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

Lin Huang, Lin Wang, Xiaomeng Hu, Sen Chen, Yunwen Tao, Haiyang Su, Jing Yang, Wei Xu, Vadanasundari Vedarethinam, Shu Wu, Bin Liu, Xinze Wan, Jiatao Lou, Qian Wang and Kun Qian ()
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Lin Huang: Shanghai Jiao Tong University
Lin Wang: Shanghai Jiao Tong University
Xiaomeng Hu: Shanghai Jiao Tong University
Sen Chen: iMS Clinic
Yunwen Tao: Southern Methodist University
Haiyang Su: Shanghai Jiao Tong University
Jing Yang: Shanghai Jiao Tong University
Wei Xu: Shanghai Jiao Tong University
Vadanasundari Vedarethinam: Shanghai Jiao Tong University
Shu Wu: iMS Clinic
Bin Liu: iMS Clinic
Xinze Wan: iMS Clinic
Jiatao Lou: Shanghai Jiao Tong University
Qian Wang: Shanghai Jiao Tong University
Kun Qian: Shanghai Jiao Tong University

Nature Communications, 2020, vol. 11, issue 1, 1-11

Abstract: 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

Date: 2020
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DOI: 10.1038/s41467-020-17347-6

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