Predictive biomarkers and clinical evidence

Predictive Biomarkers and Personalized Therapy: Use of Pharmacogenetic Testing in a Scandinavian Perspective

Precision medicine has significantly advanced through the development of predictive biomarkers based on pharmacogenetic (PGx) testing. These tests identify interactions between drugs and genetic variants that influence patient responses to treatments. Understanding genetic variations in drug-metabolizing enzymes, receptors and transporters and their impact on pharmacokinetics and pharmacodynamics allows for the prediction of drug effects and side effects, enabling tailored treatments for different patient groups. This review focuses on drugs metabolized by cytochrome P450 (CYP450) enzymes, for example, citalopram and clopidogrel or transported by the solute carrier organic anion transporter family member 1B1 (SLCO1B1), for example, atorvastatin and simvastatin, with PGx dosing guidelines, in the context of consumption in Scandinavian countries. A major barrier to the widespread adoption of PGx tests in clinical practice has been healthcare professionals' uncertainty about their efficacy, complexity in result interpretation and questions regarding the evidence base. However, recent studies have demonstrated PGx testing has the potential to improve treatment outcomes, reduce adverse drug reactions and achieve cost savings. These findings underscore the potential of PGx testing as a valuable tool in clinical decision making, promoting its use in a pre-emptive manner to enhance patient care.

Utility of public knowledge bases for the interpretation of comprehensive tumor molecular profiling results

With the growing use of comprehensive tumor molecular profiling (CTMP), the therapeutic landscape of cancer is rapidly evolving. NGS produces large amounts of genomic data requiring complex analysis and subsequent interpretation. We sought to determine the utility of publicly available knowledge bases (KB) for the interpretation of the cancer mutational profile in clinical practice. Analysis was performed across patients who previously underwent CTMP. Independent interpretation of the CTMP was performed manually, and then, the recommendations were compared to ones present in KBs (OncoKB, CIViC, CGI, CGA, VICC, MolecularMatch). A total of 222 CTMP reports from 222 patients with 932 genomic alterations (GA) were identified. For 368 targetable GA identified in 171 (77%) of the patients, 1381 therapy recommendations were compiled. Except for CGA, therapy ESCAT LOE I, II, IIIA and IIIB therapy options were equally represented in the majority of KB. Personalized treatment options with ESCAT LOE I-II were provided for 35 patients (16%); MolecularMatch/CIViC allowed to collect ESCAT I-II treatment options for 34 of them (97%), OncoKB/CGI-for 33 of them (94%). Employing VICC and CGA 6 (17%) and 20 (57%) of patients were left without ESCAT I or II treatment options. For 88 patients with ESCAT level III-B therapy recommendations: only 2 (2%), 3 (3%), 4 (5%) and 6 (7%) of patients were left without options with CIViC, MolecularMatch, CGI and OncoKB, and with VICC-12 (14%). Highest overlap ratio was observed for IIIA (0.81) biomarkers, with the comparable results for LOE I-II. Meanwhile, overlap ratio for ESCAT LOE IV was 0.22. Public KBs provide substantial information on ESCAT-I/R1 biomarkers, but the information on ESCAT II-IV and resistance biomarkers is underrepresented. Manual curation should be considered the gold standard for the CTMP interpretation.

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.

Update on Biomarkers Associated to Cardioembolic Stroke: A Narrative Review

Background: In the last years, several studies were conducted that evaluated biomarkers that could be helpful for cardioembolic stroke diagnosis, prognosis, and the determination of risk of stroke recurrence. Methods: We performed a narrative review of the main studies that evaluated biomarkers related to specific cardioembolic causes: atrial fibrillation, patent foramen ovale, atrial cardiomyopathy, and left ventricular wall motion abnormalities. Results: BNP and NT-proBNP are, among all biomarkers of cardioembolic stroke, the ones that have the highest amount of evidence for their use. NT-proBNP is currently used for the selection of patients that will be included in clinical trials that aim to evaluate the use of anticoagulation in patients suspected of having a cardioembolic stroke and for the selection of patients to undergo cardiac monitoring. NT-proBNP has also been incorporated in tools used to predict the risk of stroke recurrence (ABC-stroke score). Conclusions: NT-proBNP and BNP continue to be the biomarkers most widely studied in the context of cardioembolic stroke. The possibility of using other biomarkers in clinical practice is still distant, mainly because of the low methodological quality of the studies in which they were evaluated. Both internal and external validation studies are rarely performed for most biomarkers.

Heart Rate Variability Indices as Possible Biomarkers for the Severity of Post-traumatic Stress Disorder Following Pregnancy Loss

Background: Psychological distress, such as posttraumatic stress disorder (PTSD), is commonly evaluated using subjective questionnaires, a method prone to self-report bias. The study's working hypothesis was that levels of autonomic dysfunction determined by heart rate variability (HRV) measures are associated with the severity of PTSD in women following pregnancy loss. Methods: This was an observational prospective cohort study with 53 patients enrolled. The DSM-5 (Diagnostic and Statistical Manual of Mental Disorders) PTSD scale (PCL-5) was used to assess the severity of PTSD in women after pregnancy loss. The cardiac autonomic function was assessed using HRV measurements during a deep breathing test using an HRV scanner system with wireless ECG enabling real-time data analysis and visualization. HRV measures were: standard deviation (SD) of normal R-R wave intervals [SDNN, ms], square root of the mean of the sum of the squares of differences between adjacent normal R wave intervals [RMSSD, ms], and the number of all R-R intervals in which the change in consecutive normal sinus intervals exceeds 50 milliseconds divided by the total number of R-R intervals measured [pNN50 = (NN50/n-1)^*100%] [pNN50%]. Results: The PCL-5 scores had a statistically significant association with HRV indices (SDNN; RMSSD, and pNN50%). Patients with PTSD had similar mean heart rate values as compared to patients without PTSD (PCL-5), but significantly higher SDNN [median[IQR, interquartile range]: 90.1 (69.1-112.1) vs. 52.5 (36.8-65.6)], RMSSD [59.4 (37.5-74.9) vs. 31.9 (19.3 - 44.0)], and PNN50% values [25.7 (16.4-37.7) vs. 10.6 (1.5-21.9)]. The SDNN of the deep breathing test HRV was effective at distinguishing between patients with PTSD and those without, with an AUC = 0.83 +/- 0.06 (95 % CI 0.94, p = 0.0001) of the ROC model. Conclusions: In this study, HRV indices as biomarkers of cardiac dysautonomia were found to be significantly related to the severity of PTSD symptoms in women after pregnancy loss.