Early Detection of Malignant Pleural Mesothelioma in Asbestos-Exposed Individuals with a Noninvasive Proteomics-Based Surveillance Tool

Rachel M Ostroff,Deborah Ayers,Michele Carbone,Michael Harbut,Chandra Goparaju,Stephen A Williams,Michael R Mehan,Brad Black,Stephen Levin,Harvey I Pass,Alex Stewart,Edward N Brody,Thomas Behrens

doi:10.1371/journal.pone.0046091

Abstract

BackgroundMalignant pleural mesothelioma (MM) is an aggressive, asbestos-related pulmonary cancer that is increasing in incidence. Because diagnosis is difficult and the disease is relatively rare, most patients present at a clinically advanced stage where possibility of cure is minimal. To improve surveillance and detection of MM in the high-risk population, we completed a series of clinical studies to develop a noninvasive test for early detection.Methodology/Principal FindingsWe conducted multi-center case-control studies in serum from 117 MM cases and 142 asbestos-exposed control individuals. Biomarker discovery, verification, and validation were performed using SOMAmer proteomic technology, which simultaneously measures over 1000 proteins in unfractionated biologic samples. Using univariate and multivariate approaches we discovered 64 candidate protein biomarkers and derived a 13-marker random forest classifier with an AUC of 0.99±0.01 in training, 0.98±0.04 in independent blinded verification and 0.95±0.04 in blinded validation studies. Sensitivity and specificity at our pre-specified decision threshold were 97%/92% in training and 90%/95% in blinded verification. This classifier accuracy was maintained in a second blinded validation set with a sensitivity/specificity of 90%/89% and combined accuracy of 92%. Sensitivity correlated with pathologic stage; 77% of Stage I, 93% of Stage II, 96% of Stage III and 96% of Stage IV cases were detected. An alternative decision threshold in the validation study yielding 98% specificity would still detect 60% of MM cases. In a paired sample set the classifier AUC of 0.99 and 91%/94% sensitivity/specificity was superior to that of mesothelin with an AUC of 0.82 and 66%/88% sensitivity/specificity. The candidate biomarker panel consists of both inflammatory and proliferative proteins, processes strongly associated with asbestos-induced malignancy.SignificanceThe SOMAmer biomarker panel discovered and validated in these studies provides a solid foundation for surveillance and diagnosis of MM in those at highest risk for this disease.

Highlights

Malignant mesothelioma (MM) is a relatively rare cancer almost always caused by prolonged exposure to asbestos fibers
We report the discovery and validation of a serum-based 13-protein classifier with an AUC of 0.95 and an overall accuracy of 92% for detection of MM in the asbestos-exposed population using the SOMAscanTM proteomic assay
We analyzed a total of 259 serum samples from four independent MM biorepositories in a series of prospectively designed case/control studies with archived samples (Figure 1 and Table 1)

Summary

Introduction

Malignant mesothelioma (MM) is a relatively rare cancer almost always caused by prolonged exposure to asbestos fibers. There are about 2,500–3,000 new cases per year in the USA [1]. Over 27 million people in the US, and millions more worldwide, have been exposed to asbestos fibers and are at risk for the disease. There are 15,000–20,000 deaths per year from MM in the Western world and Japan [2]. Malignant pleural mesothelioma (MM) is an aggressive, asbestos-related pulmonary cancer that is increasing in incidence. Because diagnosis is difficult and the disease is relatively rare, most patients present at a clinically advanced stage where possibility of cure is minimal. To improve surveillance and detection of MM in the high-risk population, we completed a series of clinical studies to develop a noninvasive test for early detection

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