Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks

Abstract : Background: The ever-increasing expanse of online bioinformatics data is enabling new ways to, not only explore the visualization of these data, but also to apply novel mathematical methods to extract meaningful information for clinically relevant analysis of pathways and treatment decisions. One of the methods used for computing topological characteristics of a space at different spatial resolutions is persistent homology. This concept can also be applied to network theory, and more specifically to protein-protein interaction networks, where the number of rings in an individual cancer network represents a measure of complexity. Results: We observed a linear correlation of R = −0.55 between persistent homology and 5-year survival of patients with a variety of cancers. This relationship was used to predict the proteins within a protein-protein interaction network with the most impact on cancer progression. By re-computing the persistent homology after computationally removing an individual node (protein) from the protein-protein interaction network, we were able to evaluate whether such an inhibition would lead to improvement in patient survival. The power of this approach lied in its ability to identify the effects of inhibition of multiple proteins and in the ability to expose whether the effect of a single inhibition may be amplified by inhibition of other proteins. More importantly, we illustrate specific examples of persistent homology calculations, which correctly predict the survival benefit observed effects in clinical trials using inhibitors of the identified molecular target. Conclusions: We propose that computational approaches such as persistent homology may be used in the future for selection of molecular therapies in clinic. The technique uses a mathematical algorithm to evaluate the node (protein) whose inhibition has the highest potential to reduce network complexity. The greater the drop in persistent homology, the greater reduction in network complexity, and thus a larger potential for survival benefit. We hope that the use of advanced mathematics in medicine will provide timely information about the best drug combination for patients, and avoid the expense associated with an unsuccessful clinical trial, where drug(s) did not show a survival benefit. Reviewers: This article was reviewed by Nathan J. Bowen (nominated by I. King Jordan), Tomasz Lipniacki, and Merek Kimmel.
Type de document :
Article dans une revue
Biology Direct, BioMed Central, 2015, pp.14. 〈10.1186/s13062-015-0058-5〉
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Soumis le : dimanche 31 mai 2015 - 14:55:38
Dernière modification le : jeudi 11 janvier 2018 - 06:27:21


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Sebastien Benzekry, Jack A Tuszynski, Edward Rietman, Giannoula Lakka Klement. Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks. Biology Direct, BioMed Central, 2015, pp.14. 〈10.1186/s13062-015-0058-5〉. 〈hal-01158313〉



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