Identification of the molecular subgroups in asthma by gene expression profiles: airway inflammation implications

Background Asthma is a heterogeneous disease and different phenotypes based on clinical parameters have been identified. However, the molecular subgroups of asthma defined by gene expression profiles of induced sputum have been rarely reported. Methods We re-analyzed the asthma transcriptional profiles of the dataset of GSE45111. A deep bioinformatics analysis was performed. We classified 47 asthma cases into different subgroups using unsupervised consensus clustering analysis. Clinical features of the subgroups were characterized, and their biological function and immune status were analyzed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single sample Gene Set Enrichment Analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) and protein–protein interaction (PPI) network were performed to identify key gene modules and hub genes. Results Unsupervised consensus clustering of gene expression profiles in asthma identified two distinct subgroups (Cluster I/II), which were significantly associated with eosinophilic asthma (EA) and paucigranulocytic asthma (PGA). The differentially expressed genes (DEGs) between the two subgroups were primarily enriched in immune response regulation and signal transduction. The ssGSEA suggested the different immune infiltration and function scores between the two clusters. The WGCNA and PPI analysis identified three hub genes: THBS1, CCL22 and CCR7. ROC analysis further suggested that the three hub genes had a good ability to differentiate the Cluster I from the Cluster II. Conclusions Based on the gene expression profiles of the induced sputum, we identified two asthma subgroups, which revealed different clinical characteristics, gene expression patterns, biological functions and immune status. The transcriptional classification confirms the molecular heterogeneity of asthma and provides a framework for more in-depth research on the mechanisms of asthma.

Canine Oral Melanoma Genomic and Transcriptomic Study Defines Two Molecular Subgroups with Different Therapeutical Targets

Mucosal melanoma (MM) is a rare, aggressive clinical cancer. Despite recent advances in genetics and treatment, the prognosis of MM remains poor. Canine MM offers a relevant spontaneous and immunocompetent model to decipher the genetic bases and explore treatments for MM. We performed an integrative genomic and transcriptomic analysis of 32 canine MM samples, which identified two molecular subgroups with a different microenvironment and structural variant (SV) content. The overexpression of genes related to the microenvironment and T-cell response was associated with tumors harboring a lower content of SVs, whereas the overexpression of pigmentation-related pathways and oncogenes, such as TERT, was associated with a high SV burden. Using whole-genome sequencing, we showed that focal amplifications characterized complex chromosomal rearrangements targeting oncogenes, such as MDM2 or CDK4, and a recurrently amplified region on canine chromosome 30. We also demonstrated that the genes TRPM7, GABPB1, and SPPL2A, located in this CFA30 region, play a role in cell proliferation, and thus, may be considered as new candidate oncogenes for human MM. Our findings suggest the existence of two MM molecular subgroups that may benefit from dedicated therapies, such as immune checkpoint inhibitors or targeted therapies, for both human and veterinary medicine.

The RNA-Binding Protein Musashi1 Regulates a Network of Cell Cycle Genes in Group 4 Medulloblastoma

Medulloblastoma is the most common malignant brain tumor in children. Treatment with surgery, irradiation, and chemotherapy has improved survival in recent years, but patients are frequently left with devastating neurocognitive and other sequelae. Patients in molecular subgroups 3 and 4 still experience a high mortality rate. To identify new pathways contributing to medulloblastoma development and create new routes for therapy, we have been studying oncogenic RNA-binding proteins. We defined Musashi1 (Msi1) as one of the main drivers of medulloblastoma development. The high expression of Msi1 is prevalent in Group 4 and correlates with poor prognosis while its knockdown disrupted cancer-relevant phenotypes. Genomic analyses (RNA-seq and RIP-seq) indicated that cell cycle and division are the main biological categories regulated by Msi1 in Group 4 medulloblastoma. The most prominent Msi1 targets include CDK2, CDK6, CCND1, CDKN2A, and CCNA1. The inhibition of Msi1 with luteolin affected the growth of CHLA-01 and CHLA-01R Group 4 medulloblastoma cells and a synergistic effect was observed when luteolin and the mitosis inhibitor, vincristine, were combined. These findings indicate that a combined therapeutic strategy (Msi1 + cell cycle/division inhibitors) could work as an alternative to treat Group 4 medulloblastoma.

