Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma

作者：Adrian Lita, Joel Sjöberg, David Păcioianu, Nicoleta Siminea, Orieta Celiku, Tyrone Dowdy, Andrei Păun, Mark R. Gilbert, Houtan Noushmehr, Ion Petre, Mioara Larion · 发表于：Neuro-Oncology · 年份：2024 · DOI：10.1093/neuonc/noae101 · 被引用次数：17 · 研究领域：Spectroscopy Techniques in Biomedical and Chemical Research、Molecular Biology Techniques and Applications、Cancer Genomics and Diagnostics

BACKGROUND: Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the background coming from embedding media. METHODS: Spontaneous Raman spectroscopy was used for molecular fingerprinting of FFPE tissue from 46 patient samples with known methylation subtypes. Spectra were used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Support vector machine and random forest were used to identify the most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the frequencies identified. Mass spectrometry of glioma cell lines and TCGA were used to validate the biological findings. RESULTS: Here, we develop APOLLO (rAman-based PathOLogy of maLignant gliOma)-a computational workflow that predicts different subtypes of glioma from spontaneous Raman spectra of FFPE tissue slides. Our novel APOLLO platform distinguishes tumors from nontumor tissue and identifies novel Raman peaks corresponding to DNA and proteins that are more intense in the tumor. APOLLO differentiates isocitrate dehydrogenase 1 mutant (IDH1mut) from wild-type (IDH1WT) tumors and identifies cholesterol ester levels to be highly abundant in IDHmut glioma. Moreover, APOLLO achieves high discriminative power between finer, clinically relevant glioma methylation subtypes, distinguishing between ...