Differential scanning calorimetry of plasma in glioblastoma: toward a new prognostic / monitoring tool

Characterization of myocardial injury phenotype by thermal liquid biopsy

Background and aims

With the advent and implementation of high-sensitivity cardiac troponin assays, differentiation of patients with distinct types of myocardial injuries, including acute thrombotic myocardial infarction (TMI), acute non-thrombotic myocardial injury (nTMi), and chronic coronary atherosclerotic disease (cCAD), is of pressing clinical importance. Thermal liquid biopsy (TLB) emerges as a valuable diagnostic tool, relying on identifying thermally induced conformational changes of biomolecules in blood plasma. While TLB has proven useful in detecting and monitoring several cancers and autoimmune diseases, its application in cardiovascular diseases remains unexplored. In this proof-of-concept study, we sought to determine and characterize TLB profiles in patients with TMI, nTMi, and cCAD at multiple acute-phase time points (T 0 h, T 2 h, T 4 h, T 24 h, T 48 h) as well as a follow-up time point (Tfu) when the patient was in a stable state.

Methods

TLB profiles were collected for 115 patients (60 with TMI, 35 with nTMi, and 20 with cCAD) who underwent coronary angiography at the event presentation and had subsequent follow-up. Medical history, physical, electrocardiographic, histological, biochemical, and angiographic data were gathered through medical records, standardized patient interviews, and core laboratory measurements.

Results

Distinctive signatures were noted in the median TLB profiles across the three patient types. TLB profiles for TMI and nTMi patients exhibited gradual changes from T0 to Tfu, with significant differences during the acute and quiescent phases. During the quiescent phase, all three patient types demonstrated similar TLB signatures. An unsupervised clustering analysis revealed a unique TLB signature for the patients with TMI. TLB metrics generated from specific features of TLB profiles were tested for differences between patient groups. The first moment temperature (TFM) metric distinguished all three groups at time of presentation (T0). In addition, 13 other TLB-derived metrics were shown to have distinct distributions between patients with TMI and those with cCAD.

Conclusion

Our findings demonstrated the use of TLB as a sensitive and data-rich technique to be explored in cardiovascular diseases, thus providing valuable insight into acute myocardial injury events.

Plasma Thermogram Parameters Differentiate Status and Overall Survival of Melanoma Patients

Melanoma is the fifth most common cancer in the United States and the deadliest of all skin cancers. Even with recent advancements in treatment, there is still a 13% two-year recurrence rate, with approximately 30% of recurrences being distant metastases. Identifying patients at high risk for recurrence or advanced disease is critical for optimal clinical decision-making. Currently, there is substantial variability in the selection of screening tests and imaging, with most modalities characterized by relatively low accuracy. In the current study, we built upon a preliminary examination of differential scanning calorimetry (DSC) in the melanoma setting to examine its utility for diagnostic and prognostic assessment. Using regression analysis, we found that selected DSC profile (thermogram) parameters were useful for differentiation between melanoma patients and healthy controls, with more complex models distinguishing melanoma patients with no evidence of disease from patients with active disease. Thermogram features contributing to the third principal component (PC3) were useful for differentiation between controls and melanoma patients, and Cox proportional hazards regression analysis indicated that PC3 was useful for predicting the overall survival of active melanoma patients. With the further development and optimization of the classification method, DSC could complement current diagnostic strategies to improve screening, diagnosis, and prognosis of melanoma patients.

