Detection of cervical cancer biomarker patterns in blood plasma and urine by differential scanning calorimetry and mass spectrometry

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.

Modification of the structural stability of human serum albumin in rheumatoid arthritis

Differential scanning calorimetry (DSC) can indicate changes in structure and/or concentration of the most abundant proteins in a biological sample via heat denaturation curves (HDCs). In blood serum for example, HDC changes result from either concentration changes or altered thermal stabilities for 7-10 proteins and has previously been shown capable of differentiating between sick and healthy human subjects. Here, we compare HDCs and proteomic profiles of 50 patients experiencing joint-inflammatory symptoms, 27 of which were clinically diagnosed with rheumatoid arthritis (RA). The HDC of all 50 subjects appeared significantly different from expected healthy curves, but comparison of additional differences between the RA and the non-RA subjects allowed more specific understanding of RA samples. We used mass spectrometry (MS) to investigate the reasons behind the additional HDC changes observed in RA patients. The HDC differences do not appear to be directly related to differences in the concentrations of abundant serum proteins. Rather, the differences can be attributed to modified thermal stability of some fraction of the human serum albumin (HSA) proteins in the sample. By quantifying differences in the frequency of artificially induced post translational modifications (PTMs), we found that HSA in RA subjects had a much lower surface accessibility, indicating potential ligand or protein binding partners in certain regions that could explain the shift in HSA melting temperature in the RA HDCs. Several low abundance proteins were found to have significant changes in concentration in RA subjects and could be involved in or related to binding of HSA. Certain amino acid sites clusters were found to be less accessible in RA subjects, suggesting changes in HSA structure that may be related to changes in protein-protein interactions. These results all support a change in behavior of HSA which may give insight into mechanisms of RA pathology.

Thermodynamic Signatures of Blood Plasma Proteome in Neurodegenerative Pathologies

Discovery of diagnostic biomarkers for age-related neurodegenerative pathologies (NDDs) is essential for accurate diagnosis, following disease progression and drug development. Blood plasma and blood cells are important peripheral sources for NDDs' biomarkers that, although present in lower concentrations than in cerebrospinal fluid, would allow noninvasive diagnostics. To identify new biomarkers for Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS), in this work we have evaluated the modifications in the thermodynamic behavior of blood plasma proteome exploring differential scanning calorimetry. The plasma thermodynamics reflected the complexity and heterogeneity of the two pathologies. The unfolding temperature of the most abundant plasma protein albumin and the weighted average center of the calorimetric profile appeared as the two thermodynamic signatures that reflected modifications of the plasma proteome, i.e., strong thermal stabilization of albumin and plasma proteins' interaction network, related to both pathologies. Based on those two signatures, both PD and ALS patients were stratified in two sets, except several cases with thermodynamic parameters that strongly differed from those of the calorimetric sets. Along with modifications of the plasma thermodynamic behavior, we found altered globulin levels in all PD and ALS patients' plasma (higher level of α- and β-globulin fractions and lower level of γ-globulin fraction than the respective reference values) employing capillary electrophoresis. The presented results reveal the potential of calorimetry to indirectly identify NDDs' biomarkers in blood plasma.

Thermodynamic Sensitivity of Blood Plasma Components in Patients Afflicted with Skin, Breast and Pancreatic Forms of Cancer

According to the World Health Organization's 2018 Global Cancer Survey, cancer is the second leading cause of death. From this survey, the third most common is breast cancer, the fifth is melanoma malignum and pancreatic adenocarcinoma ranks twentieth. Undoubtedly, the early diagnosis and monitoring of these tumors and related research is important for aspects of patient care. The aim of our present review was to explain an impressive methodology that is deemed suitable in reference to studying blood sample deviations in the case of solid tumors. Essentially, we compared the heat denaturation responses of blood plasma components through differential scanning calorimetry (DSC). In the control, between five and seven separable components can be detected, in which the primary component was albumin, while in the case of tumorous patients, the peaks of immunoglobulins were dominant. Moreover, the shape of the plasma DSC curves changed with a shift in the higher temperature ranges; thus, their pattern can be used as a suitable marker of direct immunological responses. The further development of the analysis of DSC curves raises the possibility of the early diagnosis of a potential tumor, the monitoring of diseases, or testing the efficacy of the therapy from a single drop of blood.

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.

