Gene Set Analysis Research Articles

Multi-omics Pathways Workflow (MOPAW): An Automated Multi-omics Workflow on the Cancer Genomics Cloud.

In the era of big data, gene-set pathway analyses derived from multi-omics are exceptionally powerful. When preparing and analyzing high-dimensional multi-omics data, the installation process and programing skills required to use existing tools can be challenging. This is especially the case for those who are not familiar with coding. In addition, implementation with high performance computing solutions is required to run these tools efficiently. We introduce an automatic multi-omics pathway workflow, a point and click graphical user interface to Multivariate Single Sample Gene Set Analysis (MOGSA), hosted on the Cancer Genomics Cloud by Seven Bridges Genomics. This workflow leverages the combination of different tools to perform data preparation for each given data types, dimensionality reduction, and MOGSA pathway analysis. The Omics data includes copy number alteration, transcriptomics data, proteomics and phosphoproteomics data. We have also provided an additional workflow to help with downloading data from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium and preprocessing these data to be used for this multi-omics pathway workflow. The main outputs of this workflow are the distinct pathways for subgroups of interest provided by users, which are displayed in heatmaps if identified. In addition to this, graphs and tables are provided to users for reviewing. Multi-omics Pathway Workflow requires no coding experience. Users can bring their own data or download and preprocess public datasets from The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium using our additional workflow based on the samples of interest. Distinct overactivated or deactivated pathways for groups of interest can be found. This useful information is important in effective therapeutic targeting.

Abstract A010: Deciphering molecular and cellular differences between black and white colorectal cancers through in-depth transcriptomic analyses

Abstract Background Compared to all other racial groups, black Americans are most likely to be diagnosed with colorectal cancer (CRC) and are most likely to die from the disease. These racial disparities have been largely attributed to social and economic conditions that impact the prevalence of co-morbidities and access to quality health care. However, the molecular and cellular differences in CRC tumors between black and white subjects remain largely uncharacterized. Methods We analyzed paired tumor and adjacent normal RNA sequencing reads collected from 15 black and 18 white subjects at Downstate Medical Center and Stony Brook University Hospital (DSSBU). 273 additional RNA sequencing and clinicopathologic data from The Cancer Genome Atlas (TCGA) were used as a validation cohort. In the TCGA dataset, 54 black subjects were propensity score matched to 108 white subjects by age, sex, tumor location and tumor stage. Differential expression analysis was performed with EdgeR and Wilcoxon rank-sum test, which was recently recommended for human population samples. Gene Ontology (GO) enrichment analysis of DEGs (absolute log 2-fold change > 1, adjust p value < 0.05) was performed using enrichR. Gene set enrichment analysis (GSEA) of hallmark gene sets was applied to the gene list ranked by fold changes. The TPM (transcript per million) data was used to estimate relative cell abundance via xCell. Antitumor immune response likelihood scores were generated via the Estimate Systems Immune Response (EaSIeR) R package. Results EdgeR analysis identified 15 and 80 DEGs in the DSSBU and TCGA datasets, respectively. Two DEGs, CXCL10 and RPPH1 have decreased expression while EEF1A1P13 has higher expression in black samples from both datasets. CXCL10 remained a DEG under the Wilcoxon test in the TCGA dataset and in propensity score matched samples. GO enrichment analysis identified “Chemokine-mediated signaling pathway” as a significantly different biological process between tumor groups with a FDR of 0.043 and 0.0004 in TCGA and DSSBU respectively. GSEA computed a negative enrichment score for IL6 JAK STAT3 signaling, Interferon alpha response, Interferon gamma response, Allograft rejection and Myc targets V1 in the black group from both datasets. xCell analysis of the DSSBU data revealed significant enrichment for endothelial cells, adipocytes and hematopoietic stem cells in black tumor samples, while white samples were significantly enriched for basophils, T helper 2 cells and common lymphoid progenitor cells. No significant differences in cell type enrichment were identified in the TCGA dataset. Predicted immunotherapy response scores were not significantly different between races in both cohorts. Conclusion Our results show differences in genes and pathways associated with inflammation and immune cell recruitment which may impact anti-tumor activity. Our future work will include experimental validation of interesting genes accounting for the differences. Citation Format: Dimitri F. Joseph, Ellen Li, Bin Chen, Joseph F. LaComb, Alexandra Guillaume. Deciphering molecular and cellular differences between black and white colorectal cancers through in-depth transcriptomic analyses [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr A010.

Inherited rare variants in homologous recombination and neurodevelopmental genes are associated with increased risk of neuroblastoma.

