Stratifying Brain Tumour Histological Sub-Types: The Application of ATR-FTIR Serum Spectroscopy in Secondary Care

Abstract

Patients living with brain tumours have the highest average years of life lost of any cancer, ultimately reducing average life expectancy by 20 years. Diagnosis depends on brain imaging and most often confirmatory tissue biopsy for histology. The majority of patients experience non-specific symptoms, such as headache, and may be reviewed in primary care on multiple occasions before diagnosis is made. Sixty-two per cent of patients are diagnosed on brain imaging performed when they deteriorate and present to the emergency department. Histological diagnosis from invasive surgical biopsy is necessary prior to definitive treatment, because imaging techniques alone have difficulty in distinguishing between several types of brain cancer. However, surgery itself does not necessarily control tumour growth, and risks morbidity for the patient. Due to their similar features on brain scans, glioblastoma, primary central nervous system lymphoma and brain metastases have been known to cause radiological confusion. Non-invasive tests that support stratification of tumour subtype would enhance early personalisation of treatment selection and reduce the delay and risks associated with surgery for many patients. Techniques involving vibrational spectroscopy, such as attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer diagnostics. In this study, infrared spectra from 641 blood serum samples obtained from brain cancer and control patients have been collected. Firstly, we highlight the capability of ATR-FTIR to distinguish between healthy controls and brain cancer at sensitivities and specificities above 90%, before defining subtle differences in protein secondary structures between patient groups through Amide I deconvolution. We successfully differentiate several types of brain lesions (glioblastoma, meningioma, primary central nervous system lymphoma and metastasis) with balanced accuracies >80%. A reliable blood serum test capable of stratifying brain tumours in secondary care could potentially avoid surgery and speed up the time to definitive therapy, which would be of great value for both neurologists and patients.

Interrogation of IDH1 Status in Gliomas by Fourier Transform Infrared Spectroscopy

“…The classification models were retrained and tested on 100 different randomly selected training and test set partitions to provide a more reliable result. Classification results of the ATR-FTIR spectra from random forest (RF), partial least squares-discriminant analysis (PLS-DA), and support vector machine (SVM) analysis have been compared here, as described in our previous work [51,56].…”

Synchrotron-Based Fourier-Transform Infrared Micro-Spectroscopy (SR-FTIRM) Fingerprint of the Small Anionic Molecule Cobaltabis(dicarbollide) Uptake in Glioma Stem Cells

“…Therefore, the potential of SR-FTIRM to detect simple biochemical components might be used to stratify cells by their relative contents of lipids, glycogen, proteins, and other components, and this specific signature could predict therapy response. Indeed, ATR-FTIR spectroscopy has analytical capabilities for cancer diagnosis and is able to distinguish between healthy controls and brain cancer at sensitivities and specificities above 90%, and differentiate several types of brain tumors with accuracies >80% [ 68 ].…”

“…Therefore, the potential of SR-FTIRM to detect simple biochemical components might be used to stratify cells by their relative contents of lipids, glycogen, proteins, and other components, and this specific signature could predict therapy response. Indeed, ATR-FTIR spectroscopy has analytical capabilities for cancer diagnosis and is able to distinguish between healthy controls and brain cancer at sensitivities and specificities above 90%, and differentiate several types of brain tumors with accuracies >80% [68]. In addition, we analyzed by comparison spectroscopic differences between Na[o-COSAN] treated and untreated cells and we found important changes in biomolecules in both GIC7 and PG88 cell lines (Figure 11b and Supplementary Table S5), including the proteins' secondary structure increasing the tendency to α-helix, lipid modifications, and DNA chains, either in GIC7 or PG88 cells.…”

Grade Diagnosis of Human Glioma Based on Fingerprint and Artificial Neural Network

“…Smith et al [25] used the supervised machine learning algorithm of RF as a classifier to separate patients into cancer and noncancer categories based upon the intensities of wavenumbers presented in their spectra and finally achieved a sensitivity and specificity up to 92.8% and 91.5%, respectively. Cameron et al [26] assessed patients with various brain tumors by using their serum and applied the PLS-DA model to their spectral signatures collected by attenuated-total-reflection FTIR spectroscopy, achieving a sensitivity and specificity greater than 92% in the classification of brain tumors and control patients. Moreover, metastasis vs. glioblastoma with the linear SVM reported a 84.3% sensitivity, 96.2% specificity and receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.9, suggesting a high diagnostic capability [26].…”

“…Cameron et al [26] assessed patients with various brain tumors by using their serum and applied the PLS-DA model to their spectral signatures collected by attenuated-total-reflection FTIR spectroscopy, achieving a sensitivity and specificity greater than 92% in the classification of brain tumors and control patients. Moreover, metastasis vs. glioblastoma with the linear SVM reported a 84.3% sensitivity, 96.2% specificity and receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.9, suggesting a high diagnostic capability [26]. As a pattern-recognition-based approach, the artificial neural network (ANN) has been proved to be effective in the analysis of biological specimens [18].…”