scholarly journals Older Patients with NPM1 Mutated AML Have Distinctive Genomic Mutation Landscape Associated with Enrichment in Immunosuppressive Gene Signature

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1402-1402 ◽  
Author(s):  
Haitham Abdelhakim ◽  
Ahmad Elkhanany ◽  
Mohammad Telfah ◽  
Tara L. Lin ◽  
Andrew K Godwin
Keyword(s):  
Gene Expression ◽  
Exome Sequencing ◽  
Patient Group ◽  
Gene Mutations ◽  
Gene Set Enrichment Analysis ◽  
Sequencing Data ◽  
Older Patient ◽  
Gene Set Enrichment ◽  
Younger Patients ◽  
Gene Set

Background: Mutations in the nucleophosmin (NPM1) gene are associated with better responses to chemotherapy and improved survival among acute myeloid leukemia (AML) patients. However, older AML patients (≥ 60 years old) with NPM1 mutation have worse survival outcomes than younger patients (<60 years old). This may be attributed to more adverse biologic features (frequent complex karyotype, FLT3 mutations) in addition to lower odds to receive intensive curative chemotherapy due to co-morbidities. We sought to compare the outcomes of older NPM1 mutated AML patients with younger NPM1 mutated patients after exclusions of patients with adverse-risk per ELN 2017 criteria. We also compared their genomic mutation profile and gene expression utilizing the Beat AML dataset. Methods: We queried the Beat AML dataset, supported in part by the Leukemia & Lymphoma Society and the OHSU Knight Cancer Institute, for pts with NPM1 gene mutations who did not have adverse-risk ELN 2017 (poor cytogenetic profile or mutations in FLT3, TP53 or ASXL1). Descriptive statistics described baseline characteristics and responses. Kaplan-Meier with log-rank test was used for survival analysis. DNA mutation data were obtained from the exome sequencing and analyzed using the beat AML data viewer (Vizome). RNA exome sequencing data were downloaded. Differential expression of raw count RNA-Seq and gene set enrichment was done using R via limma and ClusterProfiler packages. Results: Among 562 unique patients in the Beat AML umbrella trial, there were 81 patients with newly diagnosed NPM1 mutated AML after exclusion of patients with ELN 2017 adverse-risk category. Among these patients there was 49 older patients (≥ 60 years old) and 32 younger patients (<60 years old). 39 (77.6%) in the older group received intensive induction chemotherapy and 30 patients (93.7%) in the younger group. 29 (59.1%) patients achieved complete morphologic responses in the older patient group compared to 28 (84.4%) in the younger patient group (OR 0.2, P=0.009). Median overall survival in the older patient group was 20.1 months compared to 25.4 months in the younger group (HR 0.52, P=0.08). Exome sequencing data were available for 43 and 30 patients from the older and younger group respectively. There was a median of 6.5 (2-20) and 7 (2-19) mutations in the older and younger group respectively (P=0.78). After exclusion of the benign mutations and variant of unknown significance, the median number of mutations was 4 in both group (P=0.28). Both groups shared only 24 (3.9%) of the gene mutations while there were 334 unique gene mutations in the older group and 262 in the younger group. Most common gene mutations were DNMT3a, TET2, NRAS, WT1, and PTPN11 with frequencies are shown (Figure 1). RNA sequencing data was available for 26 patients from the older group and 18 patients from the younger group. We explored the gene expression profile of the top 1000 differentially expressed genes in both groups after adjustment. There was distinctive clustering of the gene expression profile between the two groups (Figure 2). Gene set enrichment analysis identified multiple immune-related pathways among the highly enriched gene sets in both groups but with different functions in the two groups. There was significant gene set enrichment in the TGFβ signaling in the older patient group which is associated with immune suppression and microenvironment modulation. While the younger group showed significant enrichment in the TNFa, IL17, PI3K-AKT signaling which are associated with inflammation. Conclusion: Older AML patients with NPM1 mutations, and no adverse risk features, had lower rate of complete responses and a trend towards a worse survival compared to younger patients. Whole exome sequencing did not show increased mutational burden. However, 96% of the mutated genes were different between the two groups as were the gene expression profiles. Gene set enrichment analysis showed contrasting enriched immune-related pathways between both groups. The immunosuppressive TGFβ signaling gene set were significantly enriched in the older group while the inflammatory TNFa, IL17, PI3K-AKT signaling gene sets were significantly enriched in the younger group. Older AML patient with NPM1 mutations have distinctive genomic landscape compared to the younger patient which may explain in part the worse clinical outcomes in the absence of other adverse risk features. Disclosures Lin: Jazz Pharmaceuticals: Honoraria; Pfizer: Membership on an entity's Board of Directors or advisory committees.

