Medulloblastoma: From Molecular Subgroups to Molecular Targeted Therapies.

Brain tumors are the leading cause of cancer-related death in children, and medulloblastoma (MB) is the most common malignant pediatric brain tumor. Advances in surgery, radiation, and chemotherapy have improved the survival of MB patients. But despite these advances, 25-30% of patients still die from the disease, and survivors suffer severe long-term side effects from the aggressive therapies they receive. Although MB is often considered a single disease, molecular profiling has revealed a significant degree of heterogeneity, and there is a growing consensus that MB consists of multiple subgroups with distinct driver mutations, cells of origin, and prognosis. Here, we review recent progress in MB research, with a focus on the genes and pathways that drive tumorigenesis, the animal models that have been developed to study tumor biology, and the advances in conventional and targeted therapy.

Mouse models of human multiple myeloma subgroups.

Multiple myeloma (MM), a tumor of germinal center (GC)-experienced plasma cells, comprises distinct genetic subgroups, such as the t(11;14)/CCND1 and the t(4;14)/MMSET subtype. We have generated genetically defined, subgroup-specific MM models by the GC B cell-specific coactivation of mouse Ccnd1 or MMSET with a constitutively active Ikk2 mutant, mimicking the secondary NF-κB activation frequently seen in human MM. Ccnd1/Ikk2ca and MMSET/Ikk2ca mice developed a pronounced, clonally restricted plasma cell outgrowth with age, accompanied by serum M spikes, bone marrow insufficiency, and bone lesions. The transgenic plasma cells could be propagated in vivo and showed distinct transcriptional profiles, resembling their human MM counterparts. Thus, we show that targeting the expression of genes involved in MM subgroup-specific chromosomal translocations into mouse GC B cells translates into distinct MM-like diseases that recapitulate key features of the human tumors, opening the way to a better understanding of the pathogenesis and therapeutic vulnerabilities of different MM subgroups.

Specification of neuronal subtypes in the spiral ganglion begins prior to birth in the mouse.

The afferent innervation of the cochlea is comprised of spiral ganglion neurons (SGNs), which are characterized into four subtypes (Type 1A, B, and C and Type 2). However, little is known about the factors and/or processes that determine each subtype. Here, we present a transcriptional analysis of approximately 5,500 single murine SGNs collected across four developmental time points. All four subtypes are transcriptionally identifiable prior to the onset of coordinated spontaneous activity, indicating that the initial specification process is under genetic control. Trajectory analysis indicates that SGNs initially split into two precursor types (Type 1A/2 and Type 1B/C), followed by subsequent splits to give rise to four transcriptionally distinct subtypes. Differential gene expression, pseudotime, and regulon analyses were used to identify candidate transcription factors which may regulate the subtypes specification process. These results provide insights into SGN development and comprise a transcriptional atlas of SGN maturation across the prenatal period.

Pulmonary artery pressure monitoring in chronic heart failure: effects across clinically relevant subgroups in the MONITOR-HF trial.

BACKGROUND AND AIMS: In patients with chronic heart failure (HF), the MONITOR-HF trial demonstrated the efficacy of pulmonary artery (PA)-guided HF therapy over standard of care in improving quality of life and reducing HF hospitalizations and mean PA pressure. This study aimed to evaluate the consistency of these benefits in relation to clinically relevant subgroups. METHODS: The effect of PA-guided HF therapy was evaluated in the MONITOR-HF trial among predefined subgroups based on age, sex, atrial fibrillation, diabetes mellitus, left ventricular ejection fraction, HF aetiology, cardiac resynchronization therapy, and implantable cardioverter defibrillator. Outcome measures were based upon significance in the main trial and included quality of life-, clinical-, and PA pressure endpoints, and were assessed for each subgroup. Differential effects in relation to the subgroups were assessed with interaction terms. Both unadjusted and multiple testing adjusted interaction terms were presented. RESULTS: The effects of PA monitoring on quality of life, clinical events, and PA pressure were consistent in the predefined subgroups, without any clinically relevant heterogeneity within or across all endpoint categories (all adjusted interaction P-values were non-significant). In the unadjusted analysis of the primary endpoint quality-of-life change, weak trends towards a less pronounced effect in older patients (Pinteraction = .03; adjusted Pinteraction = .33) and diabetics (Pinteraction = .01; adjusted Pinteraction = .06) were observed. However, these interaction effects did not persist after adjusting for multiple testing. CONCLUSIONS: This subgroup analysis confirmed the consistent benefits of PA-guided HF therapy observed in the MONITOR-HF trial across clinically relevant subgroups, highlighting its efficacy in improving quality of life, clinical, and PA pressure endpoints in chronic HF patients.

