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- Title
Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer.
- Authors
Ishii, Hiroki; Saitoh, Masao; Sakamoto, Kaname; Sakamoto, Kei; Saigusa, Daisuke; Kasai, Hirotake; Ashizawa, Kei; Miyazawa, Keiji; Takeda, Sen; Masuyama, Keisuke; Yoshimura, Kentaro
- Abstract
<bold>Background: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>This diagnostic system combined with PESI-MS and machine learning encourages us to clinically diagnose intratumoural phenotypic heterogeneity induced by TGF-β.
- Subjects
HEAD tumors; RESEARCH; GROWTH factors; RESEARCH methodology; EVALUATION research; MEDICAL cooperation; CELLULAR signal transduction; COMPARATIVE studies; RESEARCH funding; CELL lines; NECK tumors
- Publication
British Journal of Cancer, 2020, Vol 122, Issue 7, p995
- ISSN
0007-0920
- Publication type
journal article
- DOI
10.1038/s41416-020-0732-y