Altered Thermal Behavior of Blood Plasma Proteome Related to Inflammatory Cytokines in Early Pregnancy Loss

Early pregnancy loss (EPL) is a relatively common pathology of which almost 50% of cases remain idiopathic. In the search for novel biomarkers, differential scanning calorimetry (DSC) is intensively used to characterize the thermodynamic behavior of blood plasma/serum proteome in health and disease. Herein, for the first time, we investigate the DSC denaturation profiles of blood plasma derived from patients suffering EPL compared to healthy pregnant and non-pregnant women. Data analysis reveals that 58% of the EPL thermograms differ significantly from those of healthy pregnant women. Thermal stabilization of a fraction of albumin-assigned transition with concomitant suppression of the major and enhancement of the globulin-assigned transition are characteristic features of EPL calorimetric profiles that could be used as a new indicator of a risk pregnancy. The presented results suggest an altered composition or intermolecular interactions of the plasma proteome of women with EPL. In addition, the alterations of the EPL thermograms correlate with the increased blood levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) and a higher prevalence of the polymorphism in the plasminogen activator inhibitor type-1 (PAI-1) gene, suggesting an expression of an overall enhanced immune response. The concomitant changes in plasma thermograms confirm the potential of the DSC approach for distinguishing changes in the pathological state of the blood plasma proteome.

Saliva profiling with differential scanning calorimetry: A feasibility study with ex vivo samples

Differential scanning calorimetry (DSC) has been used widely to study various biomarkers from blood, less is known about the protein profiles from saliva. The aim of the study was to investigate the use DSC in order to detect saliva thermal profiles and determine the most appropriate sampling procedure to collect and process saliva. Saliva was collected from 25 healthy young individuals and processed using different protocols based on centrifugation and filtering. The most effective protocol was centrifugation at 5000g for 10 min at 4°C followed by filtration through Millex 0.45 μm filter. Prepared samples were transferred to 3 mL calorimetric ampoules and then loaded into TAM48 calibrated to 30°C until analysis. DSC scans were recorded from 30°C to 90°C at a scan rate of 1°C/h with a pre-conditioning the samples to starting temperature for 1 h. The results show that the peak distribution of protein melting points was clearly bimodal, and the majority of peaks appeared between 40–50°C. Another set of peaks is visible between 65°C– 75°C. Additionally, the peak amplitude and area under the peak are less affected by the concentration of protein in the sample than by the individual differences between people. In conclusion, the study shows that with right preparation of the samples, there is a possibility to have thermograms of salivary proteins that show peaks in similar temperature regions between different healthy volunteers.

Sample Processing Considerations for Protein Stability Studies of Low Concentration Biofluid Samples using Differential Scanning Calorimetry

Background:

The analysis of biofluid samples with low protein content (e.g., urine or saliva) can be challenging for downstream analysis methods with limited sensitivity. To circumvent this problem, sample processing methods are employed to increase the protein concentration in analyzed samples. However, for some techniques, like differential scanning calorimetry (DSC) that characterizes thermally-induced unfolding of biomolecules, sample processing must not affect native protein structure and stability.

Methods:

We evaluated centrifugal concentration and stirred cell ultrafiltration, two common methods of sample concentration characterized by a low risk of protein denaturation, with the goal of establishing a protocol for DSC analysis of low concentration biospecimens.

Results:

Our studies indicate that both methods can affect protein stability assessed by DSC and, even after optimization of several parameters, the obtained DSC profile (thermogram) suggested that sample processing affects the structure or intermolecular interactions of component proteins contributing to altered thermal stability detectable by DSC. We also found a relationship between changes in thermograms and low protein concentration, indicating that diluting biospecimens to concentrations below 0.1 mg/mL can perturb the intermolecular environment and affect the structure of proteins present in the solution.

Conclusions:

Dilution of samples below 0.1 mg/mL, as well as concentration of samples with low protein content, resulted in affected thermogram shapes suggesting changes in protein stability. This should be taken into account when concentrating dilute samples or employing techniques that lower the protein concentration (e.g., fractionation), when downstream applications include techniques, such as DSC, that require the preservation of native protein forms.

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.

Fano Resonance-Based Blood Plasma Monitoring and Sensing using Plasmonic Nanomatryoshka

The fast label-free detection of specific antibodies and their concentration in blood plasma is useful for many applications, e.g., in Covid-19 patients. The change in biophysical properties like the refractive index of blood plasma due to the production of antibodies during infection may be very helpful in estimating the level and intensity of infection and subsequent treatment based on blood plasma therapy. In this article, Fano resonance-based refractive index sensor using plasmonic nanomatryoshka is proposed for blood plasma sensing. The interaction between hybridized modes (bright and dark modes) in optimized nanomatryoshka leads to Fano resonance, which by virtue of steeper dispersion can confine the light more efficiently compared with Lorentzian resonance. We propose the excitation of Fano resonances in sub 100-nm size nanomatryoshka based on newly emerging plasmonic materials ZrN and HfN, and one of the most widely used conventional plasmonic material, Au. Fano resonance-based plasmonic sensors leads to sensitivity = 188.5 nm/RIU, 242.5 nm/RIU, and 244.9 nm/RIU for Au, ZrN, and HfN, respectively. The corresponding figure of merit (nm/RIU) is ~ 3.5  ×  10³, 3.1  ×  10³, and 2.8  ×  10³ for Au, ZrN, and HfN, respectively. Present theoretical analysis shows that refractive index sensors with high sensitivity and figure of merit are feasible using Fano modes of plasmonic nanomatryoshka.