Neuroblastoma (NB) is the most common solid extracranial paediatric tumour. Genome-wide association studies have driven the discovery of common risk variants, but no large study has investigated the contribution of rare variants to NB susceptibility. Here, we conducted a whole-exome sequencing (WES) of 664 NB cases and 822 controls and used independent validation datasets to identify genes with rare risk variants and involved pathways. WES was performed at 50×depth and variants were jointly called in cases and controls. We developed two models to identify mutations with high clinical impact (P/LP model) and to discover less penetrant risk mutations affecting non-canonical cancer pathways (RPV model). We performed a gene-level collapsing test using Firth's logistic regression in 242 selected cancer predisposition genes (CPGs) and a gene-sets burden analysis of biologically-informed pathways. Twelve percent of patients carried P/LP variants in CPGs and showed a significant enrichment (P=2.3×10-4) compared to controls (6%). We identified P/LP variants in 45 CPGs enriched in homologous recombination (HR) pathway. The most P/LP enriched genes in NB were BRCA1, ALK and RAD51C. Additionally, we found higher RPV burden in gene-sets of neuron differentiation, neural tube development and synapse assembly, and in gene-sets associated with neurodevelopmental disorders (NDD). The high fraction of NB patients with P/LP variants indicates the need of genetic counselling. Furthermore, inherited rare variants predispose to NB development by affecting mechanisms related to HR and neurodevelopmental processes, and demonstrate that NDD genes are altered in NB at the germline level. Associazione Italiana per la Ricerca sul Cancro, Fondazione Italiana per la Lotta al Neuroblastoma, Associazione Oncologia Pediatrica e Neuroblastoma, Regione Campania, Associazione Giulio Adelfio onlus, and Italian Health Ministry.

Molecular Analyses of Chorionic-Type Intermediate Trophoblastic Lesions: Atypical Placental Site Nodules are Closer to Placental Site Nodules Than Epithelioid Trophoblastic Tumors

Gestational trophoblastic diseases derived from the chorionic-type intermediate trophoblast include benign placental site nodule (PSN) and malignant epithelioid trophoblastic tumor (ETT). Among PSNs, the World Health Organization classification introduced a new entity named atypical placental site nodule (APSN), corresponding to an ETT precursor, for which diagnostic criteria remain unclear, leading to a risk of overdiagnosis and difficulties in patient management. We retrospectively studied 8 PSNs, 7 APSNs, and 8 ETTs to better characterize this new entity and performed immunohistochemical analysis (p63, human placental lactogen, Cyclin E, and Ki67), transcriptional analysis using the NanoString method to quantify the expression of 760 genes involved in the main tumorigenesis pathways, and RNA sequencing to identify fusion transcripts. The immunohistochemical analysis did not reveal any significant difference in Cyclin E expression among the 3 groups (P = .476), whereas the Ki67 index was significantly (P < .001) higher in ETT samples than in APSN and PSN samples. None of the APSN samples harbored the LPCAT1::TERT fusion transcripts, in contrast to 1 of 6 ETT samples, as previously described in 2 of 3 ETT samples. The transcriptomic analysis allowed robust clustering of ETTs distinct from the APSN/PSN group but failed to differentiate APSNs from PSNs. Indeed, only 7 genes were differentially expressed between PSN and APSN samples; CCL19 upregulation and EPCAM downregulation were the most distinguishing features of APSNs. In contrast, 80 genes differentiated ETTs from APSNs, establishing a molecular signature for ETT. Gene set analysis identified significant enrichments in the DNA damage repair, immortality and stemness, and cell cycle signaling pathways when comparing ETTs and APSNs. These results suggested that APSN might not represent a distinct entity but rather a transitional stage between PSN and ETT. RNA sequencing and the transcriptional signature of ETT described herein could serve as triage for APSN from curettage or biopsy material, enabling the identification of cases that need further clinical investigations.

PhenoExam: gene set analyses through integration of different phenotype databases

BackgroundGene set enrichment analysis (detecting phenotypic terms that emerge as significant in a set of genes) plays an important role in bioinformatics focused on diseases of genetic basis. To facilitate phenotype-oriented gene set analysis, we developed PhenoExam, a freely available R package for tool developers and a web interface for users, which performs: (1) phenotype and disease enrichment analysis on a gene set; (2) measures statistically significant phenotype similarities between gene sets and (3) detects significant differential phenotypes or disease terms across different databases.ResultsPhenoExam generates sensitive and accurate phenotype enrichment analyses. It is also effective in segregating gene sets or Mendelian diseases with very similar phenotypes. We tested the tool with two similar diseases (Parkinson and dystonia), to show phenotype-level similarities but also potentially interesting differences. Moreover, we used PhenoExam to validate computationally predicted new genes potentially associated with epilepsy.ConclusionsWe developed PhenoExam, a freely available R package and Web application, which performs phenotype enrichment and disease enrichment analysis on gene set G, measures statistically significant phenotype similarities between pairs of gene sets G and G′ and detects statistically significant exclusive phenotypes or disease terms, across different databases. We proved with simulations and real cases that it is useful to distinguish between gene sets or diseases with very similar phenotypes.Github R package URL is https://github.com/alexcis95/PhenoExam.Shiny App URL is https://alejandrocisterna.shinyapps.io/phenoexamweb/.

GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels.

Cancer initiation and progression are likely caused by the dysregulation of biological pathways. Gene set analysis (GSA) could improve the signal-to-noise ratio and identify potential biological insights on the gene set level. However, platforms exploring cancer multi-omics data using GSA methods are lacking. In this study, we upgraded our GSCALite to GSCA (gene set cancer analysis, http://bioinfo.life.hust.edu.cn/GSCA) for cancer GSA at genomic, pharmacogenomic and immunogenomic levels. In this improved GSCA, we integrated expression, mutation, drug sensitivity and clinical data from four public data sources for 33 cancer types. We introduced useful features to GSCA, including associations between immune infiltration with gene expression and genomic variations, and associations between gene set expression/mutation and clinical outcomes. GSCA has four main functional modules for cancer GSA to explore, analyze and visualize expression, genomic variations, tumor immune infiltration, drug sensitivity and their associations with clinical outcomes. We used case studies of three gene sets: (i) seven cell cycle genes, (ii) tumor suppressor genes of PI3K pathway and (iii) oncogenes of PI3K pathway to prove the advantage of GSCA over single gene analysis. We found novel associations of gene set expression and mutation with clinical outcomes in different cancer types on gene set level, while on single gene analysis level, they are not significant associations. In conclusion, GSCA is a user-friendly web server and a useful resource for conducting hypothesis tests by using GSA methods at genomic, pharmacogenomic and immunogenomic levels.

Systematic analysis of brain and skull ischemic injury expression profiles reveals associations of the tumor immune microenvironment and cell death with ischemic stroke.

Previous studies have shown that stroke is a potential first sign of neoplasia, but the relationship between stroke and cancer remains unclear. As a complex brain disease, ischemic stroke involves cell death and immunity. Thus, it is necessary to investigate the association of the tumor immune microenvironment and cell death with ischemic stroke. We established a photothrombosis-induced ischemic injury model in mouse brain and skull. Subsequently, we sequenced the whole transcriptome of the injured mouse brain and skull and analyzed the expression profiles. To investigate the association of stroke with cell death and cancer, we systematically performed gene set enrichment analysis in pan-cell death (i.e., apoptosis, cuproptosis, ferroptosis, necroptosis, and pyroptosis) and the cancer hallmark pathways. The time-dependent immune cell abundance variations after ischemic injury were estimated. Furthermore, pan-cancer genomic and prognostic analyses of the ischemic injury-related gene sets were also performed. In this study, we found that there exist temporal and spatial differences in the gene expression patterns of both the brain and skull with ischemic injury. The skull ischemic injury-induced changes in the brain transcriptome were particularly great, but could recover in a short period, while the skull transcriptome variation resulting from brain ischemic injury was long-lasting. In addition, the expression of the genes related to ischemic injury was also associated with pan-cell death and the cancer hallmark pathways. The changes in the abundance of immune cells indicate that brain ischemic injury may disrupt the immune microenvironment for a longer time, while the skull can balance the stability of the immune microenvironment better. Moreover, the brain ischemic injury-related gene sets were highly correlated with a variety of tumors, particularly glioblastoma multiforme (GBM), kidney renal clear cell carcinoma (KIRC), brain lower grade glioma (LGG), and uveal melanoma (UVM), which carry a greater mortality risk after stroke. This systematic analysis not only helps in the understanding of the changes in the gene expression profiles of both the brain and skull with ischemic injury but also reveals the association of the tumor immune microenvironment and cell death with ischemic stroke.

Identification of CISD1 as a Prognostic Biomarker for Breast Cancer.

Although CISD1 (CDGSH iron sulfur domain 1) is upregulated in many cancer types, the potential role of CISD1 in breast cancer is still unclear. The purpose of this study was to investigate its clinical significance in breast cancer. We obtained 1109 breast cancer samples and 113 normal samples from The Cancer Genome Atlas (TCGA) and GTEx databases to demonstrate the relationship between CISD1 expression and pancancer characteristics. We analysed the relationship between CISD1 and breast cancer using the t-test and the chi-square test to evaluate the expression level of CISD1 and its clinical significance in breast cancer. The prognostic value of CISD1 in breast cancer was determined by Kaplan‒Meier and Cox regression analyses. The biological pathways were screened by gene set analysis and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single-sample gene set enrichment analysis (ssGSEA), of which the correlation between the level of immune infiltration and the expression of CISD1 in breast cancer was then analysed. Finally, we verified the conclusion by qPCR, immunohistochemistry, and CCK8. CISD1 is highly expressed in breast cancer patients. In addition, we verified a higher expression of CISD1 expressed in the BRCA (breast cancer) cell line, whereas CISD1 has a high diagnostic value, with an AUC of 0.718. Kaplan‒Meier survival and Cox regression analyses showed that high expression of CISD1 was independently associated with adverse clinical outcomes. In turn, GO and KEGG analyses showed that most genes were related to rRNA metabolic process, rRNA processing. Moreover, PCR and immunohistochemistry showed that CISD1 in breast cancer tissues was upregulated significantly, with CCK8 results showing that the proliferation of breast cancer cells decreased after CISD1 knockout. A high level of CISD1 is associated with poor prognosis and immune infiltration in breast cancer.

