Distinct Subgroups Of Patients Research Articles

Germline Variants in DNA Interstrand-Cross Link Repair Genes May Contribute to Increased Susceptibility for Serrated Polyposis Syndrome

Serrated polyposis syndrome (SPS) is characterized by the development of multiple colorectal serrated polyps and increased predisposition to colorectal cancer (CRC). However, the molecular basis of SPS, especially in cases presenting family history of SPS and/or polyps and/or CRC in first-degree relatives (SPS-FHP/CRC), is still poorly understood. In a previous study, we proposed the existence of two molecular entities amongst SPS-FHP/CRC families, proximal/whole-colon and distal SPS-FHP/CRC, according to the preferential location of lesions and somatic events involved in tumor initiation. In the present study, we aimed to investigate these distinct subgroups of SPS patients in a larger cohort at the germline level and to identify the genetic defects underlying an inherited susceptibility for these two entities. Next-generation sequencing was performed using multigene analysis with a custom-designed panel in a Miseq platform in 60 SPS patients (with and without/unknown FHP/CRC). We found germline pathogenic variants in 6/60 patients (ATM, FANCM, MITF, RAD50, RAD51C, and RNF43). We also found variants of unknown significance (VUS), with prediction of probable damaging effect in 23/60 patients (ATM, BLM, BRCA1, FAN1, ERCC2, ERCC3, FANCA, FANCD2, FANCL, MSH2, MSH6, NTHL1, PALB2, PDGFRA, PMS2, PTCH1, RAD51C, RAD51D, RECQL4, TSC2, WRN, and XRCC5 genes). Most variants were detected in gene coding for proteins of the Fanconi Anemia (FA) pathway involved in the DNA Interstrand-Cross Link repair (ICLR). Notably, variants in ICLR genes were significantly more frequent in the proximal/whole-colon than in the distal subgroup [15/44 (34%) vs 1/16 (6%), p = 0.025], as opposed to the non-ICLR genes that were slightly more frequent in the distal group [8/44 (18%) vs. 5/16 (31%), p > 0.05]. Germline defects in the DNA-ICLR genes may contribute to increased serrated colorectal polyps/carcinoma risk in SPS patients, particularly in proximal/whole-colon SPS. The inclusion of DNA-ICLR genes in the genetic diagnosis of SPS patients, mainly in those with proximal/whole-colon lesions, should be considered and validated by other studies. In addition, patients with germline defects in the DNA-ICLR genes may be more sensitive to treatment with platinum-based therapeutics, which can have implications in the clinical management of these patients.

Characteristics and outcomes of patients with moderate multivalvular disease in the context of tricuspid regurgitation

Abstract Background The clinical features and the long-term prognosis of patients with moderate tricuspid regurgitation (TR) in the context of concomitant moderate left-sided valvular disease (VD) are poorly characterized. Purpose This study sought to describe the characteristics and the outcomes of patients with combined moderate TR and moderate mitral or aortic VD. Methods Baseline clinical and follow-up data were collected from 2110 patients with moderate TR in combination with a moderate left-sided VD diagnosed at our centers from January 2012 to July 2020; the final cohort was divided in 4 groups as shown in Figure 1. The primary endpoint was all-cause death; secondary EP was the first occurrence of heart failure (HF) hospitalization. Results Group 1 patients (1529, 72.5%) had the highest prevalence of chronic kidney disease (897, 66%) and lowest left ventricular systolic function (LVEF <50%, 298, 21%), as detailed in Table 1. Coronary artery disease was most prevalent in Group 2 patients (43, 41%), which also presented with the lowest average Charlson Index scores (3.95±1.87) and most prevalent dyspnea symptoms (44, 41%). Group 3 patients (41, 1.9%) were the oldest (82±7 years), had the highest rate of comorbidities (mean Charlson score 5.2±1.6), atrial fibrillation (20, 54%) and pulmonary hypertension (17, 46%). Eventually, Group 4 patients (422, 20%) were the youngest (74±13 years), more commonly female (279, 66%) and with the highest right ventricular dysfunction rate (TAPSE < 17 mm, 114, 40%). After a median follow-up of 2.1 years, the primary endpoint occurred in 895 (42.4%) patients, with a significantly higher incidence in Group 3 patients (p<0.001) (Figure 1). The secondary endpoint occurred in 316 (15.0%) patients and, similarly, significantly more frequently in Group 3 patients (p=0.01). Subgroup analysis for the primary and secondary endpoint in patients with preserved ejection fraction (LVEF > 50%) confirmed the results of the primary analysis, with Group 3 as the cohort with the most adverse outcomes. Conclusions Patients with significant TR frequently exhibit concurrent left-sided VD, identifying distinct patient subgroups with varying clinical profiles. Notably, TR combined with moderate AS is associated with the most severe clinical presentation and the poorest long-term prognosis, even in patients with preserved LVEF.Figure 1.

