Dataset Information

External Validation of a Breath-Based Prediction Model for Malignant Pleural Mesothelioma.

ABSTRACT: During the past decade, volatile organic compounds (VOCs) in exhaled breath have emerged as promising biomarkers for malignant pleural mesothelioma (MPM). However, as these biomarkers lack external validation, no breath test for MPM has been implemented in clinical practice. To address this issue, we performed the first external validation of a VOC-based prediction model for MPM. The external validation cohort was prospectively recruited, consisting of 47 MPM patients and 76 asbestos-exposed (AEx) controls. The predictive performance of the previously developed model was assessed by determining the degree of agreement between the predicted and actual outcome of the participants (patient/control). Additionally, to optimise the performance, the model was updated by refitting it to the validation cohort. External validation revealed a poor performance of the original model as the accuracy was estimated at only 41%, indicating poor generalisability. However, subsequent updating of the model improved the differentiation between MPM patients and AEx controls significantly (73% accuracy, 92% sensitivity, and 92% negative predictive value), substantiating the validity of the original predictors. This updated model will be more generalisable to the target population and exhibits key characteristics of a potential screening test for MPM, which could significantly impact MPM management.

SUBMITTER: Janssens E

PROVIDER: S-EPMC9264774 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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External Validation of a Breath-Based Prediction Model for Malignant Pleural Mesothelioma.

Janssens Eline E Schillebeeckx Eline E Zwijsen Kathleen K Raskin Jo J Van Cleemput Joris J Surmont Veerle F VF Nackaerts Kristiaan K Marcq Elly E van Meerbeeck Jan P JP Lamote Kevin K

Cancers 20220629 13

During the past decade, volatile organic compounds (VOCs) in exhaled breath have emerged as promising biomarkers for malignant pleural mesothelioma (MPM). However, as these biomarkers lack external validation, no breath test for MPM has been implemented in clinical practice. To address this issue, we performed the first external validation of a VOC-based prediction model for MPM. The external validation cohort was prospectively recruited, consisting of 47 MPM patients and 76 asbestos-exposed (AE ...[more]

PMID: 35804954

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Project description:Malignant pleural mesothelioma (MPM) is a highly lethal, poorly understood neoplasm that is typically associated with asbestos exposure. We performed transcriptional profiling using high-density oligonucleotide microarrays containing approximately 22,000 genes to elucidate potential molecular and pathobiological pathways in MPM using discarded human MPM tumor specimens (n=40), normal lung specimens (n=4), normal pleura specimens (n=5), and MPM and SV40-immortalized mesothelial cell lines (n=5). In global expression analysis using unsupervised clustering techniques, we found two potential subclasses of mesothelioma which correlate loosely with tumor histology. We also identified sets of genes with expression levels that distinguish between multiple tumor subclasses, normal and tumor tissues, and tumors with different morphologies. Microarray gene expression data were confirmed using quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) and protein analysis for three novel candidate oncogenes (NME2, CRI1, and PDGFC) and one candidate tumor suppressor (GSN). Finally, we used bioinformatics tools (i.e. software) to create and explore complex physiological pathways that may be relevant in mesothelioma tumorigenesis, pathobiology, or both. Tissues and cell lines profiled using microarrays. Discarded MPM surgical specimens (n=40), normal pleura specimens (n=5), and normal lung specimens (n=4) were freshly collected (and snap frozen) from patients who underwent surgery at Boston's Brigham and Women's Hospital (BWH) between October 1998 and August 2000. All of these patients underwent extrapleural pneumonectomy with heated intra-pleural cisplatin chemotherapy delivered after the specimens were removed. All normal specimens were obtained from patients who were never diagnosed with MPM. Two human MPM cell lines (MS589 and MS428) were kindly provided by Jonathan A. Fletcher, M.D., Department of Pathology, BWH. The JMN1B MPM cell line19,20 has been described previously. The SV40-immortalized, non-tumorigenic mesothelial cell line (Met-5A)21 and the MPM cell line MSTO-211H22 were purchased from the American Type Culture Collection. Normal tissues were obtained from additional consented patients undergoing treatment for diseases other than MPM. All MPM samples used in these studies contained relatively pure tumor (greater than 50% tumor cells per high power field examined in a section adjacent to the tissue used). The microscopic slides from the patients' resection specimens were reviewed by one of the authors (J.G.), and the diagnosis and histologic subclassification of MPM confirmed in all cases. Linked clinical and pathological data were obtained for all patients who contributed tumor specimens. Specimens and data were rendered anonymous to protect patient confidentiality. Studies utilizing human tissues were approved by and conducted in accordance with the policies of the Institutional Review Board at BWH.

