Differential network analysis applied to preoperative breast cancer chemotherapy response

PloS One
Gregor WarsowGeorg Fuellen

Abstract

In silico approaches are increasingly considered to improve breast cancer treatment. One of these treatments, neoadjuvant TFAC chemotherapy, is used in cases where application of preoperative systemic therapy is indicated. Estimating response to treatment allows or improves clinical decision-making and this, in turn, may be based on a good understanding of the underlying molecular mechanisms. Ever increasing amounts of high throughput data become available for integration into functional networks. In this study, we applied our software tool ExprEssence to identify specific mechanisms relevant for TFAC therapy response, from a gene/protein interaction network. We contrasted the resulting active subnetwork to the subnetworks of two other such methods, OptDis and KeyPathwayMiner. We could show that the ExprEssence subnetwork is more related to the mechanistic functional principles of TFAC therapy than the subnetworks of the other two methods despite the simplicity of ExprEssence. We were able to validate our method by recovering known mechanisms and as an application example of our method, we identified a mechanism that may further explain the synergism between paclitaxel and doxorubicin in TFAC treatment: Paclitaxel may attenuate...Continue Reading

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Citations

Apr 5, 2017·Omics : a Journal of Integrative Biology·Dilara AyyildizKazim Yalcin Arga
Oct 6, 2017·Briefings in Bioinformatics·Xiaoqiang Sun, Bin Hu
Mar 14, 2021·Journal of Molecular Neuroscience : MN·Elif Emanetci, Tunahan Çakır

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Datasets Mentioned

BETA
GSE20194

Methods Mentioned

BETA
protein assay
flow cytometry

Software Mentioned

CUSP
IPA Functional Enrichment Analysis
Cytoscape
IPA Functional Enrichment
KeyPathwayMiner
IPA Enrichment Analysis
Ingenuity Functional Enrichment Analysis
ExprEssence
jActiveModules
Image Lab

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