scholarly journals Integration of GWAS Summary Statistics and Gene Expression Reveals Target Cell Types Underlying Kidney Function Traits

2020 ◽  
Vol 31 (10) ◽  
pp. 2326-2340 ◽  
Author(s):  
Yong Li ◽  
Stefan Haug ◽  
Pascal Schlosser ◽  
Alexander Teumer ◽  
Adrienne Tin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Kidney Function ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association Studies ◽  
Summary Statistics ◽  
Rna Seq ◽  
Cell Type ◽  
Gene Set Enrichment ◽  
Gene Set

BackgroundGenetic variants identified in genome-wide association studies (GWAS) are often not specific enough to reveal complex underlying physiology. By integrating RNA-seq data and GWAS summary statistics, novel computational methods allow unbiased identification of trait-relevant tissues and cell types.MethodsThe CKDGen consortium provided GWAS summary data for eGFR, urinary albumin-creatinine ratio (UACR), BUN, and serum urate. Genotype-Tissue Expression Project (GTEx) RNA-seq data were used to construct the top 10% specifically expressed genes for each of 53 tissues followed by linkage disequilibrium (LD) score–based enrichment testing for each trait. Similar procedures were performed for five kidney single-cell RNA-seq datasets from humans and mice and for a microdissected tubule RNA-seq dataset from rat. Gene set enrichment analyses were also conducted for genes implicated in Mendelian kidney diseases.ResultsAcross 53 tissues, genes in kidney function–associated GWAS loci were enriched in kidney (P=9.1E-8 for eGFR; P=1.2E-5 for urate) and liver (P=6.8·10-5 for eGFR). In the kidney, proximal tubule was enriched in humans (P=8.5E-5 for eGFR; P=7.8E-6 for urate) and mice (P=0.0003 for eGFR; P=0.0002 for urate) and confirmed as the primary cell type in microdissected tubules and organoids. Gene set enrichment analysis supported this and showed enrichment of genes implicated in monogenic glomerular diseases in podocytes. A systematic approach generated a comprehensive list of GWAS genes prioritized by cell type–specific expression.ConclusionsIntegration of GWAS statistics of kidney function traits and gene expression data identified relevant tissues and cell types, as a basis for further mechanistic studies to understand GWAS loci.

scholarly journals Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13882 ◽  
Author(s):  
Binghuang Cai ◽  
Xia Jiang
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Gene Expression Data ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Expression Data ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Pathway Gene

Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results, in the form of pathway rankings, indicate that some pathways may behave abnormally in LUSC. For example, both the cell cycle and viral carcinogenesis pathways ranked very high in LUSC. Furthermore, some pathways that are known to be associated with cancer, such as the p53 and the PI3K-Akt signal transduction pathways, were found to rank high in LUSC. Other pathways, such as bladder cancer and thyroid cancer pathways, were also ranked high in LUSC.

scholarly journals P13.10 Glioblastoma within the subventricular zone associates with increased mesenchymal transition: an intratumoral gene expression analysis

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii64-iii64
Author(s):  
S Berendsen ◽  
D Dalemans ◽  
K Draaisma ◽  
P A Robe ◽  
T J Snijders
Keyword(s):  
Gene Expression ◽  
Subventricular Zone ◽  
Epithelial Mesenchymal Transition ◽  
Expression Patterns ◽  
Sequencing Data ◽  
Gene Set Enrichment ◽  
En Bloc ◽  
Tissue Samples ◽  
Mesenchymal Transition ◽  
Gene Set

