C3G and Ig-MPGN-treatment standard.

Among the broad spectrum of membranoproliferative glomerulonephritis (MPGN), immunofluorescence distinguishes C3 glomerulopathy (C3G), with predominant C3 deposits, and immunoglobulin-associated MPGN (Ig-MPGN), with combined C3 and Ig. However, there are several intersections between C3G and Ig-MPGN. Primary C3G and Ig-MPGN share the same prevalence of low serum C3 levels and of abnormalities of the alternative pathway of complement, and patients who present a bioptic pattern of Ig-MPGN at onset may show a C3G pattern in a subsequent biopsy. There is no specific therapy for primary C3G and Ig-MPGN and prognosis is unfavourable. The only recommended indications are inhibitors of the renin-angiotensin system, lipid-lowering agents and other renoprotective agents. The other drugs used currently, such as corticosteroids and mycophenolate mofetil, are often ineffective. The anti-C5 monoclonal antibody eculizumab has been tested in several patients, with mixed results. One reason for the uncertainty is the extremely variable clinical course, most likely reflecting a heterogeneous pathogenesis. An unsupervised clustering analysis that included histologic, biochemical, genetic and clinical data available at onset in patients with primary C3G and Ig-MPGN identified four clusters characterized by specific pathogenic mechanisms. This approach may facilitate accurate diagnosis and development of targeted therapies. Several trials are ongoing with drugs targeting different molecules of the complement cascade, however it is important to consider which component of the cascade may be the most appropriate for each patient. We review the current standards of treatment and discuss novel developments in the pathophysiology, diagnosis, outcome prediction and management of C3G and Ig-MPGN.

Redistribution of CD8+ T cell subsets in metastatic renal cell carcinoma patients treated with anti-PD-1 therapy.

Renal-cell carcinoma (RCC) is responsible for the majority of tumors arising from the kidney parenchyma. Although a progressive improvement in median overall survival has been observed after the introduction of anti-PD-1 therapy, many patients do not benefit from this treatment. Therefore, we have investigated T cell dynamics to find immune modification induced by anti-PD-1 therapy. Here, we show that, after therapy, RCC patients (5 responders and 14 nonresponders) are characterized by a redistribution of different subsets across the memory T cell compartment.

Moving toward endotypes in atopic dermatitis: Identification of patient clusters based on serum biomarker analysis.

BACKGROUND: Atopic dermatitis (AD) is a complex, chronic, inflammatory skin disease with a diverse clinical presentation. However, it is unclear whether this diversity exists at a biological level. OBJECTIVE: We sought to test the hypothesis that AD is heterogeneous at the biological level of individual inflammatory mediators. METHODS: Sera from 193 adult patients with moderate-to-severe AD (six area, six sign atopic dermatitis [SASSAD] score: geometric mean, 22.3 [95% CI, 21.3-23.3] and 39.1 [95% CI, 37.5-40.9], respectively) and 30 healthy control subjects without AD were analyzed for 147 serum mediators, total IgE levels, and 130 allergen-specific IgE levels. Population heterogeneity was assessed by using principal component analysis, followed by unsupervised k-means cluster analysis of the principal components. RESULTS: Patients with AD showed pronounced evidence of inflammation compared with healthy control subjects. Principal component analysis of data on sera from patients with AD revealed the presence of 4 potential clusters. Fifty-seven principal components described approximately 90% of the variance. Unsupervised k-means cluster analysis of the 57 largest principal components delivered 4 distinct clusters of patients with AD. Cluster 1 had high SASSAD scores and body surface areas with the highest levels of pulmonary and activation-regulated chemokine, tissue inhibitor of metalloproteinases 1, and soluble CD14. Cluster 2 had low SASSAD scores with the lowest levels of IFN-α, tissue inhibitor of metalloproteinases 1, and vascular endothelial growth factor. Cluster 3 had high SASSAD scores with the lowest levels of IFN-ß, IL-1, and epithelial cytokines. Cluster 4 had low SASSAD scores but the highest levels of the inflammatory markers IL-1, IL-4, IL-13, and thymic stromal lymphopoietin. CONCLUSION: AD is a heterogeneous disease both clinically and biologically. Four distinct clusters of patients with AD have been identified that could represent endotypes with unique biological mechanisms. Elucidation of these endotypes warrants further investigation and will require future intervention trials with specific agents, such as biologics.