The substantial role of sleep, stress and physical activity in persistent high levels of fatigue in patients with inflammatory bowel disease: A longitudinal trajectory study.

Fatigue is a common and burdensome problem for patients with inflammatory bowel disease (IBD). Previous studies lack insight into the individual variability in fatigue severity and course over time, and the characteristics of patients at risk of severe and persistent fatigue. This study aimed to identify distinct groups of IBD patients based on their level and course of fatigue over five years. Subsequently, we examined the relationship between these trajectories, patient characteristics and trajectories of perceived stress, sleep and physical activity. This longitudinal cohort study used prospectively collected data from the myIBDcoach telemedicine monitoring tool, including 320 IBD patients who completed three or more online consultations between 2016 and 2021. Latent class growth analyses were performed to identify distinct trajectories. We found five subgroups with distinct trajectories of fatigue, differing in level and course over time, with 33% of patients experiencing chronic stable and high levels of fatigue. Few differences in patient characteristics were found between trajectories, yet the chronic high fatigue subgroup was more likely to report persistent stable sleep problems, perceived stress, and little physical activity over time compared to the other groups. Distinct subgroups of IBD patients can be identified based on longitudinal fatigue trajectories. The relatively stable levels of fatigue, stress, sleep, and physical activity suggest that a one-time screening of patients on these topics may be sufficient to identify those at risk. Interventions aiming to reduce fatigue should target persistent stress, sleep problems, and low levels of physical activity.

Distinct trajectories of symptomatic response in ulcerative colitis during filgotinib therapy: A post hoc analysis from the SELECTION study.

Filgotinib is an oral, once-daily, Janus kinase 1 preferential inhibitor approved for treatment of ulcerative colitis (UC) following the phase 2b/3 SELECTION trial. Identification of patient populations and factors associated with long-term treatment response trajectories may improve UC management. We aimed to identify and describe distinct patient subgroups of response to filgotinib based on partial Mayo Clinic Score (pMCS) trajectories over time. In these post hoc analyses of SELECTION, group-based trajectory modeling (GBTM) was applied to pMCS to describe groups of distinct, symptom-based patient trajectories using data from patients who responded to filgotinib 200 or 100mg and continued receiving filgotinib up to week 58. Patient demographics, disease characteristics, and week 10 response were compared between the groups. Achievement of a patient-level multi-component endpoint of comprehensive disease control (CDC) was assessed in each group. GBTM identified five distinct patient populations with different response trajectories; 67.5% of patients had beneficial trajectories. The beneficial trajectory groups generally had higher proportions of patients who were recently diagnosed (<1year), were receiving filgotinib 200mg and were biologic-naive versus the relapsing trajectory groups (4%-9% vs. 4%-5%; 43%-65% vs. 36%-46%; 54%-70% vs. 35%-58%, respectively). Furthermore, 55.4% of patients had sustained beneficial trajectories, with low baseline endoscopic subscores (≥43% of patients had a subscore of 2) and strong week 10 FCP responses (≥61% of patients with >50% decrease in FCP from baseline). Sustained beneficial trajectory groups had a higher probability of achieving CDC at week 58 than other groups (31%-32% vs. 0%-7%). Beneficial long-term response trajectories and achievement of CDC with filgotinib were associated with being biologic-naive and having less severe disease at baseline. Early estimation of sustained and CDC may facilitate patient identification and development of personalized management strategies in UC. NCT02914522.

63 Utilization of vasopressin and norepinephrine in neonates with cardiorespiratory compromise: A multicenter retrospective cohort study in Canada

