Biomarkers Of Treatment Response Research Articles

Real-world data confirm elexacftor/tezacaftor/ivacaftor modulators halves sweat chloride concentration in eligible people with cystic fibrosis.

Sweat chloride concentration, a diagnostic feature in cystic fibrosis (CF), reflects CF transmembrane conductance regulator (CFTR) activity. CFTR modulator therapies, especially elexacaftor/tezacaftor/ivacaftor (ETI), has improved CF outcomes. We report nationwide, real-world data on sweat chloride concentration in people with CF (pwCF) with and without modulator therapies. All Danish pwCF with a minimum of one F508del allele were included. Sweat chloride measurements were stratified by genotype and modulator treatment. Differences were assessed using mixed-effects models. We included 977 sweat chloride measurements from 430 pwCF, 71% of which were F508del homozygous. Heterozygous and homozygous ETI-treated pwCF had an estimated mean sweat chloride concentration of 43 mmol/L (95% confidence interval: 39; 48) and 43 mmol/L (39; 47), respectively-48% and 59% lower than those without treatment. High variation in concentrations remained regardless of treatment status. Despite ETI treatment, 27% heterozygous and 23% homozygous pwCF had elevated concentrations (≥60 mmol/L). These real-world data confirm a substantial decrease in sweat chloride concentration during modulator treatment, especially ETI, where mean concentrations halved. However, large variation remained, including persistently high concentrations. These findings emphasize the potential of sweat chloride concentration as a treatment response biomarker and the need to explore its heterogeneity and relationship with clinical outcomes.

Identifying digital biomarkers of illness activity and treatment response in bipolar disorder with a novel wearable device (TIMEBASE): protocol for a pragmatic observational clinical study.

Bipolar disorder is highly prevalent and consists of biphasic recurrent mood episodes of mania and depression, which translate into altered mood, sleep and activity alongside their physiological expressions. The IdenTifying dIgital bioMarkers of illnEss activity and treatment response in BipolAr diSordEr with a novel wearable device (TIMEBASE) project aims to identify digital biomarkers of illness activity and treatment response in bipolar disorder. We designed a longitudinal observational study including 84 individuals. Group A comprises people with acute episode of mania (n = 12), depression (n = 12 with bipolar disorder and n = 12 with major depressive disorder (MDD)) and bipolar disorder with mixed features (n = 12). Physiological data will be recorded during 48 h with a research-grade wearable (Empatica E4) across four consecutive time points (acute, response, remission and episode recovery). Group B comprises 12 people with euthymic bipolar disorder and 12 with MDD, and group C comprises 12 healthy controls who will be recorded cross-sectionally. Psychopathological symptoms, disease severity, functioning and physical activity will be assessed with standardised psychometric scales. Physiological data will include acceleration, temperature, blood volume pulse, heart rate and electrodermal activity. Machine learning models will be developed to link physiological data to illness activity and treatment response. Generalisation performance will be tested in data from unseen patients. Recruitment is ongoing. This project should contribute to understanding the pathophysiology of affective disorders. The potential digital biomarkers of illness activity and treatment response in bipolar disorder could be implemented in a real-world clinical setting for clinical monitoring and identification of prodromal symptoms. This would allow early intervention and prevention of affective relapses, as well as personalisation of treatment.

MEN1/DAXX/ATRX mutations enhance progression-free survival in gastroenteropancreatic neuroendocrine tumors treated with peptide receptor radionuclide therapy.

