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TBMT-02. APOLLO: RAMAN-BASED PATHOLOGY OF MALIGNANT GLIOMA

BACKGROUND: DNA methylation is an essential component for integrative diagnosis of gliomas. Methylation subtype prediction of gliomas is currently done via sample extraction of high-quality DNA (~1ug), methylome profiling, followed by probe identification, curation and subsequent analysis via differ...

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Detalles Bibliográficos
Autores principales: Lita, Adrian, Sjöberg, Joel, Filipescu, Stefan, Celiku, Orieta, Petre, Luigia, Gilbert, Mark, Noushmehr, Houtan, Petre, Ion, Larion, Mioara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7992223/
http://dx.doi.org/10.1093/noajnl/vdab024.084
Descripción
Sumario:BACKGROUND: DNA methylation is an essential component for integrative diagnosis of gliomas. Methylation subtype prediction of gliomas is currently done via sample extraction of high-quality DNA (~1ug), methylome profiling, followed by probe identification, curation and subsequent analysis via different random forest classifiers. However, the DNA methylation classification is not always available for all the samples. Examples include when the existing material is not suitable for methylation profiling or the sample is very limiting. Therefore, we hypothesized that Raman spectroscopy might be suitable to predict the glioma methylome, based upon its ability to create a molecular fingerprint of the tumor and would provide biological insights unknown before. METHODS: Coherent Raman Spectroscopy was used for molecular fingerprinting of the regions of interest using 1mm(2) FFPE tissue spots from 39 patient samples with LGm1 to LGm6 methylation subtypes. Spectral information was then used to train a convolutional neural network (CNN) and develop a prediction algorithm, capable of detecting the glioma methylation subtypes. 70 % of the dataset was used for model training while the remaining 30% for validation. Oversampling was used to obtain a subtype-balanced data distribution. In addition, supervised wrapper methods and random forests were used to identify the top 50 most discriminatory Raman frequencies out of 1738. RESULTS: We demonstrate that Raman spectroscopy can accurately and rapidly classify gliomas according to their methylation subtype from achieved FFPE samples, which are routinely present in pathological laboratories as a complementary mean to obtain this important classification when other analyses are not available. The most discriminatory frequencies show differential spectral intensities depending upon the glioma subtypes across the larger areas of the tissue. CONCLUSIONS: The non-destructive nature of this method and the ability to be applied on FFPE samples directly, allows the histopathologist to reuse of the same slide for subsequent staining and downstream analyses.