Tear nanoDSF Denaturation Profile Is Predictive of Glaucoma

Primary open-angle glaucoma (POAG) is a frequent blindness-causing neurodegenerative disorder characterized by optic nerve and retinal ganglion cell damage most commonly due to a chronic increase in intraocular pressure. The preservation of visual function in patients critically depends on the timeliness of detection and treatment of the disease, which is challenging due to its asymptomatic course at early stages and lack of objective diagnostic approaches. Recent studies revealed that the pathophysiology of glaucoma includes complex metabolomic and proteomic alterations in the eye liquids, including tear fluid (TF). Although TF can be collected by a non-invasive procedure and may serve as a source of the appropriate biomarkers, its multi-omics analysis is technically sophisticated and unsuitable for clinical practice. In this study, we tested a novel concept of glaucoma diagnostics based on the rapid high-performance analysis of the TF proteome by differential scanning fluorimetry (nanoDSF). An examination of the thermal denaturation of TF proteins in a cohort of 311 ophthalmic patients revealed typical profiles, with two peaks exhibiting characteristic shifts in POAG. Clustering of the profiles according to peaks maxima allowed us to identify glaucoma in 70% of cases, while the employment of artificial intelligence (machine learning) algorithms reduced the amount of false-positive diagnoses to 13.5%. The POAG-associated alterations in the core TF proteins included an increase in the concentration of serum albumin, accompanied by a decrease in lysozyme C, lipocalin-1, and lactotransferrin contents. Unexpectedly, these changes were not the only factor affecting the observed denaturation profile shifts, which considerably depended on the presence of low-molecular-weight ligands of tear proteins, such as fatty acids and iron. Overall, we recognized the TF denaturation profile as a novel biomarker of glaucoma, which integrates proteomic, lipidomic, and metallomic alterations in tears, and monitoring of which could be adapted for rapid non-invasive screening of the disease in a clinical setting.

Plasma nanoDSF Denaturation Profile at Baseline Is Predictive of Glioblastoma EGFR Status

Glioblastoma (GBM) is the most frequent and aggressive primary brain tumor in adults. Recently, we demonstrated that plasma denaturation profiles of glioblastoma patients obtained using Differential Scanning Fluorimetry can be automatically distinguished from healthy controls with the help of Artificial Intelligence (AI). Here, we used a set of machine-learning algorithms to automatically classify plasma denaturation profiles of glioblastoma patients according to their EGFR status. We found that Adaboost AI is able to discriminate EGFR alterations in GBM with an 81.5% accuracy. Our study shows that the use of these plasma denaturation profiles could answer the unmet neuro-oncology need for diagnostic predictive biomarker in combination with brain MRI and clinical data, in order to allow for a rapid orientation of patients for a definitive pathological diagnosis and then treatment. We complete this study by showing that discriminating another mutation, MGMT, seems harder, and that post-surgery monitoring using our approach is not conclusive in the 48 h that follow the surgery.

Brain targeted delivery of carmustine using chitosan coated nanoparticles via nasal route for glioblastoma treatment

This study aims to develop chitosan-coated PLGA nanoparticles intended for nose-to-brain delivery of carmustine. Formulations were prepared by the double emulsion solvent evaporation method and optimized by using Box-Behnken Design. The optimized nanoparticles were obtained to satisfactory levels in terms of particle size, PDI, entrapment efficiency, and drug loading. In vitro drug release and ex-vivo permeation showed sustained release and enhanced permeability (approx. 2 fold) of carmustine compared to drug suspension. The AUC_0-t of brain obtained with carmustine-loaded nanoparticles via nasal administration in Albino Wistar rats was 2.8 and 14.7 times that of intranasal carmustine suspension and intravenous carmustine, respectively. The MTT assay on U87 MG cell line showed a significant decrease (P < 0.05) in the IC50 value of the formulation (71.23 μg ml^-1) as compared to drug suspension (90.02 μg ml^-1).These findings suggest chitosan coated nanoparticles could be used to deliver carmustine via intranasal administration to treat Glioblastoma multiforme.

Calorimetric Markers for Detection and Monitoring of Multiple Myeloma

Simple Summary

This review highlights the potential of differential scanning calorimetry for multiple myeloma diagnosis and monitoring of the treatment outcome. The thermodynamic signatures of blood sera from patients with multiple myeloma are strongly dependent on the concentration and isotype of the secreted monoclonal immunoglobulins. Mathematical methods developed to analyze the biocalorimetry data and distinguish “diseased” from “healthy” thermogram to stratify plasma calorimetric profiles and determine specific interrelations between calorimetric and biochemical/clinical data are discussed.

