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

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.

Bowel Preparation for Colonoscopy Changes Serum Composition as Detected by Thermal Liquid Biopsy and Fluorescence Spectroscopy

(1) Background: About 50% of prescribed colonoscopies report no pathological findings. A secondary screening test after fecal immunochemical test positivity (FIT+) would be required. Considering thermal liquid biopsy (TLB) as a potential secondary test, the aim of this work was to study possible interferences of colonoscopy bowel preparation on TLB outcome on a retrospective study; (2) Methods: Three groups were studied: 1/514 FIT(+) patients enrolled in a colorectal screening program (CN and CP with normal and pathological colonoscopy, respectively), with blood samples obtained just before colonoscopy and after bowel preparation; 2/55 patients from the CN group with blood sample redrawn after only standard 8-10 h fasting and no bowel preparation (CNR); and 3/55 blood donors from the biobank considered as a healthy control group; (3) Results: The results showed that from the 514 patients undergoing colonoscopy, 247 had CN and 267 had CP. TLB parameters in these two groups were similar but different from those of the blood donors. The resampled patients (with normal colonoscopy and no bowel preparation) had similar TLB parameters to those of the blood donors. TLB parameters together with fluorescence spectra and other serum indicators (albumin and C-reactive protein) confirmed the statistically significant differences between normal colonoscopy patients with and without bowel preparation; (4) Conclusions: Bowel preparation seemed to alter serum protein levels and altered TLB parameters (different from a healthy subject). The diagnostic capability of other liquid-biopsy-based methods might also be compromised. Blood extraction after bowel preparation for colonoscopy should be avoided.

Predictive genomic tools in disease stratification and targeted prevention: a recent update in personalized therapy advancements

In the current era of medical revolution, genomic testing has guided the healthcare fraternity to develop predictive, preventive, and personalized medicine. Predictive screening involves sequencing a whole genome to comprehensively deliver patient care via enhanced diagnostic sensitivity and specific therapeutic targeting. The best example is the application of whole-exome sequencing when identifying aberrant fetuses with healthy karyotypes and chromosomal microarray analysis in complicated pregnancies. To fit into today's clinical practice needs, experimental system biology like genomic technologies, and system biology viz., the use of artificial intelligence and machine learning is required to be attuned to the development of preventive and personalized medicine. As diagnostic techniques are advancing, the selection of medical intervention can gradually be influenced by a person's genetic composition or the cellular profiling of the affected tissue. Clinical genetic practitioners can learn a lot about several conditions from their distinct facial traits. Current research indicates that in terms of diagnosing syndromes, facial analysis techniques are on par with those of qualified therapists. Employing deep learning and computer vision techniques, the face image assessment software DeepGestalt measures resemblances to numerous of disorders. Biomarkers are essential for diagnostic, prognostic, and selection systems for developing personalized medicine viz. DNA from chromosome 21 is counted in prenatal blood as part of the Down's syndrome biomarker screening. This review is based on a detailed analysis of the scientific literature via a vigilant approach to highlight the applicability of predictive diagnostics for the development of preventive, targeted, personalized medicine for clinical application in the framework of predictive, preventive, and personalized medicine (PPPM/3 PM). Additionally, targeted prevention has also been elaborated in terms of gene-environment interactions and next-generation DNA sequencing. The application of 3 PM has been highlighted by an in-depth analysis of cancer and cardiovascular diseases. The real-time challenges of genome sequencing and personalized medicine have also been discussed.

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.

Fluorescence Liquid Biopsy for Cancer Detection Is Improved by Using Cationic Dendronized Hyperbranched Polymer

(1) Background: Biophysical techniques applied to serum samples characterization could promote the development of new diagnostic tools. Fluorescence spectroscopy has been previously applied to biological samples from cancer patients and differences from healthy individuals were observed. Dendronized hyperbranched polymers (DHP) based on bis(hydroxymethyl)propionic acid (bis-MPA) were developed in our group and their potential biomedical applications explored. (2) Methods: A total of 94 serum samples from diagnosed cancer patients and healthy individuals were studied (20 pancreatic ductal adenocarcinoma, 25 blood donor, 24 ovarian cancer, and 25 benign ovarian cyst samples). (3) Results: Fluorescence spectra of serum samples (fluorescence liquid biopsy, FLB) in the presence and the absence of DHP-bMPA were recorded and two parameters from the signal curves obtained. A secondary parameter, the fluorescence spectrum score (FSscore), was calculated, and the diagnostic model assessed. For pancreatic ductal adenocarcinoma (PDAC) and ovarian cancer, the classification performance was improved when including DHP-bMPA, achieving high values of statistical sensitivity and specificity (over 85% for both pathologies). (4) Conclusions: We have applied FLB as a quick, simple, and minimally invasive promising technique in cancer diagnosis. The classification performance of the diagnostic method was further improved by using DHP-bMPA, which interacted differentially with serum samples from healthy and diseased subjects. These preliminary results set the basis for a larger study and move FLB closer to its clinical application, providing useful information for the oncologist during patient diagnosis.