Alternative genetic alterations of MYC, BCL2, and/or BCL6 in high-grade B-cell lymphoma (HGBL) and diffuse large B-cell lymphoma (DLBCL): Can we identify different prognostic subgroups?

High-grade B-cell lymphoma (HGBL)/diffuse large B-cell lymphoma (DLBCL) with rearrangements (R) in MYC and BCL2 and/or BCL6 are correlated with poor prognosis. Little is known about the impact of other genetic alterations (gain (G) or amplification (A)) of these genes. The aim of the study was to investigate whether we can identify new prognostic subgroups. Fluorescence in situ hybridization (FISH) results from 169 HGBL/DLBCL were retrospectively categorized into: (1) concurrent MYC-R and BCL2-R and/or BCL6-R-samples with MYC-R and BCL2-R (+/- BCL6-R); n = 21, and HGBL/DLBCL with MYC-R and BCL6-R; n = 11; (2) concurrent R and G/A in MYC and BCL2 and/or BCL6 called "alternative HGBL/DLBCL"-samples with (n = 16) or without (n = 6) BCL2 involvement; (3) BCL2 and/or BCL6 alterations without MYC involvement (n = 35); (4) concurrent G/A in MYC and BCL2 and/or BCL6 without R (n = 25); and (5) "No alterations" (n = 55). Patients with HGBL/DLBCL-MYC/BCL2 and "alternative" HGBL/DLBCL (with BCL2 involvement) had significantly worse survival rates compared to the "no alterations" group. G/A of these genes in the absence of rearrangements did not show any prognostic significance. HGBL/DLBCL with MYC-R and BCL6-R without BCL2 involvement showed a better survival rate compared to HGBL/DLBCL-MYC/BCL2. According to immunohistochemistry, "double/triple" expression (DEL/TEL) did not show a significantly worse outcome compared to absent DEL/TEL. This study highlights the continued value of FISH assessment of MYC, BCL2, and BCL6 in the initial evaluation of HGBL/DLBCL with different survival rates between several genetic subgroups.

Disease severity-based subgrouping of type 2 diabetes does not parallel differences in quality of life: the Maastricht Study.

AIMS/HYPOTHESIS: Type 2 diabetes is a highly heterogeneous disease for which new subgroups ('clusters') have been proposed based on disease severity: moderate age-related diabetes (MARD), moderate obesity-related diabetes (MOD), severe insulin-deficient diabetes (SIDD) and severe insulin-resistant diabetes (SIRD). It is unknown how disease severity is reflected in terms of quality of life in these clusters. Therefore, we aimed to investigate the cluster characteristics and cluster-wise evolution of quality of life in the previously defined clusters of type 2 diabetes. METHODS: We included individuals with type 2 diabetes from the Maastricht Study, who were allocated to clusters based on a nearest centroid approach. We used logistic regression to evaluate the cluster-wise association with diabetes-related complications. We plotted the evolution of HbA1c levels over time and used Kaplan-Meier curves and Cox regression to evaluate the cluster-wise time to reach adequate glycaemic control. Quality of life based on the Short Form 36 (SF-36) was also plotted over time and adjusted for age and sex using generalised estimating equations. The follow-up time was 7 years. Analyses were performed separately for people with newly diagnosed and already diagnosed type 2 diabetes. RESULTS: We included 127 newly diagnosed and 585 already diagnosed individuals. Already diagnosed people in the SIDD cluster were less likely to reach glycaemic control than people in the other clusters, with an HR compared with MARD of 0.31 (95% CI 0.22, 0.43). There were few differences in the mental component score of the SF-36 in both newly and already diagnosed individuals. In both groups, the MARD cluster had a higher physical component score of the SF-36 than the other clusters, and the MOD cluster scored similarly to the SIDD and SIRD clusters. CONCLUSIONS/INTERPRETATION: Disease severity suggested by the clusters of type 2 diabetes is not entirely reflected in quality of life. In particular, the MOD cluster does not appear to be moderate in terms of quality of life. Use of the suggested cluster names in practice should be carefully considered, as the non-neutral nomenclature may affect disease perception in individuals with type 2 diabetes and their healthcare providers.

