Integrated clustering of multiple immune marker trajectories reveals different immunotypes in severely injured patients.

BACKGROUND: The immune response of critically ill patients, such as those with sepsis, severe trauma, or major surgery, is heterogeneous and dynamic, but its characterization and impact on outcomes are poorly understood. Until now, the primary challenge in advancing our understanding of the disease has been to concurrently address both multiparametric and temporal aspects. METHODS: We used a clustering method to identify distinct groups of patients, based on various immune marker trajectories during the first week after admission to ICU. In 339 severely injured patients, we initially longitudinally clustered common biomarkers (both soluble and cellular parameters), whose variations are well-established during the immunosuppressive phase of sepsis. We then applied this multi-trajectory clustering using markers composed of whole blood immune-related mRNA. RESULTS: We found that both sets of markers revealed two immunotypes, one of which was associated with worse outcomes, such as increased risk of hospital-acquired infection and mortality, and prolonged hospital stays. This immunotype showed signs of both hyperinflammation and immunosuppression, which persisted over time. CONCLUSION: Our study suggest that the immune system of critically ill patients can be characterized by two distinct longitudinal immunotypes, one of which included patients with a persistently dysregulated and impaired immune response. This work confirms the relevance of such methodology to stratify patients and pave the way for further studies using markers indicative of potential immunomodulatory drug targets.

Association of autoantibodies with the IFN signature and NETosis in patients with systemic lupus erythematosus.

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.

[FAPI-PET-CT for quantification of the tissue response in rheumatic diseases]. / FAPI-PET/CT zur Quantifizierung der Gewebeantwort bei rheumatischen Erkrankungen.

Fibroblast activation protein (FAP) is mainly found on the surface of activated fibroblasts but is not expressed on the surface of inactive fibroblasts. Selective FAP inhibitors (FAPI), which are coupled to a radioactive tracer, can be used to quantify profibrotic and proinflammatory fibroblasts in patients using FAPI positron emission tomography (PET) computed tomography (CT). Following initial applications in neoplastic diseases, FAPI-PET/CT is also increasingly being applied in rheumatological diseases. The first studies have shown that in patients with systemic sclerosis (SSc) FAPI accumulates in actively fibrotically remodeled pulmonary and myocardial areas, that a high FAPI accumulation is associated with the risk of short-term progression and that this accumulation in the lungs regresses after successful treatment. In cases of immunoglobulin 4 (IgG4)-associated diseases (IgG4 rheumatic disease, RD), the FAPI signal correlates with the histological accumulation of activated fibroblasts and a poorer response to treatment to inhibit inflammation. Fibroblasts in chronically inflamed tissue, such as patients with inflammatory joint diseases, vasculitis or myositis, also express FAP and can be quantified by FAPI-PET/CT. The treatment-induced change of the phenotype from a destructive IL-6+/MMP3+THY1+ fibroblast subtype to an inflammation inhibiting CD200+DKK3+ subtype can be mechanistically demonstrated using FAPI-PET/CT. These studies provide indications that FAPI-PET/CT enables quantification of the tissue response in patients with fibrosing and chronic inflammatory diseases and can be used for patient stratification; however, further studies are essential for validation of the use of FAPI-PET/CT as a molecular imaging marker.

The association of lumbar intervertebral disc degeneration with low back pain is modified by underlying genetic propensity to pain.