Abstract

This review summarizes data obtained thus far on the application of differential scanning calorimetry (DSC) for the analysis of blood sera from patients diagnosed with multiple myeloma (MM) with the secretion of the most common isotypes of monoclonal proteins (M-proteins), free light chains (FLC) and non-secretory MM, as well as Waldenström macroglobulinemia and the premalignant state monoclonal gammopathy of undetermined significance. The heterogeneous nature of MM is reflected in the thermal stability profiles of the blood serum proteome of MM patients found to depend on both the level and the isotype of the secreted M-proteins or FLC. Common calorimetric markers feature the vast majority of the different myeloma types, i.e., stabilization of the major serum proteins and decrease in the albumin/globulin heat capacity ratio. A unique calorimetric fingerprint of FLC molecules forming amorphous aggregates is the low-temperature transition centered at 57 °C for a calorimetric set of FLC MM and at 46–47 °C for a single FLC MM case for which larger aggregates were formed. The calorimetric assay proved particularly advantageous for non-secretory MM and is thus a suitable tool for monitoring such patients during treatment courses. Thus, DSC provides a promising blood-based approach as a complementary tool for MM detection and monitoring.

Liquid biopsy: early and accurate diagnosis of brain tumor

Noninvasive examination is an emerging area in the field of neuro-oncology. Liquid biopsy captures the landscape of genomic alterations of brain tumors and revolutionizes the traditional diagnosis approaches. Rapidly changing sequencing technologies and more affordable prices put the screws on more application of liquid biopsy in clinical settings. In the past few years, extensive application of liquid biopsy has been seen throughout the whole diagnosis and treatment process of brain tumors, including early and accurate detection, characterization and dynamic monitoring. Here, we summarized and compared the most advanced techniques and target molecules or macrostructures related to brain tumor liquid biopsy. We further reviewed and emphasized recent progression in different clinical settings for brain tumors in blood and CSF. The preferred protocol, potential novel biomarkers and future development are discussed in the last part.

Effect of a Ketogenic Diet on Oxidative Posttranslational Protein Modifications and Brain Homogenate Denaturation in the Kindling Model of Epilepsy in Mice

This study focused on the ketogenic diet (KD) effects on oxidative posttranslational protein modification (PPM) as presumptive factors implicated in epileptogenesis. A 28-day of KD treatment was performed. The corneal kindling model of epileptogenesis was used. Four groups of adult male ICR mice (25-30 g) were randomized in standard rodent chow (SRC) group, KD-treatment group; SRC + kindling group; KD + kindling group (n = 10 each). Advanced oxidation protein products (AOPP) and protein carbonyl contents of brain homogenates together with differential scanning calorimetry (DSC) were evaluated. Two exothermic transitions (Exo1 and Exo2) were explored after deconvolution of the thermograms. Factor analysis was applied. The protective effect of KD in the kindling model was demonstrated with both decreased seizure score and increased seizure latency. KD significantly decreased glucose and increased ketone bodies (KB) in blood. Despite its antiseizure effect, the KD increased the AOPP level and the brain proteome's exothermic transitions, suggestive for qualitative modifications. The ratio of the two exothermic peaks (Exo2/Exo1) of the thermograms from the KD vs. SRC treated group differed more than twice (3.7 vs. 1.6). Kindling introduced the opposite effect, changing this ratio to 2.7 for the KD + kindling group. Kindling significantly increased glucose and KB in the blood whereas decreased the BW under the SRC treatment. Kindling decreased carbonyl proteins in the brain irrespectively of the diet. Further evaluations are needed to assess the nature of correspondence of calorimetric images of the brain homogenates with PPM.