Pre-clinical data suggest that mutations in the MEN1, DAXX, and/or ATRX genes may potentially increase radiation efficacy in cancer cells. Herein, we explore the association between response to peptide receptor radionuclide therapy (PRRT) and those mutations in patients with gastroenteropancreatic neuroendocrine tumors (GEP-NETs). We analyzed tissue-based next generation sequencing (NGS) assay results and clinicopathologic data from 28 patients with GEP-NETs treated with PRRT. Findings were correlated with progression-free survival (PFS) and objective response (ORR). Patients with mutations in MEN1, DAXX, and/or ATRX (n = 13) had a longer median PFS (26.47 vs. 12.13 months; p = 0.014) than wildtype (n = 15) patients when adjusted for surgery prior to PRRT, tumor grade, and presence of TP53 mutation. Alterations in MEN1 along with a concurrent mutation in either DAXX or ATRX (n = 6) trended towards longer PFS compared to patients without concurrent mutations (31.53 vs. 17.97 months; p = 0.09). ORR was higher in patients with a mutation in MEN1, DAXX, or ATRX (41.67% vs. 15.38%). In pancreatic NET patients, these target mutations also showed a longer PFS (28.43 vs. 9.83 months; p = 0.04). TP53 alterations showed a shorter PFS than wild-type cases (11.17 vs. 20.47 months; p = 0.009). Mutations in MEN1/DAXX/ATRX are associated with improved PFS in patients with GEP-NETs receiving PRRT and might be used as a biomarker for treatment response.

Exploring the association between melatonin and nicotine dependence (Review).

Due to the addictive qualities of tobacco products and the compulsive craving and dependence associated with their use, nicotine dependence continues to be a serious public health concern on a global scale. Despite awareness of the associated health risks, nicotine addiction contributes to numerous acute and chronic medical conditions, including cardiovascular disease, respiratory disorders and cancer. The nocturnal secretion of pineal melatonin, known as the 'hormone of darkness', influences circadian rhythms and is implicated in addiction‑related behaviors. Melatonin receptors are found throughout the brain, influencing dopaminergic neurotransmission and potentially attenuating nicotine‑seeking behavior. Additionally, the antioxidant properties of melatonin may mitigate oxidative stress from chronic nicotine exposure, reducing cellular damage and lowering the risk of nicotine‑related health issues. In addition to its effects on circadian rhythmicity, melatonin acting via specific neural receptors influences sleep and mood, and provides neuroprotection. Disruptions in melatonin signaling may contribute to sleep disturbances and mood disorders, highlighting the potential therapeutic role of melatonin in addiction and psychiatric conditions. Melatonin may influence neurotransmitter systems involved in addiction, such as the dopaminergic, glutamatergic, serotonergic and endogenous opioid systems. Preclinical studies suggest the potential of melatonin in modulating reward processing, attenuating drug‑induced hyperactivity and reducing opioid withdrawal symptoms. Chronotherapeutic approaches targeting circadian rhythms and melatonin signaling show promise in smoking cessation interventions. Melatonin supplementation during periods of heightened nicotine cravings may alleviate withdrawal symptoms and reduce the reinforcing effects of nicotine. Further research is required however, to examine the molecular mechanisms underlying the melatonin‑nicotine association and the optimization of therapeutic interventions. Challenges include variability in individual responses to melatonin, optimal dosing regimens and identifying biomarkers of treatment response. Understanding these complexities could lead to personalized treatment strategies and improve smoking cessation outcomes.

Dupilumab Reduces Urticaria Activity, Itch, and Hives in Patients with Chronic Spontaneous Urticaria Regardless of Baseline Serum Immunoglobulin E Levels.

In chronic spontaneous urticaria (CSU), interleukin (IL)-4 and IL-13 may promote mast cell activation directly via IL-4 receptor expression, or indirectly via upregulated immunoglobulin E (IgE) production. Dupilumab significantly improved CSU signs and symptoms in the phase 3, randomized, placebo-controlled LIBERTY-CSU CUPID Study A. This analysis explores the impact of dupilumab on CSU signs and symptoms and serum IgE levels in patients from LIBERTY-CSU CUPID Study A with serum total IgE above and below 100IU/mL at baseline. Patients with H1-antihistamine-refractory CSU received dupilumab (n = 70) or placebo (n = 68) for 24 weeks. Efficacy endpoints were change from baseline to weeks 12 and 24 in serum total IgE levels, Itch Severity Score over 7 days (ISS7), Urticaria Activity Score over 7 days (UAS7), and Hives Severity Score over 7days (HSS7) in dupilumab- or placebo-treated patients with serum total IgE above and below 100IU/mL at baseline. Dupilumab treatment significantly reduced median (interquartile range) IgE levels at week 12 [dupilumab: -31.9% (-41.9; -22.6); placebo: -6.3% (-21.3; 14.9)] and week 24 [dupilumab: -48.2% (-56.8; - 39.5); placebo: - 6.3% (-34.5; 14.8)]. Similar IgE reductions relative to baseline were observed in dupilumab-treated patients regardless of baseline IgE level. Dupilumab treatment improved ISS7, UAS7, and HSS7 over 12 and 24 weeks, regardless of baseline serum IgE level (interaction p ≥ 0.59 for all treatment by subgroup comparisons), with weak correlations (r < 0.2) observed between IgE level changes and ISS7, UAS7, and HSS7 outcomes. Dupilumab significantly improved CSU signs and symptoms and reduced serum IgE, regardless of baseline IgE levels. In the current analysis, baseline total IgE had no predictive value as a dupilumab treatment response biomarker in CSU. Downregulation of IgE, a key mediator of mast cell activation and histamine release, may at least partially explain the effectiveness of dupilumab in reducing CSU signs and symptoms. ClinicalTrials.gov Identifier: NCT04180488.

