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Background: While high-grade serous ovarian cancer (HGSC) has proven largely resistant to immunotherapy, sporadic incidents of partial and complete response have been observed in clinical trials and case reports. These observations suggest that a molecular basis for effective immunity may exist within a subpopulation of HGSC. Herein, we developed an algorithm, CONSTRU (Computing Prognostic Marker Dependencies by Successive Testing of Gene-Stratified Subgroups), to facilitate the discovery and characterization of molecular backgrounds of HGSC that confer resistance or susceptibility to protective anti-tumor immunity. Methods: We used CONSTRU to identify genes from tumor expression profiles that influence the prognostic power of an established immune cytolytic activity signature (CYTscore). From the identified genes, we developed a stratification signature (STRATsig) that partitioned patient populations into tertiles that varied markedly by CYTscore prognostic power. The tertile groups were then analyzed for distinguishing biological, clinical and immunological properties using integrative bioinformatics approaches. Results: Patient survival and molecular measures of immune suppression, evasion and dysfunction varied significantly across STRATsig tertiles in validation cohorts. Tumors comprising STRATsig tertile 1 (S-T1) showed no immune-survival benefit and displayed a hyper-immune suppressed state marked by activation of TGF-β, Wnt/β-catenin and adenosine-mediated immunosuppressive pathways, with concurrent T cell dysfunction, reduced potential for antigen presentation, and enrichment of cancer-associated fibroblasts. By contrast, S-T3 tumors exhibited diminished immunosuppressive signaling, heightened antigen presentation machinery, lowered T cell dysfunction, and a significant CYTscore-survival benefit that correlated with mutational burden in a manner consistent with anti-tumor immunoediting. These tumors also showed elevated activity of DNA damage/repair, cell cycle/proliferation and oxidative phosphorylation, and displayed greater proportions of Th1 CD4 + T cells. In these patients, but not those of S-T1 or S-T2, validated predictors of immunotherapy response were prognostic of longer patient survival. Further analyses showed that STRATsig tertile properties were not explained by known HGSC molecular or clinical subtypes or singular immune mechanisms. Conclusions: STRATsig is a composite of parallel immunoregulatory pathways that mirrors tumor immunogenic potential. Approximately one-third of HGSC cases classify as S-T3 and display a hypo-immunosuppressed and antigenic molecular composition that favors immunologic tumor control. These patients may show heightened responsiveness to current immunotherapies. [ABSTRACT FROM AUTHOR]

Background Staphylococcus aureus bacteremia (SAB) is a clinically heterogeneous disease. The ability to identify subgroups of patients with shared traits (subphenotypes) is an unmet need to allow patient stratification for clinical management and research. We aimed to test the hypothesis that clinically relevant subphenotypes can be reproducibly identified among patients with SAB. Methods We studied 3 cohorts of adults with monomicrobial SAB: a UK retrospective observational study (Edinburgh cohort, n = 458), the UK ARREST trial (n = 758), and the Spanish SAFO trial (n = 214). Latent class analysis was used to identify subphenotypes using routinely collected clinical data without considering outcomes. Mortality and microbiologic outcomes were then compared between subphenotypes. Results Included patients had predominantly methicillin-susceptible SAB (1366 of 1430, 95.5%). We identified 5 distinct, reproducible clinical subphenotypes: (A) SAB associated with older age and comorbidity, (B) nosocomial intravenous catheter-associated SAB in younger people without comorbidity, (C) community-acquired metastatic SAB, (D) SAB associated with chronic kidney disease, and (E) SAB associated with injection drug use. Survival and microbiologic outcomes differed between the subphenotypes. Mortality was highest in subphenotype A and lowest in subphenotypes B and E. Microbiologic outcomes were worse in subphenotype C. In a secondary analysis of the ARREST trial, adjunctive rifampicin was associated with increased mortality in subphenotype B and improved microbiologic outcomes in subphenotype C. Conclusions We have identified reproducible and clinically relevant subphenotypes within SAB and provide proof of principle of differential treatment effects. Through clinical trial enrichment and patient stratification, these subphenotypes could contribute to a personalized medicine approach to SAB. [ABSTRACT FROM AUTHOR]

The incidence of MASLD and MASH-associated fibrosis is rapidly increasing worldwide. Drug therapy is hampered by large patient variability and partial representation of human MASH fibrosis in preclinical models. Here, we investigated the mechanisms underlying patient heterogeneity using a discovery dataset and validated in distinct human transcriptomic datasets, to improve patient stratification and translation into subgroup specific patterns. Patient stratification was performed using weighted gene co-expression network analysis (WGCNA) in a large public transcriptomic discovery dataset (n = 216). Differential expression analysis was performed using DESeq2 to obtain differentially expressed genes (DEGs). Ingenuity Pathway analysis was used for functional annotation. The discovery dataset showed relevant fibrosis-related mechanisms representative of disease heterogeneity. Biological complexity embedded in genes signature was used to stratify discovery dataset into six subgroups of various sizes. Of note, subgroup-specific DEGs show differences in directionality in canonical pathways (e.g. Collagen biosynthesis, cytokine signaling) across subgroups. Finally, a multiclass classification model was trained and validated in two datasets. In summary, our work shows a potential alternative for patient population stratification based on heterogeneity in MASLD-MASH mechanisms. Future research is warranted to further characterize patient subgroups and identify protein targets for virtual screening and/or in vitro validation in preclinical models. [ABSTRACT FROM AUTHOR]

Simple Summary: Novel cancer biomarker discoveries are enabled by the application and analysis of omics technologies. This vast quantity of high-dimensional data necessitates the implementation of feature selection for analysis. The mathematical basis of selection methods varies considerably, which may influence subsequent inference. The aim of the study was to identify signature gene sets of grade 2 and 3 astrocytoma (brain cancer) and determine their impact on the classification and discovery of biological patterns. The application of feature selection methods reduced the number of genes and led to an increase in classification accuracy. Notably, no single gene was selected by all methods. Significant differences in Gene Ontology terms as well as KEGG pathways were discovered. Results demonstrated a significant difference in outcomes when classification-type algorithms were utilised compared to mixed types (selection and classification). This may result in the inadvertent omission of biological phenomena, while simultaneously achieving enhanced classification outcomes. Novel cancer biomarkers discoveries are driven by the application of omics technologies. The vast quantity of highly dimensional data necessitates the implementation of feature selection. The mathematical basis of different selection methods varies considerably, which may influence subsequent inferences. In the study, feature selection and classification methods were employed to identify six signature gene sets of grade 2 and 3 astrocytoma samples from the Rembrandt repository. Subsequently, the impact of these variables on classification and further discovery of biological patterns was analysed. Principal component analysis (PCA), uniform manifold approximation and projection (UMAP), and hierarchical clustering revealed that the data set (10,096 genes) exhibited a high degree of noise, feature redundancy, and lack of distinct patterns. The application of feature selection methods resulted in a reduction in the number of genes to between 28 and 128. Notably, no single gene was selected by all of the methods tested. Selection led to an increase in classification accuracy and noise reduction. Significant differences in the Gene Ontology terms were discovered, with only 13 terms overlapping. One selection method did not result in any enriched terms. KEGG pathway analysis revealed only one pathway in common (cell cycle), while the two methods did not yield any enriched pathways. The results demonstrated a significant difference in outcomes when classification-type algorithms were utilised in comparison to mixed types (selection and classification). This may result in the inadvertent omission of biological phenomena, while simultaneously achieving enhanced classification outcomes. [ABSTRACT FROM AUTHOR]