Thermal Liquid Biopsy (TLB) Focused on Benign and Premalignant Pancreatic Cyst Diagnosis

Background: Current efforts in the identification of new biomarkers are directed towards an accurate differentiation between benign and premalignant cysts. Thermal Liquid Biopsy (TLB) has been previously applied to inflammatory and tumor diseases and could offer an interesting point of view in this type of pathology. Methods: In this work, twenty patients (12 males and 8 females, average ages 62) diagnosed with a pancreatic cyst benign (10) and premalignant (10) cyst lesions were recruited, and biological samples were obtained during the endoscopic ultrasonography procedure. Results: Proteomic content of cyst liquid samples was studied and several common proteins in the different groups were identified. TLB cyst liquid profiles reflected protein content. Also, TLB serum score was able to discriminate between healthy and cysts patients (71% sensitivity and 98% specificity) and between benign and premalignant cysts (75% sensitivity and 67% specificity). Conclusions: TLB analysis of plasmatic serum sample, a quick, simple and non-invasive technique that can be easily implemented, reports valuable information on the observed pancreatic lesion. These preliminary results set the basis for a larger study to refine TLB serum score and move closer to the clinical application of TLB providing useful information to the gastroenterologist during patient diagnosis.

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.

Multi-group diagnostic classification of high-dimensional data using differential scanning calorimetry plasma thermograms

The thermoanalytical technique differential scanning calorimetry (DSC) has been applied to characterize protein denaturation patterns (thermograms) in blood plasma samples and relate these to a subject’s health status. The analysis and classification of thermograms is challenging because of the high-dimensionality of the dataset. There are various methods for group classification using high-dimensional data sets; however, the impact of using high-dimensional data sets for cancer classification has been poorly understood. In the present article, we proposed a statistical approach for data reduction and a parametric method (PM) for modeling of high-dimensional data sets for two- and three- group classification using DSC and demographic data. We compared the PM to the non-parametric classification method K-nearest neighbors (KNN) and the semi-parametric classification method KNN with dynamic time warping (DTW). We evaluated the performance of these methods for multiple two-group classifications: (i) normal versus cervical cancer, (ii) normal versus lung cancer, (iii) normal versus cancer (cervical + lung), (iv) lung cancer versus cervical cancer as well as for three-group classification: normal versus cervical cancer versus lung cancer. In general, performance for two-group classification was high whereas three-group classification was more challenging, with all three methods predicting normal samples more accurately than cancer samples. Moreover, specificity of the PM method was mostly higher or the same as KNN and DTW-KNN with lower sensitivity. The performance of KNN and DTW-KNN decreased with the inclusion of demographic data, whereas similar performance was observed for the PM which could be explained by the fact that the PM uses fewer parameters as compared to KNN and DTW-KNN methods and is thus less susceptible to the risk of overfitting. More importantly the accuracy of the PM can be increased by using a greater number of quantile data points and by the inclusion of additional demographic and clinical data, providing a substantial advantage over KNN and DTW-KNN methods.

Thermal Liquid Biopsy (TLB): A Predictive Score Derived from Serum Thermograms as a Clinical Tool for Screening Lung Cancer Patients

Risk population screening programs are instrumental for advancing cancer management and reducing economic costs of therapeutic interventions and the burden of the disease, as well as increasing the survival rate and improving the quality of life for cancer patients. Lung cancer, with high incidence and mortality rates, is not excluded from this situation. The success of screening programs relies on many factors, with some of them being the appropriate definition of the risk population and the implementation of detection techniques with an optimal discrimination power and strong patient adherence. Liquid biopsy based on serum or plasma detection of circulating tumor cells or DNA/RNA is increasingly employed nowadays, but certain limitations constrain its wide application. In this work, we present a new implementation of thermal liquid biopsy (TLB) for lung cancer patients. TLB provides a prediction score based on the ability to detect plasma/serum proteome alterations through calorimetric thermograms that strongly correlates with the presence of lung cancer disease (91% accuracy rate, 90% sensitivity, 92% specificity, diagnostic odds ratio 104). TLB is a quick, minimally-invasive, low-risk technique that can be applied in clinical practice for evidencing lung cancer, and it can be used in screening and monitoring actions.

MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis.

Thermal liquid biopsy for monitoring melanoma patients under surveillance during treatment: A pilot study

BACKGROUND

Differential Scanning Calorimetry (DSC) is a technique traditionally used to study thermally induced macromolecular transitions, and it has recently been proposed as a novel approach for diagnosis and monitoring of several diseases. We report a pilot study applying Thermal Liquid Biopsy (TLB, DSC thermograms of plasma samples) as a new clinical approach for diagnostic assessment of melanoma patients.

METHODS

Multiparametric analysis of DSC thermograms of patient plasma samples collected during treatment and surveillance (63 samples from 10 patients) were compared with clinical and diagnostic imaging assessment to determine the utility of thermograms for diagnostic assessment in melanoma. Nine of the ten patients were stage 2 or 3 melanoma subjects receiving adjuvant therapy after surgical resection of their melanomas. The other patient had unresectable stage 4 melanoma and was treated with immunotherapy. Two reference groups were used: (A) 36 healthy subjects and (B) 13 samples from 8 melanoma patients who had completed successful surgical management of their disease and were determined by continued clinical assessment to have no evidence of disease.

RESULTS

Plasma thermogram analysis applied to melanoma patients generally agrees with clinical evaluation determined by physical assessment or diagnostic imaging (~80% agreement). No false negatives were obtained from DSC thermograms. Importantly, this methodology was able to detect changes in disease status before it was identified clinically.

CONCLUSIONS

Thermal Liquid Biopsy could be used in combination with current clinical assessment for the earlier detection of melanoma recurrence and metastasis.

GENERAL SIGNIFICANCE

TLB offers advantages over current diagnostic techniques (PET/CT imaging), limited in frequency by radiation burden and expense, in providing a minimally-invasive, low-risk, low-cost clinical test for more frequent personalized patient monitoring to assess recurrence and facilitate clinical decision-making.

A rank weighted classification for plasma proteomic profiles based on case-based reasoning

Background

It is a challenge to precisely classify plasma proteomic profiles into their clinical status based solely on their patterns even though distinct patterns of plasma proteomic profiles are regarded as potential to be a biomarker because the profiles have large within-subject variances.

Methods

The present study proposes a rank-based weighted CBR classifier (RWCBR). We hypothesized that a CBR classifier is advantageous when individual patterns are specific and do not follow the general patterns like proteomic profiles, and robust feature weights can enhance the performance of the CBR classifier. To validate RWCBR, we conducted numerical experiments, which predict the clinical status of the 70 subjects using plasma proteomic profiles by comparing the performances to previous approaches.

Results

According to the numerical experiment, SVM maintained the highest minimum values of Precision and Recall, but RWCBR showed highest average value in all information indices, and it maintained the smallest standard deviation in F-1 score and G-measure.

Conclusions

RWCBR approach showed potential as a robust classifier in predicting the clinical status of the subjects for plasma proteomic profiles.

Comparative Metabolomics Analysis of Cervicitis in Human Patients and a Phenol Mucilage-Induced Rat Model Using Liquid Chromatography Tandem Mass Spectrometry

Cervicitis is an exceedingly common gynecological disorder that puts women at high risk of sexually transmitted infections and induces a series of reproductive system diseases. This condition also has a significant impact on quality of life and is commonly misdiagnosed in clinical practice due to its complicated pathogenesis. In the present study, we performed non-targeted plasma metabolomics analysis of cervicitis in both plasma samples obtained from human patients and plasma samples from a phenol mucilage induced rat model of cervicitis, using ultra-performance liquid chromatography coupled to quadrupole time-of-flight tandem mass spectrometry. In addition to differences in histopathology, we identified differences in the metabolic profile between the cervicitis and control groups using unsupervised principal component analysis and orthogonal projections to latent structures discriminant analysis. These results demonstrated changes in plasma metabolites, with 27 and 22 potential endogenous markers identified in rat and human samples, respectively. The metabolic pathway analysis showed that linoleic acid, arachidonic acid, ether lipid, and glycerophospholipid metabolism are key metabolic pathways involved in cervicitis. This study showed the rat model was successfully created and applied to understand the pathogenesis of cervicitis.

