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

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 subc...

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Veröffentlicht in:British journal of cancer 2020-03, Vol.122 (7), p.995-1004
Hauptverfasser: Ishii, Hiroki, Saitoh, Masao, Sakamoto, Kaname, Sakamoto, Kei, Saigusa, Daisuke, Kasai, Hirotake, Ashizawa, Kei, Miyazawa, Keiji, Takeda, Sen, Masuyama, Keisuke, Yoshimura, Kentaro
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container_end_page 1004
container_issue 7
container_start_page 995
container_title British journal of cancer
container_volume 122
creator Ishii, Hiroki
Saitoh, Masao
Sakamoto, Kaname
Sakamoto, Kei
Saigusa, Daisuke
Kasai, Hirotake
Ashizawa, Kei
Miyazawa, Keiji
Takeda, Sen
Masuyama, Keisuke
Yoshimura, Kentaro
description 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.
doi_str_mv 10.1038/s41416-020-0732-y
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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.</description><identifier>ISSN: 0007-0920</identifier><identifier>EISSN: 1532-1827</identifier><identifier>DOI: 10.1038/s41416-020-0732-y</identifier><identifier>PMID: 32020064</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>631/67/1665/3016 ; 692/308/575 ; 692/4028/546 ; Biomedical and Life Sciences ; Biomedicine ; Cancer Research ; Cell Line, Tumor ; Diagnosis ; Drug Resistance ; Epidemiology ; Head &amp; neck cancer ; Head and Neck Neoplasms - diagnosis ; Head and Neck Neoplasms - pathology ; Humans ; Learning algorithms ; Lecithin ; Lipidomics - methods ; Machine learning ; Machine Learning - standards ; Mass spectroscopy ; Medical diagnosis ; Molecular Medicine ; Oncology ; Phosphatidylcholine ; Signal Transduction ; Smad2 protein ; Squamous cell carcinoma ; Transforming Growth Factor beta - metabolism ; Transforming growth factor-b ; Transforming growth factor-b1 ; Tumor microenvironment ; Tumors</subject><ispartof>British journal of cancer, 2020-03, Vol.122 (7), p.995-1004</ispartof><rights>The Author(s), under exclusive licence to Cancer Research UK 2020</rights><rights>2020© The Author(s), under exclusive licence to Cancer Research UK 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c470t-1b0d079b82b040db069f66619e1aa891ec3f38bdbbd45e850944b12d4a99ef863</citedby><cites>FETCH-LOGICAL-c470t-1b0d079b82b040db069f66619e1aa891ec3f38bdbbd45e850944b12d4a99ef863</cites><orcidid>0000-0003-1329-2180</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7109155/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7109155/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32020064$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ishii, Hiroki</creatorcontrib><creatorcontrib>Saitoh, Masao</creatorcontrib><creatorcontrib>Sakamoto, Kaname</creatorcontrib><creatorcontrib>Sakamoto, Kei</creatorcontrib><creatorcontrib>Saigusa, Daisuke</creatorcontrib><creatorcontrib>Kasai, Hirotake</creatorcontrib><creatorcontrib>Ashizawa, Kei</creatorcontrib><creatorcontrib>Miyazawa, Keiji</creatorcontrib><creatorcontrib>Takeda, Sen</creatorcontrib><creatorcontrib>Masuyama, Keisuke</creatorcontrib><creatorcontrib>Yoshimura, Kentaro</creatorcontrib><title>Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer</title><title>British journal of cancer</title><addtitle>Br J Cancer</addtitle><addtitle>Br J Cancer</addtitle><description>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. 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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.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>32020064</pmid><doi>10.1038/s41416-020-0732-y</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0003-1329-2180</orcidid><oa>free_for_read</oa></addata></record>
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subjects 631/67/1665/3016
692/308/575
692/4028/546
Biomedical and Life Sciences
Biomedicine
Cancer Research
Cell Line, Tumor
Diagnosis
Drug Resistance
Epidemiology
Head & neck cancer
Head and Neck Neoplasms - diagnosis
Head and Neck Neoplasms - pathology
Humans
Learning algorithms
Lecithin
Lipidomics - methods
Machine learning
Machine Learning - standards
Mass spectroscopy
Medical diagnosis
Molecular Medicine
Oncology
Phosphatidylcholine
Signal Transduction
Smad2 protein
Squamous cell carcinoma
Transforming Growth Factor beta - metabolism
Transforming growth factor-b
Transforming growth factor-b1
Tumor microenvironment
Tumors
title Lipidome-based rapid diagnosis with machine learning for detection of TGF-β signalling activated area in head and neck cancer
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