scholarly journals A transcriptome and literature guided algorithm for reconstruction of pathways to assess activity of telomere maintenance mechanisms

2017 ◽  
Author(s):  
Lilit Nersisyan ◽  
Arsen Arakelyan
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Telomere Maintenance ◽  
Alternative Mechanism ◽  
Extensive Literature ◽  
Expression Data ◽  
Knowledge Based ◽  
Tumor Tissues ◽  
Extensive Literature Search ◽  
Experimental Findings

AbstractActivation of telomere maintenance mechanisms (TMMs) is a crucial factor for indefinite proliferation of cancer cells. The most common TMM is based on the action of telomerase, but in some cancers telomeres are elongated via homologous recombination based alternative mechanism (ALT). Despite their importance, little is known about TMM regulation and factors responsible for TMM phenotype choice in different cells. Currently, many studies address the involvement of few genes in TMMs, but a consensus unified picture of the full process is missing.We have developed a computational biology framework combining knowledge- and data-driven approaches to aid in understanding of TMMs. It is based on a greedy algorithm with three core modules: (1) knowledge-based construction/modification of molecular pathways for telomerase-dependent and alternative TMMs, (2) coupled with gene expression data-based validation with an in-house pathway signal flow (PSF) algorithm, and (3) iteration of these two coupled steps until converging at pathway topologies that best reflect state of the art knowledge and are in maximum accordance with the data. We have used gene expression data derived from cell lines and tumor tissues and have performed extensive literature search and multiple cycles of greedy iterations until reaching TMM assessment accuracy of 100% and 77%, respectively.Availability of TMM pathways that best reflect recent knowledge and data will facilitate better understanding of TMM processes. As novel experimental findings in TMM biology emerge, and new datasets are generated, our approach may be used to further expand/improve the pathways, possibly allowing for making distinctions not only between telomerase-dependent and ALT TMMs, but also among their different subtypes. Moreover, this method may be used for assessment of TMM phenotypes from gene expression data, which is crucial for studies where experimental detection of TMM states is missing. Furthermore, it can also be used to assess TMM activities in proliferating healthy cells.

scholarly journals Knowledge-guided gene prioritization reveals new insights into the mechanisms of chemoresistance

2016 ◽  
Author(s):  
Amin Emad ◽  
Carl R. Woese ◽  
Junmei Cairns ◽  
Krishna R. Kalari ◽  
Liewei Wang, M.D. ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Resistance ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Drug Response ◽  
Global Analysis ◽  
Extensive Literature ◽  
Expression Data ◽  
Chemotherapy Drugs ◽  
New Genes

ABSTRACTBackgroundIdentification of genes whose basal mRNA expression predicts the sensitivity of tumor cells to cytotoxic treatments can play an important role in individualized cancer medicine. It enables detailed characterization of the mechanism of action of drugs. Furthermore, screening the expression of these genes in the tumor tissue may suggest the best course of chemotherapy or a combination of drugs to overcome drug resistance.ResultsWe developed a computational method called ProGENI to identify genes most associated with the variation of drug response across different individuals, based on gene expression data. In contrast to existing methods, ProGENI also utilizes prior knowledge of protein-protein and genetic interactions, using random walk techniques. Analysis of two relatively new and large datasets including gene expression data on hundreds of cell lines and their cytotoxic responses to a large compendium of drugs reveals a significant improvement in prediction of drug sensitivity using genes identified by ProGENI compared to other methods. Our siRNA knockdown experiments on ProGENI-identified genes confirmed the role of many new genes in sensitivity to three chemotherapy drugs: cisplatin, docetaxel and doxorubicin. Based on such experiments and extensive literature survey, we demonstrate that about 73% our top predicted genes modulate drug response in selected cancer cell lines. In addition, global analysis of genes associated with groups of drugs uncovered pathways of cytotoxic response shared by each group.ConclusionsOur results suggest that knowledge-guided prioritization of genes using ProGENI gives new insight into mechanisms of drug resistance and identifies genes that may be targeted to overcome this phenomenon.

scholarly journals Transcriptional deconvolution reveals consistent functional subtypes of pancreatic cancer epithelium and stroma

2018 ◽  
Author(s):  
Jing He ◽  
H. Carlo Maurer ◽  
Sam R. Holmstrom ◽  
Tao Su ◽  
Aqeel Ahmed ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Extracellular Matrix ◽  
Gene Expression Data ◽  
Stromal Cells ◽  
Ductal Adenocarcinoma ◽  
Expression Data ◽  
Tumor Tissues ◽  
Functional Classes ◽  
Laser Capture

SummaryBulk tumor tissues comprise intermixed populations of neoplastic cells and multiple lineages of stromal cells. We used laser capture microdissection and RNA sequencing to disentangle the transcriptional programs active in the malignant epithelium and stroma of pancreatic ductal adenocarcinoma (PDA). This led to the development of a new algorithm (ADVOCATE) that accurately predicts the compartment fractions of bulk tumor samples and can computationally purify bulk gene expression data from PDA. We also present novel stromal subtypes, derived from 110 microdissected PDA stroma samples, that were enriched in extracellular matrix– and immune–associated processes. Finally, we applied ADVOCATE to systematically evaluate cross–compartment subtypes spanning four patient cohorts, revealing consistent functional classes and survival associations despite substantial compositional differences.

Integrating biological knowledge based on functional annotations for biclustering of gene expression data

2015 ◽  
Vol 119 (3) ◽  
pp. 163-180 ◽  
Author(s):  
Juan A. Nepomuceno ◽  
Alicia Troncoso ◽  
Isabel A. Nepomuceno-Chamorro ◽  
Jesús S. Aguilar-Ruiz
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Biological Knowledge ◽  
Expression Data ◽  
Functional Annotations ◽  
Knowledge Based

scholarly journals GESearch: An Interactive GUI Tool for Identifying Gene Expression Signature

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Ning Ye ◽  
Hengfu Yin ◽  
Jingjing Liu ◽  
Xiaogang Dai ◽  
Tongming Yin
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Patterns ◽  
Real Life ◽  
Gene Expression Signature ◽  
Expression Data ◽  
Knowledge Based ◽  
Sequencing Technologies ◽  
Gene Expression Signatures ◽  
Display Window

The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures.

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