Predicting Breast Cancer by Paper Spray Ion Mobility Spectrometry Mass Spectrometry and Machine Learning.

Journal: Analytical chemistry
Published Date: Jan 21, 2020

Abstract

Paper spray ionization has been used as a fast sampling/ionization method for the direct mass spectrometric analysis of biological samples at ambient conditions. Here, we demonstrated that by utilizing paper spray ionization-mass spectrometry (PSI-MS) coupled with field asymmetric waveform ion mobility spectrometry (FAIMS), predictive metabolic and lipidomic profiles of routine breast core needle biopsies could be obtained effectively. By the combination of machine learning algorithms and pathological examination reports, we developed a classification model, which has an overall accuracy of 87.5% for an instantaneous differentiation between cancerous and noncancerous breast tissues utilizing metabolic and lipidomic profiles. Our results suggested that paper spray ionization-ion mobility spectrometry-mass spectrometry (PSI-IMS-MS) is a powerful approach for rapid breast cancer diagnosis based on altered metabolic and lipidomic profiles.

Authors

  • Ying-Chen Huang
    Department of Chemistry , National Taiwan University , Taipei 10617 , Taiwan.
  • Hsin-Hsiang Chung
    Department of Chemistry , National Taiwan University , Taipei 10617 , Taiwan.
  • Ewelina P Dutkiewicz
    Department of Chemistry , National Taiwan University , Taipei 10617 , Taiwan.
  • Chih-Lin Chen
    Department of Chemistry , National Taiwan University , Taipei 10617 , Taiwan.
  • Hua-Yi Hsieh
    Department of Chemistry , National Taiwan University , Taipei 10617 , Taiwan.
  • Bo-Rong Chen
    Department of Surgery , National Taiwan University Hospital , Taipei 10002 , Taiwan.
  • Ming-Yang Wang
    Department of Surgery , National Taiwan University Hospital , Taipei 10002 , Taiwan.
  • Cheng-Chih Hsu
    Department of Chemistry, National Taiwan University, 10617, Taipei, Taiwan.

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