Abstract The incidence of MASLD and MASH-associated fibrosis is rapidly increasing worldwide. Drug therapy is hampered by large patient variability and partial representation of human MASH fibrosis in preclinical models. Here, we investigated the mechanisms underlying patient heterogeneity using a discovery dataset and validated in distinct human transcriptomic datasets, to improve patient stratification and translation into subgroup specific patterns. Patient stratification was performed using weighted gene co-expression network analysis (WGCNA) in a large public transcriptomic discovery dataset (n = 216). Differential expression analysis was performed using DESeq2 to obtain differentially expressed genes (DEGs). Ingenuity Pathway analysis was used for functional annotation. The discovery dataset showed relevant fibrosis-related mechanisms representative of disease heterogeneity. Biological complexity embedded in genes signature was used to stratify discovery dataset into six subgroups of various sizes. Of note, subgroup-specific DEGs show differences in directionality in canonical pathways (e.g. Collagen biosynthesis, cytokine signaling) across subgroups. Finally, a multiclass classification model was trained and validated in two datasets. In summary, our work shows a potential alternative for patient population stratification based on heterogeneity in MASLD-MASH mechanisms. Future research is warranted to further characterize patient subgroups and identify protein targets for virtual screening and/or in vitro validation in preclinical models.

This study, employing an interim analysis, investigates the effects of the Dermamecum protocol, a structured educational and tailored approach that stratifies ostomy patients into risk paths (green, yellow, red) based on pre-operative and post-operative characteristics. The green path indicates a low risk of peristomal skin complications (PSCs), focusing on sustaining healthy behaviours and basic stoma care. The yellow path represents a moderate risk, emphasizing the need for patients to self-monitor and recognize early signs of complications. The red path corresponds to high risk, requiring stringent monitoring and immediate access to healthcare support. The study aims to reduce PSCs and improve patient outcomes. Methods include the stratification of 226 patients, with significant differences in gender distribution, BMI categories, and stoma types across the paths. Results show an occurrence rate of PSCs of 5.9% in all risk paths (5.7% green path, 4.7% yellow path, and 7.9% red path, p = 0.685), significantly lower than the median rate of 35% reported in the literature. Multiple correspondence analysis validated the stratification, with distinct clusters for each path. Poisson regression models in the exploratory framework of an interim analysis identified male gender as the only significant predictor of PSCs, indicating the need for gender-specific interventions. The findings suggest that the Dermamecum protocol effectively reduces early PSCs, providing a foundation for further research. [ABSTRACT FROM AUTHOR]

Multi-omics data play a crucial role in precision medicine, mainly to understand the diverse biological interaction between different omics. Machine learning approaches have been extensively employed in this context over the years. This review aims to comprehensively summarize and categorize these advancements, focusing on the integration of multi-omics data, which includes genomics, transcriptomics, proteomics and metabolomics, alongside clinical data. We discuss various machine learning techniques and computational methodologies used for integrating distinct omics datasets and provide valuable insights into their application. The review emphasizes both the challenges and opportunities present in multi-omics data integration, precision medicine and patient stratification, offering practical recommendations for method selection in various scenarios. Recent advances in deep learning and network-based approaches are also explored, highlighting their potential to harmonize diverse biological information layers. Additionally, we present a roadmap for the integration of multi-omics data in precision oncology, outlining the advantages, challenges and implementation difficulties. Hence this review offers a thorough overview of current literature, providing researchers with insights into machine learning techniques for patient stratification, particularly in precision oncology. Contact: anirban@klyuniv.ac.in [ABSTRACT FROM AUTHOR]

To propose a new gene expression signature that identifies endometrial disruptions independent of endometrial luteal phase timing and predicts if patients are at risk of endometrial failure. Multicentric, prospective study. Reproductive medicine research department in a public hospital affiliated with private fertility clinics and a reproductive genetics laboratory. Caucasian women (n = 281; 39.4 ± 4.8 years old with a body mass index of 22.9 ± 3.5 kg/m2) undergoing hormone replacement therapy between July 2018 and July 2021. Endometrial samples from 217 patients met RNA quality criteria for signature discovery and analysis. Endometrial biopsies collected in the mid-secretory phase. Endometrial luteal phase timing-corrected expression of 404 genes and reproductive outcomes of the first single embryo transfer (SET) after biopsy collection to identify prognostic biomarkers of endometrial failure. Removal of endometrial timing variation from gene expression data allowed patients to be stratified into poor (n = 137) or good (n = 49) endometrial prognosis groups on the basis of their clinical and transcriptomic profiles. Significant differences were found between endometrial prognosis groups in terms of reproductive rates: pregnancy (44.6% vs. 79.6%), live birth (25.6% vs. 77.6%), clinical miscarriage (22.2% vs. 2.6%), and biochemical miscarriage (20.4% vs. 0%). The relative risk of endometrial failure for patients predicted as a poor endometrial prognosis was 3.3 times higher than those with a good prognosis. The differences in gene expression between both profiles were proposed as a biomarker, coined the endometrial failure risk (EFR) signature. Poor prognosis profiles were characterized by 59 upregulated and 63 downregulated genes mainly involved in regulation (17.0%), metabolism (8.4%), immune response, and inflammation (7.8%). This EFR signature had a median accuracy of 0.92 (min = 0.88, max = 0.94), median sensitivity of 0.96 (min = 0.91, max = 0.98), and median specificity of 0.84 (min = 0.77, max = 0.88), positioning itself as a promising biomarker for endometrial evaluation. The EFR signature revealed a novel endometrial disruption, independent of endometrial luteal phase timing, present in 73.7% of patients. This EFR signature stratified patients into 2 significantly distinct and clinically relevant prognosis profiles providing opportunities for personalized therapy. Nevertheless, further validations are needed before implementing this gene signature as an artificial intelligence (AI)-based tool to reduce the risk of patients experiencing endometrial failure. [ABSTRACT FROM AUTHOR]