SingleNucleotide Polymorphisms as Biomarkers of Mepolizumab and Benralizumab Treatment Response in Severe Eosinophilic Asthma

SingleNucleotide Polymorphisms as Biomarkers of Mepolizumab and Benralizumab Treatment Response in Severe Eosinophilic Asthma

SomaLogic proteomics reveals new biomarkers and provides mechanistic, clinical insights into Acetyl coA Carboxylase (ACC) inhibition in Non-alcoholic Steatohepatitis (NASH)

Non-alcoholic Fatty Liver Disease (NAFLD) and Non-alcoholic Steatohepatitis (NASH) are major metabolic diseases with increasing global prevalence and no approved therapies. There is a mounting need to develop biomarkers of diagnosis, prognosis and treatment response that can effectively replace current requirements for liver biopsies, which are invasive, error-prone and expensive. We performed SomaLogic serum proteome profiling with baseline (n = 231) and on-treatment (n = 72, Weeks 12 and 16, Placebo and 25 mg PF-05221304) samples from a Phase 2a trial (NCT03248882) with Clesacostat (PF-05221304), an acetyl coA carboxylase inhibitor (ACCi) in patients with NAFLD/NASH. SomaSignal NASH probability scores and expression data for 7000+ analytes were analyzed to identify potential biomarkers associated with baseline clinical measures of NAFLD/NASH [Magnetic Resonance Imaging-Proton Density Fat Fraction (MRI-PDFF), alanine aminotransferase (ALT) and aspartate aminotransferase (AST)] as well as biomarkers of treatment response to ACCi. SomaSignal NASH probability scores identified biopsy-proven/clinically defined NIT-based (Presumed) NASH classification of the cohort with > 70% agreement. Clesacostat-induced reduction in steatosis probability scores aligned with observed clinical reduction in hepatic steatosis based on MRI-PDFF. We identify a set of 69 analytes that robustly correlate with clinical measures of hepatic inflammation and steatosis (MRI-PDFF, ALT and AST), 27 of which were significantly reversed with ACC inhibition. Clesacostat treatment dramatically upregulated Wnt5a protein and Apolipoproteins C3 and E, with drug-induced changes significantly correlating to changes on MRI-PDFF. Our data demonstrate the utility of SomaLogic- analyte panel for diagnosis and treatment response in NAFLD/NASH and provide potential new mechanistic insights into liver steatosis reduction, inflammation and serum triglyceride elevation with ACC inhibition. (Clinical Trial Identifier: NCT03248882).

Based on machine learning, CDC20 has been identified as a biomarker for postoperative recurrence and progression in stage I & II lung adenocarcinoma patients.