Cell‐Level Transcriptomic Network Analysis Provides Insights into Distinct Biological Mechanisms for Alzheimer’s Disease Enabling Targeted Drug Repositioning

AbstractBackgroundThe phenotypic and genetic heterogeneity of Alzheimer’s disease (AD) complicates efforts to identify the underlying biological mechanisms. Cell‐specific biologically connected gene networks may explain some of the observed pathophysiological and cognitive heterogeneity, leading to more effective patient stratification and drug targeting strategies.MethodWe conducted cell‐level network analysis using single nuclei RNA‐sequencing data from post‐mortem prefrontal cortex areas of 24 AD and 24 control brains in the Religious Orders Study and Memory and Aging Project (ROSMAP) for discovery and 12 AD and 9 control brains in the South‐West Dementia Brain Bank study for validation. We selected the AD associated modules from the preserved modules with significant differences between AD cases and control brains. The mbPRS was validated by previously defined (1) distinct brain atrophy clusters, and (2) cognitively defined AD subgroups including AD‐Executive, AD‐Language, AD‐Memory, AD‐Visuospatial functioning, AD‐No Domains, and AD‐Multiple‐domains. We conducted an association test with each mbPRS using the assigned membership variable as a binary outcome in a logistic regression model. We identified existing drugs targeting genes from the top‐ranked modules for repurposing in AD therapeutics.ResultAmong 60 cell‐level modules, 12 modules were preserved in the validation set and significantly associated with AD (P&lt;0.05). The mbPRSs for an astrocyte and an oligodendrocyte module M45 showed significant association with diffuse brain atrophy linked with proportionally lower executive functioning scores (PM2=7.40E‐5; PM45=2.12E‐4), and medial temporal atrophy likely representing early stages of AD (PM2=2.72E‐6; PM45=2.40E‐5). The mbPRS for M2 was significantly (PM2=2.72E‐2). Pathway analysis of the module gene sets revealed potential drug candidates for repositioning, including drugs targeting ApoE and IL1b pathways.ConclusionBy integrating genetics, cell‐level transcriptome networks, and endophenotypes of AD we confirmed cell‐level polygenic risk scores can stratify individuals into different target groups that are correlated with previously classified subgroups using cognitive functions and imaging biomarker data.

Hypothesis-driven genome-wide association studies provide novel insights into genetics of reading disabilities

Reading Disability (RD) is often characterized by difficulties in the phonology of the language. While the molecular mechanisms underlying it are largely undetermined, loci are being revealed by genome-wide association studies (GWAS). In a previous GWAS for word reading (Price, 2020), we observed that top single-nucleotide polymorphisms (SNPs) were located near to or in genes involved in neuronal migration/axon guidance (NM/AG) or loci implicated in autism spectrum disorder (ASD). A prominent theory of RD etiology posits that it involves disturbed neuronal migration, while potential links between RD-ASD have not been extensively investigated. To improve power to identify associated loci, we up-weighted variants involved in NM/AG or ASD, separately, and performed a new Hypothesis-Driven (HD)–GWAS. The approach was applied to a Toronto RD sample and a meta-analysis of the GenLang Consortium. For the Toronto sample (n = 624), no SNPs reached significance; however, by gene-set analysis, the joint contribution of ASD-related genes passed the threshold (p~1.45 × 10–2, threshold = 2.5 × 10–2). For the GenLang Cohort (n = 26,558), SNPs in DOCK7 and CDH4 showed significant association for the NM/AG hypothesis (sFDR q = 1.02 × 10–2). To make the GenLang dataset more similar to Toronto, we repeated the analysis restricting to samples selected for reading/language deficits (n = 4152). In this GenLang selected subset, we found significant association for a locus intergenic between BTG3-C21orf91 for both hypotheses (sFDR q < 9.00 × 10–4). This study contributes candidate loci to the genetics of word reading. Data also suggest that, although different variants may be involved, alleles implicated in ASD risk may be found in the same genes as those implicated in word reading. This finding is limited to the Toronto sample suggesting that ascertainment influences genetic associations.

Comparative Genomics Reveal the Utilization Ability of Variable Carbohydrates as Key Genetic Features of Listeria Pathogens in Their Pathogenic Lifestyles.