Abstract BACKGROUND Involvement of the subventricular zone (SVZ) in GBM is associated with poor prognosis and suggested to associate with specific tumor-biological characteristics. The SVZ microenvironment can influence gene expression and migration in GBM cells in preclinical models. We aimed to investigate whether the SVZ microenvironment has any influence on intratumoral gene expression patterns in GBM patients. MATERIAL AND METHODS The publicly available Ivy GBM database contains clinical, radiological and whole exome sequencing data from multiple regions from en bloc resected GBMs. SVZ involvement of the various tissue samples was evaluated on MRI scans. In the tumors that contacted the SVZ, we performed gene expression analyses and gene set enrichment analyses to compare gene (set) expression in tumor regions within the SVZ to tumor regions outside the SVZ, within the same tumors. We also compared these samples to GBMs that made no contact with the SVZ. RESULTS Within GBMs that contacted the SVZ, tissue samples within the SVZ showed enrichment of gene sets involved in (epithelial-)mesenchymal transition, NF-κB and STAT3 signaling, angiogenesis and hypoxia, compared to the samples outside of the SVZ region from the same tumors (p<0.05, FDR<0.25). Comparison of GBM samples within the SVZ region to samples from tumors that did not contact the SVZ yielded similar results. In contrast, we observed no difference in gene set enrichment when comparing the samples outside of the SVZ from SVZ-contacting GBMs with samples from GBMs that did not contact the SVZ at all. CONCLUSION GBM samples in the SVZ region associate with increased (epithelial-)mesenchymal transition and angiogenesis/hypoxia signaling, possibly mediated by the SVZ microenvironment.

scholarly journals Tracking Difference in Gene Expression in a Time-Course Experiment Using Gene Set Enrichment Analysis

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e107629 ◽  
Author(s):  
Pui Shan Wong ◽  
Michihiro Tanaka ◽  
Yoshihiko Sunaga ◽  
Masayoshi Tanaka ◽  
Takeaki Taniguchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Time Course Experiment

scholarly journals Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis ◽  
Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

scholarly journals Genetic network and gene set enrichment analyses identify MND1 as potential diagnostic and therapeutic target gene for lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jinying Wei ◽  
Guangping Meng ◽  
Jing Wu ◽  
Qiang Zhang ◽  
Jie Zhang
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Therapeutic Target ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Independent Effect ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Ppi Networks

AbstractThis study aimed to characterize the key survival-specific genes for lung adenocarcinoma (LUAD) using machine-based learning approaches. Gene expression profiles were download from gene expression omnibus to analyze differentially expressed genes (DEGs) in LUAD tissues versus healthy lung tissue and to construct protein–protein interaction (PPI) networks. Using high-dimensional datasets of cancer specimens from clinical patients in the cancer genome atlas, gene set enrichment analysis was employed to assess the independent effect of meiotic nuclear divisions 1 (MND1) expression on survival status, and univariate and multivariate Cox regression analyses were applied to determine the associations of clinic-pathologic characteristics and MND1 expression with overall survival (OS). A set of 495 DEGs (145 upregulated and 350 downregulated) was detected, including 63 hub genes with ≥ 10 nodes in the PPI network. Among them, MND1 was participated in several important pathways by connecting with other genes via 17 nodes in lung cancer, and more frequently expressed in LUAD patients with advancing stage (OR = 1.68 for stage III vs. stage I). Univariate and multivariate Cox analyses demonstrated that the expression level of MND1 was significantly and negatively correlated with OS. Therefore, MND1 is a promising diagnostic and therapeutic target for LUAD.

scholarly journals XGSEA: CROSS-species Gene Set Enrichment Analysis via domain adaptation

2020 ◽  
Author(s):  
Menglan Cai ◽  
Canh Hao Nguyen ◽  
Hiroshi Mamitsuka ◽  
Limin Li
Keyword(s):  
Gene Expression ◽  
Domain Adaptation ◽  
Gene Knockout ◽  
Enrichment Analysis ◽  
Real Data ◽  
Gene Set Enrichment Analysis ◽  
Data Sets ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