The INNODIA Type 1 Diabetes Natural History Study: a European cohort of newly diagnosed children, adolescents and adults.

AIMS/HYPOTHESIS: Type 1 diabetes is an heterogenous condition. Characterising factors explaining differences in an individual's clinical course and treatment response will have important clinical and research implications. Our aim was to explore type 1 diabetes heterogeneity, as assessed by clinical characteristics, autoantibodies, beta cell function and glycaemic outcomes, during the first 12 months from diagnosis, and how it relates to age at diagnosis. METHODS: Data were collected from the large INNODIA cohort of individuals (aged 1.0-45.0 years) newly diagnosed with type 1 diabetes, followed 3 monthly, to assess clinical characteristics, C-peptide, HbA1c and diabetes-associated antibodies, and their changes, during the first 12 months from diagnosis, across three age groups: <10 years; 10-17 years; and ≥18 years. RESULTS: The study population included 649 individuals (57.3% male; age 12.1±8.3 years), 96.9% of whom were positive for one or more diabetes-related antibodies. Baseline (IQR) fasting C-peptide was 242.0 (139.0-382.0) pmol/l (AUC 749.3 [466.2-1106.1] pmol/l × min), with levels increasing with age (p<0.001). Over time, C-peptide remained lower in participants aged <10 years but it declined in all age groups. In parallel, glucose levels progressively increased. Lower baseline fasting C-peptide, BMI SD score and presence of diabetic ketoacidosis at diagnosis were associated with lower stimulated C-peptide over time. HbA1c decreased during the first 3 months (p<0.001), whereas insulin requirement increased from 3 months post diagnosis (p<0.001). CONCLUSIONS/INTERPRETATION: In this large cohort with newly diagnosed type 1 diabetes, we identified age-related differences in clinical and biochemical variables. Of note, C-peptide was lower in younger children but there were no main age differences in its rate of decline.

Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes.

AIMS/HYPOTHESIS: This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. RESULTS: Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. CONCLUSIONS/INTERPRETATION: Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.

Serum amyloid A facilitates expansion of CD4+ T cell and CD19+ B cell subsets implicated in the severity of myasthenia gravis patients.

Serum amyloid A (SAA) is a clinically useful inflammatory marker involved in the pathogenesis of autoimmune diseases. This study aimed to explore the SAA levels in a cohort of patients with myasthenia gravis (MG) in relation to disease-related clinical parameters and myasthenic crisis (MC) and elucidate the effects of SAA on immune response. A total of 82 MG patients including 50 new-onset MG patients and 32 MC patients were enrolled in this study. Baseline data and laboratory parameters of all enrolled MG patients were routinely recorded through electronic medical systems. SAA levels were measured by enzyme-linked immunosorbent assay (ELISA) kit. CD4+ T and CD19+ B cell subsets were analyzed by flow cytometry. In vitro, human recombinant SAA (Apo-SAA) was applied to stimulate peripheral blood mononuclear cells (PBMCs) from MG patients to observe the effect on T and B cell differentiation. Our results indicated that SAA levels in new-onset MG patients were higher than those in controls and were positively correlated with QMG score, MGFA classification, plasmablast cells, IL-6, and IL-17 levels. Subgroup analysis revealed that SAA levels were increased in generalized MG (GMG) patients than in ocular MG (OMG), as well as elevated in late-onset MG (LOMG) than in early-onset MG (EOMG) and higher in MGFA III/IV compared with MGFA I/II. The ROC curve demonstrated that SAA showed good diagnostic value for MC, especially when combined with NLR. In vitro, Apo-SAA promoted the Th1 cells, Th17 cells, plasmablast cells, and plasma cells differentiation in MG PBMCs. The present findings suggested that SAA was increased in MG patients and promoted expansion of CD4+ T cell and CD19+ B cell subsets, which implicated in the severity of MG patients.

