Interrogation of IDH1 Status in Gliomas by Fourier Transform Infrared Spectroscopy

Abstract

Mutations in the isocitrate dehydrogenase 1 (IDH1) gene are found in a high proportion of diffuse gliomas. The presence of the IDH1 mutation is a valuable diagnostic, prognostic and predictive biomarker for the management of patients with glial tumours. Techniques involving vibrational spectroscopy, e.g., Fourier transform infrared (FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer detection, and have the potential to contribute to diagnostics. The implementation of FTIR microspectroscopy during surgical biopsy could present a fast, label-free method for molecular genetic classification. For example, the rapid determination of IDH1 status in a patient with a glioma diagnosis could inform intra-operative decision-making between alternative surgical strategies. In this study, we utilized synchrotron-based FTIR microanalysis to probe tissue microarray sections from 79 glioma patients, and distinguished the positive class (IDH1-mutated) from the IDH1-wildtype glioma, with a sensitivity and specificity of 82.4% and 83.4%, respectively. We also examined the ability of attenuated total reflection (ATR)-FTIR spectroscopy in detecting the biomolecular events and global epigenetic and metabolic changes associated with mutations in the IDH1 enzyme, in blood serum samples collected from an additional 72 brain tumour patients. Centrifugal filtration enhanced the diagnostic ability of the classification models, with balanced accuracies up to ~69%. Identification of the molecular status from blood serum prior to biopsy could further direct some patients to alternative treatment strategies.

Liquid Biopsy for Pancreatic Cancer Detection Using Infrared Spectroscopy

“…In particular, the detection of brain cancer has been moving past the proof-of-concept stage to the clinical assessment phase, with optimal results amounting to a sensitivity of 83.3% and a specificity of 87.0% in a cohort of 104 recruited patients, and a sensitivity of 81% and a specificity of 80% in a cohort of 385 patients, with a sensitivity of 91% for glioblastomas [18,19]. These outputs were obtained using a retrospective cohort of 724 patients, with values of up to 93.2% for sensitivity and 92.0% for specificity acquired for the training set [18][19][20][21][22][23]. Recently, an update was published by Cameron et al, which included 603 recruited patients and explored the possibility of tuning classification models for the best sensitivity or specificity; the sensitivity-tuned model achieved sensitivity of 96% at 45% specificity, whilst the specificity-tuned model reached a specificity of 90% at 47% sensitivity, for discriminating brain tumors from non-cancerous symptomatic patients [24].…”

Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies

“…Raman spectroscopy (RS) [5,6] and similar optical technology (e.g., Fourier Transform Infrared Spectroscopy (FTIR) [7,8]) resulted effective tools to discriminate between cancer and normal tissue and, more recently, to investigate IDH mutational status [9,10]. RS studies using fresh tissue samples are of primary importance to improve Raman measurement in vivo, avoiding the well-known samples artefacts due to the histological blocks processing and storage [11].…”

Quantum Cascade Laser-Based Infrared Imaging as a Label-Free and Automated Approach to Determine Mutations in Lung Adenocarcinoma

“…In addition to the tumor identification in tissue samples, FTIR imaging may be employed for further analyses, such as glioma grading 24 for distinction of tumors in both colon 25 and bladder 20,26 cancer samples, as well as for mutation analysis in gliomas. 27 A workflow to distinguish thoracic and lung tumors not only by tumor type, but also to classify the subtypes of diffuse malignant mesothelioma (sarcomatoid and epithelioid, with 88% accuracy) 28 and the five World Health Organizationedefined lung adenocarcinoma histologic types (acinary, solid, papillary, micropapillary, and lepidic, with 96% accuracy) 29 was presented. The combination of FTIR imaging and laser capture microdissection (LCM) with subsequent proteomics adds molecular resolution to the spatial resolution provided by hyperspectral data sets.…”