Mar, 2020
Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer.
British journal of cancer
- Volume
- 122
- Number
- 7
- First page
- 995
- Last page
- 1004
- Language
- English
- Publishing type
- Research paper (scientific journal)
- DOI
- 10.1038/s41416-020-0732-y
- Publisher
- Springer Science and Business Media LLC
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.
- Link information
- ID information
-
- DOI : 10.1038/s41416-020-0732-y
- ISSN : 0007-0920
- eISSN : 1532-1827
- Pubmed ID : 32020064
- Pubmed Central ID : PMC7109155