期刊论文详细信息
Biology Direct
Design principles for cancer therapy guided by changes in complexity of protein-protein interaction networks
Sebastian Benzekry2  Jack A. Tuszynski1  Edward A. Rietman4  Giannoula Lakka Klement3 
[1] Department of Physics, University of Alberta, 116 St and 85 Ave, Edmonton T6G 2R3, AB, Canada
[2] UMR CNRS 5251, University of Bordeaux, 351 cours de la Libération, Talence 33405, Cedex, France
[3] Newman Lakka Institute for Personalized Cancer Care, Rare Tumors and Vascular Anomalies Center, Chef, Academic & Research Affairs, Pediatric Hematology Oncology, Floating Hospital for Children at Tufts Medical Center, 800 Washington Street, Boston 02111, MA, USA
[4] Newman-Lakka Institute, Floating Hospital for Children at Tufts Medical Center, 75 Kneeland St, Boston 02111, MA, USA
关键词: Protein interaction networks;    Cancer;    Betti number;    Persistent homology;    Topology;   
Others  :  1206325
DOI  :  10.1186/s13062-015-0058-5
 received in 2014-12-19, accepted in 2015-05-06,  发布年份 2015
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【 摘 要 】

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.

【 授权许可】

   
2015 Benzekry et al.; licensee BioMed Central.

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