L. monocytogenes and L. ivanovii, the only two pathogens of Listeria, can survive in various environments, having different pathogenic characteristics. However, the genetic basis of their excellent adaptability and differences in pathogenicity has still not been completely elucidated. We performed a comparative genomic analysis based on 275 L. monocytogenes, 10 L. ivanovii, and 22 non-pathogenic Listeria strains. Core/pan-genome analysis revealed that 975 gene families were conserved in all the studied strains. Additionally, 204, 242, and 756 gene families existed uniquely in L. monocytogenes, L. ivanovii, and both, respectively. Functional annotation partially verified that these unique gene families were closely related to their adaptability and pathogenicity. Moreover, the protein-protein interaction (PPI) network analysis of these unique gene sets showed that plenty of carbohydrate transport systems and energy metabolism enzymes were clustered in the networks. Interestingly, ethanolamine-metabolic-process-related proteins were significantly enriched in the PPI network of the unique genes of the Listeria pathogens, which can be understood as a determining factor of their pathogenicity. The utilization capacity of multiple carbon sources of Listeria pathogens, especially ethanolamine, is the key genetic basis for their ability to adapt to various environments and pathogenic lifestyles.

Comprehensive Pan-Cancer Analysis of GINS2 for Human Tumour Prognosis and as an Immunological Biomarker.

In recent years, more and more reports have shown that GINS complex subunit 2 (GINS2) plays an important role in the occurrence and progression of tumours. However, there is a lack of comprehensive and systematic research on its prognostic and immune effects in pan-cancer. Therefore, this study is aimed at investigating the prognostic value and immune-related role of GINS2 in human tumours and providing a comprehensive understanding of its carcinogenic mechanism in pan-cancer. We investigated different databases, including TIMER, TCGA, GTEX, CPTAC, GEPIA, and SangerBox. The study was carried out on the expression and prognosis of GINS2 in human tumours, immune infiltration and microenvironment, immune checkpoints, neoantigens, tumour mutational burden, microsatellite instability, mismatch repair (MMR) genes, methylation, cancer-associated fibroblasts (CAFs), and enrichment analysis of gene set. GINS2 plays a potential carcinogenic role in various human tumours through mRNA and protein levels. It is highly expressed in most cancers, and its expression is significantly correlated with tumour prognosis. In addition, the expression of GINS2 is associated with immune microenvironment and immune infiltration, especially in brain lower-grade glioma, lung squamous cell carcinoma, TGCT, breast invasive carcinoma, and glioblastoma multiforme. At the same time, GINS2 is related to immune neoantigens and the expression profiles of immune checkpoint genes in pan-cancer. It also affects the expression of DNA MMR genes and methyltransferase in pan-cancer. Finally, the correlation between GINS2 and CAF abundance in most tumours was studied, and an enrichment analysis of GINS2 and its related proteins was also carried out. This is the first study on GINS2 as a prognostic and immune mechanism in pan-cancer. GINS2 may be a valuable prognostic immunological biomarker of pan-cancer. This paper provides a relatively comprehensive understanding on the correlation of GINS2 with pan-cancer.

Genome-Wide Gene-Set Analysis Approaches in Amyotrophic Lateral Sclerosis.

The rapid increase in the number of genetic variants identified to be associated with Amyotrophic Lateral Sclerosis (ALS) through genome-wide association studies (GWAS) has created an emerging need to understand the functional pathways that are implicated in the pathology of ALS. Gene-set analysis (GSA) is a powerful method that can provide insight into the associated biological pathways, determining the joint effect of multiple genetic markers. The main contribution of this review is the collection of ALS GSA studies that employ GWAS or individual-based genotype data, investigating their methodology and results related to ALS-associated molecular pathways. Furthermore, the limitations in standard single-gene analyses are summarized, highlighting the power of gene-set analysis, and a brief overview of the statistical properties of gene-set analysis and related concepts is provided. The main aims of this review are to investigate the reproducibility of the collected studies and identify their strengths and limitations, in order to enhance the experimental design and therefore the quality of the results of future studies, deepening our understanding of this devastating disease.

Gene set proximity analysis: expanding gene set enrichment analysis through learned geometric embeddings, with drug-repurposing applications in COVID-19.

Gene set analysis methods rely on knowledge-based representations of genetic interactions in the form of both gene set collections and protein-protein interaction (PPI) networks. However, explicit representations of genetic interactions often fail to capture complex interdependencies among genes, limiting the analytic power of such methods. We propose an extension of gene set enrichment analysis to a latent embedding space reflecting PPI network topology, called gene set proximity analysis (GSPA). Compared with existing methods, GSPA provides improved ability to identify disease-associated pathways in disease-matched gene expression datasets, while improving reproducibility of enrichment statistics for similar gene sets. GSPA is statistically straightforward, reducing to a version of traditional gene set enrichment analysis through a single user-defined parameter. We apply our method to identify novel drug associations with SARS-CoV-2 viral entry. Finally, we validate our drug association predictions through retrospective clinical analysis of claims data from 8 million patients, supporting a role for gabapentin as a risk factor and metformin as a protective factor for severe COVID-19. GSPA is available for download as a command-line Python package at https://github.com/henrycousins/gspa. Supplementary data are available at Bioinformatics online.

