Identification of Highly Sensitive Pleural Effusion Protein Biomarkers for Malignant Pleural Mesothelioma by Affinity-Based Quantitative Proteomics

Our plain-English summary of Identification of Highly Sensitive Pleural Effusion Protein Biomarkers for Malignant Pleural Mesothelioma by Affinity-Based Quantitative Proteomics, by N.B. Palstrøm, M. Overgaard, P. Licht, et al (Cancers, 2023).

The study

• Authors: N.B. Palstrøm, M. Overgaard, P. Licht, H.C. Beck
• Journal: Cancers
• Published: 2023;15(3)
• Cited by: 6 articles, as counted by NIH iCite
• Read it: PubMed · Publisher · Free full text

Abstract

Malignant pleural mesothelioma (MPM) is an asbestos-associated, highly aggressive cancer characterized by late-stage diagnosis and poor prognosis. Gold standards for diagnosis are pleural biopsy and cytology of pleural effusion (PE), both of which are limited by low sensitivity and markedly inter-observer variations. Therefore, the assessment of PE biomarkers is considered a viable and objective diagnostic tool for MPM diagnosis. We applied a novel affinity-enrichment mass spectrometry-based proteomics method for explorative analysis of pleural effusions from a prospective cohort of 84 patients referred for thoracoscopy due to clinical suspicion of MPM. Protein biomarkers with a high capability to discriminate MPM from non-MPM patients were identified, and a Random Forest algorithm was applied for building classification models. Immunohistology of pleural biopsies confirmed MPM in 40 patients and ruled out MPM in 44 patients. Proteomic analysis of pleural effusions identified panels of proteins with excellent diagnostic properties (90-100% sensitivities, 89-98% specificities, and AUC 0.97-0.99) depending on the specific protein combination. Diagnostic proteins associated with cancer growth included galactin-3 binding protein, testican-2, haptoglobin, Beta ig-h3, and protein AMBP. Moreover, we also confirmed previously reported diagnostic accuracies of the MPM markers fibulin-3 and mesothelin measured by two complementary mass spectrometry-based methods. In conclusion, a novel affinity-enrichment mass spectrometry-based proteomics identified panels of proteins in pleural effusion with extraordinary diagnostic accuracies, which are described here for the first time as biomarkers for MPM.

In plain English

Researchers at Odense University Hospital, Denmark, analysed pleural effusion (fluid around the lung) collected in 2012 to 2016 from 84 patients referred for thoracoscopy for suspected malignant pleural mesothelioma (MPM). All were described as having a history of asbestos exposure. Biopsies confirmed MPM in 40. The other 44, who had benign effusions, served as controls. A feature-selection step picked 16 candidate proteins that were raised in MPM fluid, and Random Forest machine learning then built protein combinations from them. A five-protein combination (testican-2, haptoglobin, Beta ig-h3, protein AMBP, galectin-3-binding protein) separated MPM from benign cases with sensitivity 1.00, specificity 0.98 and AUC 0.99 (95% CI 0.99 to 1.00). This was estimated by cross-validation within the same patients, not in a separate group. Measured by discovery-based mass spectrometry, the previously proposed markers mesothelin and fibulin-3 had AUCs of 0.49 (95% CI 0.38 to 0.64) and 0.60 (95% CI 0.48 to 0.72). The authors concluded they had identified novel MPM biomarkers, which they said need validation in a separate cohort. They had no patient data beyond histology, age and sex to divide patients by asbestos exposure or tumor stage. They also did not account for MPM subtypes when selecting markers.

We wrote this plain-English summary from the full article with the help of AI, and a second AI model checked each sentence against the article. It is not the authors' wording; the abstract and the article are their own account.

What the study did and found

• Design: Prospective cohort; diagnostic biomarker discovery study using mass spectrometry-based proteomics of pleural effusion. Candidates were selected by Boruta feature selection and a t-test filter, then Random Forest classifiers were built and assessed by leave-one-out cross-validation
• Who was studied: 84 patients referred for thoracoscopy for suspected MPM (authors at Odense University Hospital, Denmark), with pleural effusion collected September 2012 to November 2016. MPM was confirmed in 40 patients (35 male, 5 female; mean age 71.54; 10 biphasic, 26 epithelioid, 4 unknown histology). The 44 controls had benign effusions (36 male, 8 female; mean age 68.88)
• Exposure: All patients were described as having a history of asbestos exposure. No fiber types, jobs or doses were given, and no individual exposure data were available for analysis
• Main results:
  • Statistical analysis of the two datasets (Boruta feature selection plus a requirement of significant upregulation by t-test) identified 16 candidate proteins upregulated in MPM pleural effusions compared with controls
  • A 3-protein classifier from the high-abundance dataset (galectin-3-binding protein, haptoglobin, periostin) had sensitivity 0.97, specificity 0.93, accuracy 0.95 and AUC 0.99 (95% CI 0.98 to 1.00) for MPM versus non-MPM patients
  • A 4-protein classifier from the affinity-enriched low-abundance dataset (testican-2, nipped-B-like protein, Beta ig-h3, protein AMBP) had sensitivity 0.90, specificity 0.89, accuracy 0.89 and AUC 0.97 (95% CI 0.94 to 1.00)
  • A combined 5-protein classifier (testican-2, haptoglobin, Beta ig-h3, protein AMBP, galectin-3-binding protein) had sensitivity 1.00, specificity 0.98, accuracy 0.99 and AUC 0.99 (95% CI 0.99 to 1.00)
  • In this cohort, measured by discovery-based mass spectrometry, mesothelin had an AUC of 0.49 (95% CI 0.38 to 0.64) and fibulin-3 had an AUC of 0.60 (95% CI 0.48 to 0.72). A targeted mass spectrometry assay gave comparable results
  • Proteins in the MPM classifiers showed no significant differences between biphasic and epithelioid subtypes. Separate classifiers built to distinguish the two subtypes had sensitivity 0.83 to 0.90, specificity 0.50 to 0.60, and AUCs of 0.86 (95% CI 0.74 to 0.97) and 0.82 (95% CI 0.65 to 0.99)
• The authors' conclusion: The authors concluded that they identified novel MPM biomarkers in pleural effusions using mass spectrometry-based proteomics with machine learning. They said future studies should validate these findings in a separate cohort of patients and investigate the impact of MPM subtypes
• Limitations the authors state:
  • The impact of the different MPM subtypes on biomarker selection was not taken into account; more samples from each subtype would have been needed
  • No patient data beyond MPM histology, age and sex were available, so patients could not be divided by asbestos exposure or tumor stage
  • The findings have not yet been validated in a separate cohort of patients

Topics

Medical terms in this study, explained in our Lexicon: Pleural Effusion, Biomarker, Pleural Mesothelioma, Prognosis, Pleural Biopsy, Cytology, Thoracoscopy, Fibulin-3.

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