By utilizing machine learning, we can identify genes that are associated with recurrence, invasion, and tumor stemness, thus uncovering new therapeutic targets. To begin, we obtained a gene set related to recurrence and invasion from the GEO database, a comprehensive gene expression database. We then employed the Weighted Gene Co-expression Network Analysis (WGCNA) to identify core gene modules and perform functional enrichment analysis on them. Next, we utilized the random forest and random survival forest algorithms to calculate the genes within the key modules, resulting in the identification of three crucial genes. Subsequently, one of these key genes was selected for prognosis analysis and potential drug screening using the Kaplan-Meier tool. Finally, in order to examine the role of CDC20 in lung adenocarcinoma (LUAD), we conducted a variety of in vitro and in vivo experiments, including wound healing assay, colony formation assays, Transwell migration assays, flow cytometric cell cycle analysis, western blotting, and a mouse tumor model experiment. First, we collected a total of 279 samples from two datasets, GSE166722 and GSE31210, to identify 91 differentially expressed genes associated with recurrence, invasion, and stemness in lung adenocarcinoma. Functional enrichment analysis revealed that these key gene clusters were primarily involved in microtubule binding, spindle, chromosomal region, organelle fission, and nuclear division. Next, using machine learning, we identified and validated three hub genes (CDC45, CDC20, TPX2), with CDC20 showing the highest correlation with tumor stemness and limited previous research. Furthermore, we found a close association between CDC20 and clinical pathological features, poor overall survival (OS), progression-free interval (PFI), progression-free survival (PFS), and adverse prognosis in lung adenocarcinoma patients. Lastly, our functional research demonstrated that knocking down CDC20 could inhibit cancer cell migration, invasion, proliferation, cell cycle progression, and tumor growth possibly through the MAPK signaling pathway. CDC20 has emerged as a novel biomarker for monitoring treatment response, recurrence, and disease progression in patients with lung adenocarcinoma. Due to its significance, further research studying CDC20 as a potential therapeutic target is warranted. Investigating the role of CDC20 could lead to valuable insights for developing new treatments and improving patient outcomes.

Examining immune-inflammatory mechanisms of probiotic supplementation in depression: secondary findings from a randomized clinical trial

We recently indicated that four-week probiotic supplementation significantly reduced depression along with microbial and neural changes in people with depression. Here we further elucidated the biological modes of action underlying the beneficial clinical effects of probiotics by focusing on immune-inflammatory processes. The analysis included a total of N = 43 participants with depression, from which N = 19 received the probiotic supplement and N = 24 received a placebo over four weeks, in addition to treatment as usual. Blood and saliva were collected at baseline, at post-intervention (week 4) and follow-up (week 8) to assess immune-inflammatory markers (IL-1β, IL-6, CRP, MIF), gut-related hormones (ghrelin, leptin), and a stress marker (cortisol). Furthermore, transcriptomic analyses were conducted to identify differentially expressed genes. Finally, we analyzed the associations between probiotic-induced clinical and immune-inflammatory changes. We observed a significant group x time interaction for the gut hormone ghrelin, indicative of an increase in the probiotics group. Additionally, the increase in ghrelin was correlated with the decrease in depressive symptoms in the probiotics group. Transcriptomic analyses identified 51 up- and 57 down-regulated genes, which were involved in functional pathways related to enhanced immune activity. We identified a probiotic-dependent upregulation of the genes ELANE, DEFA4 and OLFM4 associated to immune activation and ghrelin concentration. These results underscore the potential of probiotic supplementation to produce biological meaningful changes in immune activation in patients with depression. Further large-scale mechanistic trials are warranted to validate and extend our understanding of immune-inflammatory measures as potential biomarkers for stratification and treatment response in depression. Trial Registration: www.clinicaltrials.gov, identifier: NCT02957591.

Metabolomic characterization of human glioblastomas and patient plasma: a pilot study

