Differential gene expression signatures between colorectal cancers with and without KRAS mutations: Crosstalk between the KRAS pathway and other signalling pathways

2011 ◽  
Vol 47 (13) ◽  
pp. 1946-1954 ◽  
Author(s):  
Toshiaki Watanabe ◽  
Takashi Kobunai ◽  
Yoko Yamamoto ◽  
Keiji Matsuda ◽  
Soichiro Ishihara ◽  
...  
Development ◽  
2021 ◽  
Vol 148 (11) ◽  
Author(s):  
Lluc Mosteiro ◽  
Hanaa Hariri ◽  
Jelle van den Ameele

ABSTRACT The intimate relationships between cell fate and metabolism have long been recognized, but a mechanistic understanding of how metabolic pathways are dynamically regulated during development and disease, how they interact with signalling pathways, and how they affect differential gene expression is only emerging now. We summarize the key findings and the major themes that emerged from the virtual Keystone Symposium ‘Metabolic Decisions in Development and Disease’ held in March 2021.


2020 ◽  
Author(s):  
Prachi Bajpai ◽  
Amr Elholy ◽  
Michael Behring ◽  
Dongquan Chen ◽  
Kevin Hale ◽  
...  

2019 ◽  
Author(s):  
Gonzalo S. Nido ◽  
Fiona Dick ◽  
Lilah Toker ◽  
Kjell Petersen ◽  
Guido Alves ◽  
...  

AbstractBackgroundThe etiology of Parkinson’s disease (PD) is largely unknown. Genome-wide transcriptomic studies in bulk brain tissue have identified several molecular signatures associated with the disease. While these studies have the potential to shed light into the pathogenesis of PD, they are also limited by two major confounders: RNA post mortem degradation and heterogeneous cell type composition of bulk tissue samples. We performed RNA sequencing following ribosomal RNA depletion in the prefrontal cortex of 49 individuals from two independent case-control cohorts. Using cell-type specific markers, we estimated the cell-type composition for each sample and included this in our analysis models to compensate for the variation in cell-type proportions.ResultsRibosomal RNA depletion results in substantially more even transcript coverage, compared to poly(A) capture, in post mortem tissue. Moreover, we show that cell-type composition is a major confounder of differential gene expression analysis in the PD brain. Correcting for cell-type proportions attenuates numerous transcriptomic signatures that have been previously associated with PD, including vesicle trafficking, synaptic transmission, immune and mitochondrial function. Conversely, pathways related to endoplasmic reticulum, lipid oxidation and unfolded protein response are strengthened and surface as the top differential gene expression signatures in the PD prefrontal cortex.ConclusionsDifferential gene expression signatures in PD bulk brain tissue are significantly confounded by underlying differences in cell-type composition. Modeling cell-type heterogeneity is crucial in order to unveil transcriptomic signatures that represent regulatory changes in the PD brain and are, therefore, more likely to be associated with underlying disease mechanisms.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 1365-1365
Author(s):  
Mara Rosenberg ◽  
Cristina E. Tognon ◽  
Kevin M Watanabe-Smith ◽  
Uma Borate

