Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer

Hiroki Ishii, Masao Saitoh, Kaname Sakamoto, Kei Sakamoto, Daisuke Saigusa, Hirotake Kasai, Kei Ashizawa, Keiji Miyazawa, Sen Takeda, Keisuke Masuyama, Kentaro Yoshimura

研究成果: Article査読

4 被引用数 (Scopus)


Background: Several pro-oncogenic signals, including transforming growth factor beta (TGF-β) signalling from tumour microenvironment, generate intratumoural phenotypic heterogeneity and result in tumour progression and treatment failure. However, the precise diagnosis for tumour areas containing subclones with cytokine-induced malignant properties remains clinically challenging. Methods: We established a rapid diagnostic system based on the combination of probe electrospray ionisation-mass spectrometry (PESI-MS) and machine learning without the aid of immunohistological and biochemical procedures to identify tumour areas with heterogeneous TGF-β signalling status in head and neck squamous cell carcinoma (HNSCC). A total of 240 and 90 mass spectra were obtained from TGF-β-unstimulated and -stimulated HNSCC cells, respectively, by PESI-MS and were used for the construction of a diagnostic system based on lipidome. Results: This discriminant algorithm achieved 98.79% accuracy in discrimination of TGF-β1-stimulated cells from untreated cells. In clinical human HNSCC tissues, this approach achieved determination of tumour areas with activated TGF-β signalling as efficiently as a conventional histopathological assessment using phosphorylated-SMAD2 staining. Furthermore, several altered peaks on mass spectra were identified as phosphatidylcholine species in TGF-β-stimulated HNSCC cells. Conclusions: This diagnostic system combined with PESI-MS and machine learning encourages us to clinically diagnose intratumoural phenotypic heterogeneity induced by TGF-β.

ジャーナルBritish Journal of Cancer
出版ステータスPublished - 2020 3 31

ASJC Scopus subject areas

  • 腫瘍学
  • 癌研究


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