Associations between low- and high-fat dairy intake and recurrence risk in people with stage I-III colorectal cancer differ by sex and primary tumour location.

We previously demonstrated that intake of low-fat dairy, but not high-fat dairy, was associated with a decreased colorectal cancer (CRC) recurrence risk. These risks, however, may differ by sex, primary tumour location, and disease stage. Combining data from two similar prospective cohort studies of people with stage I-III CRC enabled these subgroup analyses. Participants completed a food frequency questionnaire at diagnosis (n = 2283). We examined associations between low- and high-fat dairy intake and recurrence risk using multivariable Cox proportional hazard models, stratified by sex, and primary tumour location (colon and rectum), and disease stage (I/II and III). Upper quartiles were compared to lower quartiles of intake, and recurrence was defined as a locoregional recurrence and/or metastasis. During a median follow-up of 5.0 years, 331 recurrences were detected. A higher intake of low-fat dairy was associated with a reduced risk of recurrence (hazard ratio [HR]: 0.60, 95% confidence interval [CI]: 0.43-0.83), which seemed more pronounced in men (HR: 0.51, 95% CI: 0.34-0.77) than in women (HR: 0.84, 95% CI: 0.47-1.49). A higher intake of high-fat dairy was associated with an increased risk of recurrence in participants with colon cancer (HR: 1.60, 95% CI: 1.03-2.50), but not rectal cancer (HR: 0.88, 95% CI: 0.54-1.45). No differences in associations were observed between strata of disease stage. Concluding, our findings imply that dietary advice regarding low-fat dairy intake may be especially important for men with CRC, and that dietary advice regarding high-fat dairy intake may be specifically important in people with colon cancer.

ARX, PDX1, ISL1, and CDX2 Expression Distinguishes 5 Subgroups of Pancreatic Neuroendocrine Tumors With Correlations to Histology, Hormone Expression, and Outcome.

Many pancreatic neuroendocrine tumors (PanNETs) fall into 2 major prognostic subtypes based on DAXX/ATRX-induced alternative lengthening of telomerase phenotype and alpha- and beta-cell-like epigenomic profiles. However, these PanNETs are still flanked by other PanNETs that do not fit into either subtype. Furthermore, despite advanced genotyping, PanNETs are generally not well-characterized in terms of their histologic and hormonal phenotypes. We aimed to identify new subgroups of PanNETs by extending the currently used transcription factor signatures and investigating their correlation with histologic, hormonal, molecular, and prognostic findings. One hundred eighty-five PanNETs (nonfunctioning 165 and functioning 20), resected between 1996 and 2023, were classified into 5 subgroups (A1, A2, B, C, and D) by cluster analysis based on ARX, PDX1, islet-1 (ISL1), and CDX2 expressions and correlated with trabecular vs solid histology, expression of insulin, glucagon, polypeptide (PP), somatostatin, serotonin, gastrin, calcitonin, adrenocorticotropic hormone (ACTH), DAXX/ATRX, MEN1, and alternative lengthening of telomerase status by fluorescence in situ hybridization, and disease-free survival. A1 (46%, ARX+/ISL1+/PDX1-/CDX2-) and A2 (15%, ARX+/ISL1+/PDX1+/CDX2-) showed trabecular histology and glucagon/PP expression, with A2 also showing gastrin expression. B (18%, PDX1+/ISL1+/ARX-/CDX2-) showed solid histology, insulin, and somatostatin expression (P < .001). It included all insulinomas and had the best outcome (P < .01). C (15%, ARX-/PDX1-/ISL1-/CDX2-) showed solid histology and frequent expression of serotonin, calcitonin, and ACTH. D (5%, PDX1+/CDX2+/ISL1-/ARX-) showed solid histology, expressed ACTH/serotonin, and was an independent poor prognosticator (P < .01). Differential expressions of ARX, PDX1, ISL1, and CDX2 stratified PanNETs into 5 subgroups with different histologies, hormone expressions, and outcomes. Subgroups A1 and A2 resembled the alpha-cell-like type, and subgroup B, the beta-cell-like type. Subgroup C with almost no transcription factor signature was unclear in cell lineage, whereas the PDX+/CDX2+ signature of subgroup D suggested a pancreatic/intestinal cell lineage. Assigning PanNETs to the subgroups may help establish the diagnosis, predict the outcome, and guide the treatment.