Project description:Introduction: Dysregulated genes play a critical role in the development and progression of cancer, suggesting their potential as novel independent biomarkers for cancer diagnosis and prognosis. Prognostic model-based gene expression profiles are not widely utilized in clinical medicine. We investigated the prognostic significance of an expression profile-based gene signature for outcome prediction in patients with malignant pleural mesothelioma (MPM). Methods: The gene expression profiles of a large cohort of patients with MPM were obtained and analyzed by repurposing publicly available microarray data. A gene-based risk score model was developed with the training dataset and then validated with the TCGA-MESO (mesothelioma) dataset. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance of survival prediction. The biological function of the prognostic genes was predicted using bioinformatics analysis. Results: Three genes in the training dataset (GSE2549) were identified as significantly associated with the overall survival (OS) of patients with MPM and were combined to develop a three-gene prognostic signature to stratify patients into low-risk and high-risk groups. The MPM patients of the training dataset in the low-risk group exhibited longer OS than those in the high-risk group (HR = 0.25, 95% CI = 0.11-0.56, P < 0.001). Similar prognostic values for the three-gene signature were observed in the validated TCGA-MESO cohort (HR = 0.53 95% CI = 0.33-0.85, P = 0.008). ROC analysis also demonstrated the good performance in predicting 3-year OS in the GEO and TCGA cohorts (KM-AUC for GEO = 0.989, KM-AUC for TCGA = 0.618). The C-statistic for the 3-gene model was 0.761. Validation with TCGA-MESO confirmed the model's ability to discriminate between risk groups in an alternative data set with fair performance (C-statistic: 0.68). Functional enrichment analysis suggested that these three genes may be involved in genetic and epigenetic events with known links to MPM. Conclusions: This study has identified and validated a novel 3-gene model to reliably discriminate patients at high and low risk of death in unselected populations of patients with MPM. Further larger, prospective multi-institutional cohort studies are necessary to validate this model.

Project description:Malignant pleural mesothelioma (MPM) is a tumour related to a historical exposure to asbestos fibres. Currently, the definite diagnosis is made only by the histological examination of a biopsy obtained through an invasive thoracoscopy. However, diagnosis is made too late for curative treatment because of non-specific symptoms mainly appearing at advanced stage disease. Hence, due to its biologic aggressiveness and the late diagnosis, survival rate is low and the patients' outcome poor. In addition, radiological imaging, like computed tomographic scans, and blood biomarkers are found not to be sensitive enough to be used as an early diagnostic tool. Detection in an early stage is assumed to improve the patients' outcome but is hampered due to non-specific and late symptomology. Hence, there is a need for a new screening and diagnostic test which could improve the patients' outcome. Despite extensive research has focused on blood biomarkers, not a single has been shown clinically useful, and therefore research recently shifted to "breathomics" techniques to recognize specific volatile organic compounds (VOCs) in the breath of the patient as potential non-invasive biomarkers for disease. In this review, we summarize the acquired knowledge about using breath analysis for diagnosing and monitoring MPM and asbestos-related disorders (ARD). Gas chromatography-mass spectrometry (GC-MS), the gold standard of breath analysis, appears to be the method with the highest accuracy (97%) to differentiate MPM patients from at risk asbestos-exposed subjects. There have already been found some interesting biomarkers that are significantly elevated in asbestosis (NO, 8-isoprostane, leukotriene B4, ?-Pinene…) and MPM (cyclohexane) patients. Regrettably, the different techniques and the plethora of studies suffer some limitations. Most studies are pilot studies with the inclusion of a limited number of patients. Nevertheless, given the promising results and easy sampling methods, we can conclude that breath analysis may become a useful tool in the future to screen for MPM, but further research is warranted.

Dataset Information

External Validation of a Breath-Based Prediction Model for Malignant Pleural Mesothelioma.

Publications

External Validation of a Breath-Based Prediction Model for Malignant Pleural Mesothelioma.

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