IDH1 mutation in human glioma induces chemical alterations that are amenable to optical Raman spectroscopy

Uckermann, Ortrud; Yao, Wenmin; Juratli, Tareq A.; Galli, Roberta; Leipnitz, Elke; Meinhardt, Matthias; Koch, Edmund; Schackert, Gabriele; Steiner, Gerald; Kirsch, Matthias

doi:10.1007/s11060-018-2883-8

Cited by 41 publications

(29 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Others have reported the use of Raman and attenuated total reflectance-Fourier transform infrared spectroscopy spectroscopy to distinguish IDH-mutant versus IDH-wild-type but have used snap frozen sections before spectroscopy, which still requires laboratory-based sample preparation. 36,37 These vibrational spectroscopy studies have not attempted to distinguish oligodendrogliomas as a separate group, which is essential for clinical decision-making. Raman spectroscopy has also been used intraoperatively in the form of a probe device to attempt to differentiate tumor boundaries.…”

Section: Discussionmentioning

confidence: 99%

Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy

Livermore

Isabelle

Bell

et al. 2019

Neuro-Oncology Advances

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy

Livermore

Isabelle

Bell

et al. 2019

Neuro-Oncology Advances

View full text Add to dashboard Cite

show abstract

“…Desroches et al constructed a SVM model, which was evaluated by a leave‐one‐out cross‐validation approach, giving a performance with a sensitivity of 80% and a specificity of 90%. Linear discriminant analysis (LDA), which was based on Mahalanobis distances, has also been employed to predict the IDH1‐mutation status with a correct rate of 89% for the test set and 88% for the training set . Additionally, the boosted trees machine learning method has been presented for on‐site detection of human brain cancer with a sensitivity of 93% and a specificity of 91% .…”

Section: Resultsmentioning

confidence: 99%

Detection of glioma by surface‐enhanced Raman scattering spectra with optimized mathematical methods

Sun

Fang

Zhang

et al. 2019

J Raman Spectroscopy

View full text Add to dashboard Cite

This work aimed to establish a fast and accurate method to detect glioma by combining surface‐enhanced Raman scattering (SERS) and mathematical analysis. At first, 785‐nm laser was selected as the optimum laser to acquire Raman spectra of human brain tissue. Second, it was verified that Raman data in the range of 1,200–1,600 cm−1 could improve the performance of classifier. Based on the analytical results of 1,200–1,600 cm−1 data, the sensitivity and specificity of partial least square (PLS) analysis and back‐propagation neural network (BPNN) were as high as 100%, whereas the sensitivity and specificity of support vector machine (SVM) were 96% and 100%, respectively. Among them, PLS was more potential in the detection of glioma, because of its lower computational cost compared with SVM and BPNN. Moreover, the correlation between observed Raman peaks and 2‐hydroxyglutarate (2HG; 512, 790, 1,204, 1,302, and 1,463 cm−1) was observed, suggesting 2HG as a potential marker of glioma using its Raman spectroscopic signatures. After all, SERS combining with mathematical analysis could be a promising tool for the accurate detection of glioma.

show abstract

“…[131][132][133]136,148 Most studies involving brain tissue rely on filter methods to identify important features, 149,150 and in some cases, features are selected manually. 148,151,152 In proteomics and genomics studies, performance-based and optimization-embedded have proven superior to filter methods in recovering truly important features in sparse, high-dimensional datasets. 153,154 Embedded methods have recently started to emerge in the biomedical Raman literature to identify crucial features, but not as part of a supervised learning task.…”

Section: Supervised Machine Learningmentioning

confidence: 99%

Rise of Raman spectroscopy in neurosurgery: a review

et al. 2020

View full text Add to dashboard Cite

Significance: Although the clinical potential for Raman spectroscopy (RS) has been anticipated for decades, it has only recently been used in neurosurgery. Still, few devices have succeeded in making their way into the operating room. With recent technological advancements, however, vibrational sensing is poised to be a revolutionary tool for neurosurgeons. Aim: We give a summary of neurosurgical workflows and key translational milestones of RS in clinical use and provide the optics and data science background required to implement such devices. Approach: We performed an extensive review of the literature, with a specific emphasis on research that aims to build Raman systems suited for a neurosurgical setting. Results: The main translatable interest in Raman sensing rests in its capacity to yield label-free molecular information from tissue intraoperatively. Systems that have proven usable in the clinical setting are ergonomic, have a short integration time, and can acquire high-quality signal even in suboptimal conditions. Moreover, because of the complex microenvironment of brain tissue, data analysis is now recognized as a critical step in achieving high performance Raman-based sensing. Conclusions: The next generation of Raman-based devices are making their way into operating rooms and their clinical translation requires close collaboration between physicians, engineers, and data scientists.

show abstract

IDH1 mutation in human glioma induces chemical alterations that are amenable to optical Raman spectroscopy

Abstract: Raman spectroscopy constitutes a simple, rapid and safe procedure for determination of the IDH1 mutation that shows great promise for clinically relevant in situ diagnostics.

Cited by 41 publications

References 50 publications

Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy

Rapid intraoperative molecular genetic classification of gliomas using Raman spectroscopy

Detection of glioma by surface‐enhanced Raman scattering spectra with optimized mathematical methods

Rise of Raman spectroscopy in neurosurgery: a review

Contact Info

Product

Resources

About