Risk subgroups and intervention effects among infants at high risk for peanut allergy: A model for clinical decision making.

BACKGROUND: The Learning Early About Peanut Allergy (LEAP) trial showed that early dietary introduction of peanut reduced the risk of developing peanut allergy by age 60 months in infants at high risk for peanut allergy. In this secondary analysis of LEAP data, we aimed to determine risk subgroups within these infants and estimate their respective intervention effects of early peanut introduction. METHODS: LEAP raw data were retrieved from ITNTrialShare.org. Conditional random forest was applied to participants in the peanut avoidance arm to select statistically important features for the classification and regression tree (CART) analysis to group infants based on their risk of peanut allergy at 60 months of age. Intervention effects were estimated for each derived risk subgroup using data from both arms. Our main model was generated based on baseline data when the participants were 4-11 months old. Specific IgE measurements were truncated to account for the limit of detection commonly used by laboratories in clinical practice. RESULTS: The model found infants with higher predicted probability of peanut allergy at 60 months of age had a similar relative risk reduction, but a greater absolute risk reduction in peanut allergy with early introduction of peanut, than those with lower probability. The intervention effects were significant across all risk subgroups. Participants with baseline peanut sIgE ≥0.22 kU/L (n = 78) had an absolute risk reduction of 40.4% (95% CI 27.3, 51.9) whereas participants with baseline peanut sIgE<0.22 kU/L and baseline Ara h 2 sIgE <0.10 kU/L (n = 226) had an absolute risk reduction of 6.5% (95% CI 2.6, 11.0). These findings were consistent in sensitivity analyses using alternative models. CONCLUSION: In this study, risk subgroups were determined among infants from the LEAP trial based on the probability of developing peanut allergy and the intervention effects of early peanut introduction were estimated. This may be relevant for further risk assessment and personalized clinical decision-making.

Elevated unphosphorylated STAT1 and IRF9 in T and B cells of primary sjögren's syndrome: Novel biomarkers for disease activity and subsets.

OBJECTIVES: Autoreactive B cells and interferon (IFN) signature are hallmarks of primary sjögren's syndrome (pSS), but how IFN signaling pathways influence autoantibody production and clinical manifestations remain unclear. More detailed studies hold promise for improved diagnostic methodologies and personalized treatment. METHODS: We analyzed peripheral blood T and B cell subsets from 34 pSS patients and 38 healthy donors (HDs) at baseline and upon stimulation regarding their expression levels of type I and II IFN signaling molecules (STAT1/2, IRF1, IRF9). Additionally, we investigated how the levels of these molecules correlated with serological and clinical characteristics and performed ROC analysis. RESULTS: Patients showed elevated IFN pathway molecules, including STAT1, STAT2 and IRF9 among most T and B cell subsets. We found a reduced ratio of phosphorylated STAT1 and STAT2 in patients in comparison to HDs, although B cells from patients were highly responsive by increased phosphorylation upon IFN stimulation. Correlation matrices showed further interrelations between STAT1, IRF1 and IRF9 in pSS. Levels of STAT1 and IRF9 in T and B cells correlated with the IFN type I marker Siglec-1 (CD169) on monocytes. High levels of STAT1 and IRF9 within pSS B cells were significantly associated with hypergammaglobulinemia as well as anti-SSA/anti-SSB autoantibodies. Elevated STAT1 levels were found in patients with extraglandular disease and could serve as a biomarker for this subgroup (p < 0.01). Notably, IRF9 levels in T and B cells correlated with EULAR Sjögren's syndrome disease activity index (ESSDAI). CONCLUSION: Here, we provide evidence that in active pSS patients, enhanced IFN signaling incl. unphosphorylated STAT1 and STAT2 with IRFs entertain chronic T and B cell activation. Furthermore, increased STAT1 levels candidate as biomarker of extraglandular disease, while IRF9 levels can serve as biomarker for disease activity.