Magnetic Resonance Imaging Characteristics of Molecular Subgroups in Pediatric H3 K27M Mutant Diffuse Midline Glioma

Purpose Recent research identified histone H3 K27M mutations to be associated with a dismal prognosis in pediatric diffuse midline glioma (pDMG); however, data on detailed MRI characteristics with respect to H3 K27 mutation status and molecular subgroups (H3.1 and H3.3 K27M mutations) are limited. Methods Standardized magnetic resonance imaging (MRI) parameters and epidemiologic data of 68 pDMG patients (age <18 years) were retrospectively reviewed and compared in a) H3 K27M mutant versus H3 K27 wildtype (WT) tumors and b) H3.1 versus H3.3 K27M mutant tumors. Results Intracranial gliomas (n = 58) showed heterogeneous phenotypes with isointense to hyperintense signal in T2-weighted images and frequent contrast enhancement. Hemorrhage and necrosis may be present. Comparing H3 K27M mutant to WT tumors, there were significant differences in the following parameters: i) tumor localization (p = 0.001), ii) T2 signal intensity (p = 0.021), and iii) T1 signal homogeneity (p = 0.02). No significant imaging differences were found in any parameter between H3.1 and H3.3 K27M mutant tumors; however, H3.1 mutant tumors occurred at a younger age (p = 0.004). Considering spinal gliomas (n = 10) there were no significant imaging differences between the analyzed molecular groups. Conclusion With this study, we are the first to provide detailed MR imaging data on H3 K27M mutant pDMG with respect to molecular subgroup status in a large patient cohort. Our findings may support diagnosis and future targeted therapeutic trials of pDMG within the framework of the radiogenomics concept.

An Overview of Intracranial Ependymomas in Adults

Ependymomas are rare primary central nervous system tumors. They can form anywhere along the neuraxis, but in adults, these tumors predominantly occur in the spine and less frequently intracranially. Ependymal tumors represent a heterogenous group of gliomas, and the WHO 2016 classification is based essentially on a grading system, with ependymomas classified as grade I, II (classic), or III (anaplastic). In adults, surgery is the primary initial treatment, while radiotherapy is employed as an adjuvant treatment in some cases of grade II and in all cases of anaplastic ependymoma; chemotherapy is reserved for recurrent cases. In recent years, important and interesting advances in the molecular characterization of ependymomas have been made, allowing for the identification of nine molecular subgroups of ependymal tumors and moving toward subgroup-specific patients with improved risk stratification for treatment-decisions and future prospective trials. New targeted agents or immunotherapies for ependymoma patients are being explored for recurrent disease. This review summarizes recent molecular advances in the diagnosis and treatment of intracranial ependymomas including surgery, radiation therapy and systemic therapies.

Integrative analysis of lung molecular signatures reveals key drivers of idiopathic pulmonary fibrosis

Background Idiopathic pulmonary fibrosis (IPF) is a devastating disease with a high clinical burden. The molecular signatures of IPF were analyzed to distinguish molecular subgroups and identify key driver genes and therapeutic targets. Methods Thirteen datasets of lung tissue transcriptomics including 585 IPF patients and 362 normal controls were obtained from the databases and subjected to filtration of differentially expressed genes (DEGs). A functional enrichment analysis, agglomerative hierarchical clustering, network-based key driver analysis, and diffusion scoring were performed, and the association of enriched pathways and clinical parameters was evaluated. Results A total of 2,967 upregulated DEGs was filtered during the comparison of gene expression profiles of lung tissues between IPF patients and healthy controls. The core molecular network of IPF featured p53 signaling pathway and cellular senescence. IPF patients were classified into two molecular subgroups (C1, C2) via unsupervised clustering. C1 was more enriched in the p53 signaling pathway and ciliated cells and presented a worse prognostic score, while C2 was more enriched for cellular senescence, profibrosing pathways, and alveolar epithelial cells. The p53 signaling pathway was closely correlated with a decline in forced vital capacity and carbon monoxide diffusion capacity and with the activation of cellular senescence. CDK1/2, CKDNA1A, CSNK1A1, HDAC1/2, FN1, VCAM1, and ITGA4 were the key regulators as evidence by high diffusion scores in the disease module. Currently available and investigational drugs showed differential diffusion scores in terms of their target molecules. Conclusions An integrative molecular analysis of IPF lungs identified two molecular subgroups with distinct pathobiological characteristics and clinical prognostic scores. Inhibition against CDKs or HDACs showed great promise for controlling lung fibrosis. This approach provided molecular insights to support the prediction of clinical outcomes and the selection of therapeutic targets in IPF patients.

TAMI-74. SPATIOTEMPORAL MULTI-OMIC LANDSCAPE OF HUMAN MEDULLOBLASTOMA AT SINGLE CELL RESOLUTION

Medulloblastoma is the most common malignant childhood tumor type with distinct molecular subgroups. While advances in the comprehensive treatment have been made, the mortality in the high-risk group is still very high, driven by an incomplete understanding of cellular diversity. Here we use single-nucleus RNA expression, chromatin accessibility and spatial transcriptomic profiling to generate an integrative multi-omic map in 40 human medulloblastomas spanning all molecular subgroups and human postnatal cerebella, which is supplemented by the bulk whole genome and RNA sequences across 300 cases. This approach provides spatially resolved insights into the medulloblastoma and cerebellum transcriptome and epigenome with identification of distinct cell-type in the tumor microenvironment. Medulloblastoma exhibited three tumor subpopulations including the quiescent, the differentiated, and a stem-like (proliferating) population unique to cancer, which localized to an immunosuppressive-vascular niche. We identified and validated mechanisms of stem-like to differentiated process among the malignant cells that drive tumor progression. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing stem-like malignant cells as a hub for intercellular communication. Multiple features of potential immunosuppression and angiogenesis were observed, including Treg cells and endothelial cells co-localization in compartmentalized tumor stroma. Collectively, our study provides an integrative molecular landscape of human medulloblastoma and represents a reference to advance mechanistic and therapeutic studies of pediatric neuro-oncological disease.