Abstract Background Data regarding neonatal clinical experience with Vasopressin (VA) and Norepinephrine (NE) are limited to small studies. The lack of large-scale studies with safety and efficacy data is preventing the widespread adoption of these agents. Objectives To describe epidemiological data on utilization of NE and VA in neonates including their indications, effectiveness and overall outcomes across 3 tertiary Neonatal Intensive Care Units (NICU) in Canada. Design/Methods This retrospective cohort study involved neonates who received NE or VA between January 1, 2015-December 31, 2020 at 3 tertiary NICUs. Data regarding demographic information, indications of use, dosing details, and clinical outcomes were described using mean, standard deviations (SD), frequencies and percentages. When appropriate, a comparative analysis was performed using Mann-Whitney U tests or the chi-square test as appropriate. Post-initiation changes in physiological variables and laboratory parameters over time were analyzed using repeated measure ANOVA. Results 133 neonates (VA:70, NE:63) were included in this study. Baseline demographic features, clinical characteristics at initiation, indication of use, dosing details and clinical response in neonates exposed to both agents are summarized in Table 1. VA was commonly used for oxygenation failure due to persistent pulmonary hypertension of the newborn (PPHN), while NE was commonly used for hypotension due to septic shock. NE was frequently used as a 1st line inotropic agent, whereas VA was used more as an additive agent [30 (47.6%) vs 12 (18.6%); p=&amp;lt;0.001]. During treatment, lower sodium levels were recorded in the VA group vs the NE group [126.5 (8.6) vs 132 (7); p=.035], but other safety parameters were similar. The time to reach predefined stability parameters were similar in both groups. Significant improvement in physiological parameters were seen over time with both agents (Figure 1). Mortality within 7 days of use was high in the cohort, but comparable between VA and NE groups [18 (25.7%) vs 22 (34.9%), p=0.206]. Other neonatal morbidities were comparable between groups (Table 1). Conclusion This multicenter study provides pragmatic insights into the current use of NE and VA in neonates. Our study reports the effective use of each agent in distinct patient subgroups. This cohort represented neonates with high illness severity; however, exposure to either agent did not independently increase the risk of mortality or adverse outcomes. Further prospective studies exploring its optimal use and cardiopulmonary effects would be interesting.

Data-driven Machine Learning Models for Risk Stratification and Prediction of Emergence Delirium in Pediatric Patients Underwent Tonsillectomy/Adenotonsillectomy.

In the pediatric surgical population, Emergence Delirium (ED) poses a significant challenge. This study aims to develop and validate machine learning (ML) models to identify key features associated with ED and predict its occurrence in children undergoing tonsillectomy or adenotonsillectomy. The analysis involved data cleaning, exploratory data analysis (EDA), supervised predictive modeling, and unsupervised learning on a medical dataset (n = 423). After preliminary data cleaning, EDA encompassed plotting histograms, boxplots, pairplots, and correlation heatmaps to understand variable distributions and relationships. Four predictive models were trained including logistic regression (LR), random forest (RF), Support Vector Machine (SVM), and Gradient Boosting (XGBoost). The models were evaluated and compared using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC), precision, recall, and feature importance. The RF model showed better performance and was used for the test (AUC-ROC 0.96, precision 1.00, and recall 0.92 on the validation set). K-means clustering was applied to find groups within the data. Elbow method and silhouette scores were used to determine the optimal number of clusters. The formed clusters were analyzed by aggregating features to understand the characteristics of each cluster. EDA revealed significant positive correlations between age, weight, American Society of Anesthesiologists (ASA) health score, and surgery duration with the risk of developing ED. Among the ML models, RF achieved the highest performance. Key predictive variables, based on the model's feature importance, included delirium screening scales, extubation time, and time to regain consciousness. Unsupervised K-means clustering identified 2-3 optimal clusters, which represented distinct patient subgroups: younger, healthier, low-risk individuals (cluster 0), and older patients with increasing chronic disease burden, higher delirium screening scores, and consequently higher post-operative delirium risk (clusters 1 and 2). ML techniques are valuable tools for extracting insights and making accurate predictions from healthcare data. High-performing algorithm-based models can be implemented for clinical decision support systems, facilitating early identification and intervention for ED in pediatric patients. By investigating various variables, it is possible to assess risk and implement preventive measures effectively. Furthermore, unsupervised clustering reveals distinct patient subgroups, enabling personalized perioperative management strategies and enhancing overall patient care.

Inflammatory and Lipid Biomarkers in Early Atherosclerosis: A Comprehensive Analysis

Introduction: Atherosclerosis is a leading cause of cardiovascular disease, characterized by lipid accumulation and chronic inflammation within arterial walls. Early detection in young adults is crucial for preventing adverse cardiovascular events. This study investigates the associations between inflammatory indices, lipid biomarkers, and the presence of atherosclerosis in patients aged 18 to 55 years. Methods: A cross-sectional study was conducted involving 89 participants divided into two groups: 62 patients with documented atherosclerosis (main group) and 27 healthy controls without significant atherosclerosis. Comprehensive data—including demographic information, medication use, imaging results, laboratory parameters, and calculated inflammatory indices (SIRI, SII, AISI, NLR, PLR, MLR)—were collected. Statistical analyses included correlation assessments, group comparisons using the Mann–Whitney U test, logistic regression modeling, feature importance analysis with Random Forest and Gradient Boosting classifiers, receiver operating characteristic (ROC) curves, and K-means clustering. Results: Significant differences were observed between the main and control groups. Patients with atherosclerosis exhibited elevated inflammatory indices (SIRI, NLR, MLR, SII) and lipid profile abnormalities (higher TC and LDL-C, lower HDL-C). Lp(a) and ANGPTL3 levels were significantly higher in the main group (p &lt; 0.001 and p &lt; 0.01, respectively). Logistic regression identified SIRI and ANGPTL3 as significant predictors of atherosclerosis, with the model demonstrating high accuracy (77%) and sensitivity (93%). Feature importance analysis confirmed the significance of SIRI and ANGPTL3, alongside traditional lipid biomarkers, in predicting disease presence. ROC analysis showed excellent model performance (AUC &gt; 0.80). Clustering analysis revealed two distinct patient subgroups characterized by predominant inflammatory profiles or lipid metabolism disturbances. Conclusions: Systemic inflammation and lipid abnormalities play significant roles in early atherosclerosis among young adults. Elevated SIRI and ANGPTL3 levels are potent predictors of disease presence. The integration of inflammatory indices and lipid biomarkers into predictive models enhances risk stratification and supports personalized medicine approaches.