Clinically unfavorable transcriptome subtypes of non-WNT/non-SHH medulloblastomas are associated with a predominance in proliferating and progenitor-like cell subpopulations.

The non-WNT/non-SHH (Grp3/Grp4) medulloblastomas (MBs) include eight second-generation subgroups (SGS; I-VIII) each with distinct molecular and clinical characteristics. Recently, we also identified two prognostically relevant transcriptome subtypes within each SGS MB, which are associated with unique gene expression signatures and signaling pathways. These prognostic subsets may be in connection to the intra-tumoral cell landscape that underlies SGS MB clinical-molecular diversity. Here, we performed a deconvolution analysis of the Grp3/Grp4 MB bulk RNA profiles using the previously identified single-cell RNA-seq reference dataset and focusing on variability in the cellular composition of SGS MB. RNA deconvolution analysis of the Grp3/Grp4 MB disclosed the subgroup-specific neoplastic cell subpopulations. Neuronally differentiated axodendritic GP3-C1 and glutamatergic GP4-C1 subpopulations were distributed within Grp3- and Grp4-associated SGS MB, respectively. Progenitor GP3-B2 subpopulation was prominent in aggressive SGS II MB, whereas photoreceptor/visual perception GP3/4-C2 cell content was typical for SGS III/IV MB. The current study also revealed significant variability in the proportions of cell subpopulations between clinically relevant SGS MB transcriptome subtypes, where unfavorable cohorts were enriched with cell cycle and progenitor-like cell subpopulations and, vice versa, favorable subtypes were composed of neuronally differentiated cell fractions predominantly. A higher than median proportion of proliferating and progenitor cell subpopulations conferred the shortest survival of the Grp3 and Grp 4 MB, and similar survival associations were identified for all SGS MB except SGS IV MB. In summary, the recently identified clinically relevant Grp3/Grp4 MB transcriptome subtypes are composed of different cell populations. Future studies should aim to validate the prognostic and therapeutic role of the identified Grp3/Grp4 MB inter-tumoral cellular heterogeneity. The application of the single-cell techniques on each SGS MB separately could help to clarify the clinical significance of subgroup-specific variability in tumor cell content and its relation with prognostic transcriptome signatures identified before.

Cognitive and inflammatory heterogeneity in severe mental illness: Translating findings from blood to brain.

Recent findings link cognitive impairment and inflammatory-immune dysregulation in schizophrenia (SZ) and bipolar (BD) spectrum disorders. However, heterogeneity and translation between the periphery and central (blood-to-brain) mechanisms remains a challenge. Starting with a large SZ, BD and healthy control cohort (n = 1235), we aimed to i) identify candidate peripheral markers (n = 25) associated with cognitive domains (n = 9) and elucidate heterogenous immune-cognitive patterns, ii) evaluate the regulation of candidate markers using human induced pluripotent stem cell (iPSC)-derived astrocytes and neural progenitor cells (n = 10), and iii) evaluate candidate marker messenger RNA expression in leukocytes using microarray in available data from a subsample of the main cohort (n = 776), and in available RNA-sequencing deconvolution analysis of postmortem brain samples (n = 474) from the CommonMind Consortium (CMC). We identified transdiagnostic subgroups based on covariance between cognitive domains (measures of speed and verbal learning) and peripheral markers reflecting inflammatory response (CRP, sTNFR1, YKL-40), innate immune activation (MIF) and extracellular matrix remodelling (YKL-40, CatS). Of the candidate markers there was considerable variance in secretion of YKL-40 in iPSC-derived astrocytes and neural progenitor cells in SZ compared to HC. Further, we provide evidence of dysregulated RNA expression of genes encoding YKL-40 and related signalling pathways in a high neuroinflammatory subgroup in the postmortem brain samples. Our findings suggest a relationship between peripheral inflammatory-immune activity and cognitive impairment, and highlight YKL-40 as a potential marker of cognitive functioning in a subgroup of individuals with severe mental illness.

UCHL-3 as a potential biomarker of ovarian cancer.

