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MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance

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  • Francisca Barceló
  • Rosa Gomila
  • Ivan de Paul
  • Xavier Gili
  • Jaume Segura
  • Albert Pérez-Montaña
  • Teresa Jimenez-Marco
  • Antonia Sampol
  • José Portugal

Abstract

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis.

Suggested Citation

  • Francisca Barceló & Rosa Gomila & Ivan de Paul & Xavier Gili & Jaume Segura & Albert Pérez-Montaña & Teresa Jimenez-Marco & Antonia Sampol & José Portugal, 2018. "MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-14, August.
    • Handle: RePEc:plo:pone00:0201793
    DOI: 10.1371/journal.pone.0201793
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    References listed on IDEAS

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    1. Kristjan Korjus & Martin N Hebart & Raul Vicente, 2016. "An Efficient Data Partitioning to Improve Classification Performance While Keeping Parameters Interpretable," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-16, August.
    2. Nichola C Garbett & Michael L Merchant & C William Helm & Alfred B Jenson & Jon B Klein & Jonathan B Chaires, 2014. "Detection of Cervical Cancer Biomarker Patterns in Blood Plasma and Urine by Differential Scanning Calorimetry and Mass Spectrometry," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-12, January.
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