The Utility of Differential Scanning Calorimetry Curves of Blood Plasma for Diagnosis, Subtype Differentiation and Predicted Survival in Lung Cancer

Early detection of lung cancer (LC) significantly increases the likelihood of successful treatment and improves LC survival rates. Currently, screening (mainly low-dose CT scans) is recommended for individuals at high risk. However, the recent increase in the number of LC cases unrelated to the well-known risk factors, and the high false-positive rate of low-dose CT, indicate a need to develop new, non-invasive methods for LC detection. Therefore, we evaluated the use of differential scanning calorimetry (DSC) for LC patients' diagnosis and predicted survival. Additionally, by applying mass spectrometry, we investigated whether changes in O- and N-glycosylation of plasma proteins could be an underlying mechanism responsible for observed differences in DSC curves of LC and control subjects. Our results indicate selected DSC curve features could be useful for differentiation of LC patients from controls with some capable of distinction between subtypes and stages of LC. DSC curve features also correlate with LC patients' overall/progression free survival. Moreover, the development of classification models combining patients' DSC curves with selected plasma protein glycosylation levels that changed in the presence of LC could improve the sensitivity and specificity of the detection of LC. With further optimization and development of the classification method, DSC could provide an accurate, non-invasive, radiation-free strategy for LC screening and diagnosis.

Red Blood Cells' Thermodynamic Behavior in Neurodegenerative Pathologies and Aging

The main trend of current research in neurodegenerative diseases (NDDs) is directed towards the discovery of novel biomarkers for disease diagnostics and progression. The pathological features of NDDs suggest that diagnostic markers can be found in peripheral fluids and cells. Herein, we investigated the thermodynamic behavior of the peripheral red blood cells (RBCs) derived from patients diagnosed with three common NDDs—Parkinson’s disease (PD), Alzheimer’s disease (AD), and amyotrophic lateral sclerosis (ALS) and compared it with that of healthy individuals, evaluating both fresh and aged RBCs. We established that NDDs can be differentiated from the normal healthy state on the basis of the variation in the thermodynamic parameters of the unfolding of major RBCs proteins—the cytoplasmic hemoglobin (Hb) and the membrane Band 3 (B3) protein. A common feature of NDDs is the higher thermal stability of both Hb and B3 proteins along the RBCs aging, while the calorimetric enthalpy can distinguish PD from ALS and AD. Our data provide insights into the RBCs thermodynamic behavior in two complex and tightly related phenomena—neurodegenerative pathologies and aging, and it suggests that the determined thermodynamic parameters are fingerprints of the altered conformation of Hb and B3 protein and modified RBCs’ aging in the studied NDDs.

Artificial Intelligence in Brain Tumour Surgery-An Emerging Paradigm

Artificial intelligence (AI) platforms have the potential to cause a paradigm shift in brain tumour surgery. Brain tumour surgery augmented with AI can result in safer and more effective treatment. In this review article, we explore the current and future role of AI in patients undergoing brain tumour surgery, including aiding diagnosis, optimising the surgical plan, providing support during the operation, and better predicting the prognosis. Finally, we discuss barriers to the successful clinical implementation, the ethical concerns, and we provide our perspective on how the field could be advanced.

Thermal Liquid Biopsy (TLB) of Blood Plasma as a Potential Tool to Help in the Early Diagnosis of Multiple Sclerosis

Background: Multiple sclerosis (MS) is frequently characterized by a variety of clinical signs, often exhibiting little specificity. The diagnosis requires a combination of medical observations and instrumental tests, and any support for its objective assessment is helpful. Objective: Herein, we describe the application of thermal liquid biopsy (TLB) of blood plasma samples, a methodology for predicting the occurrence of MS with a noninvasive, quick blood test. Methods: TLB allows one to define an index (TLB score), which provides information about overall real-time alterations in plasma proteome that may be indicative of MS. Results: This pilot study, based on 85 subjects (45 MS patients and 40 controls), showed good performance indexes (sensitivity and specificity both around 70%). The diagnostic methods better discriminate between early stage and low-burden MS patients, and it is not influenced by gender, age, or assumption of therapeutic drugs. TLB is more accurate for patients having low disability level (≤ 3.0, measured by the expanded disability status scale, EDSS) and a relapsing–remitting diagnosis. Conclusion: Our results suggest that TLB can be applied to MS, especially in an initial phase of the disease when diagnosis is difficult and yet more important (in such cases, accuracy of prediction is close to 80%), as well as in personalized patient periodic monitoring. The next step will be determining its utility in differentiating between MS and other disorders, in particular in inflammatory diseases.

