Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy

Abstract

Background The molecular genetic classification of gliomas, particularly the identification of isocitrate dehydrogenase (IDH) mutations, is critical for clinical and surgical decision-making. Raman spectroscopy probes the unique molecular vibrations of a sample to accurately characterize its molecular composition. No sample processing is required allowing for rapid analysis of tissue. The aim of this study was to evaluate the ability of Raman spectroscopy to rapidly identify the common molecular genetic subtypes of diffuse glioma in the neurosurgical setting using fresh biopsy tissue. In addition, classification models were built using cryosections, formalin-fixed paraffin-embedded (FFPE) sections and LN-18 (IDH-mutated and wild-type parental cell) glioma cell lines. Methods Fresh tissue, straight from neurosurgical theatres, underwent Raman analysis and classification into astrocytoma, IDH-wild-type; astrocytoma, IDH-mutant; or oligodendroglioma. The genetic subtype was confirmed on a parallel section using immunohistochemistry and targeted genetic sequencing. Results Fresh tissue samples from 62 patients were collected (36 astrocytoma, IDH-wild-type; 21 astrocytoma, IDH-mutated; 5 oligodendroglioma). A principal component analysis fed linear discriminant analysis classification model demonstrated 79%–94% sensitivity and 90%–100% specificity for predicting the 3 glioma genetic subtypes. For the prediction of IDH mutation alone, the model gave 91% sensitivity and 95% specificity. Seventy-nine cryosections, 120 FFPE samples, and LN18 cells were also successfully classified. Meantime for Raman data collection was 9.5 min in the fresh tissue samples, with the process from intraoperative biopsy to genetic classification taking under 15 min. Conclusion These data demonstrate that Raman spectroscopy can be used for the rapid, intraoperative, classification of gliomas into common genetic subtypes.

Glioma biopsies Classification Using Raman Spectroscopy and Machine Learning Models on Fresh Tissue Samples

“…However, a learning effect was observed during the study, with lower analytical time with later samples. Data regarding diagnostic power in discriminating relevant molecular features are lacking [ 50 ] and should also be addressed to probe the diagnostic accuracy in this relevant issue. The limited number of cases also hamper a broader analysis of the data.…”

Raman microspectroscopy and Raman imaging reveal biomarkers specific for thoracic aortic aneurysms

“… 59 , 60 , 61 Raman measurements are now used for rapid intraoperative diagnosis by stimulated Raman histology for brain tumors. 62 , 63 , 64 Combined with automated spectral decomposition methods, Raman techniques have the potential to extract target signals from big datasets without the need for extensive training of the end user. Fiberoptic confocal Raman probes have already been applied in preclinical setups.…”

Ultra-Rapid Somatic Variant Detection via Real-Time Threshold Sequencing

“…Immunohistochemistry (IHC) can detect certain biomarkers that correlate to actionable mutations 11,12 , but is difficult to extend and apply to other mutation hot spot loci or oncogenes. Similarly, Raman spectroscopy has been shown to enable rapid diagnosis of specific IDH mutations and 1p19q co-deletion 13 , but is currently limited in scope from diagnosing other clinically relevant targets. Intraoperative fluorescent in-situ hybridization (FISH) 14,15 and targeted quantitative polymerase chain reaction (qPCR) assays 8,16,17 have been shown to provide rapid, and specific detection of targeted mutations or other cancer biomarkers.…”