Metabolomic profiling and accurate diagnosis of basal cell carcinoma by MALDI imaging and machine learning

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Basal cell carcinoma (BCC), the most common keratinocyte cancer, presents a substantial public health challenge due to its high prevalence. Traditional diagnostic methods, which rely on visual examination and histopathological analysis, do not include metabolomic data. This exploratory study aims to molecularly characterize BCC and diagnose tumour tissue by applying matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) and machine learning (ML). BCC tumour development was induced in a mouse model and tissue sections containing BCC (n = 12) were analysed. The study design involved three phases: (i) Model training, (ii) Model validation and (iii) Metabolomic analysis. The ML algorithm was trained on MS data extracted and labelled in accordance with histopathology. An overall classification accuracy of 99.0% was reached for the labelled data. Classification of unlabelled tissue areas aligned with the evaluation of a certified Mohs surgeon for 99.9% of the total tissue area, underscoring the model's high sensitivity and specificity in identifying BCC. Tentative metabolite identifications were assigned to 189 signals of importance for the recognition of BCC, each indicating a potential tumour marker of diagnostic value. These findings demonstrate the potential for MALDI-MSI coupled with ML to characterize the metabolomic profile of BCC and to diagnose tumour tissue with high sensitivity and specificity. Further studies are needed to explore the potential of implementing integrated MS and automated analyses in the clinical setting.

OriginalsprogEngelsk
Artikelnummere15141
TidsskriftExperimental Dermatology
Vol/bind33
Udgave nummer7
Antal sider11
ISSN0906-6705
DOI
StatusUdgivet - 2024

Bibliografisk note

Funding Information:
The authors would like to express their gratitude to Uffe Hoegh Olesen, Katrine Togsverd-Bo, Catrine Fischer Goldschmidt and Diana Hoeeg for assistance during the study. Funding from the Danish Cancer Society (R209-A12968-18-S24), the Independent Research Fund Denmark | Technology and Production (grant no. 9041-00203B), the Carlsberg Foundation (grant no. CF14-0214), the Lundbeck Foundation (grant no. R307-2018-3318) and Copenhagen University Hospital, Bispebjerg and Frederiksberg is gratefully acknowledged. The research was conducted as a part of the Danish Research Center for Skin Cancer (www.vfhk.org/resca.org), a public-private research partnership between the Private Hospital Molholm, Aalborg University Hospital and Copenhagen University Hospital, Bispebjerg and Frederiksberg. Coauthor, Fernanda E. Pinto, was funded by the Research fund of the Capital Region of Denmark (grant no. A7106).

Funding Information:
The authors would like to express their gratitude to Uffe Hoegh Olesen, Katrine Togsverd\u2010Bo, Catrine Fischer Goldschmidt and Diana Hoeeg for assistance during the study. Funding from the Danish Cancer Society (R209\u2010A12968\u201018\u2010S24), the Independent Research Fund Denmark | Technology and Production (grant no. 9041\u201000203B), the Carlsberg Foundation (grant no. CF14\u20100214), the Lundbeck Foundation (grant no. R307\u20102018\u20103318) and Copenhagen University Hospital, Bispebjerg and Frederiksberg is gratefully acknowledged. The research was conducted as a part of the Danish Research Center for Skin Cancer ( www.vfhk.org/resca.org ), a public\u2010private research partnership between the Private Hospital Molholm, Aalborg University Hospital and Copenhagen University Hospital, Bispebjerg and Frederiksberg. Coauthor, Fernanda E. Pinto, was funded by the Research fund of the Capital Region of Denmark (grant no. A7106).

Publisher Copyright:
© 2024 The Author(s). Experimental Dermatology published by John Wiley & Sons Ltd.

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