Calorimetric markers for monitoring of multiple myeloma and Waldenström's macroglobulinemia patients

The blood proteome has been studied extensively for identification of novel reliable disease biomarkers. In recent years, differential scanning calorimetry has emerged as a new tool for characterization of the thermodynamic properties of the major serum/plasma proteins and for the establishment of calorimetric markers for a variety of diseases. Here we applied calorimetry to monitor the effect of treatment of patients diagnosed with multiple myeloma and Waldenström's macroglobulinemia on the calorimetric profiles of patients' blood sera. The parameters derived from the calorimetric profiles were compared with the primary serum biomarkers, monoclonal immunoglobulin (M protein) concentration, and κ/λ free light chain ratio. For the secretory cases, the calorimetric parameters thermogram's shape similarity and weighted average center strongly depended on the M protein level but had lower sensitivity and specificity. By contrast, for non-secretory cases, the calorimetric parameters did not depend on the κ/λ free light chains ratio and exhibited significantly higher sensitivity and specificity than M protein levels. A combination of the immunological and calorimetric tests was found to greatly improve the sensitivity and specificity of the clinical status evaluation. The pronounced differences in blood sera thermograms before and during monitoring reflected the individual patients' response to treatment received and showed maintenance of heterogeneity during the disease course.

Characterization and classification of lupus patients based on plasma thermograms

Objective

Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent health conditions and SLE diagnostic criteria.

Methods

Thermograms were visualized graphically for important differences between covariates and summarized using various measures. A modified linear discriminant analysis was used to segregate SLE versus control subjects on the basis of the thermograms. Classification accuracy was measured based on multiple training/test splits of the data and compared to classification based on SLE serological markers.

Results

Median sensitivity, specificity, and overall accuracy based on classification using plasma thermograms was 86%, 83%, and 84% compared to 78%, 95%, and 86% based on a combination of five antibody tests. Combining thermogram and serology information together improved sensitivity from 78% to 86% and overall accuracy from 86% to 89% relative to serology alone. Predictive accuracy of thermograms for distinguishing SLE and osteoarthritis / rheumatoid arthritis patients was comparable. Both gender and anemia significantly interacted with disease status for plasma thermograms (p<0.001), with greater separation between SLE and control thermograms for females relative to males and for patients with anemia relative to patients without anemia.

Conclusion

Plasma thermograms constitute an additional biomarker which may help improve diagnosis of SLE patients, particularly when coupled with standard diagnostic testing. Differences in thermograms according to patient sex, ethnicity, clinical and environmental factors are important considerations for application of thermograms in a clinical setting.

Candidate biomarkers in the cervical vaginal fluid for the (self-)diagnosis of cervical precancer

Purpose

Despite improvement in vaccines against human papilloma virus (HPV), the causative agent of cervical cancer, screening women for cervical precancer will remain indispensable in the coming 30–40 years. A simple test that could be performed at home or at a doctor’s practice and that informs the woman whether she is at risk would significantly help make a broader group of patients who aware that they need medical treatment. Cervical vaginal fluid (CVF) is a body fluid that is very well suited for such a test.

Methods

Narrative review of cervical (pre)cancer candidate biomarkers from cervicovaginal fluid, is based on a detailed review of the literature. We will also discuss the possibilities that these biomarkers create for the development of a self-test or point-of-care test for cervical (pre)cancer.

Results

Several DNA, DNA methylation, miRNA, and protein biomarkers were identified in the cervical vaginal fluid; however, not all of these biomarkers are suited for development of a simple diagnostic assay.

Conclusions

Proteins, especially alpha-actinin-4, are most suited for development of a simple assay for cervical (pre)cancer. Accuracy of the test could further be improved by combination of several proteins or by combination with a new type of biomarker, e.g., originating from the cervicovaginal microbiome or metabolome.

Application and interpretation of functional data analysis techniques to differential scanning calorimetry data from lupus patients

Background

DSC is used to determine thermally-induced conformational changes of biomolecules within a blood plasma sample. Recent research has indicated that DSC curves (or thermograms) may have different characteristics based on disease status and, thus, may be useful as a monitoring and diagnostic tool for some diseases. Since thermograms are curves measured over a range of temperature values, they are considered functional data. In this paper we apply functional data analysis techniques to analyze differential scanning calorimetry (DSC) data from individuals from the Lupus Family Registry and Repository (LFRR). The aim was to assess the effect of lupus disease status as well as additional covariates on the thermogram profiles, and use FD analysis methods to create models for classifying lupus vs. control patients on the basis of the thermogram curves.