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• Neurodevelopmental disorders share a common genetic foundation with immune disorders but exhibit a higher degree of polygenicity. • This study identified thirty genomic loci with significant associations in both types of diseases, including eight novel loci. • The shared loci were mapped to genes enriched in three classes of pathways. • The pleiotropic loci show a significant association with blood cell traits, potentially serving as more accessible and feasible biomarkers for patient stratification. The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta -analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta -analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders. [ABSTRACT FROM AUTHOR]

Objective: Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by a variety of disease symptoms and an unpredictable clinical course. To improve treatment outcome, stratification based on immunological manifestations commonly seen in patients with SLE such as autoantibodies, type I interferon (IFN) signature and neutrophil extracellular trap (NET) release may help. It is assumed that there is an association between these immunological phenomena, since NET release induces IFN production and IFN induces autoantibody formation via B-cell activation. Here we studied the association between autoantibodies, the IFN signature, NET release, and clinical manifestations in patients with SLE. Methods: We performed principal component analysis (PCA) and hierarchical clustering of 57 SLE-related autoantibodies in 25 patients with SLE. We correlated each autoantibody to the IFN signature and NET inducing capacity. Results: We observed two distinct clusters: one cluster contained mostly patients with a high IFN signature. Patients in this cluster often present with cutaneous lupus, and have higher anti-dsDNA concentrations. Another cluster contained a mix of patients with a high and low IFN signature. Patients with high and low NET inducing capacity were equally distributed between the clusters. Variance between the clusters is mainly driven by antibodies against histones, RibP2, RibP0, EphB2, RibP1, PCNA, dsDNA, and nucleosome. In addition, we found a trend towards increased concentrations of autoantibodies against EphB2, RibP1, and RNP70 in patients with an IFN signature. We found a negative correlation of NET inducing capacity with anti-FcER (r = −0.530; p = 0.007) and anti-PmScl100 (r = −0.445; p = 0.03). Conclusion: We identified a subgroup of patients with an IFN signature that express increased concentrations of antibodies against DNA and RNA-binding proteins, which can be useful for further patient stratification and a more targeted therapy. We did not find positive associations between autoantibodies and NET inducing capacity. Our study further strengthens the evidence of a correlation between RNA-binding autoantibodies and the IFN signature.

Asthma has become one of the most serious chronic respiratory diseases threatening people's lives worldwide. The pathogenesis of asthma is complex and driven by numerous cells and their interactions, which contribute to its genetic and phenotypic heterogeneity. The clinical characteristic is insufficient for the precision of patient classification and therapies; thus, a combination of the functional or pathophysiological mechanism and clinical phenotype proposes a new concept called “asthma endophenotype” representing various patient subtypes defined by distinct pathophysiological mechanisms. High-throughput omics approaches including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiome enable us to investigate the pathogenetic heterogeneity of diverse endophenotypes and the underlying mechanisms from different angles. In this review, we provide a comprehensive overview of the roles of diverse cell types in the pathophysiology and heterogeneity of asthma and present a current perspective on their contribution into the bidirectional interaction between airway inflammation and airway remodeling. We next discussed how integrated analysis of multi-omics data via machine learning can systematically characterize the molecular and biological profiles of genetic heterogeneity of asthma phenotype. The current application of multi-omics approaches on patient stratification and therapies will be described. Integrating multi-omics and clinical data will provide more insights into the key pathogenic mechanism in asthma heterogeneity and reshape the strategies for asthma management and treatment.

Abstract Background Patients suffering from chronic spontaneous urticaria (CSU) are typically classified as type I or type IIb autoimmune CSU, but further patient stratification is hindered by the lack of biomarkers. Objectives We investigated whether the histamine content of individual basophils differ between patient subtypes in CSU to evaluate its potential as a biomarker. Methods A total of 101 patients diagnosed with CSU were included in the study. The histamine content per circulating basophil was derived from the basophil count in peripheral blood and levels of total cellular blood histamine. These measures, together with results from the serum‐induced basophil histamine release assay (s‐BHRA), were correlated to information on demographics, clinical characteristics, patient reported outcomes and laboratory analyses. Results The histamine content per basophil was significantly different between s‐BHRA positive and ‐negative patients (0.175 vs. 1.40 pg/cell, p

Triple-negative breast cancer (TNBC) is the most challenging breast cancer subtype. Molecular stratification and target therapy bring clinical benefit for TNBC patients, but it is difficult to implement comprehensive molecular testing in clinical practice. Here, using our multi-omics TNBC cohort (N = 425), a deep learning-based framework was devised and validated for comprehensive predictions of molecular features, subtypes and prognosis from pathological whole slide images. The framework first incorporated a neural network to decompose the tissue on WSIs, followed by a second one which was trained based on certain tissue types for predicting different targets. Multi-omics molecular features were analyzed including somatic mutations, copy number alterations, germline mutations, biological pathway activities, metabolomics features and immunotherapy biomarkers. It was shown that the molecular features with therapeutic implications can be predicted including the somatic PIK3CA mutation, germline BRCA2 mutation and PD-L1 protein expression (area under the curve [AUC]: 0.78, 0.79 and 0.74 respectively). The molecular subtypes of TNBC can be identified (AUC: 0.84, 0.85, 0.93 and 0.73 for the basal-like immune-suppressed, immunomodulatory, luminal androgen receptor, and mesenchymal-like subtypes respectively) and their distinctive morphological patterns were revealed, which provided novel insights into the heterogeneity of TNBC. A neural network integrating image features and clinical covariates stratified patients into groups with different survival outcomes (log-rank P < 0.001). Our prediction framework and neural network models were externally validated on the TNBC cases from TCGA (N = 143) and appeared robust to the changes in patient population. For potential clinical translation, we built a novel online platform, where we modularized and deployed our framework along with the validated models. It can realize real-time one-stop prediction for new cases. In summary, using only pathological WSIs, our proposed framework can enable comprehensive stratifications of TNBC patients and provide valuable information for therapeutic decision-making. It had the potential to be clinically implemented and promote the personalized management of TNBC.

