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

IF 16.4 1区 医学 Q1 CLINICAL NEUROLOGY Neuro-oncology Pub Date : 2024-11-04 DOI:10.1093/neuonc/noae101
Adrian Lita, Joel Sjöberg, David Păcioianu, Nicoleta Siminea, Orieta Celiku, Tyrone Dowdy, Andrei Păun, Mark R Gilbert, Houtan Noushmehr, Ion Petre, Mioara Larion
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Abstract

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 the CpG island hypermethylated phenotype (G-CIMP)-high and G-CIMP-low molecular phenotypes within the IDH1mut types.

Conclusions: Our results demonstrate the potential of label-free Raman spectroscopy to classify glioma subtypes from FFPE slides and to extract meaningful biological information thus opening the door for future applications on these archived tissues in other cancers.

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基于拉曼的机器学习平台揭示了 IDHmut 和 IDHwt 胶质瘤之间独特的代谢差异。
背景:福尔马林固定、石蜡包埋(FFPE)组织切片通常用于癌症诊断和临床决策,并储存在生物库中,但由于包埋介质产生的背景,它们在基于拉曼光谱的研究中的应用受到了限制:方法:利用自发拉曼光谱对来自 46 个已知甲基化亚型患者样本的 FFPE 组织进行分子指纹分析。利用光谱构建肿瘤/非肿瘤、IDH1WT/IDH1mut 和甲基化亚型分类器。支持向量机和随机森林用于识别最具鉴别力的拉曼频率。受激拉曼光谱用于验证所识别的频率。胶质瘤细胞系的质谱分析和 TCGA 被用来验证生物学发现:在此,我们开发了基于拉曼光谱的恶性胶质瘤病理学(APOLLO)--一种计算工作流程,它能根据 FFPE 组织切片的自发拉曼光谱预测胶质瘤的不同亚型。我们新颖的 APOLLO 平台可区分肿瘤和非肿瘤组织,并识别出肿瘤中强度更高的 DNA 和蛋白质对应的新拉曼峰。APOLLO 能区分异柠檬酸脱氢酶 1 突变体(IDH1mut)和野生型(IDH1WT)肿瘤,并能识别 IDHmut 胶质瘤中高度丰富的胆固醇酯水平。此外,APOLLO在更精细的临床相关胶质瘤甲基化亚型之间具有很高的鉴别力,能区分IDH1mut类型中的CpG岛高甲基化表型(G-CIMP)和G-CIMP低分子表型:我们的研究结果表明,无标记拉曼光谱具有从 FFPE 切片中对胶质瘤亚型进行分类和提取有意义的生物信息的潜力,从而为今后在其他癌症的这些存档组织中的应用打开了大门。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neuro-oncology
Neuro-oncology 医学-临床神经学
CiteScore
27.20
自引率
6.30%
发文量
1434
审稿时长
3-8 weeks
期刊介绍: Neuro-Oncology, the official journal of the Society for Neuro-Oncology, has been published monthly since January 2010. Affiliated with the Japan Society for Neuro-Oncology and the European Association of Neuro-Oncology, it is a global leader in the field. The journal is committed to swiftly disseminating high-quality information across all areas of neuro-oncology. It features peer-reviewed articles, reviews, symposia on various topics, abstracts from annual meetings, and updates from neuro-oncology societies worldwide.
期刊最新文献
Consensus recommendations for an integrated diagnostic approach to peripheral nerve sheath tumors arising in the setting of Neurofibromatosis type 1 (NF1). Validation and next-generation update of a DNA methylation-based recurrence predictor for meningioma: a multicenter prospective study. Potential of ex vivo organotypic slice cultures in neuro-oncology. Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma. Disturbance in cerebral blood microcirculation and hypoxic-ischemic microenvironment are associated with the development of brain metastasis.
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