Background Glioblastoma (GBM) is a clinically challenging primary brain tumor with poor survival outcome despite surgical resection and intensive chemoradiation. The metabolic heterogeneity of GBM can become biomarkers for treatment response, resistance, and outcome prediction. The aim of the study is to investigate metabolic distinctions between primary and recurrent GBM tissue and patient plasma to establish feasibility for metabolic profiling. Methods A single-center cohort study analyzed tissue and blood samples from 15 patients with GBM using untargeted metabolomic/lipidomic assays. Metabolomic, lipidomic, and biogenic amine analyses were conducted on GBM tissue and patient plasma at diagnosis and recurrence using untargeted mass spectrometry. The study utilized a small but longitudinally collected cohort to evaluate alteration in metabolites, lipids, and biogenic amines between specimens at diagnosis and recurrence. Results Exploratory analysis revealed significant alteration in metabolites, lipids, and biogenic amines between diagnostic and recurrent states in both tumor and plasma specimens. Notable metabolites differed at recurrence, including N-alpha-methylhistamine, glycerol-3-phosphate, phosphocholine, and succinic acid in tissue, and indole-3-acetate, and urea in plasma. Principal component analysis revealed distinct metabolomic profiles between tumor tissue and patient plasma. Distinct metabolic profiles were observed in GBM tissue and patient plasma at recurrence, demonstrating the feasibility of using metabolomic methodologies for longitudinal studies. One patient exhibited a unique tumor resistance signature at diagnosis, possibly indicating a high-risk metabolomic phenotype. Conclusions In this small cohort, the findings suggest the potential of metabolomic signatures of GBM tissue and patient plasma for risk stratification, outcome prediction, and the development of novel adjuvant metabolic-targeting therapies. The findings suggest metabolic discrepancies at diagnosis and recurrence in tissue and plasma, highlighting potential implications for evaluation of clinical response. The identification of significant changes in metabolite abundance emphasizes the need for larger studies using targeted metabolomics to validate and further explore these profiles.

Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia.

Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes in patients. The clinical challenge lies in identifying those patients at highest risk for developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathologic, histologic or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality of OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Machine-learning based biomarkers, such as S100A7, demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry (mIHC) workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple "hidden" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.

Advances in metabolomics in critically ill patients with Sepsis and Septic Shock.

Sepsis accounts for high cases of morbidity and mortality in hospitalized patients. It has a very complex pathophysiology and swiftly progresses to a severe form of the disease, such as septic shock leading to organ dysfunction, organ failure, and death. Metabolomics has transformed sepsis's clinical and research topography with its application in prognosis, diagnosis, and risk assessment in patients with sepsis and septic shock. Metabolites in blood and urine are detected and analyzed, which helps in understanding the pathogenesis of the disease and aid in better disease management by identifying biomarkers early on. Metabolomics, sepsis and septic shock were the keywords were searched in PubMed and Scopus, from its inception to Dec 2023. This article provides information regarding metabolic profiling performed in sepsis and septic shock We demonstrated that metabolomics will change the world of sepsis by analyzing and detecting the diagnosis, prognosis, mortality, and treatment response biomarkers.

Inflammatory Cytokines and Clinical Outcome Following Biological Therapy in Adult Bio-Naïve Psoriasis Patients.

Inflammatory cytokines may hold the key to the clinical evolution of psoriasis. The aims of this study are to find a correlation between levels of inflammatory cytokines such as TNF-α, IL-23, IL-17A, and IL-17F and disease duration and severity scores in psoriasis; to test if the decrease in any of the aforementioned cytokines is correlated with an amelioration in disease severity scores; and to analyze if any of the four biologic agents used are linked with a greater decrease in overall cytokine levels. We enrolled 23 adult patients under treatment with ixekizumab, secukinumab, guselkumab, or adalimumab and measured psoriasis disease severity scores PASI (Psoriasis Area Severity Index) and DLQI (Dermatology Life Quality Index), as well as the levels of the aforementioned cytokines at the start of therapy and after 3 months of continuous treatment. Inclusion criteria were the presence of psoriasis, age above 18 years and the need to initiate biological therapy (lack of response to standard treatment). Biological therapies resulted in an amelioration of PASI and DLQI scores, as well as levels of TNF-α, IL-23 and IL-17F. Disease duration and PASI and DLQI scores did not correlate with cytokine levels except DLQI and IL-23 score, in a paradoxically inversely proportional manner. IL-23, in particular, could be a useful biomarker for checking treatment response in psoriasis.

