INTRODUCTION: Soluble mesothelin (SM) and megakaryocyte potentiating factor (MPF) are serum biomarkers of mesothelioma. This study aims to examine the longitudinal behavior of SM and MPF in controls to gain insight in the optimal use of these biomarkers in screening. METHODS: Asbestos-exposed individuals, with no malignant disease at inclusion, were surveilled for 2 years with annual measurements of SM and MPF. Fixed thresholds were set at 2.10 nmol/L for SM and 13.10 ng/ml for MPF. Longitudinal biomarker analysis, using a random intercept model, estimated the association with age and glomerular filtration rate (GFR), and the intraclass correlation. The latter represents the proportion of total biomarker variance accounted for by the between-individual variance. RESULTS: A total of 215 participants were included, of whom 179 and 137 provided a second sample and third sample, respectively. Two participants with normal SM and MPF levels presented afterward with mesothelioma and lung cancer, respectively. Participants with elevated biomarker levels were typically older and had a lower GFR. During follow-up, biomarker levels significantly increased. Longitudinal analysis indicated that this was in part due to aging, while changes in GFR had a less pronounced effect on serial biomarker measurements. SM and MPF had a high intraclass correlation of 0.81 and 0.78, respectively, which implies that a single biomarker measurement and fixed threshold are suboptimal in screening. CONCLUSIONS: The longitudinal behavior of SM and MPF in controls indicates that a biomarker-based screening approach can benefit from the incorporation of serial measurements and individual-specific screening rules, adjusted for age and GFR. Large-scale validation remains nevertheless mandatory to elucidate whether such an approach can improve the early detection of mesothelioma.
INTRODUCTION: Soluble mesothelin (SM) and megakaryocyte potentiating factor (MPF) are serum biomarkers of mesothelioma. This study aims to examine the longitudinal behavior of SM and MPF in controls to gain insight in the optimal use of these biomarkers in screening. METHODS:Asbestos-exposed individuals, with no malignant disease at inclusion, were surveilled for 2 years with annual measurements of SM and MPF. Fixed thresholds were set at 2.10 nmol/L for SM and 13.10 ng/ml for MPF. Longitudinal biomarker analysis, using a random intercept model, estimated the association with age and glomerular filtration rate (GFR), and the intraclass correlation. The latter represents the proportion of total biomarker variance accounted for by the between-individual variance. RESULTS: A total of 215 participants were included, of whom 179 and 137 provided a second sample and third sample, respectively. Two participants with normal SM and MPF levels presented afterward with mesothelioma and lung cancer, respectively. Participants with elevated biomarker levels were typically older and had a lower GFR. During follow-up, biomarker levels significantly increased. Longitudinal analysis indicated that this was in part due to aging, while changes in GFR had a less pronounced effect on serial biomarker measurements. SM and MPF had a high intraclass correlation of 0.81 and 0.78, respectively, which implies that a single biomarker measurement and fixed threshold are suboptimal in screening. CONCLUSIONS: The longitudinal behavior of SM and MPF in controls indicates that a biomarker-based screening approach can benefit from the incorporation of serial measurements and individual-specific screening rules, adjusted for age and GFR. Large-scale validation remains nevertheless mandatory to elucidate whether such an approach can improve the early detection of mesothelioma.
Authors: Nico van Zandwijk; Christopher Clarke; Douglas Henderson; A William Musk; Kwun Fong; Anna Nowak; Robert Loneragan; Brian McCaughan; Michael Boyer; Malcolm Feigen; David Currow; Penelope Schofield; Beth Ivimey Nick Pavlakis; Jocelyn McLean; Henry Marshall; Steven Leong; Victoria Keena; Andrew Penman Journal: J Thorac Dis Date: 2013-12 Impact factor: 2.895
Authors: Rosa Filiberti; Paola Marroni; Manlio Mencoboni; Virginia Mortara; Pietro Caruso; Alex Cioè; Luigi Michelazzi; Domenico F Merlo; Andrea Bruzzone; Barbara Bobbio; Lisette Del Corso; Roberto Galli; Paola Taveggia; Guglielmo Dini; Fabio Spigno Journal: Med Oncol Date: 2013-01-01 Impact factor: 3.064
Authors: Nabil P Rizk; Elliot L Servais; Laura H Tang; Camelia S Sima; Hans Gerdes; Martin Fleisher; Valerie W Rusch; Prasad S Adusumilli Journal: Cancer Epidemiol Biomarkers Prev Date: 2012-01-11 Impact factor: 4.254
Authors: Kevin Hollevoet; Johannes B Reitsma; Jenette Creaney; Bogdan D Grigoriu; Bruce W Robinson; Arnaud Scherpereel; Alfonso Cristaudo; Harvey I Pass; Kristiaan Nackaerts; José A Rodríguez Portal; Joachim Schneider; Thomas Muley; Francesca Di Serio; Paul Baas; Marco Tomasetti; Alex J Rai; Jan P van Meerbeeck Journal: J Clin Oncol Date: 2012-03-12 Impact factor: 44.544
Authors: Michael K Felten; Khaled Khatab; Lars Knoll; Thomas Schettgen; Hendrik Müller-Berndorff; Thomas Kraus Journal: Int Arch Occup Environ Health Date: 2013-02-20 Impact factor: 3.015
Authors: Georg Johnen; Katarzyna Gawrych; Irina Raiko; Swaantje Casjens; Beate Pesch; Daniel G Weber; Dirk Taeger; Martin Lehnert; Jens Kollmeier; Torsten Bauer; Arthur W Musk; Bruce W S Robinson; Thomas Brüning; Jenette Creaney Journal: BMC Cancer Date: 2017-05-30 Impact factor: 4.430
Authors: Jenette Creaney; Sophie Sneddon; Ian M Dick; Hanne Dare; Neil Boudville; Arthur William Musk; Steven J Skates; Bruce W S Robinson Journal: Dis Markers Date: 2013-08-06 Impact factor: 3.434
Authors: Georg Johnen; Katarzyna Burek; Irina Raiko; Katharina Wichert; Beate Pesch; Daniel G Weber; Martin Lehnert; Swaantje Casjens; Olaf Hagemeyer; Dirk Taeger; Thomas Brüning Journal: Sci Rep Date: 2018-09-25 Impact factor: 4.379