Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer
| Autor: | Kentaro Yoshimura, Hiroki Ishii, Daisuke Saigusa, Masao Saitoh, Kei Sakamoto, Keiji Miyazawa, Kei Ashizawa, Kaname Sakamoto, Hirotake Kasai, Keisuke Masuyama, Sen Takeda |
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| Rok vydání: | 2020 |
| Předmět: |
Cancer Research
Machine learning computer.software_genre Diagnostic system Article Treatment failure Machine Learning 03 medical and health sciences 0302 clinical medicine Transforming Growth Factor beta Cell Line Tumor medicine Humans Tgf β signalling 030304 developmental biology 0303 health sciences business.industry Genetic heterogeneity Head and neck cancer Oral cancer detection Translational research Lipidome medicine.disease Head and neck squamous-cell carcinoma Oncology Head and Neck Neoplasms Surgical oncology 030220 oncology & carcinogenesis Lipidomics Artificial intelligence business computer Signal Transduction Transforming growth factor |
| Zdroj: | British Journal of Cancer |
| ISSN: | 1532-1827 0007-0920 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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