An AI-Powered Blood Test to Detect Cancer Using NanoDSF

Glioblastoma is the most frequent and aggressive primary brain tumor. Its diagnosis is based on resection or biopsy that could be especially difficult and dangerous in the case of deep location or patient comorbidities. Monitoring disease evolution and progression also requires repeated biopsies that are often not feasible. Therefore, there is an urgent need to develop biomarkers to diagnose and follow glioblastoma evolution in a minimally invasive way. In the present study, we described a novel cancer detection method based on plasma denaturation profiles obtained by a non-conventional use of differential scanning fluorimetry. Using blood samples from 84 glioma patients and 63 healthy controls, we showed that their denaturation profiles can be automatically distinguished with the help of machine learning algorithms with 92% accuracy. Proposed high throughput workflow can be applied to any type of cancer and could become a powerful pan-cancer diagnostic and monitoring tool requiring only a simple blood test.

Exothermic transitions in the heat capacity profiles of human cerebrospinal fluid

In this work, we examined by DSC protein denaturation heat capacity profiles for two body fluids, cerebrospinal fluid (CSF) and blood plasma obtained from brain tumor (mainly glioblastoma) patients and healthy volunteers. We observed large distinctions between the heat capacity profiles of CSF and blood plasma, although their protein compositions are believed to have much in common. A prominent, previously unreported CSF feature was the existence of a pre-denaturation exothermic transition peaking at ~ 50-52 °C, recorded for both control and brain tumor CSF. This appears to be the first observation of a pre-denaturation exotherm in a human body fluid. In all studied samples, the exotherms deconvoluted with high precision into a sum of two Gaussian peaks. These exotherms are apparently specific, originating from brain tissue-soluble proteins in the CSF not present in blood plasma. Malignant brain tumors (glioblastoma multiforme, Grade IV, and low-grade glioma, Grade II) reduced twofold the enthalpy of the exotherms relative to the control. These results suggest that the amount and/or conformational state of the CSF proteins (e.g., intrinsic disorder) giving rise to pre-denaturation exothermic events substantially changed upon brain tumor progression. Concomitantly, the enthalpy of the CSF endothermic peaks was partially redistributed from a lower-temperature (main) transition to a higher-temperature transition. The presented data demonstrated that the heat capacity profiles of intrinsic CSF proteins constitute a sensitive biomarker of glioblastoma and other brain malignancies.

Plasmatic Signature of Disease by Differential Scanning Calorimetry (DSC)

Differential scanning calorimetry (DSC) has been used for several decades to characterize thermal stability of macromolecules such as proteins and DNA. It allows to determine the denaturation temperature and enthalpy of individual domains of proteins, thus giving new insights into their domain organization and ligand interaction. Over the past decade, it has been shown that this technique can also be used to study biofluids such as plasma or cerebrospinal fluid to obtain denaturation profiles. An increasing number of studies demonstrated that such profiles obtained from patients were significantly different from profiles obtained using biofluids of healthy individuals. This opens interesting perspectives for new diagnostics and monitoring tools for a large number of diseases. Nevertheless, the extensive studies of plasma samples from patients with different pathologies as well as the development of standardized methods of data analysis are necessary to reach the promising diagnostic potential of this methodology. Using plasma samples from healthy individuals and glioblastoma patients, we outline the steps necessary to obtain a plasmatic calorimetric profile with VP-DSC instrument and describe a cluster analysis of obtained data.