N100 as a response prediction biomarker for accelerated 1 Hz right DLPFC-rTMS in major depression

Background and objectiveRepetitive transcranial magnetic stimulation (rTMS) is a safe and effective treatment for major depressive disorder (MDD); however, this treatment currently lacks reliable biomarkers of treatment response. TMS-evoked potentials (TEPs), measured using TMS-electroencephalography (TMS-EEG), have been suggested as potential biomarker candidates, with the N100 peak being one of the most promising. This study investigated the association between baseline N100 amplitude and 1 Hz right dorsolateral prefrontal cortex (R-DLPFC) accelerated rTMS (arTMS) treatment in MDD. MethodsBaseline TMS-EEG sessions were performed for 23 MDD patients. All patients then underwent 40 sessions of 1 Hz R-DLPFC (F4) arTMS over 5 days and a follow-up TMS-EEG session one week after the end of theses arTMS sessions. ResultsBaseline N100 amplitude at F4 showed a strong positive association (p < .001) with treatment outcome. The association between the change in N100 amplitude (baseline to follow-up) and treatment outcome did not remain significant after Bonferroni correction (p = .06, corrected; p = .03, uncorrected). Furthermore, treatment responders had a significantly larger mean baseline F4 TEP amplitude during the N100 time frame compared to non-responders (p < .001). Topographically, after Bonferroni correction, F4 is the only electrode at which its baseline N100 amplitude showed a significant positive association (p < .001) with treatment outcome. LimitationsLack of control group and auditory masking. ConclusionBaseline N100 amplitude showed a strong association with treatment outcome and thus demonstrated great potential to be utilized as a cost-effective and widely adoptable biomarker of rTMS treatment in MDD.

Molecular and Pathobiology of Canine Mammary Tumour: Defining a Translational Model for Human Breast Cancer.

Canine mammary tumours (CMT) have histological, clinicopathological and molecular resemblances to human breast cancer (HBC), positioning them as viable models for studying the human disease. CMT initiation and progression occur spontaneously in immune-competent animals, which challenge the suggested limitations of genetically modified mice, also enabling the evaluation of immunotherapies in canine patients. Dogs have shorter life expectancy compared to humans, and cancer advances more rapidly in this species. This makes it possible to perform studies about the clinical efficacy of new therapeutic modalities in a much shorter time than in human patients. The identification of biomarkers for tumour subtypes, progression and treatment response paves the way for the development of novel therapeutic and diagnostic approaches. This review addresses the similarities between CMT and HBC and the molecular signatures identified in CMT samples that have been explored to date. We proposed a detailed molecular exploration of the CMT stroma using state-of-the-art methods in transcriptomics and proteomics. Using CMT as an analog for HBC not only helps to understand the complexities of the disease, but also to advance comparative oncology to the next level to prove the claim of dogs as a valid translational model.

Metabolic Response to Small Molecule Therapy in Colorectal Cancer Tracked with Raman Spectroscopy and Metabolomics.

Despite numerous screening tools for colorectal cancer (CRC), 25% of patients are diagnosed with advanced disease. Novel diagnostic technologies that are early, accurate, and rapid are imperative to assess the therapeutic efficacy of clinical drugs and identify new biomarkers of treatment response. Here Raman spectroscopy (RS) was used to track metabolic reprogramming in KRAS-mutant HCT116 and SW837 cells, and KRAS wild-type CC cells. RS combined with multivariate analysis methods distinguished nonresponsive, partially responsive, and responsive cells treated with cetuximab, a monoclonal antibody for EGFR inhibition, sotorasib, a clinically approved KRAS inhibitor, and various doses of trametinib, an inhibitor of the MAPK pathway. Cells treated with a combination of subtoxic doses of trametinib and BKM120, an inhibitor of the PI3K pathway, showed a synergistic response between the two pathways. Using a supervised machine learning regression model, we established a scoring methodology trained to a priori predict therapeutic response to new treatment combinations. RS metabolites were verified with mass spectrometry, and enrichment pathways were identified, including amino acid, purine, and nicotinate and nicotinamide metabolism that differentiated monotherapy from combination therapy. Our approach may ultimately be applicable to patient-derived primary cells and cultures of patient tumors to predict effective drugs for individualized care.

Early ctDNA kinetics as a dynamic biomarker of cancer treatment response.

