Differentiation of primary CNS lymphoma and glioblastoma using Raman spectroscopy and machine learning algorithms

Objective and Methods: Timely discrimination between primary CNS lymphoma (PCNSL) and glioblastoma is crucial for diagnostics and therapy, but most importantly also determines the intraoperative surgical course. Advanced radiological methods allow this to a certain extent but ultimately, biopsy is still necessary for final diagnosis. As an upcoming method that enables tissue analysis by tracking changes in the vibrational state of molecules via inelastic scattered photons, we used Raman Spectroscopy (RS) as a label free method to examine specimens of both tumor entities intraoperatively, as well as postoperatively in formalin fixed paraffin embedded (FFPE) samples. Results: We applied and compared statistical performance of linear and nonlinear machine learning algorithms (Logistic Regression, Random Forest and XGBoost), and found that Random Forest classification distinguished the two tumor entities with a balanced accuracy of 82,4% in intraoperative tissue condition and with 94% using measurements of distinct tumor areas on FFPE tissue. Taking a deeper insight into the spectral properties of the tumor entities, we describe different tumor-specific Raman shifts of interest for classification. Conclusions: Due to our findings, we propose RS as an additional tool for fast and non-destructive, perioperative tumor tissue discrimination, which may augment treatment options at an early stage. RS may further serve as a useful additional tool for neuropathological diagnostics with little requirements for tissue integrity

TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes

The blood–brain barrier (BBB) is a selectively permeable boundary that separates the circulating blood from the extracellular fluid of the brain and is an essential component for brain homeostasis. In glioblastoma (GBM), the BBB of peritumoral vessels is often disrupted. Pericytes, being important to maintaining BBB integrity, can be functionally modified by GBM cells which induce proliferation and cell motility via the TGF-β-mediated induction of central epithelial to mesenchymal transition (EMT) factors. We demonstrate that pericytes strengthen the integrity of the BBB in primary endothelial cell/pericyte co-cultures as an in vitro BBB model, using TEER measurement of the barrier integrity. In contrast, this effect was abrogated by TGF-β or conditioned medium from TGF-β secreting GBM cells, leading to the disruption of a so far intact and tight BBB. TGF-β notably changed the metabolic behavior of pericytes, by shutting down the TCA cycle, driving energy generation from oxidative phosphorylation towards glycolysis, and by modulating pathways that are necessary for the biosynthesis of molecules used for proliferation and cell division. Combined metabolomic and transcriptomic analyses further underscored that the observed functional and metabolic changes of TGF-β-treated pericytes are closely connected with their role as important supporting cells during angiogenic processes

STUDY OF EARLY MELANOMA BRAIN METASTASIS MECHANISMS USING IN VITRO AND IN VIVO MODELS OF TUMOR INVASION

Of all skin cancers, melanoma is the most fatal. Of all cancer types, melanoma is also the cancer with the highest level of brain tropism. Approximately 50% of patients with stage IV melanoma are diagnosed with melanoma brain metastases. A percentage that rises when postmortem patients are also taken into account. Following lung cancer and breast cancer, melanoma is the leading cause of malignant metastasis to the central nervous system. Of all metastatic brain tumors, melanoma represents 6-12% of cases. The overall survival rate following a diagnosis of melanoma brain metastases has been historically low. However, over the past ten years, advances in targeted therapies as well as in immunotherapies have significantly improved the survival rate of patients with advanced melanoma. Melanoma brain metastases most frequently occur at the junction between the gray and the white matter and in the frontal lobe. In order to reach the brain parenchyma, metastases must cross the brain vasculature. The specific properties of the blood vessels that perfuse the central nervous system are referred to as the blood-brain barrier. They allow these vessels to finely regulate the flow of cells, ions and molecules between the bloodstream and the brain parenchyma in order to preserve brain homeostasis for the proper functioning of neurons and the protection of the brain against toxic and pathogenic agents. Abnormalities in this functional interfacing barrier that separates the brain from the bloodstream are a critical element in the development and progression of several neurological pathologies. A poor understanding of the early mechanisms of metastasis crossing the blood-brain barrier constitutes an obstacle to the development of effective preventive therapeutic strategies as well as a particularly challenging domain of interest as it is one of the most crucial and least documented steps in the metastasizing process to the brain. Here, we focused on the ideation and consequent creation of effective in vitro and in vivo models to help identify and characterize as meticulously as possible, the players that are implicated in the crossing of melanoma metastases through the blood-brain barrier to reach the brain parenchyma. We used human immortalized cells (endothelial cells, pericytes and astrocytes) in triple coculture to recreate a blood-brain barrier in vitro and be able to investigate eventual changes in the gene expression of the tumor cells crossing the model. In parallel, we have set up an in vivo murine model to recreate the process of brain metastasis by injecting melanoma tumor cells into the left ventricle of the heart and thus be able to study the early stages of blood-brain barrier invasion. The analysis of the murine tissues was performed by Correlative light-electron microscopy (CLEM) and the results obtained revealed the presence of cells in the brain that present artifacts that have the same appearance as melanosomes. Experiments using focused ion beam scanning electron microscopes (FIB-SEM) as well as nanoscale secondary ion mass spectrometry (NanoSIMS) may be conducted to take the investigation further