Background: Huntington's disease (HD) is a progressive neurodegenerative disease caused by a CAG trinucleotide expansion in the huntingtin gene. The length of the CAG repeat is inversely correlated with disease onset. HD is characterized by hyperkinetic movement disorder, psychiatric symptoms, and cognitive deficits, which greatly impact patient's quality of life. Despite this clear genetic course, high variability of HD patients' symptoms can be observed. Current clinical diagnosis of HD solely relies on the presence of motor signs, disregarding the other important aspects of the disease. By incorporating a broader approach that encompasses motor as well as non-motor aspects of HD, predictive, preventive, and personalized (3P) medicine can enhance diagnostic accuracy and improve patient care. Methods: Multisymptom disease trajectories of HD patients collected from the Enroll-HD study were first aligned on a common disease timescale to account for heterogeneity in disease symptom onset and diagnosis. Following this, the aligned disease trajectories were clustered using the previously published Variational Deep Embedding with Recurrence (VaDER) algorithm and resulting progression subtypes were clinically characterized. Lastly, an AI/ML model was learned to predict the progression subtype from only first visit data or with data from additional follow-up visits. Results: Results demonstrate two distinct subtypes, one large cluster (n = 7122) showing a relative stable disease progression and a second, smaller cluster (n = 411) showing a dramatically more progressive disease trajectory. Clinical characterization of the two subtypes correlates with CAG repeat length, as well as several neurobehavioral, psychiatric, and cognitive scores. In fact, cognitive impairment was found to be the major difference between the two subtypes. Additionally, a prognostic model shows the ability to predict HD subtypes from patients' first visit only. Conclusion: In summary, this study aims towards the paradigm shift from reactive to preventive and personalized medicine by showing that non-motor symptoms are of vital importance for predicting and categorizing each patients' disease progression pattern, as cognitive decline is oftentimes more reflective of HD progression than its motor aspects. Considering these aspects while counseling and therapy definition will personalize each individuals' treatment. The ability to provide patients with an objective assessment of their disease progression and thus a perspective for their life with HD is the key to improving their quality of life. By conducting additional analysis on biological data from both subtypes, it is possible to gain a deeper understanding of these subtypes and uncover the underlying biological factors of the disease. This greatly aligns with the goal of shifting towards 3P medicine. [ABSTRACT FROM AUTHOR]

Mesenchymal stromal cells (MSCs) are promising cell therapy candidates, but their debated efficacy in clinical trials still limits successful adoption. Here, we discuss proceedings from a roundtable session titled "Failure to Launch Mesenchymal Stromal Cells 10 Years Later: What's on the Horizon?" held at the International Society for Cell & Gene Therapy 2023 Annual Meeting. Panelists discussed recent progress toward developing patient-stratification approaches for MSC treatments, highlighting the role of baseline levels of inflammation in mediating MSC treatment efficacy. In addition, MSC critical quality attributes (CQAs) are beginning to be elucidated and applied to investigational MSC products, including immunomodulatory functional assays and other potency markers that will help to ensure product consistency and quality. Lastly, next-generation MSC products, such as culture-priming strategies, were discussed as a promising strategy to augment MSC basal fitness and therapeutic potency. Key variables that will need to be considered alongside investigations of patient stratification approaches, CQAs and next-generation MSC products include the specific disease target being evaluated, route of administration of the cells and cell manufacturing parameters; these factors will have to be matched with postulated mechanisms of action towards treatment efficacy. Taken together, patient stratification metrics paired with the selection of therapeutically potent MSCs (using rigorous CQAs and/or engineered MSC products) represent a path forward to improve clinical successes and regulatory endorsements. [Display omitted] [ABSTRACT FROM AUTHOR]

This research seeks to assess the potential of regionally integrated health management for specific sub-populations, including the incorporation of selfmanagement initiatives. It will achieve this by conducting a thorough stratification analysis of hospital data, utilizing the Adjusted Clinical Groups (ACG) classification system. The approach involves a retrospective review of healthcare data spanning five years, which includes patient demographics, health outcomes, and healthcare utilization metrics. We intend to use the ACG method to classify the patient population into pertinent groups that mirror their health requirements and resource use. The insights obtained from this analysis will be used to create a localized adaptation of the Kaiser Permanente Pyramid Model of Care. This adaptation aims to identify the distribution of costs among patients treated in the Rivierenland Hospital. We anticipate that stratifying data with the ACG method will identify distinct multimorbid subgroups. These subgroups will have unique healthcare requirements. Early interventions and customized health management strategies, based on these insights, could enhance health outcomes and resource efficiency for high-risk patients. This analysis will serve as a foundation for constructive discussions with hospital management and clinical staff, fostering a deeper comprehension of the patients' burden of disease. It might also foster multidisciplinary collaboration opportunities between medical specialties as with regional healthcare partners such as general practitioners (GPs), mental health and other long-term care organizations. Moreover, we anticipate that self-care initiatives, supported by customized health information, will encourage increased patient engagement and strategies for enhancing lifestyle improvements. This strategy is expected to enable the personalization of advanced care planning based on individual needs profiles, thereby improving the management of complex and chronic conditions, and encouraging self-care practices. Our anticipated findings highlight the potential benefits of a data-informed approach to advancing healthcare outcomes and present opportunities for future investigations to refine and implement such integrated care models across the region. [ABSTRACT FROM AUTHOR]

BackgroundDespite clinical suspicion, most non-invasive ischemia tests for coronary artery disease (CAD) reveal unremarkable results. Patients with a coronary artery calcium score (CACS) of zero rarely have an abnormal positron emission tomography (PET) and could be deferred from further testing. However, most patients have some extent of coronary calcification.ObjectivesCACS percentiles could be useful to exclude abnormal perfusion in patients with CACS >0, but data from patients with 82Rb PET are lacking. The aim of this study was to assess the diagnostic utility of CACS percentiles in comparison to zero calcium and absolute CACS classes.MethodsConsecutive patients with suspected CAD (n = 1,792) referred for 82Rb PET were included and analyzed for abnormal PET (SSS ≥4) and relevant ischemia (>10% myocardium). Test characteristics were calculated.ResultsThe mean age was 65 ± 11 years, 43% were female, and typical angina was reported in 21%. Abnormal PET/relevant ischemia (>10%) were observed in 19.8%/9.3%. Overall, the sensitivity/negative predictive value (NPV) of a 90.9% in all age groups.ConclusionIn patients >50 years, the 10%). They could be used to extend the scope of application of CACS 0 by 8%–10% to 32%–34% overall of patients who could be deferred from further testing.

Background: The role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy. Methods: This study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS. Results: MCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and β-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors. Conclusion: The combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.