BACKGROUND CONTEXT: Associations between magnetic resonance imaging (MRI)-detected lumbar intervertebral disc degeneration (LDD) and LBP are often of modest magnitude. This association may be larger in specific patient subgroups. PURPOSE: To examine whether the association between LDD and LBP is modified by underlying genetic predispositions to pain. STUDY DESIGN: Cross-sectional study in UK Biobank (UKB) and TwinsUK. PATIENT SAMPLES: A genome-wide association study (GWAS) of the number of anatomical chronic pain locations was conducted in 347,538 UKB participants. The GWAS was used to develop a genome-wide polygenic risk score (PRS) in a holdout sample of 30,000 UKB participants. The PRS model was then used in analyses of 645 TwinsUK participants with standardized LDD MRI assessments. OUTCOME MEASURES: Ever having had LBP associated with disability lasting ≥1 month (LBP1). METHODS: Using the PRS as a proxy for "genetically-predicted propensity to pain", we stratified TwinsUK participants into PRS quartiles. A "basic" model examined the association between an LDD summary score (LSUM) and LBP1, adjusting for covariates. A "fully-adjusted" model also adjusted for PRS quartile and LSUM x PRS quartile interaction terms. RESULTS: In the basic model, the odds ratio (OR) of LBP1 was 1.8 per standard deviation of LSUM (95% confidence interval [CI] 1.4 -2.3). In the fully-adjusted model, there was a statistically significant LSUM-LBP1 association in quartile 4, the highest PRS quartile (OR = 2.5 [95% CI 1.7-3.7], p=2.6×10-6), and in quartile 3 (OR=2.0, [95% CI 1.3-3.0]; p=0.002), with small-magnitude and/or non-significant associations in the lowest two PRS quartiles. PRS quartile was a significant effect modifier of the LSUM-LBP1 association (interaction p≤0.05). CONCLUSIONS: Genetically-predicted propensity to pain modifies the LDD-LBP association, with the strongest association present in people with the highest genetic propensity to pain. Lumbar MRI findings may have stronger connections to LBP in specific subgroups of people.

Deep learning framework for comprehensive molecular and prognostic stratifications of triple-negative breast cancer.

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.

Unraveling progression subtypes in people with Huntington's disease.

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. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00368-2.

Clinically relevant immune subtypes based on alternative splicing landscape of immune-related genes for lung cancer advanced PPPM approach.

Background: Alternative splicing (AS) occurs in the process of gene post-transcriptional process, which is very important for the correct synthesis and function of protein. The change of AS pattern may lead to the change of expression level or function of lung cancer-related genes, and then affect the occurrence and development of lung cancers. The specific AS pattern might be used as a biomarker for early warning and prognostic assessment of a cancer in the framework of predictive, preventive, and personalized medicine (PPPM; 3PM). AS events of immune-related genes (IRGs) were closely associated with tumor progression and immunotherapy. We hypothesize that IRG-AS events are significantly different in lung adenocarcinomas (LUADs) vs. controls or in lung squamous cell carcinomas (LUSCs) vs. controls. IRG-AS alteration profiling was identified to construct IRG-differentially expressed AS (IRG-DEAS) signature models. Study on the selective AS events of specific IRGs in lung cancer patients might be of great significance for further exploring the pathogenesis of lung cancer, realizing early detection and effective monitoring of lung cancer, finding new therapeutic targets, overcoming drug resistance, and developing more effective therapeutic strategies, and better used for the prediction, diagnosis, prevention, and personalized medicine of lung cancer. Methods: The transcriptomic, clinical, and AS data of LUADs and LUSCs were downloaded from TCGA and its SpliceSeq databases. IRG-DEAS events were identified in LUAD and LUSC, followed by their functional characteristics, and overall survival (OS) analyses. OS-related IRG-DEAS prognostic models were constructed for LUAD and LUSC with Lasso regression, which were used to classify LUADs and LUSCs into low- and high-risk score groups. Furthermore, the immune cell distribution, immune-related scores, drug sensitivity, mutation status, and GSEA/GSVA status were analyzed between low- and high-risk score groups. Also, low- and high-immunity clusters and AS factor (SF)-OS-related-AS co-expression network and verification of cell function of CELF6 were analyzed in LUAD and LUSC. Results: Comprehensive analysis of transcriptomic, clinical, and AS data of LUADs and LUSCs identified IRG-AS events in LUAD (n = 1607) and LUSC (n = 1656), including OS-related IRG-AS events in LUAD (n = 127) and LUSC (n = 105). A total of 66 IRG-DEAS events in LUAD and 89 IRG-DEAS events in LUSC were identified compared to controls. The overlapping analysis between IRG-DEASs and OS-related IRG-AS events revealed 14 OS-related IRG-DEAS events for LUAD and 16 OS-related IRG-DEAS events for LUSC, which were used to identify and optimize a 12-OS-related-IRG-DEAS signature prognostic model for LUAD and an 11-OS-related-IRG-DEAS signature prognostic model for LUSC. These two prognostic models effectively divided LUAD or LUSC samples into low- and high-risk score groups that were closely associated with OS, clinical characteristics, and tumor immune microenvironment, with significant gene sets and pathways enriched in the two groups. Moreover, weighted gene co-expression network (WGCNA) and nonnegative matrix factorization method (NMF) analyses identified four OS-relevant subtypes of LUAD and six OS-relevant subtypes of LUSC, and ssGSEA identified five immunity-relevant subtypes of LUAD and five immunity-relevant subtypes of LUSC. Interestingly, splicing factors-OS-related-AS network revealed hub molecule CELF6 was significantly related to the malignant phenotype in lung cancer cells. Conclusions: This study established two reliable IRG-DEAS signature prognostic models and constructed interesting splicing factor-splicing event networks in LUAD and LUSC, which can be used to construct clinically relevant immune subtypes, patient stratification, prognostic prediction, and personalized medical services in the PPPM practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00366-4.

Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach.

Background: DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC. Method: Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified. Results: A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC. Conclusions: This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00359-3.

Identification of endophenotypes supporting outcome prediction in hemodialysis patients based on mechanistic markers of statin treatment.

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.

Shared molecular mechanisms and transdiagnostic potential of neurodevelopmental disorders and immune disorders.

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.

A comprehensive review of machine learning techniques for multi-omics data integration: challenges and applications in precision oncology.

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.

Identification of an immune-related signature as a prognostic classifier for patients with early-stage head and neck squamous cell carcinoma.

Background: Head and neck squamous cell carcinoma (HNSCC) is the most common type and accounts for 90% of all head and neck cancer cases. Despite advances in early diagnosis and treatment strategies-chemotherapy, surgical resection, and radiotherapy-5-year survival remains grim. For patients with early-stage HNSCC, accurately predicting clinical outcomes is challenging. Considering the pivotal role of the immune system in HNSCC, we developed a reliable immune-related gene signature (IRGS) and explored its predictive accuracy in patients with early-stage HNSCC. Methods: We examined immune gene expression profiles and clinical information from 230 early-stage HNSCC specimens, including 100 cases from The Cancer Genome Atlas (TCGA), 49 cases from the Gene Expression Omnibus (GEO; GSE65858), and 81 cases from an independent clinical cohort. The prognostic signature was constructed using Kaplan-Meier analysis and the least absolute shrinkage and selection operator (LASSO) Cox algorithm. We also explored the IRGS-related biological pathways and immune landscape using bioinformatics analysis. Results: A nine-immune-gene signature was generated to significantly stratify patients into high and low-risk groups. High risk patients exhibited shorter survival time [hazard ratio (HR) =13.795, 95% confidence interval (CI): 3.275-58.109, P<0.001]. The signature demonstrated robust prognostic ability in the training and validation sets and could independently predict overall survival (OS) and relapse-free survival (RFS). Subsequently, the receiver operating characteristic (ROC) curve and C-index confirmed the signature's predictive accuracy compared to clinical parameters. Additionally, cases classified as low risk showed more immune cell infiltration than high-risk cases. Conclusions: Our novel IRGS is a reliable and robust classifier for accurate patient stratification and prognostic evaluation. Future studies will attempt to affirm the signature's clinical application to early-stage HNSCC.

Prognostic value of B-score for predicting joint replacement in the context of osteoarthritis phenotypes: Data from the osteoarthritis initiative.