Identification and Analysis of Potential Autophagy-Related Biomarkers in Endometriosis by WGCNA

Background: Endometriosis is a serious gynecological disorder characterized by debilitating pain, infertility and the establishment of innervated endometriosis lesions outside the uterus. Early detection and accurate diagnosis are pivotal in endometriosis. The work screened autophagy-related genes (ATGs) as potential biomarkers to reveal new molecular subgroups for the early diagnosis of endometriosis.Materials and Methods: The gene lists of ATGs from five databases were integrated. Then, weighted gene co-expression network analysis (WGCNA) was used to map the genes to the gene profile of endometriosis samples in GSE51981 to obtain functional modules. GO and KEGG analyses were performed on the ATGs from the key modules. Differentially expressed ATGs were identified by the limma R package and further validated in the external datasets of GSE7305 and GSE135485. The DESeq2 R package was utilized to establish multifactorial network. Subsequently, one-way analysis of variance (ANOVA) was performed to identify new molecular subgroups. Real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting were used to confirm the differential expression of hub ATGs, and the receiver operating characteristic (ROC) curve analysis and Spearman correlation analysis were applied to assess the diagnostic value of hub ATGs in 40 clinical samples and human primary endometrial stromal cells (ESCs).Results: We screened 4 key modules and 12 hub ATGs and found the key genes to be strongly correlated with endometriosis. The pathways of ATGs were mainly enriched in autophagy, apoptosis, ubiquitin-protein ligase binding, and MAPK signaling pathway. The expression levels of EZH2 (Enhancer of Zeste homolog 2) and RND3 (also known as RhoE) had statistically significant changes with higher values in the endometriosis group compared with the controls, both in the tissue samples and primary ESCs. Besides, they also showed higher specificity and sensitivity by the receiver operating characteristic analysis and Spearman correlation analysis for the diagnosis of endometriosis. The TF-mRNA-miRNA-lncRNA multifactorial network was successfully constructed. Four new molecular subgroups were identified, and we preliminarily showed the ability of IQCG to independently differentiate subgroups.Conclusion: EZH2 and RND3 could be candidate biomarkers for endometriosis, which would contribute to the early diagnosis and intervention in endometriosis.

Statistical partitioning of Ki-67 distribution can refine Molecular Classifica-tion of invasive Breast Carcinoma (Preprint)

BACKGROUND Breast carcinoma is one of the most common histological types of Breast Cancer, exploring a new approach that allows to do a quantitative description in order to characterize its heterogeneity and refine its classification is one of the main interests for pathologists. OBJECTIVE The purpose of our study is to explore further statistically significant subdivisions beyond breast cancer molecular classification that is routinely established in pathology departments. METHODS We conducted a 5-year retrospective study on 1266 invasive breast carcinomas of moroccan pa-tients, collected at the Pathology Department of Ibn-Rochd University Hospital in Casablanca, and followed at King MohammedVI National Centre for the Treatment of Cancers. We elaborated an Estimation-Maximization clustering, based on the main Breast cancer prognosis biomarkers: Ki-67, HER2, oestrogen and progesterone receptors, evaluated by Immunohistochemistry. RESULTS Each molecular subgroup could be partitioned into two further subdivisions: Cluster1, with average Ki-67 of 16.26%(±11.9) across all molecular subgroups and higher frequency within luminal sub-groups, and Cluster2, with average Ki-67 of 68.8%(±18) across all molecular subgroups; and higher frequency in HER2 as well as in triple negative subgroups. Overall Survival of the two clusters was significantly different, with 5-year rates of 52 and 37 months for Cluster1 and Cluster2, respectively (p=0.000001). Moreover, patient survival within the same molecular subgroup varied remarkably depending on cluster membership. Three independent datasets (Algerian, TCGA-BRCA and METABRIC) were also analysed to assess the reproducibility of this new “2-clusters partition” through several clustering methods and validation measures. Two different al-gorithms to evaluate the prognostic importance, VSURF and MinimalDepth, confirmed that this new subdivision is able to predict patient survival better than several histoprognostic features. CONCLUSIONS Our results highlight a new refinement of the breast cancer molecular classification and provide a simple and improved way to classify tumors that could be applied in low to medium income countries. This is the first study of its kind addressed in an African context.