Abstract Introduction: Acute Myeloid Leukemia (AML) is a heterogenous malignancy with the most common genomic alterations including mutations in NPM1, DNMT3A, and FLT3. Multiple FLT3-inhibitors have been developed and Midostaurin is the first to be FDA approved. However, while Midostaurin is now part of standard of care for FLT3 mutant AML patients undergoing induction chemotherapy, there is still a spectrum of clinical response. We hypothesized that additional biological factors could play a role in this variation of response. We previously observed that FLT3-ITD wildtype samples with KRAS mutations correlated with increased resistance to Midostaurin (1). Here, we utilized the Beat AML dataset containing over 900 AML patient samples processed through an ex vivo drug sensitivity screen to understand further whether genomic alterations could influence the range of Midostaurin response. Methods:We identified 180 (46 FLT3-ITD positive, 134 FLT3-ITD negative) de-novo primary AML peripheral blood or bone marrow samples from distinct patients within the Beat AML dataset. All samples included RNA-Sequencing profiling, mutational analyses, and ex-vivo Midostaurin drug screening data. Drug sensitivity was measured as area under a seven-point drug concentration curve (AUC). AUC was calculated as the area under the fitted probit curve (via direct integration) using all seven dose ranges as x-values and cell viability with limits from 0 to 100% as the y-value. RNA-Sequencing was normalized to counts per million (cpm) and the overall gene expression was chosen as the transcript of that gene with greatest average expression across the cohort. Differential gene expression was performed with the EdgeR package in R version 3.4.0. Results: As expected FLT3-ITD mutant samples, on average, are more sensitive to Midostaurin than FLT3-ITD wildtype sample. However, within the FLT3-ITD mutant cohort we observed a distribution of sensitivity responses (AUC mean of 55.4, IQR 35.5 - 75.3). Analysis of differential gene expression performed on the top 20% and bottom 20% of AUC values identified RGL4 over-expression across the entire resistant cohort (n = 9; Figure A). This was validated in an orthogonal data-set of 79 samples (21 mutant FLT3-ITD, 58 wildtype FLT3-ITD). To gain even more power within the validation set, we looked at all FLT3-ITD positive AUC values and observed a positive correlation between Midostaurin AUC and RGL4 expression (Spearman's rank correlation coefficient of 0.72, p < 0.05). Conclusions: In summary, we found that RGL4 over-expression correlated with resistance to Midostaurin in FLT3-ITD mutant samples in our ex vivo drug screen. RGL4 (ral guanine nucleotide dissociation stimulator like 4) encodes a protein similar to the guanine nucleotide exchange factor for Ral known as Ral GDS (guanine dissociation stimulator). It is highly expressed in the bone marrow and has the potential to activate the Ras-Raf-MEK-ERK pathway. As we had previously observed KRAS mutations correlating with resistance in FLT3-WT samples, this additional finding of RGL4 over expression supports the involvement of RAS pathway activation as a potential mechanism of resistance to Midostaurin. Additional in vitro studies are necessary to establish and further understand this mechanism as well as to test the efficacy of a Midostaurin / MEK inhibitor combination to treat resistant samples. 1. Watanabe-Smith, K., Rosenberg, M., Bucy, T., Tyner, J. W., & Borate, U.(2017). Factors Predicting Response and Resistance to Midostaurin in FLT3 Positive and FLT3 Negative AML in 483 Primary AML Patient Samples. Blood,130(Suppl 1), 296. Figure. Figure. Disclosures Borate: Novartis: Consultancy; Agios: Consultancy.


2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Huaming Wen ◽  
Ryan A. Gallo ◽  
Xiaosheng Huang ◽  
Jiamin Cai ◽  
Shaoyi Mei ◽  
...  

Purpose. Based on the differential gene expression analysis for predictive biomarkers with RNA-Sequencing data from Fuchs endothelial corneal dystrophy (FECD) patients, we are aiming to evaluate the efficacy of Library of Integrated Network-based Cellular Signatures (LINCS) perturbagen prediction software to identify novel pharmacotherapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotype in FECD. Methods. A publicly available RNA-seq dataset was used to compare corneal endothelial specimens from controls and patients with FECD. Based on the differential gene expression analysis for predictive biomarkers, we evaluated the efficacy of LINCS perturbagen prediction software to identify novel therapeutic targets that can revert the pathogenic gene expression signatures and reverse disease phenotypes in FECD. Results. The RNA-seq dataset of the corneal endothelial cells from FECD patients revealed the differential gene expression signatures of FECD. Many of the differential expressed genes are related to canonical pathways of the FECD pathogenesis, such as extracellular matrix reorganization and immunological response. The expression levels of genes VSIG2, IL18, and ITGB8 were significantly increased in FECD compared with control. Meanwhile, the expression levels of CNGA3, SMOX, and CERS1 were significantly lower in the FECD than in control. We employed LINCS L1000 Characteristic Direction Signature Search Engine (L1000-CDS2) to investigate pathway-based molecular treatment. L1000-CDS2 predicted that small molecule drugs such as histone deacetylase (HDAC) inhibitors might be a potential candidate to reverse the pathological gene expression signature in FECD. Conclusions. Based on differential gene expression signatures, several candidate drugs have been identified to reverse the disease phenotypes in FECD. Gene expression signature with LINCS small molecule prediction software can discover novel preclinical drug candidates for FECD.


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