Methods

Thermograms were collected for 300 lupus patients and 300 controls without lupus who were matched with diseased individuals based on sex, race, and age. First, functional regression with a functional response (DSC) and categorical predictor (disease status) was used to determine how thermogram curve structure varied according to disease status and other covariates including sex, race, and year of birth. Next, functional logistic regression with disease status as the response and functional principal component analysis (FPCA) scores as the predictors was used to model the effect of thermogram structure on disease status prediction. The prediction accuracy for patients with Osteoarthritis and Rheumatoid Arthritis but without Lupus was also calculated to determine the ability of the classifier to differentiate between Lupus and other diseases. Data were divided 1000 times into separate 2/3 training and 1/3 test data for evaluation of predictions. Finally, derivatives of thermogram curves were included in the models to determine whether they aided in prediction of disease status.

Results

Functional regression with thermogram as a functional response and disease status as predictor showed a clear separation in thermogram curve structure between cases and controls. The logistic regression model with FPCA scores as the predictors gave the most accurate results with a mean 79.22% correct classification rate with a mean sensitivity = 79.70%, and specificity = 81.48%. The model correctly classified OA and RA patients without Lupus as controls at a rate of 75.92% on average with a mean sensitivity = 79.70% and specificity = 77.6%. Regression models including FPCA scores for derivative curves did not perform as well, nor did regression models including covariates.

Conclusion

Changes in thermograms observed in the disease state likely reflect covalent modifications of plasma proteins or changes in large protein-protein interacting networks resulting in the stabilization of plasma proteins towards thermal denaturation. By relating functional principal components from thermograms to disease status, our Functional Principal Component Analysis model provides results that are more easily interpretable compared to prior studies. Further, the model could also potentially be coupled with other biomarkers to improve diagnostic classification for lupus.

First-void urine: A potential biomarker source for triage of high-risk human papillomavirus infected women

Great interest has been directed towards the use of first-void urine as a liquid biopsy for high-risk human papillomavirus DNA testing. Despite the high correlations established between urinary and cervical infections, human papillomavirus testing is unable to distinguish between productive and transforming high-risk infections that have the tendency to progress to cervical cancer. Thus far, investigations have been primarily confined to the identification of biomarkers for triage of high-risk human papillomavirus-positive women in cervicovaginal specimens and tissue biopsies. This paper reviews urinary biomarkers for cervical cancer and triage of high-risk human papillomavirus infections and elaborates on the opportunities and challenges that have emerged regarding the use of first-void urine as a liquid biopsy for the analysis of both morphological- (conventional cytology and novel immunohistochemical techniques) and molecular-based (HPV16/18 genotyping, host/viral gene methylation, RNA, and proteins) biomarkers. A literature search was performed in PubMed and Web of Science for studies investigating the use of urine as a biomarker source for cervical cancer screening. Five studies were identified reporting on biomarkers that are still in preclinical exploratory or clinical assay development phases and on assessments of non-invasive (urine) samples. Although large-scale validation studies are still needed, we conclude that methylation of both host and viral genes in urine has been proven feasible for use as a molecular cervical cancer triage and screening biomarker in phase two studies. This is especially promising and underscores our hypothesis that human papillomavirus DNA and candidate human and viral biomarkers are washed away with the initial, first-void urine, together with exfoliated cells, debris and impurities that line the urethra opening. Similar to the limitations of self-collected cervicovaginal samples, first-void urine will likely not fulfil the high-quality cellularity standards required for morphological biomarkers. Molecular biomarkers will likely overcome this issue to yield high-throughput, objective, and reproducible results. When using proper sampling, transport, storage, preanalytical biomarker concentration techniques, and clinically validated assays, first-void urine is expected to be a valuable source of molecular biomarkers for cervical cancer screening. Furthermore, as first-void urine can be easily and non-invasively collected, it is a highly preferred technique among women and offers the ability to test both primary high-risk human papillomavirus and biomarkers in the same sample. In addition, the use of first-void urine confers opportunities to reduce loss-to follow-up and non-adherence to screening subjects.

Mass spectrometry as a tool for biomarkers searching in gynecological oncology

Tumors of the female reproductive tract are an important target for the development of diagnostic, prognostic and therapeutic strategies. Recent research has turned to proteomics based on mass spectrometry techniques, to achieve more effective diagnostic results. Mass spectrometry (MS) enables identification and quantification of multiple molecules simultaneously in a single experiment according to mass to charge ratio (m/z). Several proteomic strategies may be applied to establish the function of a particular protein/peptide or to identify a novel disease and specific biomarkers related to it. Therefore, MS could facilitate treatment in patients with tumors by helping researchers discover new biomarkers and narrowly targeted drugs. This review presents a comprehensive discussion of mass spectrometry as a tool for biomarkers searching that may lead to the discovery of easily available diagnostic tests in gynecological oncology with emphasis on clinical proteomics over the past decade. The article provides an insight into different MS based proteomic approaches.