A clinical prognostic model of oxidative stress-related genes linked to tumor immune cell infiltration and the prognosis of ovarian cancer patients.

Background: According to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV. Methods: In this study, all data related to the transcriptome and clinical status of the patients were retrieved from "The Cancer Genome Atlas" (TCGA) and "Gene Expression Omnibus" (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model. Results: Two subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of MARVELD1 and VSIG4 was high in the OV samples. Conclusions: Analysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.

Using Cluster Analysis to Overcome the Limits of Traditional Phenotype-Genotype Correlations: The Example of RYR1-Related Myopathies.

Thanks to advances in gene sequencing, RYR1-related myopathy (RYR1-RM) is now known to manifest itself in vastly heterogeneous forms, whose clinical interpretation is, therefore, highly challenging. We set out to develop a novel unsupervised cluster analysis method in a large patient population. The objective was to analyze the main RYR1-related characteristics to identify distinctive features of RYR1-RM and, thus, offer more precise genotype-phenotype correlations in a group of potentially life-threatening disorders. We studied 600 patients presenting with a suspicion of inherited myopathy, who were investigated using next-generation sequencing. Among them, 73 index cases harbored variants in RYR1. In an attempt to group genetic variants and fully exploit information derived from genetic, morphological, and clinical datasets, we performed unsupervised cluster analysis in 64 probands carrying monoallelic variants. Most of the 73 patients with positive molecular diagnoses were clinically asymptomatic or pauci-symptomatic. Multimodal integration of clinical and histological data, performed using a non-metric multi-dimensional scaling analysis with k-means clustering, grouped the 64 patients into 4 clusters with distinctive patterns of clinical and morphological findings. In addressing the need for more specific genotype-phenotype correlations, we found clustering to overcome the limits of the "single-dimension" paradigm traditionally used to describe genotype-phenotype relationships.

Patterns of Lumbar Spine Malalignment Leading to Revision Surgery for Proximal Junctional Kyphosis: A Cluster Analysis of Over- Versus Under-Correction.

STUDY DESIGN: Retrospective cohort study. OBJECTIVE: Investigate the patterns of fused lumbar alignment in patients requiring revision surgery for proximal junctional kyphosis (PJK). METHODS: Fifty patients (67.8 yo, 76% female) with existing thoraco-lumbar fusion (T10/12 to pelvis) and indicated for surgical correction for PJK were included. To investigate patterns of radiographic alignment prior to PJK revision, unsupervised 2-step cluster analysis was run on parameters describing the fused lumbar spine (PI-LL) to identify natural independent groups within the cohort. Clusters were compared in terms of demographics, pre-operative alignment, surgical parameters, and post-operative alignment. Associations between pre- and post-revision PJK angles were investigated using a Pearson correlation analysis. RESULTS: Analysis identified 2 distinct patterns: Under-corrected (UC, n = 12, 32%) vs over-corrected (OC, n = 34, 68%) with a silhouette of .5. The comparison demonstrated similar pelvic incidence (PI) and PJK angle but significantly greater deformity for the UC vs OC group in terms of PI-LL, PI-LL offset, pelvic tilt, and sagittal vertebral axis. The surgical strategy for PJK correction did not differ between the 2 groups in terms of approach, American Society of Anesthesiologists grade, decompression, use of osteotomy, interbody fusion, or fusion length. The post-revision PJK angle significantly correlated with the amount of PJK correction within the OC group but not within the UC group. CONCLUSIONS: This study identified 2 patterns of lumbar malalignment associated with severe PJK: over vs under corrected. Despite the difference in PJK etiology, both patterns underwent the same revision strategy. Future analysis should look at the effect of correcting focal deformity alone vs correcting focal deformity and underlying malalignment simultaneously on recurrent PJK rate.

Identification of Necroptosis-related Subtypes and Characterization of Tumor Microenvironment Infiltration in Non-small Cell Lung Cancer.

