Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy. Issue 1 (28th May 2019)

• Record Type: Journal Article
• Authors: Livermore, Laurent James; Isabelle, Martin; Bell, Ian Mac; Scott, Connor; Walsby-Tickle, John; Gannon, Joan; Plaha, Puneet; Vallance, Claire; Ansorge, Olaf
• Is Part Of: Neuro-oncology advances. Volume 1:Issue 1(2019)
• Publication Date: 2019-05-28
• Subjects: brain tumor -- intraoperative -- isocitrate hydrogenase -- Raman spectroscopy -- personalized medicine; 616.99481
• DOI: 10.1093/noajnl/vdz008
• Languages: English
• ISSNs: 2632-2498

Abstract

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.