Application of Raman Spectroscopy for Detection of Histologically Distinct Areas in Formalin-fixed Paraffin-embedded (FFPE) Glioblastoma

Background Although microscopic assessment is still the diagnostic gold standard in pathology, non-light microscopic methods such as new imaging methods and molecular pathology have considerably contributed to more precise diagnostics. As an upcoming method, Raman spectroscopy (RS) offers a "molecular fingerprint" which could be used to differentiate tissue heterogeneity or diagnostic entities. RS has been successfully applied on fresh and frozen tissue, however more aggressively, chemically treated tissue such as formalin-fixed, paraffin-embedded (FFPE) samples are challenging for RS. Methods To address this issue, we examined FFPE samples of morphologically highly heterogeneous glioblastoma (GBM) using RS in order to classify histologically defined GBM areas according to RS spectral properties. We have set up a SVM (support vector machine)-based classifier in a training cohort and corroborated our findings in a validation cohort. Results Our trained classifier identified distinct histological areas such as tumor core and necroses in GBM with an overall accuracy of 70.5% based on spectral properties of RS. With an absolute misclassification of 21 out of 471 Raman measurements, our classifier has the property of precisely distinguishing between normal appearing brain tissue and necrosis. When verifying the suitability of our classifier system in a second independent dataset, very little overlap between necrosis and normal appearing brain tissue can be detected. Conclusion These findings show that histologically highly variable samples such as GBM can be reliably recognized by their spectral properties using RS. As a conclusion, we propose that RS may serve useful as a future method in the pathological toolbox

Intraoperative discrimination of native meningioma and dura mater by Raman spectroscopy

Meningiomas are among the most frequent tumors of the central nervous system. For a total resection, shown to decrease recurrences, it is paramount to reliably discriminate tumor tissue from normal dura mater intraoperatively. Raman spectroscopy (RS) is a non-destructive, label-free method for vibrational analysis of biochemical molecules. On the microscopic level, RS was already used to differentiate meningioma from dura mater. In this study we test its suitability for intraoperative macroscopic meningioma diagnostics. RS is applied to surgical specimen of intracranial meningiomas. The main purpose is the differentiation of tumor from normal dura mater, in order to potentially accelerate the diagnostic workflow. The collected meningioma and dura mater samples (n = 223 tissue samples from a total of 59 patients) are analyzed under untreated conditions using a new partially robotized RS acquisition system. Spectra (n = 1273) are combined with the according histopathological analysis for each sample. Based on this, a classifier is trained via machine learning. Our trained classifier separates meningioma and dura mater with a sensitivity of 96.06 [Formula: see text] 0.03% and a specificity of 95.44 [Formula: see text] 0.02% for internal fivefold cross validation and 100% and 93.97% if validated with an external test set. RS is an efficient method to discriminate meningioma from healthy dura mater in fresh tissue samples without additional processing or histopathological imaging. It is a quick and reliable complementary diagnostic tool to the neuropathological workflow and has potential for guided surgery. RS offers a safe way to examine unfixed surgical specimens in a perioperative setting

Machine Learning-Assisted Classification of Paraffin-Embedded Brain Tumors with Raman Spectroscopy