BACKGROUND: Necroptosis is correlated with the development, prognosis, and treatment of tumors. However, the function of necroptosis-associated genes (NRGs) in the tumor microenvironment (TME) of non-small cell lung cancer (NSCLC) remains unclear. METHODS: In this study, 1210 NSCLC samples were classified into different subtypes based on the expression of 66 NRGs by unsupervised clustering analysis, and further analyzed the TME characteristics of these subtypes. In addition, we identified common differentially expressed genes (co-DEGs) in NRG subtypes and constructed the NRG score using principal component analysis (PCA) to assess the NRG-mediated TME characteristics of patients with NSCLC. RESULTS: Using unsupervised cluster analysis, 1210 NSCLC samples were divided into NRGcluster A and B subtypes. The NRGcluster B survived significantly better than the NRGcluster A. TME characterization revealed that NRGcluster B was upregulated in immune and stromal signaling activation, whereas NRGcluster A was upregulated in oncogenic signaling. The NRG score constructed based on co-DEGs of the two NRG-related subtypes was positively correlated with immune cell infiltration and negatively correlated with the number of cancer stem cells (CSCs) and tumor mutational burden (TMB). In addition, survival was significantly worse in the low-NRG-score group compared to the high-NRG-score group. Finally, the assessment of immunotherapeutic efficacy showed that immunotherapeutic response was significantly worse in the low-NRG-score group compared to the high- NRG-score group. CONCLUSION: This research reveals that NRGs are associated with the complexity and diversity of TME in NSCLC. Adopting the NRG score to quantitatively assess NRG-mediated TME in individual patients with NSCLC may help in planning clinical treatment strategies.

Establishment and Systematic Evaluation of Gastric Cancer Classification Model Based on Pyroptosis.

Background: Gastric cancer (GC) is considered the fifth most prevalent type of cancer and the third leading cause of cancer deaths worldwide. This in-depth investigation was performed to generate fresh concepts for the clinical classification, diagnosis, and prognostic evaluation of GC. Methods: The data were retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Unsupervised cluster analysis was used to divide up the GC patients using pyroptosis-related differentially expressed genes (DEGs), which were discovered to be significantly linked with GC prognosis. The therapeutic importance of pyroptosis in GC patients was discovered using PCA analysis of genes associated with pyroptosis. The models were then carefully scrutinized. Results: Three hub genes, ELANE, IL6, and TIRAP, exhibit significant predictive importance among the 15 pyroptosis-related genes. Unsupervised clustering analysis revealed that the DEGs were enriched in the pathway of cytokine-cytokine receptor interactions, and Clusters 1 and 2 had statistically distinct prognoses. PCA analysis revealed significant differences in the area under the curve, immunological checkpoints, immunogenic cell death, and prognostic value between the high- and low-risk groups. Conclusions: These two GC classification models, based on pyroptosis, have significant clinical value for patients with GC.

A cluster of blood-based protein biomarkers reflecting coagulation relates to the burden of cerebral small vessel disease.

Biological processes underlying cerebral small vessel disease (cSVD) are largely unknown. We hypothesized that identification of clusters of inter-related bood-based biomarkers that are associated with the burden of cSVD provides leads on underlying biological processes. In 494 participants (mean age 67.6 ± 8.7 years; 36% female; 75% cardiovascular diseases; 25% reference participants) we assessed the relation between 92 blood-based biomarkers from the OLINK cardiovascular III panel and cSVD, using cluster-based analyses. We focused particularly on white matter hyperintensities (WMH). Nineteen biomarkers individually correlated with WMH ratio (r range: 0.16-0.27, Bonferroni corrected p-values <0.05), of which sixteen biomarkers formed one biomarker cluster. Pathway analysis showed that this biomarker cluster predominantly reflected coagulation processes. This cluster related also significantly to other cSVD manifestations (lacunar infarcts, microbleeds, and enlarged perivascular spaces), which supports generalizability beyond WMHs. To study possible causal effects of biological processes reflected by the cluster we performed a mediation analysis that showed a mediation effect of the cluster on the relation between age and WMH ratio (proportion mediated 17%), and hypertension and WMH-volume (proportion mediated 21%). In conclusion, we identified a cluster of blood-based biomarkers reflecting coagulation, that is related to manifestations of cSVD, corroborating involvement of coagulation abnormalities in the etiology of cSVD.

Distinct immune and inflammatory response patterns contribute to the identification of poor prognosis and advanced clinical characters in bladder cancer patients.