Meta-analysis of genome-wide association studies of hoarding symptoms in 27,651 individuals

Hoarding Disorder (HD) is a mental disorder characterized by persistent difficulties discarding or parting with possessions, often resulting in cluttered living spaces, distress, and impairment. Its etiology is largely unknown, but twin studies suggest that it is moderately heritable. In this study, we pooled phenotypic and genomic data from seven international cohorts (N = 27,651 individuals) and conducted a genome wide association study (GWAS) meta-analysis of parent- or self-reported hoarding symptoms (HS). We followed up the results with gene-based and gene-set analyses, as well as leave-one-out HS polygenic risk score (PRS) analyses. To examine a possible genetic association between hoarding symptoms and other phenotypes we conducted cross-trait PRS analyses. Though we did not report any genome-wide significant SNPs, we report heritability estimates for the twin-cohorts between 26–48%, and a SNP-heritability of 11% for an unrelated sub-cohort. Cross-trait PRS analyses showed that the genetic risk for schizophrenia and autism spectrum disorder were significantly associated with hoarding symptoms. We also found suggestive evidence for an association with educational attainment. There were no significant associations with other phenotypes previously linked to HD, such as obsessive-compulsive disorder, depression, anxiety, or attention-deficit hyperactivity disorder. To conclude, we found that HS are heritable, confirming and extending previous twin studies but we had limited power to detect any genome-wide significant loci. Much larger samples will be needed to further extend these findings and reach a “gene discovery zone”. To move the field forward, future research should not only include genetic analyses of quantitative hoarding traits in larger samples, but also in samples of individuals meeting strict diagnostic criteria for HD, and more ethnically diverse samples.

Metabolic Dysfunctions Driven By PRPF8 Explain Cases with Ring Sideroblasts without SF3B1 Mutations: Similarity/ Differences of Genotype/ Phenotype Association

SF3B1 mutations (SF3B1MT) mostly identify a group of MDS with ringed sideroblasts (RS), isolated erythroid dysplasia and favorable prognosis. Besides SF3B1, we and others have demonstrated that other genes might be involved in RS formation either due to convergent metabolic pathways or erythroid driver genes. MDS subtypes characterized by multilineage dysplasia lacking SF3B1MT and presenting with RS are paradigmatic examples of such a possibility. In addition, RS in AML is often unrecognized by hematopathologists due to the focus on the presence of blasts as a predominant feature and/ or the reduction of erythroid precursors inherent to blast infiltration. However, when we previously studied defects in PRPF81, a central but unappreciated target of somatic lesions in MDS, we found a correlation with the RS phenotype. To follow up on this initial finding, we compiled a well-annotated cohort of patients seen at the Cleveland Clinic to assess phenotype/ genotype association of PRPF8MT and RS. NGS results including PRPF8 mutational status of 3,545 cases with myeloid neoplasia were reviewed. Calls were retained after assessment of pathogenicity by the ACMG and VarSome classifiers. Excluding cases with concomitant SF3B1MT, we found PRPF8MT (median VAF 38% [3-79]) in 78 (2%) patients. In total, 83% of the mutations were missense, whereas 15% were truncating. Homology sequence between yeast and humans denoted that hotspots mapped to highly conserved amino acids. D1598 and C1594, localizing in the U6-snRNA interacting domain (1442-1601), were mutated in 14% and 5% of the patients, respectively. T418S mapped in a central domain of several SFs (PROCN) and was found in 9% of the cases, whereas the F1099fs alteration was detected in 5% of our cohort. Prussian blue stain showed the presence of RS in 25% of PRPF8MT. Since PRPF8 maps to the proximal regions of 17p, we also noticed that three informative cases carried somatic PRPF8MT and 17p11.2 deletion or loss of chr.17 (1/2), all showing the presence of RS. We thus screened a more complete cohort of chr.17 abnormalities (88% in the context of a complex karyotype) to investigate their contribution to RS formation. While 66% had RS, the high blast count may have concealed their presence in cases lacking this feature because of their predominant AML or high risk MDS. Notably, we also found 8 additional patients in whom SNP-A showed cryptic losses such as del17p11.2 and 17p13.1 (n=7) or UPD (n=2) containing elevated RS. In this cohort, TP53 was the most commonly mutated gene (49%) followed by ASXL1 and DNMT3A (10% each), BCOR/L1 and TET2 (7.5%). In regard to other SFMT, no patient had SRSF2MT, three had U2AF1S34/Q157, two carried somatic DDX41c.139-5C>T/A225D (with -5/del5q) and one patient had a LUC7L2G48* without -7/del7p. While the coincidental presence of marrow RS in TP53MT lacking SF3B1MT has been previously demonstrated2, no study has investigated whether RS might be a result of shared metabolic feedback mechanisms common to SFMT. We thus analyzed RNA splicing and gene expression (GE)3 of patients with single PRPF8MT (n=34) and SF3B1MT (n=281) finding overlapping splicing features in the a-hemoglobin synthesis HBA2 gene. Upregulation of HBA2 has been described in SF3B1MT patients4,5 to correlate with impaired erythrocyte maturation and its accumulation in early erythrocytes. Interestingly enough, given our data on chr.17 abnormalities, we also found that cases with single TP53MT (n=52) displayed high covariance features for HBA2, a possible culprit in cases carrying RS but unmutated for SF3B1 and PRPF8. Indeed by applying gene-set overrepresentation analysis to PRPF8MT and TP53MT GE profiles, we clustered the top 5 categories (FDR< .001) in erythrocytes differentiation (KLF1, EPB42), homeostasis and development (ALAS2, GATA1, RHAG, SLC4A1) suggesting that these genes might inversely correlate with HBA2 expression and be responsible for the formation of RS. Our study demonstrates that PRPF8MT is another molecular lesion phenocopying SF3B1MT via a compensatory metabolic mechanism responsible for the failure of terminal erythroid differentiation. In analogy, given the proximal genomic location of PRPF8 and TP53 we do not exclude that this commonality might be involved in TP53MT with RS as well. These likely phenocopying mechanisms may enable therapeutic opportunities (luspatercept) in cases lacking SF3B1MT presenting with RS and transfusion-dependent anemia.