Cluster Analysis Identifies Patients With Severe Eosinophilic Asthma Who Achieve Super-Response and Remission With Mepolizumab.

This study identifies two distinct subgroups of patients with severe eosinophilic asthma who respond differently to mepolizumab. Cluster analysis reveals that patients with a family history of asthma, positive skin prick tests and higher baseline lung function have better treatment outcomes, highlighting the value of personalised treatment strategies.

Patient feasibility as a novel approach for integrating IRT and LCA statistical models into patient-centric qualitative data-a pilot study.

Clinical research increasingly recognizes the role and value of patient-centric data incorporation in trial design, aiming for more relevant, feasible, and engaging studies for participating patients. Despite recognition, research on analytical models regarding qualitative patient data analysis has been insufficient. This pilot study aims to explore and demonstrate the analytical framework of the "patient feasibility" concept-a novel approach for integrating patient-centric data into clinical trial design using psychometric latent class analysis (LCA) and interval response theory (IRT) models. A qualitative survey was designed to capture the diverse experiences and attitudes of patients in an oncological indication. Results were subjected to content analysis and categorization as a preparatory phase of the study. The analytical phase further employed LCA and hybrid IRT models to discern distinct patient subgroups and characteristics related to patient feasibility. LCA identified three latent classes each with distinct characteristics pertaining to a latent trait defined as patient feasibility. Covariate analyses further highlighted subgroup behaviors. In addition, IRT analyses using the two-parameter logistic model, generalized partial credit model, and nominal response model highlighted further distinct characteristics of the studied group. The results provided insights into perceived treatment challenges, logistic challenges, and limiting factors regarding the standard of care therapy and clinical trial attitudes.

Identification of SUMOylation-related signature genes associated with immune infiltration in ulcerative colitis through bioinformatics analysis and experimental validation

ObjectiveUlcerative colitis (UC) is a chronic inflammatory disorder challenging to diagnose clinically. We focused on identifying and validating SUMOylation-related signature genes in UC and their association with immune infiltration. MethodsFive eligible gene expression profiles were selected from the Gene Expression Omnibus (GEO) database and merged into a single dataset comprising 260 UC patients and 76 healthy controls (HC). Differentially expressed genes (DEGs) were identified, and these were intersected with SUMOylation-related genes to obtain differentially expressed SUMOylation-related genes (DESRGs). Next, we identify the signature genes and validate them through comprehensive analyses employing GO, KEGG, GSVA, Lasso-cox regression, ROC curves, and clustering analysis. The infiltrating immune cells were analyzed using the CIBERSORT algorithm and Pearson correlation analysis. Finally, in vitro and in vivo experiments validated the identified signature genes. ResultsPALMD, THRB, MAGED1, PARP1, and SLC16A1 were identified. Next, an excellent predictive model for UC was established and distinct subgroups of patients associated with SUMOylation were identified. Moreover, the NF-κB signaling pathway likely plays a pivotal role in the regulation of SUMOylation in UC. Additionally, we validated that the alterations in PALMD, THRB, and MAGED1 expression in LPSinduced Caco-2 cells concurred with our bioinformatics findings, particularly demonstrating statistically significant differences in PALMD and THRB expression. Finally, in a DSS-induced mouse colitis model, we observed a significant upregulation of PALMD expression. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation. ConclusionThis study comprehensively elucidates the biological roles of SUMOylation-related genes in UC, identifying PALMD, MAGED1, THRB, PARP1, and SLC16A1 as signature genes that represent promising biomarkers for UC diagnosis and prognosis.

Cluster analysis of 100 Marfan patients based on aortic 4D flow MRI and Z-score: insights into disease heterogeneity and stratification of subgroups.