Due to the molecular heterogeneity, most bladder cancer (BLCA) patients show no pathological responses to immunotherapy and chemotherapy yet suffer from their toxicity. This study identified and validated three distinct and stable molecular clusters of BLCA in cross-platform databases based on personalized immune and inflammatory characteristics. H&E-stained histopathology images confirmed the distinct infiltration of immune and inflammatory cells among clusters. Cluster-A was characterized by a favorable prognosis and low immune and inflammatory infiltration but showed the highest abundance of prognosis-related favorable immune cell and inflammatory activity. Cluster-B featured the worst prognosis and high immune infiltration, but numerous unfavorable immune cells exist. Cluster-C had a favorable prognosis and the highest immune and inflammatory infiltration. Based on machine learning, a highly precise predictive model (immune and inflammatory responses signature, IIRS), including FN1, IL10, MYC, CD247, and TLR2, was developed and validated to identify the high IIRS-score group that had a poor prognosis and advanced clinical characteristics. Compared to other published models, IIRS showed the highest AUC in 5 years of overall survival (OS) and a favorable predictive value in predicting 1- and 3- year OS. Moreover, IIRS showed an excellent performance in predicting immunotherapy and chemotherapy's response. According to immunohistochemistry and qRT-PCR, IIRS genes were differentially expressed between tumor tissues with corresponding normal or adjacent tissues. Finally, immunohistochemical and H&E-stained analyses were performed on the bladder tissues of 13 BLCA patients to further demonstrate that the IIRS score is a valid substitute for IIR patterns and can contribute to identifying patients with poor clinical and histopathology characteristics. In conclusion, we established a novel IIRS depicting an IIR pattern that could independently predict OS and acts as a highly precise predictive biomarker for advanced clinical characters and the responses to immunotherapy and chemotherapy.

Human phenotype ontology annotation and cluster analysis for pulmonary atresia to unravel clinical outcomes.

Background: Pulmonary atresia (PA) is a heterogeneous congenital heart defect and ventricular septal defect (VSD) is the most vital factor for the conventional classification of PA patients. The simple dichotomy could not fully describe the cardiac morphologies and pathophysiology in such a complex disease. We utilized the Human Phenotype Ontology (HPO) database to explore the phenotypic patterns of PA and the phenotypic influence on prognosis. Methods: We recruited 786 patients with diagnoses of PA between 2008 and 2016 at Fuwai Hospital. According to cardiovascular phenotypes of patients, we retrieved 52 HPO terms for further analyses. The patients were classified into three clusters based on unsupervised hierarchical clustering. We used Kaplan-Meier curves to estimate survival, the log-rank test to compare survival between clusters, and univariate and multivariate Cox proportional hazards regression modeling to investigate potential risk factors. Results: According to HPO term distribution, we observed significant differences of morphological abnormalities in 3 clusters. We defined cluster 1 as being associated with Tetralogy of Fallot (TOF), VSD, right ventricular hypertrophy (RVH), and aortopulmonary collateral arteries (ACA). ACA was not included in the cluster classification because it was not an HPO term. Cluster 2 was associated with hypoplastic right heart (HRH), atrial septal defect (ASD) and tricuspid disease as the main morphological abnormalities. Cluster 3 presented higher frequency of single ventricle (SV), dextrocardia, and common atrium (CA). The mortality rate in cluster 1 was significantly lower than the rates in cluster 2 and 3 (p = 0.04). Multivariable analysis revealed that abnormal atrioventricular connection (AAC, p = 0.011) and persistent left superior vena cava (LSVC, p = 0.003) were associated with an increased risk of mortality. Conclusions: Our study reported a large cohort with clinical phenotypic, surgical strategy and long time follow-up. In addition, we provided a precise classification and successfully risk stratification for patients with PA.

Profiles of immune infiltration in the tumor microenvironment of hepatocellular carcinoma.