Raman spectroscopy (RS) has demonstrated its utility in neurooncological diagnostics, spanning from intraoperative tumor detection to the analysis of tissue samples peri- and postoperatively. In this study, we employed Raman spectroscopy (RS) to monitor alterations in the molecular vibrational characteristics of a broad range of formalin-fixed, paraffin-embedded (FFPE) intracranial neoplasms (including primary brain tumors and meningiomas, as well as brain metastases) and considered specific challenges when employing RS on FFPE tissue during the routine neuropathological workflow. We spectroscopically measured 82 intracranial neoplasms on CaF2 slides (in total, 679 individual measurements) and set up a machine learning framework to classify spectral characteristics by splitting our data into training cohorts and external validation cohorts. The effectiveness of our machine learning algorithms was assessed by using common performance metrics such as AUROC and AUPR values. With our trained random forest algorithms, we distinguished among various types of gliomas and identified the primary origin in cases of brain metastases. Moreover, we spectroscopically diagnosed tumor types by using biopsy fragments of pure necrotic tissue, a task unattainable through conventional light microscopy. In order to address misclassifications and enhance the assessment of our models, we sought out significant Raman bands suitable for tumor identification. Through the validation phase, we affirmed a considerable complexity within the spectroscopic data, potentially arising not only from the biological tissue subjected to a rigorous chemical procedure but also from residual components of the fixation and paraffin-embedding process. The present study demonstrates not only the potential applications but also the constraints of RS as a diagnostic tool in neuropathology, considering the challenges associated with conducting vibrational spectroscopic analysis on formalin-fixed, paraffin-embedded (FFPE) tissue

Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms

peer reviewedUnderstanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas—vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%—but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control

Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms

Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas—vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%—but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control

Computational Assessment of Spectral Heterogeneity within Fresh Glioblastoma Tissue Using Raman Spectroscopy and Machine Learning Algorithms

Understanding and classifying inherent tumor heterogeneity is a multimodal approach, which can be undertaken at the genetic, biochemical, or morphological level, among others. Optical spectral methods such as Raman spectroscopy aim at rapid and non-destructive tissue analysis, where each spectrum generated reflects the individual molecular composition of an examined spot within a (heterogenous) tissue sample. Using a combination of supervised and unsupervised machine learning methods as well as a solid database of Raman spectra of native glioblastoma samples, we succeed not only in distinguishing explicit tumor areas—vital tumor tissue and necrotic tumor tissue can correctly be predicted with an accuracy of 76%—but also in determining and classifying different spectral entities within the histomorphologically distinct class of vital tumor tissue. Measurements of non-pathological, autoptic brain tissue hereby serve as a healthy control since their respective spectroscopic properties form an individual and reproducible cluster within the spectral heterogeneity of a vital tumor sample. The demonstrated decipherment of a spectral glioblastoma heterogeneity will be valuable, especially in the field of spectroscopically guided surgery to delineate tumor margins and to assist resection control

TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes

The blood–brain barrier (BBB) is a selectively permeable boundary that separates the circulating blood from the extracellular fluid of the brain and is an essential component for brain homeostasis. In glioblastoma (GBM), the BBB of peritumoral vessels is often disrupted. Pericytes, being important to maintaining BBB integrity, can be functionally modified by GBM cells which induce proliferation and cell motility via the TGF-β-mediated induction of central epithelial to mesenchymal transition (EMT) factors. We demonstrate that pericytes strengthen the integrity of the BBB in primary endothelial cell/pericyte co-cultures as an in vitro BBB model, using TEER measurement of the barrier integrity. In contrast, this effect was abrogated by TGF-β or conditioned medium from TGF-β secreting GBM cells, leading to the disruption of a so far intact and tight BBB. TGF-β notably changed the metabolic behavior of pericytes, by shutting down the TCA cycle, driving energy generation from oxidative phosphorylation towards glycolysis, and by modulating pathways that are necessary for the biosynthesis of molecules used for proliferation and cell division. Combined metabolomic and transcriptomic analyses further underscored that the observed functional and metabolic changes of TGF-β-treated pericytes are closely connected with their role as important supporting cells during angiogenic processes