Amyotrophic lateral sclerosis (ALS) is a motor neurodegenerative disease characterized by poor prognosis. Currently, screening and diagnostic methods for ALS remain challenging, often leading to diagnosis at an advanced stage of the disease. This delay hinders the timely initiation of therapy, negatively impacting patient well-being. Additionally, misdiagnosis with other neurodegenerative disorders that present similar profiles often occurs. Therefore, there is an urgent need for a cost-effective, rapid, and user-friendly tool capable of predicting ALS onset. In this pilot study, we demonstrate that infrared spectroscopy, coupled with chemometric analysis, can effectively identify and predict disease profiles from blood samples drawn from ALS patients. The selected predictive spectral markers, which are used in various discriminant models, achieved an AUROC sensitivity of almost 80% for distinguishing ALS patients from controls. Furthermore, the differentiation of ALS at both the initial and advanced stages from other neurodegenerative disorders showed even higher AUROC values, with sensitivities of 87% (AUROC: 0.70–0.97). These findings highlight the elevated potential of ATR-FTIR spectroscopy for routine clinical screening and early diagnosis of ALS.

INTRODUCTION: Existing blood‐based biomarkers for Alzheimer's disease (AD) mainly focus on its pathological features. However, studies on blood‐based biomarkers associated with other biological processes for a comprehensive evaluation of AD status are limited. METHODS: We developed a blood‐based, multiplex biomarker assay for AD that measures the levels of 21 proteins involved in multiple biological pathways. We evaluated the assay's performance for classifying AD and indicating AD‐related endophenotypes in three independent cohorts from Chinese or European‐descent populations. RESULTS: The 21‐protein assay accurately classified AD (area under the receiver operating characteristic curve [AUC] = 0.9407 to 0.9867) and mild cognitive impairment (MCI; AUC = 0.8434 to 0.8945) while also indicating brain amyloid pathology. Moreover, the assay simultaneously evaluated the changes of five biological processes in individuals and revealed the ethnic‐specific dysregulations of biological processes upon AD progression. DISCUSSION: This study demonstrated the utility of a blood‐based, multi‐pathway biomarker assay for early screening and staging of AD, providing insights for patient stratification and precision medicine. Highlights: The authors developed a blood‐based biomarker assay for Alzheimer's disease.The 21‐protein assay classifies AD/MCI and indicates brain amyloid pathology.The 21‐protein assay can simultaneously assess activities of five biological processes.Ethnic‐specific dysregulations of biological processes in AD were revealed. [ABSTRACT FROM AUTHOR]

The key role of cancer stem cells (CSCs) in tumor development and therapy resistance makes them essential biomarkers and therapeutic targets. Numerous agents targeting CSCs, either as monotherapy or as part of combination therapy, are currently being tested in clinical trials to treat solid tumors and hematologic malignancies. Data from ongoing and future clinical trials testing novel approaches to target tumor stemness-related biomarkers and pathways may pave the way for further clinical development of CSC-targeted treatments and CSC-guided selection of therapeutic regimens. In this concise review, we discuss recent progress in developing CSC-directed treatment approaches, focusing on clinical trials testing CSC-directed therapies. We also consider the further development of CSC-assay-guided patient stratification and treatment personalization. [ABSTRACT FROM AUTHOR]

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex disease characterized by profound fatigue, post-exertional malaise (PEM), and neurocognitive dysfunction. Immune dysregulation and gastrointestinal symptoms are commonly observed in ME/CFS patients. Despite affecting approximately 0.89% of the general population, the underlying pathophysiological mechanisms remain poorly understood. This study aimed to elucidate the relationship between immunological characteristics and intestinal barrier function in ME/CFS patients. ME/CFS patients were stratified into two groups based on their immune competence. After documentation of detailed medical records, serum and plasma samples were collected for the assessment of inflammatory immune mediators and biomarkers for intestinal barrier integrity by ELISA. We found reduced complement protein C4a levels in immunodeficient ME/CFS patients suggesting a subgroup-specific innate immune dysregulation. ME/CFS patients without immunodeficiencies exhibit a mucosal barrier leakage, as indicated by elevated levels of Lipopolysaccharide-binding protein (LBP). Stratifying ME/CFS patients based on immune competence enabled the distinction of two subgroups with different pathophysiological patterns. The study highlights the importance of emphasizing precise patient stratification in ME/CFS, particularly in the context of defining suitable treatment strategies. Given the substantial health and socioeconomic burden associated with ME/CFS, urgent attention and research efforts are needed to define causative treatment approaches. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, characterized by high tumor heterogeneity and a poor prognosis. Inter- and intra-tumoral heterogeneity in PDAC is a major obstacle to effective PDAC treatment; therefore, it is highly desirable to explore the tumor heterogeneity and underlying mechanisms for the improvement of PDAC prognosis. Gene copy number variations (CNVs) are increasingly recognized as a common and heritable source of inter-individual variation in genomic sequence. In this review, we outline the origin, main characteristics, and pathological aspects of CNVs. We then describe the occurrence of CNVs in PDAC, including those that have been clearly shown to have a pathogenic role, and further highlight some key examples of their involvement in tumor development and progression. The ability to efficiently identify and analyze CNVs in tumor samples is important to support translational research and foster precision oncology, as copy number variants can be utilized to guide clinical decisions. We provide insights into understanding the CNV landscapes and the role of both somatic and germline CNVs in PDAC, which could lead to significant advances in diagnosis, prognosis, and treatment. Although there has been significant progress in this field, understanding the full contribution of CNVs to the genetic basis of PDAC will require further research, with more accurate CNV assays such as single-cell techniques and larger cohorts than have been performed to date. [ABSTRACT FROM AUTHOR]

Osteoarthritis (OA) affects over 500 million people worldwide. OA patients are symptomatically treated, and current therapies exhibit marginal efficacy and frequently carry safety-risks associated with chronic use. No disease-modifying therapies have been approved to date leaving surgical joint replacement as a last resort. To enable effective patient care and successful drug development there is an urgent need to uncover the pathobiological drivers of OA and how these translate into disease endotypes. Endotypes provide a more precise and mechanistic definition of disease subgroups than observable phenotypes, and a panel of tissue- and pathology-specific biochemical markers may uncover treatable endotypes of OA. We have searched PubMed for full-text articles written in English to provide an in-depth narrative review of a panel of validated biochemical markers utilized for endotyping of OA and their association to key OA pathologies. As utilized in IMI-APPROACH and validated in OAI-FNIH, a panel of biochemical markers may uncover disease subgroups and facilitate the enrichment of treatable molecular endotypes for recruitment in therapeutic clinical trials. Understanding the link between biochemical markers and patient-reported outcomes and treatable endotypes that may respond to given therapies will pave the way for new drug development in OA. [ABSTRACT FROM AUTHOR]