BACKGROUND: Thus far, few studies have systematically analyzed the profiles of immune cells infiltrated in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC). Therefore, the purpose of our study was to comprehensively analyze the 22 tumor-infiltrating immune cells (TIICs) and the immune subtypes of HCC, as well as the factors associated with the prognosis of HCC patients. METHODS: In this study, we evaluated the abundance of 22 tumor-infiltrating immunocytes of 371 HCC patients from The Cancer Genome Atlas (TCGA) database by using the CIBERSORT algorithm, and defined immune subtypes of HCC according to unsupervised cluster analysis. The immune score of HCC patients was calculated by the prognostic regression model, while the survival analysis was evaluated by the Kaplan-Meier method. In addition, the consistency index of TIICs and principal component analysis (PCA) of immunomodulator genes were estimated. RESULTS: The results of this study showed that three distinct immune subtypes of HCC were stratified, and the C1 subtype and C3 subtype were correlated with a good prognosis. The cellular composition of three immune subtypes was different. Moreover, immunomodulator gene and programmed cell death protein 1/programmed death-ligand 1 (PD-1/PD-L1) expression in the C1 subtype was significantly higher (P<0.05). CONCLUSIONS: This suggested that the low immune score of HCC patients is associated with better clinical outcomes. In addition, the interaction network of cluster of differentiation CD8+ T cells was mainly concentrated in the C1 subtype. Taken together, this study showed that tumor-infiltrating immune cells can perhaps be an important determinant of clinical outcomes of patients with HCC and may provide biomarkers to reflect the immunotherapy response. Notably, the C1 subtype of HCC may be used as an important predictive factor for immunotherapy response.

CD8+ T Cell-Based Molecular Classification With Heterogeneous Immunogenomic Landscapes and Clinical Significance of Clear Cell Renal Cell Carcinoma.

The tumor microenvironment (TME) exerts a high impact on tumor biology and immunotherapy. The heterogeneous phenotypes and the clinical significance of CD8+ T cells in TME have not been fully elucidated. Here, a comprehensive immunogenomic analysis based on multi-omics data was performed to investigate the clinical significance and tumor heterogeneity between CD8+ T cell-related molecular clusters. We identified two distinct molecular clusters of ccRCC (C1 and C2) in TCGA and validated in E-MTAB-1980 cohorts. The C1 cluster was characterized by unfavorable prognosis, increased expression levels of CD8+ T cell exhaustion markers, high immune infiltration levels as well as more immune escape mechanisms. The C2 cluster was featured by favorable prognosis, elevated expression levels of CD8+ T cell effector markers, low load of copy number loss and low frequency of 9p21.3 deletion. Moreover, the effect of molecular classifications on Nivolumab therapeutic efficacy in the CheckMate 025 cohort was examined, and the C2 cluster exhibited a better prognosis. Taken together, we determine two CD8+ T cell-related molecular clusters in ccRCC, and provide new insights for evaluating the functions of CD8+ T cells. Our molecular classification is a potential strategy for prognostic prediction and immunotherapeutic guidance for ccRCC patients.

Gene expression profiles define molecular subtypes of prostate cancer bone metastases with different outcomes and morphology traceable back to the primary tumor.

Bone metastasis is the lethal end-stage of prostate cancer (PC), but the biology of bone metastases is poorly understood. The overall aim of this study was therefore to explore molecular variability in PC bone metastases of potential importance for therapy. Specifically, genome-wide expression profiles of bone metastases from untreated patients (n = 12) and patients treated with androgen-deprivation therapy (ADT, n = 60) were analyzed in relation to patient outcome and to morphological characteristics in metastases and paired primary tumors. Principal component analysis and unsupervised classification were used to identify sample clusters based on mRNA profiles. Clusters were characterized by gene set enrichment analysis and related to histological and clinical parameters using univariate and multivariate statistics. Selected proteins were analyzed by immunohistochemistry in metastases and matched primary tumors (n = 52) and in transurethral resected prostate (TUR-P) tissue of a separate cohort (n = 59). Three molecular subtypes of bone metastases (MetA-C) characterized by differences in gene expression pattern, morphology, and clinical behavior were identified. MetA (71% of the cases) showed increased expression of androgen receptor-regulated genes, including prostate-specific antigen (PSA), and glandular structures indicating a luminal cell phenotype. MetB (17%) showed expression profiles related to cell cycle activity and DNA damage, and a pronounced cellular atypia. MetC (12%) exhibited enriched stroma-epithelial cell interactions. MetB patients had the lowest serum PSA levels and the poorest prognosis after ADT. Combined analysis of PSA and Ki67 immunoreactivity (proliferation) in bone metastases, paired primary tumors, and TUR-P samples was able to differentiate MetA-like (high PSA, low Ki67) from MetB-like (low PSA, high Ki67) tumors and demonstrate their different prognosis. In conclusion, bone metastases from PC patients are separated based on gene expression profiles into molecular subtypes with different morphology, biology, and clinical outcome. These findings deserve further exploration with the purpose of improving treatment of metastatic PC.