T315I-Mutated BCR-ABL Induces a Distinct and Specific Molecular Signature With High Expression Of Zinc Finger (ZNF) Transcription Factors

Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 4899-4899
Author(s):  
Christophe Desterke ◽  
Djamel Aggoune ◽  
Marie Laure Bonnet ◽  
Nais Prade ◽  
Jean-Claude Chomel ◽  
...  

Abstract Chronic myeloid leukemia (CML) is the paradigm of malignancy treated by targeted therapies by the use of tyrosine kinase inhibitors (TKI), essentially Imatinib, Dasatinib and Nilotinib. Despite their major efficiency, especially as first line therapies, resistance to these drugs develop partly due to genetic instability inherent to CML. BCR-ABL-kinase mutations remain the first cause of resistance, which appears to be due to clonal selection of cells bearing a given mutation under TKI therapies. Amongst these mutations, the “gatekeeper” T315I mutant is a major concern as it confers resistance to all three TKI clinically used and patients with this mutation have a poor prognosis. The inaccessibility of the TKI to the ABL kinase pocket might not be the only “mechanistic” cause of resistance and it has been suggested that T315I-mutated BCR-ABL (Skaggs BJ et al, 2006) could induce a specific phosphoproteome signature. To evaluate this possibility, we decided to determine if a specific gene expression profiling can be associated with T315I-mutated BCR-ABL, as compared to native BCR-ABL. The human hematopoietic cell line UT7 was transfected with retroviral vectors encoding for native BCR-ABL (UT7.11) or BCR-ABL with the T315I mutation (UT7.T315I). The cell lines were characterized by their cell growth, Western blotting and sequencing. UT7.11 cells were sensitive to Imatinib, Dasatinib and Nilotinib as well as to Ponatinib whereas UT7-T315I cells were resistant to all three TKI except for Ponatinib. Affymetrix microarrays were performed in triplicate on each of three groups (UT7, UT7.11, UT7.T315I). The datas were normalized using the dchip software. Bioinformatics analyzes were performed with R software (packages FactoMineR, limma, PAMR) Mev in TM4 software, enrichment analysis with the GSEA software (Broad institute). The principal component analysis (PCA) showed that the overall RNA expression of UT7.T315I was different from that of UT7.11 (native BCR-ABL) and parental UT7. On factorial map, UT7.11 was found more distant from parental UT7 than UT7.T315I. The contrast analysis of the linear model by the algorithm limma between the 3 groups, showed a strong differential signature of UT7.11 as compared to parental UT7 and UT7.T315I (respectively 4792 and 4813 genes). Only 800 genes were found to be differentially expressed between UT7.T315I and parental UT7. In hierarchical clustering, the total signature obtained in limma confirmed a closed profile between parental UT7 and UT7.T315I. Among the results of the limma model, we identified a 286 specific genes signature for UT7.T315I (both different from parental UT7 and UT7.11 and also not regulated between UT7.11 and UT7). This specific list of UT7.T315I was validated with the T315I group sample segregation by different multivariate methods: PCA, hierarchical clustering and non-negative matrix factorization. Among this T315I-specific gene list limma, 34 ZNF family genes were found (11.88%). Predicting class algorithm based on shunkren centroid (PAMR) separated the three group samples with low classification error and a global list of 368 genes: only 75 genes predicted UT7.T315I group and from this list 13 were in the ZNF gene family (13.33%). By the method of gene set enrichment analysis (GSEA), we explored the top 100 ranked genes as upregulated in UT7.T315I by comparing the two other sample groups. This gene set showed a high representation of ZNF family genes (25%). The design of a gene set with ZNF family genes selected showed a positive enrichment of ZNF (NES = +1.35, p-value <0.001) in the UT7.T315I by comparing the two other groups. The majority of these genes is localized in 19q13.41 (ZNF cluster 282). They exhibit C2H2 and Kruppel-associated box (KRAB) domains in their sequence. Interestingly the overexpression of KRAB-ZNF transcription factors has been recently reported in patients with gastrointestinal stromal tumors (GIST) as conferring resistance to Imatinib Mesylate (Rink L., PLOS One 2013). In conclusion, our work revealed for the first time a specific signature of the T315I mutation which includes a strong representation of the ZNF family. The identification of this signature could be interest for future drug screening strategies in advanced phase CML patients progressing under Ponatinib. Current experiments are underway to validate these results by analyzing the expression of ZNF family of genes in primary CML cells with T315I mutation. Disclosures: Turhan: Bristol Myers Squibb, Novartis: Consultancy, Honoraria.

2020 ◽  
Author(s):  
H. Robert Frost

AbstractSingle cell RNA sequencing (scRNA-seq) is a powerful tool for analyzing complex tissues with recent advances enabling the transcriptomic profiling of thousands to tens-of-thousands of individual cells. Although scRNA-seq provides unprecedented insights into the biology of heterogeneous cell populations, analyzing such data on a gene-by-gene basis is challenging due to the large number of tested hypotheses, high level of technical noise and inflated zero counts. One promising approach for addressing these challenges is gene set testing, or pathway analysis. By combining the expression data for all genes in a pathway, gene set testing can mitigate the impacts of sparsity and noise and improve interpretation, replication and statistical power. Unfortunately, statistical and biological differences between single cell and bulk expression measurements make it challenging to use gene set testing methods originally developed for bulk tissue on scRNA-seq data and progress on single cell-specific methods has been limited. To address this challenge, we have developed a new gene set testing method, variance-adjusted Mahalanobis (VAM), that seamlessly integrates with the Seurat framework and is designed to accommodate the technical noise, sparsity and large sample sizes characteristic of scRNA-seq data. The VAM method computes cell-specific pathway scores to transform a cell-by-gene matrix into a cell-by-pathway matrix that can be used for both exploratory data visualization and statistical gene set enrichment analysis. Because the distribution of these scores under the null of uncorrelated technical noise has an accurate gamma approximation, inference can be performed at both the population and single cell levels. As we demonstrate using both simulation studies and real data analyses, the VAM method provides superior classification accuracy at a lower computation cost relative to existing single sample gene set testing approaches.