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 â€‹< â€‹0.001). Overall, 9.4% of participants had a KR over 9 years, with a shorter time to event in clusters with high B-score at baseline. Those clusters were characterized clinically by a high rate of comorbidities and potential signs of inflammation. Both phenotype and B-score independently predicted KR, with better prediction if combined (P â€‹< â€‹0.001). B-score added predictive value in groups with less pain and radiographic severity but limited physical activity. Conclusions: B-scores correlated with phenotypes based on clinical patient profiles. B-score and phenotype independently predicted KR surgery, with higher predictive value if combined. This can be used for patient stratification in drug development and potentially risk prediction in clinical practice.

Predicting risk of endometrial failure: a biomarker signature that identifies a novel disruption independent of endometrial timing in patients undergoing hormonal replacement cycles.

OBJECTIVE: 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. DESIGN: Multicentric, prospective study. SETTING: Reproductive medicine research department in a public hospital affiliated with private fertility clinics and a reproductive genetics laboratory. PATIENTS: 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. INTERVENTION(S): Endometrial biopsies collected in the mid-secretory phase. MAIN OUTCOME MEASURE(S): 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. RESULTS: 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. CONCLUSION(S): 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.

Clinically relevant stratification of lung squamous carcinoma patients based on ubiquitinated proteasome genes for 3P medical approach.

Relevance: The proteasome is a crucial mechanism that regulates protein fate and eliminates misfolded proteins, playing a significant role in cellular processes. In the context of lung cancer, the proteasome's regulatory function is closely associated with the disease's pathophysiology, revealing multiple connections within the cell. Therefore, studying proteasome inhibitors as a means to identify potential pathways in carcinogenesis and metastatic progression is crucial in in-depth insight into its molecular mechanism and discovery of new therapeutic target to improve its therapy, and establishing effective biomarkers for patient stratification, predictive diagnosis, prognostic assessment, and personalized treatment for lung squamous carcinoma in the framework of predictive, preventive, and personalized medicine (PPPM; 3P medicine). Methods: This study identified differentially expressed proteasome genes (DEPGs) in lung squamous carcinoma (LUSC) and developed a gene signature validated through Kaplan-Meier analysis and ROC curves. The study used WGCNA analysis to identify proteasome co-expression gene modules and their interactions with the immune system. NMF analysis delineated distinct LUSC subtypes based on proteasome gene expression patterns, while ssGSEA analysis quantified immune gene-set abundance and classified immune subtypes within LUSC samples. Furthermore, the study examined correlations between clinicopathological attributes, immune checkpoints, immune scores, immune cell composition, and mutation status across different risk score groups, NMF clusters, and immunity clusters. Results: This study utilized DEPGs to develop an eleven-proteasome gene-signature prognostic model for LUSC, which divided samples into high-risk and low-risk groups with significant overall survival differences. NMF analysis identified six distinct LUSC clusters associated with overall survival. Additionally, ssGSEA analysis classified LUSC samples into four immune subtypes based on the abundance of immune cell infiltration with clinical relevance. A total of 145 DEGs were identified between high-risk and low-risk score groups, which had significant biological effects. Moreover, PSMD11 was found to promote LUSC progression by depending on the ubiquitin-proteasome system for degradation. Conclusions: Ubiquitinated proteasome genes were effective in developing a prognostic model for LUSC patients. The study emphasized the critical role of proteasomes in LUSC processes, such as drug sensitivity, immune microenvironment, and mutation status. These data will contribute to the clinically relevant stratification of LUSC patients for personalized 3P medical approach. Further, we also recommend the application of the ubiquitinated proteasome system in multi-level diagnostics including multi-omics, liquid biopsy, prediction and targeted prevention of chronic inflammation and metastatic disease, and mitochondrial health-related biomarkers, for LUSC 3PM practice. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00352-w.

Impacts of a multipurpose outpatient hospital pharmacy in the framework of 3P medicine.