Point-of-care test for cervical cancer in LMICs

Cervical cancer screening using Papanicolaou's smear test has been highly effective in reducing death from this disease. However, this test is unaffordable in low- and middle-income countries, and its complexity has limited wide-scale uptake. Alternative tests, such as visual inspection with acetic acid or Lugol's iodine and human papillomavirus DNA, are sub-optimal in terms of specificity and sensitivity, thus sensitive and affordable tests with high specificity for on-site reporting are needed. Using proteomics and bioinformatics, we have identified valosin-containing protein (VCP) as differentially expressed between normal specimens and those with cervical intra-epithelial neoplasia grade 2/3 (CIN2/CIN3+) or worse. VCP-specific immunohistochemical staining (validated by a point-of-care technology) provided sensitive (93%) and specific (88%) identification of CIN2/CIN3+ and may serve as a critical biomarker for cervical-cancer screening. Future efforts will focus on further refinements to enhance analytic sensitivity and specificity of our proposed test, as well as on prototype development.

A Comprehensive Analysis of Metabolomics and Transcriptomics in Cervical Cancer

Cervical cancer (CC) still remains a common and deadly malignancy among females in developing countries. More accurate and reliable diagnostic methods/biomarkers should be discovered. In this study, we performed a comprehensive analysis of metabolomics (285 samples) and transcriptomics (52 samples) on the potential diagnostic implication and metabolic characteristic description in cervical cancer. Sixty-two metabolites were different between CC and normal controls (NOR), in which 5 metabolites (bilirubin, LysoPC(17:0), n-oleoyl threonine, 12-hydroxydodecanoic acid and tetracosahexaenoic acid) were selected as candidate biomarkers for CC. The AUC value, sensitivity (SE), and specificity (SP) of these 5 biomarkers were 0.99, 0.98 and 0.99, respectively. We further analysed the genes in 7 significantly enriched pathways, of which 117 genes, that were expressed differentially, were mainly involved in catalytic activity. Finally, a fully connected network of metabolites and genes in these pathways was built, which can increase the credibility of our selected metabolites. In conclusion, our biomarkers from metabolomics could set a path for CC diagnosis and screening. Our results also showed that variables of both transcriptomics and metabolomics were associated with CC.

Intercriteria analysis of calorimetric data of blood serum proteome

BACKGROUND

Biological microcalorimetry has entered into a phase where its potential for disease diagnostics is readily recognized. A wide variety of oncological and immunological disorders have been characterized by differential scanning calorimetry (DSC) and characteristic thermodynamic profiles were reported. Now the challenge before DSC is not the experimental data collection but the development of analysis protocols for reliable data stratification/classification and discrimination of disease specific features (calorimetric markers).

METHODS

In this work we apply InterCriteria Analysis (ICA) approach combined with Pearson's and Spearman's correlation analysis to a large dataset of calorimetric and biochemical parameters derived for the serum proteome of patients diagnosed with multiple myeloma (MM).

RESULTS

We have identified intercriteria dependences that are general for the various types of MM and thus can be regarded as a characteristic of this largely heterogeneous disease: strong contribution of the monoclonal (M) protein concentration to the excess heat capacity of the immunoglobulins-assigned thermal transition; shift of the albumin assigned calorimetric transition to allocation where it overlaps with the globulins assigned transition and strong shift of the globulins assigned transition temperature attributable to M proteins conformational changes.

CONCLUSIONS

Our data justify the applicability of ICA for deciphering of the complex thermodynamic behavior of the MM blood serum proteome.

GENERAL SIGNIFICANCE

The applied approach is suitable for more general application in the analysis of biocalorimetric data since it can help identify the biological relevance of the distinguished thermodynamic features observed for variety of diseases.

Investigation of early and advanced stages in ovarian cancer using human plasma by differential scanning calorimetry and mass spectrometry

Ovarian cancer is recognized with high mortality due to asymptomatic nature of the disease and difficulties in diagnosing early stage of the cancer. The present study evaluates the use of differential scanning calorimetry (DSC) in differentiating the severity of ovarian cancer from healthy women. 47 diseased women were subdivided into four stages with respect to clinical relevance and severity. Stages I-II were regarded as early stages and stages III-IV were regarded as advanced stages. The two average transition temperatures (T m ) increased with disease severity from 64.84 and 70.32 °C (healthy) to 68.46 and 75.24 °C (stage IV), respectively. T m were increased depending on clinical groups. In addition, the change in heat capacity was also dependent on the disease severity. To further support and investigate the nature of the proposed interactions, matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis is employed. The results suggest the differences in peptide expression between early and advanced stage of ovarian cancer, affected abundant proteins in plasma. The combined DSC and MS approach was supportive in identifying a unique signature of ovarian cancer stages, and demonstrates the potential of DSC as a complementary diagnostic tool in the evaluation of early stage ovarian cancer.

