Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

Journal: Nature communications
Published Date: Jul 16, 2020

Abstract

Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

Authors

  • Lin Huang
    Division of Vascular Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510800, China; National-Guangdong Joint Engineering Laboratory for Diagnosis and Treatment of Vascular Disease, First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
  • Lin Wang
    Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
  • Xiaomeng Hu
    Department of Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 200030, Shanghai, P. R. China.
  • Sen Chen
    iMS Clinic, 310052, Hangzhou, P. R. China.
  • Yunwen Tao
    Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, TX, 75275-0314, USA.
  • Haiyang Su
    State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 200030, Shanghai, P. R. China.
  • Jing Yang
    Beijing Novartis Pharma Co. Ltd., Beijing, China.
  • Wei Xu
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023 China.
  • Vadanasundari Vedarethinam
    State Key Laboratory for Oncogenes and Related Genes, School of Biomedical Engineering, Shanghai Jiao Tong University, 200030, Shanghai, P. R. China.
  • Shu Wu
    Department of Plastic Surgery, Second Affiliated Hospital of Nanchang University, Nanchang Jiangxi, 330006, P.R.China.
  • Bin Liu
    Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, China; Department of Endocrinology, Neijiang First People's Hospital, Chongqing, China.
  • Xinze Wan
    iMS Clinic, 310052, Hangzhou, P. R. China.
  • Jiatao Lou
    Department of Laboratory Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, 200030, Shanghai, P. R. China.
  • Qian Wang
    Department of Radiation Oncology, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Kun Qian
    Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology), Ministry of Education, Beijing, China.

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