Objective: Developing new therapies for knee osteoarthritis (KOA) requires improved prediction of disease progression. This study evaluated the prognostic value of clinical clusters and machine-learning derived quantitative 3D bone shape B-score for predicting total and partial knee replacement (KR). Design: This retrospective study used longitudinal data from the Osteoarthritis Initiative. A previous study used patients' clinical profiles to delineate phenotypic clusters. For these clusters, the distribution of B-scores was assessed (employing Tukey's method). The value of both cluster allocation and B-score for KR-prediction was then evaluated using multivariable Cox regression models and Kaplan-Meier curves for time-to-event analyses. The impact of using B-score vs. cluster was evaluated using a likelihood ratio test for the multivariable Cox model; global performances were assessed by concordance statistics (Harrell's C-index) and time dependent receiver operating characteristic (ROC) curves. Results: B-score differed significantly for the individual clinical clusters (p ​

Background: Statins are widely used to reduce the risk of cardiovascular disease (CVD). Patients with end-stage renal disease (ESRD) on hemodialysis have significantly increased risk of developing CVD. Statin treatment in these patients however did not show a statistically significant benefit in large trials on a patient cohort level. Methods: We generated gene expression profiles for statins to investigate the impact on cellular programs in human renal proximal tubular cells and mesangial cells in-vitro. We subsequently selected biomarkers from key statin-affected molecular pathways and assessed these biomarkers in plasma samples from the AURORA cohort, a double-blind, randomized, multi-center study of patients on hemodialysis or hemofiltration that have been treated with rosuvastatin. Patient clusters (phenotypes) were created based on the identified biomarkers using Latent Class Model clustering and the associations with outcome for the generated phenotypes were assessed using Cox proportional hazards regression models. The multivariable models were adjusted for clinical and biological covariates based on previously published data in AURORA. Results: The impact of statin treatment on mesangial cells was larger as compared with tubular cells with a large overlap of differentially expressed genes identified for atorvastatin and rosuvastatin indicating a predominant drug class effect. Affected molecular pathways included TGFB-, TNF-, and MAPK-signaling and focal adhesion among others. Four patient clusters were identified based on the baseline plasma concentrations of the eight biomarkers. Phenotype 1 was characterized by low to medium levels of the hepatocyte growth factor (HGF) and high levels of interleukin 6 (IL6) or matrix metalloproteinase 2 (MMP2) and it was significantly associated with outcome showing increased risk of developing major adverse cardiovascular events (MACE) or cardiovascular death. Phenotype 2 had high HGF but low Fas cell surface death receptor (FAS) levels and it was associated with significantly better outcome at 1 year. Conclusions: In this translational study, we identified patient subgroups based on mechanistic markers of statin therapy that are associated with disease outcome in patients on hemodialysis.

BackgroundChronic systemic inflammation has been linked to premature mortality and limited somatic as well as mental health with consequences for capability to work and everyday functioning. We recently identified three biochemical clusters of endocrine and immune parameters (C-reactive protein (CRP), interleukin-6 (IL-6), fibrinogen, cortisol and creatinine) in participants, age 35-81 years, of the open access Midlife in the United States Study (MIDUS) dataset. These clusters have been validated in an independent cohort of Japanese mid-life adults. Among these clusters, the one characterized by high inflammation coupled with low cortisol and creatinine concentrations was associated with the highest disease burden, referred to as high-risk cluster in the following. The current study aims to further examine the nature of this cluster and specifically whether it predicts mortality and the reported inability to work the last 30 days 10 years after the biomarker assessment.Methods and findingsLongitudinally assessed health data from N ​= ​1234 individuals were analyzed in the current study. Logistic regression analyses were performed to predict mortality within one decade after first assessment (T0 ​= ​first assessment; T1 ​= ​second assessment). General linear models were used to predict the number of days study participants were unable to work due to health issues in the last 30 days (assessed at T1, analyses restricted to individuals

Abstract Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80–90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .

Joseph William Clinton, Raymond Keith Cross Department of Medicine, Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, MD, USACorrespondence: Joseph William Clinton, Department of Medicine, Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, 22 South Greene Street, Baltimore, MD, 21201, USA, Tel +1 703 955 2907, Fax +1 410 328 8318, Email joseph.clinton2907@gmail.comAbstract: Crohn’s disease is a complex, relapsing and remitting inflammatory disorder of the gastrointestinal tract with a variable disease course. While the treatment options for Crohn’s disease have dramatically increased over the past two decades, predicting individual patient response to treatment remains a challenge. As a result, patients often cycle through multiple different therapies before finding an effective treatment which can lead to disease complications, increased costs, and decreased quality of life. Recently, there has been increased emphasis on personalized medicine in Crohn’s disease to identify individual patients who require early advanced therapy to prevent complications of their disease. In this review, we summarize our current approach to management of Crohn’s disease by identifying risk factors for severe or disabling disease and tailoring individual treatments to patient-specific goals. Lastly, we outline our knowledge gaps in implementing personalized Crohn’s disease treatment and describe the future directions in precision medicine.Keywords: inflammatory bowel disease, precision medicine, personalized medicine, biomarkers, biologic therapy, patient stratification

Abstract Objective To investigate the prognostic prediction of a new indicator, combined by tumor grade and Ki-67, in patients with resected pancreatic ductal adenocarcinoma (PDAC). Methods Data were retrospectively collected from consecutive patients who underwent primary resection of pancreas from December 2012 to December 2017. Tumor grade and Ki-67 were reviewed from routine pathological reports. G-Ki67 was classified as three categories as I (G1/2 and Ki-67

Xi Zhang, Ruzhe Li, Guonian Wang Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, People’s Republic of ChinaCorrespondence: Guonian Wang, Department of Anesthesiology, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, Heilongjiang, 150081, People’s Republic of China, Email guonianwang609cn@aliyun.comPurpose: Programmed cell death ligand 1 (PDL1) has the predictive and prognostic value in a great deal of cancers. This study aims to explore the expression of PDL1 in stage III breast cancer (BC) and its correlation with clinical outcome.Methods: The protein expression of PDL1 in tumor tissues was determined by immunohistochemistry (IHC). The correlations between PDL1 and clinicopathological variables were performed by χ²-tests or Fisher’s exact tests. The Cox proportional hazards model was used for univariate and multivariate analysis of the potential prognostic factors. Survival curves were estimated based on Kaplan–Meier analyses, and Log Rank test was used to contrast factors influencing the survival outcome.Results: On the basis of the semiquantitative scoring method for PDL1 expression, the patients were divided into low PDL1 expression group (109 cases) and high PDL1 expression group (107 cases). PDL1 expression was correlated with positive lymph nodes, positive axillary lymph nodes, postoperative radiotherapy, and CK5/6 expression (P < 0.05). The PDL1 expression in tumor tissues was discovered to be a potential prognostic risk factor with the disease-free survival (DFS) and overall survival (OS) for stage III BC. Moreover, patients with high PDL1 expression showed longer lifetime (DFS and OS) compared to those with low PDL1 expression in total patient population (P < 0.05). Moreover, the nomogram showed that the prediction line is in good agreement with the reference line for postoperative 1-, 3-, and 5-year lifetime. The DCA curve showed that the 3- and 5-year lifetime by nomogram had so much better divination of the clinical application than only by PDL1.Conclusion: PDL1 is a latent prognostic factor in stage III BC and is closely related to some clinicopathological features. PDL1 expression in tumor tissues is significantly associated with better lifetime rate in stage III BC.Keywords: breast cancer, programmed death ligand-1, improved individual outcomes, patient stratification, tumor cell