4D flow MRI-derived variables from Marfan patients are highly heterogeneous. Our aim was to identify distinct Marfan patient subgroups based on aortic 4D flow MRI and Z-score for stratification of distinct hemodynamic profiles and clinical features by means of hierarchical cluster analysis. One hundred Marfan patients underwent baseline aortic 4D flow MRI at 3 T. Z-scores, degree of helical and vortical flow, wall shear stress, flow displacement, and peak velocity were determined in the ascending aorta. Sex, age, BMI, antihypertensive medication, and dural ectasia were recorded. Hierarchical cluster analysis was performed using 4D flow MRI variables and Z-scores as input. Cluster analysis resulted in three distinct clusters characterized by different Z-scores (mean ± SD); cluster 1: 0.4 ± 1.1 vs. cluster 2: 3.1 ± 1.1 vs. cluster 3: 3.6 ± 1.9. The three clusters delivered differences in helical and vortical flow patterns (global p = 0.003 and p < 0.001, respectively), wall shear stress (0.49 ± 0.11 vs. 0.44 ± 0.12 vs. 0.37 ± 0.09 N/m2, global p < 0.001), flow displacement (0.11 ± 0.05 vs. 0.16 ± 0.08 vs. 0.15 ± 0.07, global p = 0.006), and peak velocity (76.3 ± 9.0 vs. 60.1 ± 7.3 vs. 56.0 ± 7.8 cm/s, global p < 0.001). Patients in cluster 1 and 2 were relevantly younger than in cluster 3 (32.3 ± 13.8 vs. 32.8 ± 12.6 vs. 40.2 ± 15.0 years, all pairwise ∆p < 0.0297). Hierarchical cluster analysis based on aortic 4D flow MRI and Z-score revealed three distinct subgroups of Marfan patients, each characterized by specific hemodynamic profiles and clinical features. Follow-up of our patients is warranted to assess if 4D flow MRI- and Z-score-based stratification can predict future aortic diameter growth and ultimately improve outcomes. A combination of Z-score and 4D flow MRI-derived parameters may help identify subgroups of Marfan patients representing different stages or phenotypes of aortic disease, which require specific management strategies. Four-dimensional (4D) flow MRI-derived variables of Marfan patients are highly heterogeneous across varying Z-scores. Cluster analysis based on 4D flow MRI and Z-score revealed three distinct subgroups of Marfan patients. A combination of Z-score and 4D flow MRI-derived parameters may identify different stages of aortic disease in Marfan patients.

Clinical Characteristics of Distinct Subgroups of Patients with Primary Sjögren's Syndrome Classified by Serological Profiles: A Comparison Study.

Sjögren's syndrome (SS) is an autoimmune disease characterized by heterogeneous clinical presentation and the presence of various autoantibodies. This study aimed to determine the differences in clinical findings according to antibody positivity in patients with primary Sjögren syndrome (pSS) in the Turkish population. A retrospective study was conducted and 402 patients (378 women and 24 men) with pSS were analyzed. The patients were categorized into three subgroups based on serological tests. These were (1) quadruple seropositivity (positive for anti-Sjögren's syndrome-related antigen A antibodies (anti-SSA; anti-Ro) and anti-Sjögren's syndrome-related antigen B antibodies (anti-SSB; anti-La), rheumatoid factor (RF), and antinuclear antibody (ANA); (2) double seropositivity (positive for ANA and anti-SSA/Ro antibodies); and (3) quadruple seronegativity (negative for ANA, RF, anti-SSA/Ro and anti-SSB/La antibodies). The number of quadruple-seropositive patients was 72 (18.6%), double-seropositive 174 (43.2%), and quadruple-seronegative was 85 (21.1%). The age at diagnosis of quadruple-seropositive pSS was 42.4 ± 10.8, which was significantly younger than that of patients with double-seropositive and quadruple-seronegative pSS (p = 0.021, p = 0.112). In terms of organ involvement, salivary gland enlargement, arthralgia, arthritis, Raynaud's phenomenon, lymphadenopathy, cutaneous vasculitis, interstitial lung disease, neurological involvement, autoimmune thyroiditis, renal interstitial disease, anemia, leukopenia, hypergammaglobulinemia, and hypocomplementemia were more common in quadruple-seropositive patients with pSS than in quadruple-seronegative patients (p < 0.0001). The results of this study confirmed the strong impact of immunological markers on the pSS phenotype at the time of diagnosis. Immunological patterns play a central role in the phenotypic expression of the disease, even during the initial diagnostic phase, and can guide physicians in designing personalized treatment plans for patients with pSS.

Federated unsupervised random forest for privacy-preserving patient stratification.

