for pleural mesothelioma (PM). Soluble mesothelin-related protein (SMRP) and cancer antigen 125...
BACKGROUND
Tumor recurrence remains the main barrier to survival after surgery for pleural mesothelioma (PM). Soluble mesothelin-related protein (SMRP) and cancer antigen 125 (CA-125) are established blood-based biomarkers for monitoring PM. We prospectively studied the utility of these biomarkers after pleurectomy decortication (PD).
METHODS
Patients who underwent PD and achieved complete macroscopic resection with available preoperative SMRP levels were included. Tumor marker levels were determined within 60 days of three timepoints: (1) preoperation, (2) post-operation, and (3) recurrence.
RESULTS
Of 356 evaluable patients, 276 (78%) had recurrence by the end of follow-up interval. Elevated preoperative SMRP levels were associated with epithelioid histology (p < 0.013), advanced TNM (p < 0.001) stage, and clinical stage (p < 0.001). Preoperative CA-125 levels were not significantly associated with clinical covariates. Neither biomarker was associated with survival or disease-free survival. With respect to nonpleural and nonlymphatic recurrences, mean SMRP levels were elevated in patients with pleural (p = 0.021) and lymph node (p = 0.042) recurrences. CA-125 levels were significantly higher in patients with abdominal (p < 0.001) and lymph node (p = 0.004) recurrences. Among patients with all three timepoints available, we observed an average decrease in SMRP levels by 1.93 nmol/L (p < 0.001) postoperatively and again an average increase at recurrence by 0.79 nmol/L (p < 0.001). There were no significant changes in levels of CA-125 across the study timepoints (p = 0.47).
CONCLUSIONS
Longitudinal changes in SMRP levels corresponded with a radiographic presence of disease in a subset of patients. SMRP surveillance could aid in detection of local recurrences, whereas CA-125 could be helpful in recognizing abdominal recurrences.
Topics: Humans; Male; Female; CA-125 Antigen; Aged; Pleural Neoplasms; Middle Aged; Biomarkers, Tumor; Mesothelioma; Neoplasm Recurrence, Local; Mesothelin; Mesothelioma, Malignant; Prospective Studies; Adult; Aged, 80 and over; GPI-Linked Proteins; Lung Neoplasms
PubMed: 38627917
DOI: 10.1111/1759-7714.15264 -
Therapeutic Advances in Respiratory... 2024The prognosis of malignant pleural effusion (MPE) is poor. A timely and accurate diagnosis is the prerequisite for managing MPE patients. Carbohydrate antigen 72-4...
BACKGROUND
The prognosis of malignant pleural effusion (MPE) is poor. A timely and accurate diagnosis is the prerequisite for managing MPE patients. Carbohydrate antigen 72-4 (CA72-4) is a diagnostic tool for MPE.
OBJECTIVE
We aimed to evaluate the diagnostic accuracy of pleural fluid CA72-4 for MPE.
DESIGN
A prospective, preregistered, and double-blind diagnostic test accuracy study.
METHODS
We prospectively enrolled participants with undiagnosed pleural effusions from two centers in China (Hohhot and Changshu). CA72-4 concentration in pleural fluid was measured by electrochemiluminescence. Its diagnostic accuracy for MPE was evaluated by a receiver operating characteristic (ROC) curve. The net benefit of CA72-4 was determined by a decision curve analysis (DCA).
RESULTS
In all, 153 participants were enrolled in the Hohhot cohort, and 58 were enrolled in the Changshu cohort. In both cohorts, MPE patients had significantly higher CA72-4 levels than benign pleural effusion (BPE) patients. At a cutoff value of 8 U/mL, pleural fluid CA72-4 had a sensitivity, specificity, and area under the ROC curve (AUC) of 0.46, 1.00, and 0.79, respectively, in the Hohhot cohort. In the Changshu cohort, CA72-4 had a sensitivity, specificity, and AUC of 0.27, 0.94, and 0.86, respectively. DCA revealed the relatively high net benefit of CA72-4 determination. In patients with negative cytology, the AUC of CA72-4 was 0.67.
CONCLUSION
Pleural fluid CA72-4 helps differentiate MPE and BPE in patients with undiagnosed pleural effusions.
Topics: Humans; Diagnostic Tests, Routine; Pleural Effusion; Pleural Effusion, Malignant; Prospective Studies
PubMed: 38189269
DOI: 10.1177/17534666231222333 -
Molecular Oncology Apr 2024Mesothelioma is a type of late-onset cancer that develops in cells covering the outer surface of organs. Although it can affect the peritoneum, heart, or testicles, it... (Review)
Review
Mesothelioma is a type of late-onset cancer that develops in cells covering the outer surface of organs. Although it can affect the peritoneum, heart, or testicles, it mainly targets the lining of the lungs, making pleural mesothelioma (PMe) the most common and widely studied mesothelioma type. PMe is caused by exposure to fibres of asbestos, which when inhaled leads to inflammation and scarring of the pleura. Despite the ban on asbestos by most Western countries, the incidence of PMe is on the rise, also facilitated by a lack of specific symptomatology and diagnostic methods. Therapeutic options are also limited to mainly palliative care, making this disease untreatable. Here we present an overview of biological aspects underlying PMe by listing genetic and molecular mechanisms behind its onset, aggressive nature, and fast-paced progression. To this end, we report on the role of deubiquitinase BRCA1-associated protein-1 (BAP1), a tumour suppressor gene with a widely acknowledged role in the corrupted signalling and metabolism of PMe. This review aims to enhance our understanding of this devastating malignancy and propel efforts for its investigation.