Circulating tumor DNA assays are promising tools for the prediction of cancer treatment response. Here, we build a framework for the design of ctDNA biomarkers of therapy response that incorporate variations in ctDNA dynamics driven by specific treatment mechanisms. We develop mathematical models of ctDNA kinetics driven by tumor response to several therapy classes, and utilize them to simulate randomized virtual patient cohorts to test candidate biomarkers. Using this approach, we propose specific biomarkers, based on ctDNA longitudinal features, for targeted therapy, chemotherapy and radiation therapy. We evaluate and demonstrate the efficacy of these biomarkers in predicting treatment response within a randomized virtual patient cohort dataset. These biomarkers are based on novel proposals for ctDNA sampling protocols, consisting of frequent sampling within a compact time window surrounding therapy initiation - which we hypothesize to hold valuable prognostic information on longer-term treatment response. This study highlights a need for tailoring ctDNA sampling protocols and interpretation methodology to specific biological mechanisms of therapy response, and it provides a novel modeling and simulation framework for doing so. In addition, it highlights the potential of ctDNA assays for making early, rapid predictions of treatment response within the first days or weeks of treatment, and generates hypotheses for further clinical testing.

Updates on the Natural History and Clinical Characteristics of NSAID-ERD

Nonsteroidal Anti-Inflammatory Drug Exacerbated Respiratory Disease (NSAID-ERD) is a distinct clinical syndrome characterized by NSAID hypersensitivity, asthma, and nasal polyposis. Its diagnosis is challenging due to variable presentations and a lack of simple tests, leading to diagnostic delays. Recent research has revealed its genetic predispositions, environmental triggers, and associations with atopy and second-hand tobacco smoke exposure or smoking cessation. Despite its severity, diagnostic awareness remains low, leading to the delay in effective management. Therapeutically, NSAID-ERD necessitates multidisciplinary approaches, often combining surgical interventions with medical management, including aspirin desensitization and biologic agents. However, predictive biomarkers for treatment response remain elusive. Understanding the underlying mechanisms driving NSAID-ERD pathogenesis and identifying reliable biomarkers are crucial for enhancing diagnostic accuracy and refining targeted therapeutic strategies for this debilitating condition. This review aims to provide a thorough understanding of NSAID-ERD, covering its history, clinical features, epidemiology, diagnosis, systemic and molecular biomarkers, available treatment options, and avenues for future research.

Evaluating the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) as prognostic and treatment response biomarkers in stage IV colorectal cancer patients

Evaluating the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) as prognostic and treatment response biomarkers in stage IV colorectal cancer patients

Asymmetric Dimethylarginine and NT-proBNP Levels Provide SynergisticInformation in PulmonaryArterial Hypertension.

Plasma asymmetric dimethylarginine (ADMA) is elevated in pulmonary arterial hypertension (PAH) and is associated with unfavorable outcomes. The aim of this study was to assess changes in ADMA plasma levels for monitoring disease progression and outcomes during PAH-specific therapy. ADMA was measured at baseline and after at least 6months of follow-up using enzyme-linked immunosorbent assay and high-performance liquid chromatography. Changes in ADMA were analyzed in relation to changes in established PAH markers, including hemodynamic status, N-terminal pro-brain natriuretic peptide (NT-proBNP) and risk assessment scores. Impact on survival was assessed using Kaplan-Meier curves and Cox proportional hazards models. Between 2008 and 2019, ADMA samples were collected prospectively from 215 patients with PAH. Change in ADMA plasma level was a predictor of disease progression and survival. ΔADMA (median-0.03μmol/L; 95%CI:-0.145 to 0.0135) was correlated with change in mean pulmonary arterial pressure (P< 0.005; rS=0.287) but was not significantly correlated with ΔNT-proBNP (P=0.056; rS=0.135). Patients with decreased ADMA plasma levels at follow-up had better 3-year and 5-year survival rates (88% and 80%, respectively, vs 72% and 53% in those without decreases in ADMA) (P< 0.005; pulmonary hypertension-related mortality or lung transplantation). Patients with decreases in both ADMA and NT-proBNP had better survival rates compared with patients in whom only 1 parameter improved (P< 0.005). ΔADMA was a significant predictor of survival in Cox regression analysis and also when corrected for ΔNT-proBNP (HRs: 1.27 and 1.35, respectively; P< 0.005). ADMA and NT-proBNP provide synergistic prognostic information for patients with PAH. ADMA could be used as an objective and distinct biomarker for monitoring treatment response in PAH.