2018 ◽  
Author(s):  
Rani K. Powers ◽  
Andrew Goodspeed ◽  
Harrison Pielke-Lombardo ◽  
Aik-Choon Tan ◽  
James C. Costello

AbstractMotivationGene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment.ResultsWe compiled a compendium of 442 small molecule transcriptomic experiments and used GSEA to characterize common patterns of positively and negatively enriched gene sets. To identify experiment-specific gene set enrichment, we developed the GSEA-InContext method that accounts for gene expression patterns within a user-defined background set of experiments to identify statistically significantly enriched gene sets. We evaluated GSEA-InContext on experiments using small molecules with known targets and show that it successfully prioritizes gene sets that are specific to each experiment, thus providing valuable insights that complement standard GSEA analysis.Availability and ImplementationGSEA-InContext is implemented in Python. Code, the background expression compendium, and results are available at: https://github.com/CostelloLab/GSEA-InContext


2017 ◽  
Author(s):  
Mingze He ◽  
Peng Liu ◽  
Carolyn J. Lawrence-Dill

AbstractGenome-wide molecular gene expression studies generally compare expression values for each gene across multiple conditions followed by cluster and gene set enrichment analysis to determine whether differentially expressed genes are enriched in specific biochemical pathways, cellular components, biological processes, and/or molecular functions, etc. This approach to analyzing differences in gene expression enables discovery of gene function, but is not useful to determine whether pre-defined groups of genes share or diverge in their expression patterns in response to treatments nor to assess the correctness of pre-defined gene set groupings. Here we present a simple method that changes the dimension of comparison by treating genes as variable traits to directly assess significance of differences in expression levels among pre-defined gene groups. Because expression distributions are typically skewed (thus unfit for direct assessment using Gaussian statistical methods) our method involves transforming expression data to approximate a normal distribution followed by dividing the genes into groups, then applying Gaussian parametric methods to assess significance of observed differences. This method enables the assessment of differences in gene expression distributions within and across samples, enabling hypothesis-based comparison among groups of genes. We demonstrate this method by assessing the significance of specific gene groups’ differential response to heat stress conditions in maize.AbbreviationsGO– gene ontology HSP – heat shock proteinKEGG– Kyoto Encyclopedia of Genes and GenomesHSF TF– heat shock factor transcription factorHSBP– heat shock binding proteinRNA– ribonucleic acidTE– transposable elementTF– transcription factorTPM– transcripts per kilobase millions


2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.


2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jovana Maksimovic ◽  
Alicia Oshlack ◽  
Belinda Phipson

AbstractDNA methylation is one of the most commonly studied epigenetic marks, due to its role in disease and development. Illumina methylation arrays have been extensively used to measure methylation across the human genome. Methylation array analysis has primarily focused on preprocessing, normalization, and identification of differentially methylated CpGs and regions. GOmeth and GOregion are new methods for performing unbiased gene set testing following differential methylation analysis. Benchmarking analyses demonstrate GOmeth outperforms other approaches, and GOregion is the first method for gene set testing of differentially methylated regions. Both methods are publicly available in the missMethyl Bioconductor R package.


2021 ◽  
Vol 12 (1) ◽  
pp. 009-019
Author(s):  
Ying Yang ◽  
Jin Wang ◽  
Shihai Xu ◽  
Wen Lv ◽  
Fei Shi ◽  
...  

Abstract Background In cancer, kappa B-interacting protein (IKBIP) has rarely been reported. This study aimed at investigating its expression pattern and biological function in brain glioma at the transcriptional level. Methods We selected 301 glioma patients with microarray data from CGGA database and 697 glioma patients with RNAseq data from TCGA database. Transcriptional data and clinical data of 998 samples were analyzed. Statistical analysis and figure generating were performed with R language. Results We found that IKBIP expression showed positive correlation with WHO grade of glioma. IKBIP was increased in isocitrate dehydrogenase (IDH) wild type and mesenchymal molecular subtype of glioma. Gene ontology analysis demonstrated that IKBIP was profoundly associated with extracellular matrix organization, cell–substrate adhesion and response to wounding in both pan-glioma and glioblastoma. Subsequent gene set enrichment analysis revealed that IKBIP was particularly correlated with epithelial-to-mesenchymal transition (EMT). To further elucidate the relationship between IKBIP and EMT, we performed gene set variation analysis to screen the EMT-related signaling pathways and found that IKBIP expression was significantly associated with PI3K/AKT, hypoxia and TGF-β pathway. Moreover, IKBIP expression was found to be synergistic with key biomarkers of EMT, especially with N-cadherin, vimentin, snail, slug and TWIST1. Finally, higher IKBIP indicated significantly shorter survival for glioma patients. Conclusions IKBIP was associated with more aggressive phenotypes of gliomas. Furthermore, IKBIP was significantly involved in EMT and could serve as an independent prognosticator in glioma.


Sign in / Sign up

Export Citation Format

Share Document