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.

scholarly journals HisCoM-PAGE: Hierarchical Structural Component Models for Pathway Analysis of Gene Expression Data

Genes ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 931 ◽  
Author(s):  
Mok ◽  
Kim ◽  
Lee ◽  
Choi ◽  
Lee ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Expression Data ◽  
Pathway Analysis ◽  
Structural Component ◽  
Biological Data ◽  
Gene Set Enrichment Analysis ◽  
Expression Data ◽  
Global Test ◽  
Causal Pathways

Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios.

scholarly journals Cross-platform Comparison of Two Pancreatic Cancer Phenotypes

2010 ◽  
Vol 9 ◽  
pp. CIN.S5755 ◽  
Author(s):  
Robert B. Scharpf ◽  
Christine A. Iacobuzio-Donahue ◽  
Leslie Cope ◽  
Ingo Ruczinski ◽  
Elizabeth Garrett-Mayer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Expression Data ◽  
Pancreatic Cancer Cell ◽  
Expression Data ◽  
Pancreatic Cancer Cells ◽  
Technological Artifacts ◽  
Platform Comparison ◽  
Cross Platform ◽  
Cancer Phenotypes

Model-based approaches for combining gene expression data from multiple high throughput platforms can be sensitive to technological artifacts when the number of samples in each platform is small. This paper proposes simple tools for quantifying concordance in a small study of pancreatic cancer cells lines with an emphasis on visualizations that uncover intra- and inter-platform variation. Using this approach, we identify several transcripts from the integrative analysis whose over-or under-expression in pancreatic cancer cell lines was validated by qPCR.

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.

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