Prevalences of non-communicable diseases such as depression and a range of somatic diseases are continuously increasing requiring simple and inexpensive ways to identify high-risk individuals to target with predictive and preventive approaches. Using k-mean cluster analytics, in study 1, we identified biochemical clusters (based on C-reactive protein, interleukin-6, fibrinogen, cortisol, and creatinine) and examined their link to diseases. Analyses were conducted in a US American sample (from the Midlife in the US study, N = 1234) and validated in a Japanese sample (from the Midlife in Japan study, N = 378). In study 2, we investigated the link of the biochemical clusters from study 1 to childhood maltreatment (CM). The three identified biochemical clusters included one cluster (with high inflammatory signaling and low cortisol and creatinine concentrations) indicating the highest disease burden. This high-risk cluster also reported the highest CM exposure. The current study demonstrates how biomarkers can be utilized to identify individuals with a high disease burden and thus, may help to target these high-risk individuals with tailored prevention/intervention, towards personalized medicine. Furthermore, our findings raise the question whether the found biochemical clusters have predictive character, as a tool to identify high-risk individuals enabling targeted prevention. The finding that CM was mostly prevalent in the high-risk cluster provides first hints that the clusters could indeed have predictive character and highlight CM as a central disease susceptibility factor and possibly as a leverage point for disease prevention/intervention.Supplementary informationThe online version contains supplementary material available at 10.1007/s13167-021-00255-0.

Abstract Background With the emergence of microglia-modulating therapies there is an urgent need for reliable biomarkers to evaluate microglial activation states. Methods Using mouse models and human induced pluripotent stem cell-derived microglia (hiMGL), genetically modified to yield the most opposite homeostatic (TREM2-knockout) and disease-associated (GRN-knockout) states, we identified microglia activity-dependent markers. Non-targeted mass spectrometry was used to identify proteomic changes in microglia and cerebrospinal fluid (CSF) of Grn- and Trem2-knockout mice. Additionally, we analyzed the proteome of GRN- and TREM2-knockout hiMGL and their conditioned media. Candidate marker proteins were tested in two independent patient cohorts, the ALLFTD cohort (GRN mutation carriers versus non-carriers), as well as the proteomic data set available from the EMIF-AD MBD study. Results We identified proteomic changes between the opposite activation states in mouse microglia and CSF, as well as in hiMGL cell lysates and conditioned media. For further verification, we analyzed the CSF proteome of heterozygous GRN mutation carriers suffering from frontotemporal dementia (FTD). We identified a panel of six proteins (FABP3, MDH1, GDI1, CAPG, CD44, GPNMB) as potential indicators for microglial activation. Moreover, we confirmed three of these proteins (FABP3, GDI1, MDH1) to be significantly elevated in the CSF of Alzheimer’s (AD) patients. Remarkably, each of these markers differentiated amyloid-positive cases with mild cognitive impairment (MCI) from amyloid-negative individuals. Conclusions The identified candidate proteins reflect microglia activity and may be relevant for monitoring the microglial response in clinical practice and clinical trials modulating microglial activity and amyloid deposition. Moreover, the finding that three of these markers differentiate amyloid-positive from amyloid-negative MCI cases in the AD cohort suggests that these proteins associate with a very early immune response to seeded amyloid. This is consistent with our previous findings in the Dominantly Inherited Alzheimer’s Disease Network (DIAN) cohort, where soluble TREM2 increases as early as 21 years before symptom onset. Moreover, in mouse models for amyloidogenesis, seeding of amyloid is limited by physiologically active microglia further supporting their early protective role. The biological functions of some of our main candidates (FABP3, CD44, GPNMB) also further emphasize that lipid dysmetabolism may be a common feature of neurodegenerative disorders.

Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80–90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk. [ABSTRACT FROM AUTHOR]

Objective: To investigate the prognostic prediction of a new indicator, combined by tumor grade and Ki-67, in patients with resected pancreatic ductal adenocarcinoma (PDAC). Methods: Data were retrospectively collected from consecutive patients who underwent primary resection of pancreas from December 2012 to December 2017. Tumor grade and Ki-67 were reviewed from routine pathological reports. G-Ki67 was classified as three categories as I (G1/2 and Ki-67 < 40%), II (G1/2 and Ki-67 ≥ 40%), and III(G3/4 and all Ki-67). Results: Cox regression analyses revealed that tumor stage (II vs. I: hazard ratio (HR), 3.781; 95% confidence index (CI), 2.844–5.025; P < 0.001; III vs. I: HR, 7.476; 95% CI, 5.481–10.20; P < 0.001) and G-Ki67 (II vs. I: HR, 1.299; 95% CI, 1.038–1.624; P = 0.022; III vs. I: HR, 1.942; 95% CI, 1.477–2.554; P < 0.001) were independent prognostic factors in the developing cohort. The result was rectified in the validation cohort. In subgroups analysis, G-Ki67 (II vs. I: HR, 1.866 ; 95% CI, 1.045–3.334; P = 0.035; III vs. I: HR, 2.333 ; 95% CI, 1.156–4.705; P = 0.018) also had a high differentiation for survival prediction. Conclusion: Our findings indicate that three-categories of G-Ki67 in resectable PDAC according to the routine pathological descriptions provided additional prognostic information complementary to the TNM staging system. [ABSTRACT FROM AUTHOR]