AbstractGene set enrichment analysis (GSEA) has been widely used to identify gene sets with statistically significant difference between cases and controls against a large gene set. GSEA needs both phenotype labels and expression of genes. However, gene expression are assessed more often for model organisms than minor species. More importantly, gene expression could not be measured under specific conditions for human, due to high healthy risk of direct experiments, such as non-approved treatment or gene knockout, and then often substituted by mouse. Thus predicting enrichment significance (on a phenotype) of a given gene set of a species (target, say human), by using gene expression measured under the same phenotype of the other species (source, say mouse) is a vital and challenging problem, which we call CROSS-species Gene Set Enrichment Problem (XGSEP). For XGSEP, we propose XGSEA (Cross-species Gene Set Enrichment Analysis), with three steps of: 1) running GSEA for a source species to obtain enrichment scores and p-values of source gene sets; 2) representing the relation between source and target gene sets by domain adaptation; and 3) using regression to predict p-values of target gene sets, based on the representation in 2). We extensively validated XGSEA by using four real data sets under various settings, proving that XGSEA significantly outperformed three baseline methods. A case study of identifying important human pathways for T cell dysfunction and reprogramming from mouse ATAC-Seq data further confirmed the reliability of XGSEA. Source code is available through https://github.com/LiminLi-xjtu/XGSEAAuthor summaryGene set enrichment analysis (GSEA) is a powerful tool in the gene sets differential analysis given a ranked gene list. GSEA requires complete data, gene expression with phenotype labels. However, gene expression could not be measured under specific conditions for human, due to high risk of direct experiments, such as non-approved treatment or gene knockout, and then often substituted by mouse. Thus no availability of gene expression leads to more challenging problem, CROSS-species Gene Set Enrichment Problem (XGSEP), in which enrichment significance (on a phenotype) of a given gene set of a species (target, say human) is predicted by using gene expression measured under the same phenotype of the other species (source, say mouse). In this work, we propose XGSEA (Cross-species Gene Set Enrichment Analysis) for XGSEP, with three steps of: 1) GSEA; 2) domain adaptation; and 3) regression. The results of four real data sets and a case study indicate that XGSEA significantly outperformed three baseline methods and confirmed the reliability of XGSEA.

scholarly journals Immune gene expression networks in sepsis: A network biology approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0247669
Author(s):  
Kyung Soo Kim ◽  
Dong Wook Jekarl ◽  
Jaeeun Yoo ◽  
Seungok Lee ◽  
Myungshin Kim ◽  
...  
Keyword(s):  
Gene Expression ◽  
Adaptive Immunity ◽  
Enrichment Analysis ◽  
Clustering Coefficient ◽  
Gene Set Enrichment Analysis ◽  
Sepsis Group ◽  
Immune Gene ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Presentation Pathway

To study the dysregulated host immune response to infection in sepsis, gene expression profiles from the Gene Expression Omnibus (GEO) datasets GSE54514, GSE57065, GSE64456, GSE95233, GSE66099 and GSE72829 were selected. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) immune system pathways, 998 unique genes were selected, and genes were classified as follows based on gene annotation from KEGG, Gene Ontology, and Reactome: adaptive immunity, antigen presentation, cytokines and chemokines, complement, hematopoiesis, innate immunity, leukocyte migration, NK cell activity, platelet activity, and signaling. After correlation matrix formation, correlation coefficient of 0.8 was selected for network generation and network analysis. Total transcriptome was analyzed for differentially expressed genes (DEG), followed by gene set enrichment analysis. The network topological structure revealed that adaptive immunity tended to form a prominent and isolated cluster in sepsis. Common genes within the cluster from the 6 datasets included CD247, CD8A, ITK, LAT, and LCK. The clustering coefficient and modularity parameters were increased in 5/6 and 4/6 datasets in the sepsis group that seemed to be associated with functional aspect of the network. GSE95233 revealed that the nonsurvivor group showed a prominent and isolated adaptive immunity cluster, whereas the survivor group had isolated complement-coagulation and platelet-related clusters. T cell receptor signaling (TCR) pathway and antigen processing and presentation pathway were down-regulated in 5/6 and 4/6 datasets, respectively. Complement and coagulation, Fc gamma, epsilon related signaling pathways were up-regulated in 5/6 datasets. Altogether, network and gene set enrichment analysis showed that adaptive-immunity-related genes along with TCR pathway were down-regulated and isolated from immune the network that seemed to be associated with unfavorable prognosis. Prominence of platelet and complement-coagulation-related genes in the immune network was associated with survival in sepsis. Complement-coagulation pathway was up-regulated in the sepsis group that was associated with favorable prognosis. Network and gene set enrichment analysis supported elucidation of sepsis pathogenesis.

Sign in / Sign up

Export Citation Format

Share Document