OBJECTIVE: This study explored promising prognostic and immune therapeutic candidate biomarkers for OC and determined the expression, prognostic value, and immune effects of UCHL3. METHODS: UCHL3 expression and clinical data were investigated using bioinformatic analysis. CCK8 and transwell assays were conducted to evaluate the impact of UCHL3 on proliferation and migration, and the effects of UCHL3 were further validated in a mouse model. Univariate and least absolute shrinkage and selection operator regression analyses were performed to construct a novel UCHL3-related prognostic risk model. Gene set enrichment analysis (GSEA) and immune analysis were performed to identify the significantly involved functions of UCHL3. Finally, bioinformatic analysis and immunohistochemistry were performed to explore the effect of UCHL3 on chemotherapy. RESULTS: UCHL3 expression was upregulated and associated with worse overall survival (OS) in OC. UCHL3 depletion repressed cell proliferation and migration both in vitro and in vivo. Furthermore, 237 genes were differentially expressed between the high and low UCHL3 expression groups. Subsequently, a UCHL3-related prognostic signature was built based on six prognostic genes (PI3, TFAP2B, MUC7, PSMA2, PIK3C2G, and NME1). Independent prognostic analysis suggested that age, tumor mutational burden, and RiskScore can be used as independent prognostic factors. The immune infiltration analysis and GSEA suggested that UCHL3 expression was related to the immune response. In addition, UCHL3 expression was higher in platinum-resistant OC patients than in platinum-sensitive patients. UCHL3 overexpression was associated with poorer OS. CONCLUSION: UCHL3 overexpression contributes to aggressive progression, poor survival, and chemoresistance in OC. Therefore, UCHL3 may be a candidate prognostic biomarker and potential target for controlling progression and platinum resistance in OC.

Acute limbic system connectivity predicts chronic cognitive function in mild traumatic brain injury: An individualized differential structural covariance network study.

Mild traumatic brain injury (mTBI) is a known risk factor for neurodegenerative diseases, yet the precise pathophysiological mechanisms remain poorly understand, often obscured by group-level analysis in non-invasive neuroimaging studies. Individual-based method is critical to exploring heterogeneity in mTBI. We recruited 80 mTBI patients and 40 matched healthy controls, obtaining high-resolution structural MRI for constructing Individual Differential Structural Covariance Networks (IDSCN). Comparisons were conducted at both the individual and group levels. Connectome-based Predictive Modeling (CPM) was applied to predict cognitive performance based on whole-brain connectivity. During the acute stage of mTBI, patients exhibited significant heterogeneity in the count and direction of altered edges, obscured by group-level analysis. In the chronic stage, the number of altered edges decreased and became more consistent, aligning with clinical observations of acute cognitive impairment and gradual improvement. Subgroup analysis based on loss of consciousness/post-traumatic amnesia revealed distinct patterns of alterations. The temporal lobe, particularly regions related to the limbic system, significantly predicted cognitive function from acute to chronic stage. The use of IDSCN and CPM has provided valuable individual-level insights, reconciling discrepancies from previous studies. Additionally, the limbic system may be an appropriate target for future intervention efforts.

Development and Validation of a Machine Learning Prognostic Model of m5C Related immune Genes in Lung Adenocarcinoma.