Differential scanning calorimetry as a complementary diagnostic tool for the evaluation of biological samples

BACKGROUND

Differential scanning calorimetry (DSC) is a tool for measuring the thermal stability profiles of complex molecular interactions in biological fluids. DSC profiles (thermograms) of biofluids provide specific signatures which are being utilized as a new diagnostic approach for characterizing disease but the development of these approaches is still in its infancy.

METHODS

This article evaluates several approaches for the analysis of thermograms which could increase the utility of DSC for clinical application. Thermograms were analyzed using localized thermogram features and principal components (PCs). The performance of these methods was evaluated alongside six models for the classification of a data set comprised of 300 systemic lupus erythematosus (SLE) patients and 300 control subjects obtained from the Lupus Family Registry and Repository (LFRR).

RESULTS

Classification performance was substantially higher using the penalized algorithms relative to localized features/PCs alone. The models were grouped into two sets, the first having smoother solution vectors but lower classification accuracies than the second with seemingly noisier solution vectors.

CONCLUSIONS

Coupling thermogram technology with modern classification algorithms provides a powerful diagnostic approach for analysis of biological samples. The solution vectors from the models may reflect important information from the thermogram profiles for discriminating between clinical groups.

GENERAL SIGNIFICANCE

DSC thermograms show sensitivity to changes in the bulk plasma proteome that correlate with clinical status. To move this technology towards clinical application the development of new approaches is needed to extract discriminatory parameters from DSC profiles for the comparison and diagnostic classification of patients.

Characterization of monoclonal gammopathy of undetermined significance by calorimetric analysis of blood serum proteome

Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant proliferative disorder that may progress to multiple myeloma, a malignant plasma cell neoplasia. We evaluated differential scanning calorimetry (DSC) as an experimental tool for differentiating serum samples of MGUS patients from healthy individuals. DSC thermograms can be used for monitoring changes in the serum proteome associated with MGUS. MGUS patients showed great variability in serum thermogram characteristics, which depended on the IgG, IgA or IgM isotypes and/or the κ or λ light chains. Thermogram feature parameters distinguished patients with MGUS from healthy people. Serum samples, named as non-MGUS, were also collected from patients with subjacent immunological pathologies who were discarded of having MGUS through serum immunofixation. They were used to verify the sensitivity of DSC for discriminating MGUS from related blood dyscrasias. Only some DSC thermogram feature parameters differentiated, to a lesser extent, between MGUS and non-MGUS individuals. We contemplate DSC as a tool for early diagnosis and monitoring of MGUS.

Calorimetric markers of Bence Jones and nonsecretory multiple myeloma serum proteome

The present work provides a thermodynamic description of blood serum from patients diagnosed with Bence Jones myeloma (BJMM) and nonsecretory myeloma (NSMM) by means of differential scanning calorimetry (DSC), serum protein electrophoresis, and free light chain assay. Specific alterations in the thermodynamic behavior of both BJMM and NSMM proteome have been revealed. On the basis of the transition temperature of the main transition in the calorimetric profiles and the shape similarity criterion, we defined BJMM and NSMM sets/subsets of thermograms with very similar thermodynamic features. We show that some of the BJMM and NSMM subsets correlate with previously defined secretory myeloma subsets (Todinova et al. Anal. Chem. 2011, 83, 7992). The established analogies strongly suggest that common molecular markers contribute to the calorimetric profiles of the different, secretory and nonsecretory, myeloma types; our data show robust evidence that these are ligands stabilizing the major serum proteins. We demonstrate that the DSC approach might be highly beneficial, especially for NSMM patients, since the characteristic modifications in the DSC profiles might serve as calorimetric markers when no monoclonal proteins can be detected in the bloodstream and the diagnosis heavily relies on invasive methods.

Clinical application of plasma thermograms. Utility, practical approaches and considerations

Differential scanning calorimetry (DSC) studies of blood plasma are part of an emerging area of the clinical application of DSC to biofluid analysis. DSC analysis of plasma from healthy individuals and patients with various diseases has revealed changes in the thermal profiles of the major plasma proteins associated with the clinical status of the patient. The sensitivity of DSC to the concentration of proteins, their interactions with other proteins or ligands, or their covalent modification underlies the potential utility of DSC analysis. A growing body of literature has demonstrated the versatility and performance of clinical DSC analysis across a range of biofluids and in a number of disease settings. The principles, practice and challenges of DSC analysis of plasma are described in this article.