In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a finer-grained understanding of disease variability. Meanwhile, clinical datasets are often small and must be aggregated from multiple hospitals. Online data sharing, however, is seen as a significant challenge due to privacy concerns, potentially impeding big data's role in medical advancements using machine learning. This work establishes a powerful framework for advancing precision medicine through unsupervised random forest-based clustering in combination with federated computing. We introduce a novel multi-omics clustering approach utilizing unsupervised random forests. The unsupervised nature of the random forest enables the determination of cluster-specific feature importance, unraveling key molecular contributors to distinct patient groups. Our methodology is designed for federated execution, a crucial aspect in the medical domain where privacy concerns are paramount. We have validated our approach on machine learning benchmark datasets as well as on cancer data from The Cancer Genome Atlas. Our method is competitive with the state-of-the-art in terms of disease subtyping, but at the same time substantially improves the cluster interpretability. Experiments indicate that local clustering performance can be improved through federated computing. The proposed methods are available as an R-package (https://github.com/pievos101/uRF).

Chromosome 9p trisomy increases stem cells clonogenic potential and fosters T-cell exhaustion in JAK2-mutant myeloproliferative neoplasms

JAK2V617F is the most recurrent genetic mutation in Philadelphia-negative chronic Myeloproliferative Neoplasms (MPNs). Since the JAK2 locus is located on Chromosome 9, we hypothesized that Chromosome 9 copy number abnormalities may be a disease modifier in JAK2V617F-mutant MPN patients. In this study, we identified a subset of MPN patients with partial or complete Chromosome 9 trisomy (+9p patients), who differ from JAK2V617F-homozygous MPN patients as they carry three JAK2 alleles as well as three copies of all neighboring gene loci, including CD274, encoding immunosuppressive Programmed death-ligand 1 (PD-L1) protein. Investigation of the clonal hierarchy revealed that the JAK2V617F occurs first, followed by +9p. Functionally, CD34+ cells from +9p MPN patients demonstrated increased clonogenicity, generating a greater number of primitive colonies, due to high OCT4 and NANOG expression, with knock-down of these genes leading to a genotype-specific decrease in colony numbers. Moreover, our analysis revealed increased PD-L1 surface expression in malignant monocytes from +9p patients, while analysis of the T cell compartment unveiled elevated levels of exhausted cytotoxic T cells. Overall, here we identify a distinct novel subgroup of MPN patients, who feature a synergistic interplay between +9p and JAK2V617F that shapes immune escape characteristics and increased stemness in CD34+ cells.

Serum biomarkers at disease onset for personalized therapy in multiple sclerosis.

The potential of combining serum neurofilament light chain (sNfL) and glial fibrillary acidic protein (sGFAP) levels to predict disability worsening in multiple sclerosis (MS) remains underexplored. We aimed to investigate whether sNfL and sGFAP values identify distinct subgroups of patients according to the risk of disability worsening and their response to disease-modifying treatments (DMTs). This multicentre study, conducted across thirteen European hospitals, spanned from July 15, 1994, to August 18, 2022, with follow-up until September 26, 2023. We enrolled MS patients who had serum samples collected within 12 months from disease onset and before initiating DMTs. Multivariable regression models were used to estimate the risk of relapse-associated worsening (RAW), progression independent of relapse activity (PIRA), and Expanded Disability Status Scale (EDSS) score of 3. Of the 725 patients included, median age was 34.2 years (IQR, 27.6-42.4), and 509 patients (70.2%) were female. Median follow-up duration was 6.43 years (IQR, 4.65-9.81). Higher sNfL values associated with an elevated risk of RAW (HR of 1.45; 95% CI 1.19-1.76; P < 0.001), PIRA (HR of 1.43; 95% CI 1.13-1.81; P = 0.003), and reaching an EDSS of 3 (HR of 1.55; 95% CI 1.29-1.85; P < 0.001). Moreover, higher sGFAP levels were linked to a higher risk of achieving an EDSS score of 3 (HR of 1.36; 95% CI 1.06-1.74; P = 0.02) and, in patients with low sNfL values, to PIRA (HR of 1.86; 95% CI 1.01-3.45; P = 0.04). We further examined the combined effect of sNfL and sGFAP levels. Patients with low sNfL and sGFAP values (NLGL) exhibited a low risk of all outcomes and served as reference. Untreated patients with high sNfL levels showed a higher risk of RAW, PIRA, and reaching an EDSS of 3. Injectable or oral DMTs reduced the risk of RAW in these patients but failed to mitigate the risk of PIRA and reaching an EDSS of 3. Conversely, high-efficacy DMTs counteracted the heightened risk of these outcomes, except for the risk of PIRA in patients with high sNfL and sGFAP levels. Patients with low sNfL and high sGFAP values (NLGH) showed an increased risk of PIRA and achieving an EDSS of 3, which remained unchanged with either high-efficacy or other DMTs. In conclusion, evaluating sNfL and sGFAP levels at disease onset in MS may identify distinct phenotypes associated with diverse immunological pathways of disability acquisition and therapeutic response.

Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications. A novel, graph-based model capable of predicting treatment responses, combining Graph Neural Network and Transformer was developed. This method differs from conventional approaches by transforming a patient's EHR data into a graph structure. By defining patient subgroups based on this representation via K-Means Clustering, we were able to enhance the performance of drug response predictions. Leveraging EHR data from 11627 Mayo Clinic HF patients, our model significantly outperformed traditional models in predicting drug response using NT-proBNP as a HF biomarker across five medication categories (best RMSE of 0.0043). Four distinct patient subgroups were identified with differential characteristics and outcomes, demonstrating superior predictive capabilities over existing HF subtypes (best mean RMSE of 0.0032). These results highlight the power of graph-based modeling of EHR in improving HF treatment strategies. The stratification of patients sheds light on particular patient segments that could benefit more significantly from tailored response predictions. Longitudinal EHR data have the potential to enhance personalized prognostic predictions through the application of graph-based AI techniques.

The genomic and molecular landscape of splenic marginal zone lymphoma, biological and clinical implications.

Splenic marginal zone lymphoma (SMZL) is a rare, predominantly indolent B-cell lymphoma constituting fewer than 2% of lymphoid neoplasms. However, around 30% of patients have a shorter survival despite currently available treatments and the prognosis is especially poor for the 5-15% of cases that transform to a large cell lymphoma. Mounting evidence suggests that the molecular pathogenesis of SMZL is critically shaped by microenvironmental triggering and cell-intrinsic aberrations. Immunogenetic investigations have revealed biases in the immunoglobulin gene repertoire, indicating a role of antigen selection. Furthermore, cytogenetic studies have identified recurrent chromosomal abnormalities such as deletion of the long arm of chromosome 7, though specific disease-associated genes remain elusive. Our knowledge of SMZL's mutational landscape, based on a limited number of cases, has identified recurring mutations in KLF2, NOTCH2, and TP53, as well as genes clustering within vital B-cell differentiation pathways. These mutations can be clustered within patient subgroups with different patterns of chromosomal lesions, immunogenetic features, transcriptional signatures, immune microenvironments, and clinical outcomes. Regarding SMZL epigenetics, initial DNA methylation profiling has unveiled epigenetically distinct patient subgroups, including one characterized by elevated expression of Polycomb repressor complex 2 (PRC2) components. Furthermore, it has also demonstrated that patients with evidence of high historical cell division, inferred from methylation data, exhibit inferior treatment-free survival. This review provides an overview of our current understanding of SMZL's molecular basis and its implications for patient outcomes. Additionally, it addresses existing knowledge gaps, proposes future research directions, and discusses how a comprehensive molecular understanding of the disease will lead to improved management and treatment choices for patients.

Cluster analysis of clinical manifestations assigns systemic lupus erythematosus-phenotype subgroups: A multicentre study on 440 patients

ObjectiveSystemic lupus erythematous (SLE) is a heterogenous disease characterised by a large panel of autoantibodies and a wide spectrum of clinical signs and symptoms that engender different outcomes. We aimed to identify distinct, homogeneous SLE patients’ phenotypes. MethodsThis retrospective study enrolled SLE patients meeting the Systemic Lupus International Collaborating Clinics (SLICC) classification criteria, enrolled in the French multicentre “APS (antiphospholipid syndrome) and SLE” Registry. Based on 29 variables selected to cover a broad range of clinical and laboratory (excluding autoantibodies) SLE manifestations, unsupervised multiple correspondence analysis followed by hierarchical ascendent-clustering analysis assigned different phenotypes. ResultsWe included 440 patients, mostly women (94.3%). Median age at SLE diagnosis was 24 (IQR 19–32) years. Cluster analysis yielded three distinct subgroups based on cumulative clinical manifestations, not autoantibody pattern. Cluster 1 (n=91) comprised mostly Caucasian patients, with APS-associated clinical and biological manifestations, e.g., livedo, seizure, thrombocytopaenia and haemolytic anaemia. Cluster 2 (n=221), the largest, included patients with mild clinical manifestations, mainly articular, more frequently associated with Sjögren's syndrome and with less frequent autoantibody-positivity. Cluster 3 (n=128) consisted of patients with the largest panel of SLE-specific clinical manifestations (cutaneous, articular, proliferative nephritis, pleural, cardiac and haematological), the most frequent autoantibody-positivity, low complement levels, and more often of Asian and sub-Saharan African origin. ConclusionThis unsupervised clustering method distinguished three distinct SLE patient subgroups, highlighting SLE heterogeneity.

The Role of Fibroblast Activation Protein in Glioblastoma and Gliosarcoma: A Comparison of Tissue, 68Ga-FAPI-46 PET Data, and Survival Data.