Integrated analysis identifies the IL6/JAK/STAT signaling pathway and the estrogen response pathway associated with the pathogenesis of intracranial aneurysms.

We intended to identify the potential key biomarker and pathways that correlated with infiltrating immune cells during the pathogenesis of intracranial aneurysms (IA), to develop a diagnostic model, and to predict therapeutic drugs. Three datasets containing intracranial aneurysm tissue samples and normal artery control samples from Gene Expression Omnibus (GEO) were included. Gene-set variation analysis(GSVA) and gene set enrichment analysis (GSEA) were conducted to find the significant differentially expressed pathways in IA formation. The least absolute shrinkage and selection operator (LASSO) regression and the multivariate logistic regression analysis were performed to identify the characteristic genes in the IL6/JAK/STAT signaling pathway (ISP) and the estrogen response pathway (ERP). A diagnostic model was constructed. xCell was used to identify immune cell types in IA pathogenesis. We used the weighted gene co-expression network analysis (WGCNA) algorithm to explore the correlations between the key modules and the four traits. Potential therapeutic drugs were investigated in Enrichr and Drugbank database. The ISP is significant positively correlated with IA onset. The biological function of the ISP is positively correlated with that of the ERP, and is significantly associated with immune cells activities. CSF2RB, FAS, IL6, PTPN1, STAT2, TGFB1 of the ISP gene set and ALDH3A2, COX6C, IGSF1, KRT18, MICB, NPY1R of the ERP gene set were proved to be the characteristic genes. The STAT2 gene can be the potential biomarker of IA onset. The immune score of IA samples was significantly higher than the controls. The STAT2 gene expression is associated with infiltration of immune cells. The WGCNA results were consistent with our finds. Acetaminophen can be a potential therapeutic drug for IA targeting STAT2. We identified that the ISP was one of the most significant positively correlated pathways in IA onset, and it was activated in this process concordant with the ERP and immune responses. Except for beneficial effects, complex and multiple roles of estrogen may be involved in IA formation. STAT2 could be a potential biomarker and a promising therapeutic target of IA pathogenesis.

TMET-29. THE GLUTAMINE TRANSPORTER SLC1A5 IS ASSOCIATED WITH REWIRING OF AMINO ACID, LIPID, AND IMMUNE PATHWAYS AND TUMOR PROGNOSIS IN PEDIATRIC BRAIN CANCERS