Protein tyrosine nitration is a selectively and reversible important post-translational modification, which is closely related to oxidative stress. Astrocytoma is the most common neuroepithelial tumor with heterogeneity and complexity. In the past, the diagnosis of astrocytoma was based on the histological and clinical features, and the treatment methods were nothing more than surgery-assisted radiotherapy and chemotherapy. Obviously, traditional methods short falls an effective treatment for astrocytoma. In late 2021, the World Health Organization (WHO) adopted molecular biomarkers in the comprehensive diagnosis of astrocytoma, such as IDH-mutant and DNA methylation, which enabled the risk stratification, classification, and clinical prognosis prediction of astrocytoma to be more correct. Protein tyrosine nitration is closely related to the pathogenesis of astrocytoma. We hypothesize that nitroproteome is significantly different in astrocytoma relative to controls, which leads to establishment of nitroprotein biomarkers for patient stratification, diagnostics, and prediction of disease stages and severity grade, targeted prevention in secondary care, treatment algorithms tailored to individualized patient profile in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). Nitroproteomics based on gel electrophoresis and tandem mass spectrometry is an effective tool to identify the nitroproteins and effective biomarkers in human astrocytomas, clarifying the biological roles of oxidative/nitrative stress in the pathophysiology of astrocytomas, functional characteristics of nitroproteins in astrocytomas, nitration-mediated signal pathway network, and early diagnosis and treatment of astrocytomas. The results finds that these nitroproteins are enriched in mitotic cell components, which are related to transcription regulation, signal transduction, controlling subcellular organelle events, cell perception, maintaining cell homeostasis, and immune activity. Eleven statistically significant signal pathways are identified in astrocytoma, including remodeling of epithelial adherens junctions, germ cell-sertoli cell junction signaling, 14-3-3-mediated signaling, phagosome maturation, gap junction signaling, axonal guidance signaling, assembly of RNA polymerase III complex, and TREM1 signaling. Furthermore, protein tyrosine nitration is closely associated with the therapeutic effects of protein drugs, and molecular mechanism and drug targets of cancer. It provides valuable data for studying the protein nitration biomarkers, molecular mechanisms, and therapeutic targets of astrocytoma towards PPPM (3P medicine) practice. [ABSTRACT FROM AUTHOR]

C-reactive protein is a well-studied host response biomarker, whose diagnostic performance depends on its accurate classification into concentration zones defined by clinical scenario-specific cutoff values. We validated a newly developed, bead-based, bound-free phase detection immunoassay (BFPD-IA) versus a commercial CE-IVD enzyme-linked immunosorbent assay (ELISA) kit and a commercial CE-IVD immunoturbidimetric assay (ITA) kit. The latter was performed on a fully automated DPC Konelab 60i clinical analyzer used in routine diagnosis. We classified 53 samples into concentration zones derived from four different sets of cutoff values that are related to antibiotic prescription scenarios in the case of respiratory tract infections. The agreements between the methods were ELISA/ITA at 87.7%, ELISA/BFPD-IA at 87.3%, and ITA/-BFPD-IA at 93.9%, reaching 98–99% in all cases when considering the calculated relative combined uncertainty of the single measurement of each sample. In a subgroup of 37 samples, which were analyzed for absolute concentration quantification, the scatter plot slopes' correlations were as follows: ELISA/ITA 1.15, R2 = 0.97; BFPD-IA/ELISA 1.12, R2 = 0.95; BFPD-IA/ITA 0.95, R2 = 0.93. These very good performances and the agreement between BFPD-IA and ITA (routine diagnostic), combined with BFPD-IA's functional advantages over ITA (and ELISA)—such as quick time to result (~20 min), reduced consumed reagents (only one assay buffer and no washing), few and easy steps, and compatibility with nucleic-acid-amplification instruments—render it a potential approach for a reliable, cost-efficient, evidence-based point-of-care diagnostic test for guiding antibiotic prescriptions. [ABSTRACT FROM AUTHOR]

Background: Long COVID is a debilitating chronic condition that has affected over 100 million people globally. It is characterized by a diverse array of symptoms, including fatigue, cognitive dysfunction and respiratory problems. Studies have so far largely failed to identify genetic associations, the mechanisms behind the disease, or any common pathophysiology with other conditions such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) that present with similar symptoms. Methods: We used a combinatorial analysis approach to identify combinations of genetic variants significantly associated with the development of long COVID and to examine the biological mechanisms underpinning its various symptoms. We compared two subpopulations of long COVID patients from Sano Genetics' Long COVID GOLD study cohort, focusing on patients with severe or fatigue dominant phenotypes. We evaluated the genetic signatures previously identified in an ME/CFS population against this long COVID population to understand similarities with other fatigue disorders that may be triggered by a prior viral infection. Finally, we also compared the output of this long COVID analysis against known genetic associations in other chronic diseases, including a range of metabolic and neurological disorders, to understand the overlap of pathophysiological mechanisms. Results: Combinatorial analysis identified 73 genes that were highly associated with at least one of the long COVID populations included in this analysis. Of these, 9 genes have prior associations with acute COVID-19, and 14 were differentially expressed in a transcriptomic analysis of long COVID patients. A pathway enrichment analysis revealed that the biological pathways most significantly associated with the 73 long COVID genes were mainly aligned with neurological and cardiometabolic diseases. Expanded genotype analysis suggests that specific SNX9 genotypes are a significant contributor to the risk of or protection against severe long COVID infection, but that the gene-disease relationship is context dependent and mediated by interactions with KLF15 and RYR3. Comparison of the genes uniquely associated with the Severe and Fatigue Dominant long COVID patients revealed significant differences between the pathways enriched in each subgroup. The genes unique to Severe long COVID patients were associated with immune pathways such as myeloid differentiation and macrophage foam cells. Genes unique to the Fatigue Dominant subgroup were enriched in metabolic pathways such as MAPK/JNK signaling. We also identified overlap in the genes associated with Fatigue Dominant long COVID and ME/CFS, including several involved in circadian rhythm regulation and insulin regulation. Overall, 39 SNPs associated in this study with long COVID can be linked to 9 genes identified in a recent combinatorial analysis of ME/CFS patient from UK Biobank. Among the 73 genes associated with long COVID, 42 are potentially tractable for novel drug discovery approaches, with 13 of these already targeted by drugs in clinical development pipelines. From this analysis for example, we identified TLR4 antagonists as repurposing candidates with potential to protect against long term cognitive impairment pathology caused by SARS-CoV-2. We are currently evaluating the repurposing potential of these drug targets for use in treating long COVID and/or ME/CFS. Conclusion: This study demonstrates the power of combinatorial analytics for stratifying heterogeneous populations in complex diseases that do not have simple monogenic etiologies. These results build upon the genetic findings from combinatorial analyses of severe acute COVID-19 patients and an ME/CFS population and we expect that access to additional independent, larger patient datasets will further improve the disease insights and validate potential treatment options in long COVID. [ABSTRACT FROM AUTHOR]