Topics: Humans; Mesothelioma; Mesothelioma, Malignant; Pleural Neoplasms; Asbestos; Lung Neoplasms
PubMed: 38459714
DOI: 10.1002/1878-0261.13591 -
European Respiratory Review : An... Jun 2023Deep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Deep learning (DL), a subset of artificial intelligence (AI), has been applied to pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been performed.
METHODS
A search of multiple electronic databases through September 2022 was performed to identify studies that applied DL for pneumothorax diagnosis using imaging. Meta-analysis a hierarchical model to calculate the summary area under the curve (AUC) and pooled sensitivity and specificity for both DL and physicians was performed. Risk of bias was assessed using a modified Prediction Model Study Risk of Bias Assessment Tool.
RESULTS
In 56 of the 63 primary studies, pneumothorax was identified from chest radiography. The total AUC was 0.97 (95% CI 0.96-0.98) for both DL and physicians. The total pooled sensitivity was 84% (95% CI 79-89%) for DL and 85% (95% CI 73-92%) for physicians and the pooled specificity was 96% (95% CI 94-98%) for DL and 98% (95% CI 95-99%) for physicians. More than half of the original studies (57%) had a high risk of bias.
CONCLUSIONS
Our review found the diagnostic performance of DL models was similar to that of physicians, although the majority of studies had a high risk of bias. Further pneumothorax AI research is needed.
Topics: Humans; Pneumothorax; Artificial Intelligence; Deep Learning; Sensitivity and Specificity; Diagnostic Imaging
PubMed: 37286217
DOI: 10.1183/16000617.0259-2022 -
Journal of Translational Medicine Sep 2023Metagenomic next-generation sequencing (mNGS) has become a powerful tool for pathogen detection, but the value of human sequencing reads generated from it is...
BACKGROUND
Metagenomic next-generation sequencing (mNGS) has become a powerful tool for pathogen detection, but the value of human sequencing reads generated from it is underestimated.
METHODS
A total of 138 patients with pleural effusion (PE) were diagnosed with tuberculous pleurisy (TBP, N = 82), malignant pleural effusion (MPE, N = 35), or non-TB infection (N = 21), whose PE samples all underwent mNGS analysis. Clinical TB tests including culture, Acid-Fast Bacillus (AFB) test, Xpert, and T-SPOT, were performed. To utilize mNGS for MPE identification, 25 non-MPE samples (20 TBP and 5 non-TB infection) were randomly selected to set human chromosome copy number baseline and generalized linear modeling was performed using copy number variant (CNV) features of the rest 113 samples (35 MPE and 78 non-MPE).
RESULTS
The performance of TB detection was compared among five methods. T-SPOT demonstrated the highest sensitivity (61% vs. culture 32%, AFB 12%, Xpert 35%, and mNGS 49%) but with the highest false-positive rate (10%) as well. In contrast, mNGS was able to detect TB-genome in nearly half (40/82) of the PE samples from TBP subgroup, with 100% specificity. To evaluate the performance of using CNV features of the human genome for MPE prediction, we performed the leave-one-out cross-validation (LOOCV) in the subcohort excluding the 25 non-MPE samples for setting copy number standards, which demonstrated 54.1% sensitivity, 80.8% specificity, 71.7% accuracy, and an AUC of 0.851.
CONCLUSION
In summary, we exploited the value of human and non-human sequencing reads generated from mNGS, which showed promising ability in simultaneously detecting TBP and MPE.
Topics: Humans; Tuberculosis, Pleural; Pleural Effusion, Malignant; Pleural Effusion; High-Throughput Nucleotide Sequencing; Metagenomics; Sensitivity and Specificity
PubMed: 37777783
DOI: 10.1186/s12967-023-04492-x -
Cells Jul 2023A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observer variability in...
A Pleural effusion cytology is vital for treating metastatic breast cancer; however, concerns have arisen regarding the low accuracy and inter-observer variability in cytologic diagnosis. Although artificial intelligence-based image analysis has shown promise in cytopathology research, its application in diagnosing breast cancer in pleural fluid remains unexplored. To overcome these limitations, we evaluate the diagnostic accuracy of an artificial intelligence-based model using a large collection of cytopathological slides, to detect the malignant pleural effusion cytology associated with breast cancer. This study includes a total of 569 cytological slides of malignant pleural effusion of metastatic breast cancer from various institutions. We extracted 34,221 augmented image patches from whole-slide images and trained and validated a deep convolutional neural network model (DCNN) (Inception-ResNet-V2) with the images. Using this model, we classified 845 randomly selected patches, which were reviewed by three pathologists to compare their accuracy. The DCNN model outperforms the pathologists by demonstrating higher accuracy, sensitivity, and specificity compared to the pathologists (81.1% vs. 68.7%, 95.0% vs. 72.5%, and 98.6% vs. 88.9%, respectively). The pathologists reviewed the discordant cases of DCNN. After re-examination, the average accuracy, sensitivity, and specificity of the pathologists improved to 87.9, 80.2, and 95.7%, respectively. This study shows that DCNN can accurately diagnose malignant pleural effusion cytology in breast cancer and has the potential to support pathologists.
Topics: Humans; Female; Pleural Effusion, Malignant; Deep Learning; Artificial Intelligence; Breast Neoplasms; Neural Networks, Computer
PubMed: 37508511
DOI: 10.3390/cells12141847