Despite their unique histologic features, gliosarcomas belong to the group of glioblastomas and are treated according to the same standards. Fibroblast activation protein (FAP) is a component of a tumor-specific subpopulation of fibroblasts that plays a critical role in tumor growth and invasion. Some case studies suggest an elevated expression of FAP in glioblastoma and a particularly strong expression in gliosarcoma attributed to traits of predominant mesenchymal differentiation. However, the prognostic impact of FAP and its diagnostic and therapeutic potential remain unclear. Here, we investigate the clinical relevance of FAP expression in gliosarcoma and glioblastoma and how it correlates with 68Ga-FAP inhibitor (FAPI)-46 PET uptake. Methods: Patients diagnosed with gliosarcoma or glioblastoma without sarcomatous differentiation with an overall survival of less than 2.5 y were enrolled. Histologic examination included immunohistochemistry and semiquantitative scoring of FAP (0-3, with higher values indicating stronger expression). Additionally, 68Ga-FAPI-46 PET scans were performed in a subset of glioblastomas without sarcomatous differentiation patients. The clinical SUVs were correlated with FAP expression levels in surgically derived tumor tissue and relevant prognostic factors. Results: Of the 61 patients who were enrolled, 13 of them had gliosarcoma. Immunohistochemistry revealed significantly more FAP in gliosarcomas than in glioblastomas without sarcomatous differentiation of tumor tissue (P < 0.0001). In the latter, FAP expression was confined to the perivascular space, whereas neoplastic cells additionally expressed FAP in gliosarcoma. A significant correlation of immunohistochemical FAP with SUVmean and SUVpeak of 68Ga-FAPI-46 PET indicates that clinical tracer uptake represents FAP expression of the tumor. Although gliosarcomas express higher levels of FAP than do glioblastomas without sarcomatous differentiation, overall survival does not significantly differ between the groups. Conclusion: The analysis reveals a significant correlation between SUVmean and SUVpeak in 68Ga-FAPI-46 PET and immunohistochemical FAP expression. This study indicates that FAP expression is much more abundant in the gliosarcoma subgroup of glioblastomas. This could open not only a diagnostic but also a therapeutic gap, since FAP could be explored as a theranostic target to enhance survival in a distinct subgroup of high-risk brain tumor patients with poor survival prognosis.

Longitudinal patterns of patient-reported cognitive impairment in patients with non-Hodgkin lymphoma (NHL) treated with chemotherapy: A group-based trajectory analysis of a nationwide community-based cohort.

7071 Background: Cognitive problems are an important clinical concern for cancer patients. We previously published that patients with lymphoma experienced worsening in cognitive functions and perceived function from pre-to post-treatment. Limited data exists in NHL, importantly previously reported group means of cognitive function may not capture interindividual heterogeneity. Therefore, we identified distinct subgroups of NHL patients with similar trajectories of cognitive function over time and investigated potential predictors. Methods: We included 181 NHL patients receiving chemotherapy (mean age [SD]: 59.0 [11.8]; 63.5% male; 54% diffuse large B-cell, 22% follicular lymphoma) enrolled in the National Cancer Institute Community Oncology Research Program. Cognitive function was assessed by the patient-reported Functional Assessment of Cancer-Therapy-Cognitive Function (total score and perceived cognitive impairment [PCI]) prior to chemotherapy (T1), after chemotherapy (T2), and 6 months following T2 (T3). Patient and cancer characteristics were collected through questionnaires or medical records at T1. We utilized group-based trajectory modeling (GBTM) to identify latent clusters of individuals with similar cognitive function patterns over time. Model selection and assessment were based on the Bayes factor, group size, model diagnostics, and clinical interpretability. Multinomial logistic regression models were identified characteristics associated with group membership. Results: Four groups were identified by GBTM: Group A had high perceived impairment, which decreased at T2 and remained high at T3 (n=17, 9.4%); Group B had moderate perceived impairment, with a continuous decline at T2 and T3 (n=21, 11.6%); Group C had low perceived impairment and remained stable at T2 and T3 (n=95, 52.5%); Group D had moderate perceived impairment, without changes at T2 and T3 (n=48, 26.5%). Similar trajectory groups were identified for the PCI subscale. Compared to ≥ some college education, ≤ high school was associated with an increased odds of membership in Group D relative to Group C (OR 3.45, 95% CI 1.44, 8.30). Patients with a higher fatigue score (MFSI) were more likely to report worse cognitive impairment (Group A vs. C: OR 1.10, 95% CI 1.07, 1.14; Group B vs. C: OR 1.05, 95% CI 1.02, 1.08; Group D vs. C: 1.05, 95% CI 1.03, 1.07). Stage, lymphoma subtype, and treatment regimen were not associated with group membership. Conclusions: Our analysis revealed that patients with perceived impairment experienced stable impairment or further decline over time, which is associated with education and fatigue; a subgroup of patients did not experience cognitive worsening. These data help identify patients with different patterns of cognitive impairment to inform personalized interventions.