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Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma

Author

Listed:
  • Meng Nie

    (Tsinghua University)

  • Ke Yao

    (Tsinghua University)

  • Xinsheng Zhu

    (Tongji University School of Medicine)

  • Na Chen

    (Tsinghua University)

  • Nan Xiao

    (Tsinghua University)

  • Yi Wang

    (Tsinghua University)

  • Bo Peng

    (Tsinghua University)

  • LiAng Yao

    (Tsinghua University)

  • Peng Li

    (Fudan University
    Shanghai Qi Zhi Institute
    Tsinghua University)

  • Peng Zhang

    (Tongji University School of Medicine)

  • Zeping Hu

    (Tsinghua University)

Abstract

Metabolic reprogramming evolves during cancer initiation and progression. However, thorough understanding of metabolic evolution from preneoplasia to lung adenocarcinoma (LUAD) is still limited. Here, we perform large-scale targeted metabolomics on resected lesions and plasma obtained from invasive LUAD and its precursors, and decipher the metabolic trajectories from atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC), revealing that perturbed metabolic pathways emerge early in premalignant lesions. Furthermore, three panels of plasma metabolites are identified as non-invasive predictive biomarkers to distinguish IAC and its precursors with benign diseases. Strikingly, metabolomics clustering defines three metabolic subtypes of IAC patients with distinct clinical characteristics. We identify correlation between aberrant bile acid metabolism in subtype III with poor clinical features and demonstrate dysregulated bile acid metabolism promotes migration of LUAD, which could be exploited as potential targetable vulnerability and for stratifying patients. Collectively, the comprehensive landscape of the metabolic evolution along the development of LUAD will improve early detection and provide impactful therapeutic strategies.

Suggested Citation

  • Meng Nie & Ke Yao & Xinsheng Zhu & Na Chen & Nan Xiao & Yi Wang & Bo Peng & LiAng Yao & Peng Li & Peng Zhang & Zeping Hu, 2021. "Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26685-y
    DOI: 10.1038/s41467-021-26685-y
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    1. Yuefan Huang & Vakul Mohanty & Merve Dede & Kyle Tsai & May Daher & Li Li & Katayoun Rezvani & Ken Chen, 2023. "Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Yangzi Chen & Bohong Wang & Yizi Zhao & Xinxin Shao & Mingshuo Wang & Fuhai Ma & Laishou Yang & Meng Nie & Peng Jin & Ke Yao & Haibin Song & Shenghan Lou & Hang Wang & Tianshu Yang & Yantao Tian & Pen, 2024. "Metabolomic machine learning predictor for diagnosis and prognosis of gastric cancer," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    3. Yao Yao & Xueping Wang & Jian Guan & Chuanbo Xie & Hui Zhang & Jing Yang & Yao Luo & Lili Chen & Mingyue Zhao & Bitao Huo & Tiantian Yu & Wenhua Lu & Qiao Liu & Hongli Du & Yuying Liu & Peng Huang & T, 2023. "Metabolomic differentiation of benign vs malignant pulmonary nodules with high specificity via high-resolution mass spectrometry analysis of patient sera," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

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