Challenge in the framework of Predictive Preventive and Personalised Medicine: In recent years, we have been witnessing a change in the performance of hospital pharmacists, aimed at increasing their participation in the pharmacotherapeutic process of patients. The drug cycle, characterised as multidisciplinary, is very complex. It is essential for the multidisciplinary team to have a broad vision of the medication system in order to guarantee safety and quality.Considering the challenges of current healthcare systems and paradigm shift from reactive to predictive medicine, a new professional environment should be created to promote the implementation of Predictive, Preventive and Personalised Medicine in healthcare. Objectives and study design: To optimise care times in multipurpose outpatient hospital both in the preparation of ready-to-use medications and in the dispensing of medications for home treatment.To increase the confidence and value of hospital pharmacists in the process of patient and family care.The design of the study was carried out by the following:-Coordinating the schedules of the multi-pathology day hospital with the software and records of Medication Preparation in the pharmacy service.-Opening a Pharmacy Outpatient Clinic associated with the multi-pathology day hospital.-Planning and scheduling patient treatments. Achievements: With the implementation of this programme, the visibility of hospital pharmacists in the multidisciplinary team was increased.This Pharmacy Outpatient Clinic allowed the coordination of the pharmaceutical care process in the day hospital.This project increased the credibility of the Pharmacy Service in the improvement of the integral process of the medicine. Predictive approach: The presence of pharmacists in the multi-pathology day hospital has a predictive approach. A change is made in the workflow that allows to generate a speed of intervention by acting before prescribing, dispensing and administering the treatment to the patient. Targeted prevention: The presence of pharmacists in the multipurpose day hospital unit and their collaboration with other professionals and the patient bring about a selective prevention that decreases the possibility of medication errors occurring. Personalisation of medical services: With the individualised dispensing of treatments, a step forward is taken in the personalisation of medical services, which avoids medication errors in labelling and administration and improves safety in the overall medication circuit in the hospital.

From biochemical markers to molecular endotypes of osteoarthritis: a review on validated biomarkers.

INTRODUCTION: 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. AREAS COVERED: 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. EXPERT OPINION: 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.

Characterizing the real-world economic burden of metastatic castration-sensitive prostate cancer in the United States.

AIMS: To describe healthcare resource utilization (HRU) and costs of patients with metastatic castration-sensitive prostate cancer (mCSPC). METHODS: Linked data from Flatiron Metastatic PC Core Registry and Komodo's Healthcare Map were evaluated (01/2016-12/2021). Patients with chart-confirmed diagnoses for metastatic PC without confirmed castration resistance in Flatiron who initiated androgen deprivation therapy (ADT) monotherapy or advanced therapy for mCSPC in 2017 or later (index date) with a corresponding pharmacy or medical claim in Komodo Health were included. Advanced therapies considered were androgen-receptor signaling inhibitors, chemotherapies, estrogens, immunotherapies, poly ADP-ribose polymerase inhibitors, and radiopharmaceuticals. Patients with <12 months of continuous insurance eligibility before index were excluded. Per-patient-per-month (PPPM) all-cause and PC-related HRU and costs (medical and pharmacy; from a payer's perspective in 2022 $USD) were described in the 12-month baseline period and follow-up period (from the index date to castration resistance, end of continuous insurance eligibility, end of data availability, or death). RESULTS: Of 871 patients included (mean age: 70.6 years), 52% initiated ADT monotherapy as their index treatment without documented advanced therapy use. During baseline, 31% of patients had a PC-related inpatient admission and 94% had a PC-related outpatient visit; mean all-cause costs were $2551 PPPM and PC-related costs were $839 PPPM with $787 PPPM attributable to medical costs. Patients had a mean follow-up of 15 months, during which 38% had a PC-related inpatient admission and 98% had a PC-related outpatient visit; mean all-cause costs were $5950 PPPM with PC-related total costs of $4363 PPPM, including medical costs of $2012 PPPM. LIMITATIONS: All analyses were descriptive; statistical testing was not performed. Treatment effectiveness and clinical outcomes were not assessed. CONCLUSION: This real-world study demonstrated a significant economic burden in mCSPC patients, and a propensity to use ADT monotherapy in clinical practice despite the availability and guideline recommendations of advanced life-prolonging therapies.