BACKGROUND: The aim of this retrospective research was to develop an immune-related genes significantly associated with m5C methylation methylation (m5C-IRGs)-related signature associated with lung adenocarainoma (LUAD). METHODS: We introduced transcriptome data to screen out m5C-IRGs in The Cancer Genome Atlas (TCGA)-LUAD dataset. Subsequently, the m5C-IRGs associated with survival were certificated by Kaplan Meier (K-M) analysis. The univariate Cox, least absolute shrinkage and selection operator (LASSO) regression, and xgboost.surv tool were adopted to build a LUAD prognostic signature. We further conducted gene functional enrichment, immune microenvironment and immunotherapy analysis between 2 risk subgroups. Finally, we verified m5C-IRGs-related prognostic gene expression in transcription level. RESULTS: A total of 76 m5C-IRGs were identified in TCGA-LUAD dataset. Furthermore, 27 m5C-IRGs associated with survival were retained. Then, a m5C-IRGs prognostic signature was build based on the 3 prognostic genes (HLA-DMB, PPIA, and GPI). Independent prognostic analysis suggested that stage and RiskScore could be used as independent prognostic factors. We found that 4104 differentially expressed genes (DEGs) between the 2 risk subgroups were mainly concerned in immune receptor pathways. We found certain distinction in LUAD immune microenvironment between the 2 risk subgroups. Then, immunotherapy analysis and chemotherapeutic drug sensitivity results indicated that the m5C-IRGs-related gene signature might be applied as a therapy predictor. Finally, we found significant higher expression of PPIA and GPI in LUAD group compared to the normal group. CONCLUSIONS: The prognostic signature comprised of HLA-DMB, PPIA, and GPI based on m5C-IRGs was established, which might provide theoretical basis and reference value for the research of LUAD. PUBLIC DATASETS ANALYZED IN THE STUDY: TCGA-LUAD dataset was collected from the TCGA (https://portal.gdc.cancer.gov/) database, GSE31210 (validation set) was retrieved from GEO (https://www.ncbi.nlm.nih.gov/geo/) database.

Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies.

BACKGROUND: Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological and behavioral substrates are associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning (ML) shows promise in predicting treatment response in MDD, but its application is limited by challenges to the clinical interpretability of ML models, and clinicians often lack confidence in model results. In order to improve the interpretability of ML models in clinical practice, our goal was to demonstrate the derivation of treatment-relevant patient profiles comprised of clinical and demographic information using a novel ML approach. METHODS: We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a ML model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Prototypes were evaluated for interpretability by assessing differences in feature distributions (e.g. age, sex, symptom severity) and treatment-specific outcomes. RESULTS: A 3-prototype model achieved an area under the receiver operating curve of 0.66 and an expected absolute improvement in remission rate for those receiving the best predicted treatment of 6.5% (relative improvement of 15.6%) compared to the population remission rate. We identified three treatment-relevant patient clusters. Cluster A patients tended to be younger, to have increased levels of fatigue, and more severe symptoms. Cluster B patients tended to be older, female, have less severe symptoms, and the highest remission rates. Cluster C patients had more severe symptoms, lower remission rates, more psychomotor agitation, more intense suicidal ideation, and more somatic genital symptoms. CONCLUSION: It is possible to produce novel treatment-relevant patient profiles using ML models; doing so may improve interpretability of ML models and the quality of precision medicine treatments for MDD.

Metabolic Syndrome, Modifiable Lifestyle Factors, and Sleep-Disordered Breathing: The Hispanic Community Health Study.

BACKGROUND: US Hispanics/Latinos are disproportionately susceptible to metabolic syndrome (MetS), attributed in part to systemic inequities related to health and lifestyle factors such as low physical activity (PA) levels, diet quality, alcohol use, tobacco use, and sleep disorder. Gender and heritage group differences are vastly understudied and need to be examined in this heterogeneous population. PURPOSE: To examine the relationships between select health and lifestyle factors and MetS among Hispanic gender and heritage subgroups (Hypothesis 1) and determine whether gender and heritage moderate those relationships (Hypothesis 2). METHODS: Participants included 14,155 Hispanic Americans aged 18-76 (59% female, mean age 45.92 ± 13.97) from seven heritage subgroups. This secondary analysis of cross-sectional data from the observational Hispanic Community Health Study/Study of Latinos (HCHS/SOL) dataset used hierarchical multinomial logistic regression to test Hypothesis 1; the dependent variable, MetS, included three categories delineating absence of MetS and presence of MetS with or without related medication use. Hayes' PROCESS macro tested Hypothesis 2. RESULTS: Low PA and sleep-disordered breathing (SDB) each had significant (p < .001) predictive value of MetS group membership, whereas both low and high alcohol use (p < .001) were associated with decreased MetS risk. Cigarette pack-years were not significantly associated with MetS outcomes. Gender moderated the association between MetS and alcohol use (p < .001), cigarette pack-years (p < .001), and SDB (p < .001) such that the effects on MetS were higher in females than males. The association between MetS and diet quality (p < .001) was stronger among males than in females. CONCLUSIONS: Gender and heritage differences were prominent among study variables.