Abstract Glutamine transporters play an important role in supporting increased tumor nutritional demands, often through overexpression of the solute carrier (SLC) family of membrane transporters. We aimed to understand the relationship of SLC transporter expression with pediatric brain tumor subtypes, lipid metabolism and their potential prognostic significance using data from the Pediatric Brain Tumor Atlas (PBTA). Using the expression of amino acid transporter genes, we found that elevated expression of glutamine transporters (SLC1A5, SLC7A5, SLC7A11, SLC38A5, SLC38A3) predicted shorter progression-free survival (PFS) in low-grade gliomas (LGGs) and poorer overall survival in pediatric ependymomas, high-grade gliomas (HGGs), and medulloblastomas. We focused specifically on SLC1A5 given the availability of imaging probes (18 F-Fluoroglutamine and 18F-Fluciclovine) for the corresponding amino acid transporter (ASCT2). Kaplan-Meier analysis found that higher expression of SLC1A5 was associated with shorter OS in ependymoma and medulloblastoma (p = 0.032 and p = 9.8e-4) and shorter PFS in LGG (p = 0.022). We found a universally higher expression of ATP citrate lyase (ACLY) and Acetyl-CoA carboxylase 1 (ACC) relative to normal brain tissue across all histologies. BRAF driven low grade gliomas and SHH-driven medulloblastomas showed differential expression of ACLY compared to other histologies. In addition, Acyl-CoA synthetase short chain family member 1 and 2 (ACSS1 and 2) were universally downregulated in pediatric gliomas, indicating that pediatric CNS tumors seem to rely on ACLY over ACSS for lipid synthesis. Gene set analysis showed higher expression and network rewiring of amino acid, lipid, and immune pathways in SLC1A5-high expressing clusters. SLC1A5 correlated negatively with pathways associated with lipid metabolic breakdown/degradation and correlated positively with pathways associated with lipid biosynthesis. In conclusion, our work demonstrates that glutamine transporters, particularly SLC1A5, represent compelling targets in pediatric brain tumors and can be exploited with theranostic approaches and amino-acid PET imaging.

Abstract 15779: Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes and Neurons as Models for Toxoplasma Gondii Infection

Introduction: Toxoplasma gondii infects all nucleated cells in warm-blooded vertebrates. Infection may be asymptomatic in healthy people but may result in the lifelong chronic persistence of cysts containing bradyzoites in specific tissues such as muscle and neurons. Hypothesis: Stem cell-derived neurons and cardiomyocytes will recapitulate the tissue-specific parasite-host interaction underlying chronic persistence in Toxoplasma infection. Methods: Human induced pluripotent stem cell-derived cardiomyocytes (iCMs), cardiac fibroblasts (iCFs) and neurons (iNEs) were used as biologically relevant models to study the interactions of T. gondii with human hosts in vitro . iCM, iCF and iNE were infected with T. gondii TypeI/III EGS strain (dual fluorescent stage-specific reporter strain: mCherry driven by SAG1 promoter and sfGFP driven by the LDH2 promoter). Results: We observed higher rate of bradyzoite formation in iCM and iNE, compared to iCF from the same donor at 72 hours post-infection by fluorescence microscopy of live cells and flow cytometry. Furthermore, we have analyzed the changes in the gene expression of host cells during Toxoplasma infection using high-throughput RNA-seq. Compared to uninfected iCMs, iCMs infected for 24 hours showed 1090 upregulated and 753 downregulated differentially expressed genes (DEGs); iCMs infected for 72 hours showed 1956 upregulated and 1253 downregulated DEGs. Compared to uninfected iCFs, iCFs infected for 24 hours showed 1654 upregulated and 1971 downregulated DEGs; iCFs infected for 72 hours showed 2742 upregulated and 1916 downregulated DEGs. In comparison to uninfected iNEs, iNEs infected for 24 hours showed 190 upregulated and 321 downregulated DEGs; iNEs infected for 72 hours showed 244 upregulated and 91 downregulated DEGs. Enrichment analysis of gene sets regulated by infection will be presented. Conclusions: Our in vitro generated human iCMs, iCF and iNEs have provided us a valuable tool for better understanding the impact on the host cells during Toxoplasma infection. A detailed analysis of validation and intervention of the top dysregulated genes in all different cell types will inform the design of potential therapeutic targets.

Gene expression-based dissection of inter-histotypes, intra-histotype and intra-tumor heterogeneity in pediatric tumors

Intra-tumor heterogeneity (ITH) fosters tumor evolution, resistance to therapy, and relapse. Recently, many evidence have been accumulated on the occurrence of genetic ITH in pediatric cancers. With this study we aimed to address the downstream effects that genetic and epigenetic ITH, and tumor-microenvironment interactions may produce within a tumor mass. To this aim, we investigated by high-throughput gene expression multiple samples of 5 hepatoblastomas, 5 neuroblastomas, 5 rhabdomyosarcomas, and 5 Wilms tumors. Principal component analysis, single sample hallmark gene sets analysis, and weighted gene co-expression network analysis were performed on gene expression data. We observed that the different tumors clustered by histotype, and then by case, and in addition, a variable degree of ITH was visible in all the investigated cases. The ITH highlighted in this study can represent a challenge in tumor treatment since we demonstrated that different druggable hallmarks and targets may be heterogeneously present within the same tumor mass, and this can potentially lead to therapeutic failure. Despite this heterogeneity, we could highlight some commonalities among the different histotypes investigated, supporting the feasibility to move in the clinic from a histotype-driven to a target-driven, sometimes agnostic, approach at least in some cases.