title: 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.
authors.affiliation: Inria team MC2, Institut de Mathématiques de Bordeaux, Bordeaux, France. sebastien.benzekry@inria.fr.|UMR CNRS 5251, University of Bordeaux, 351 cours de la Libération, Talence, Cedex, 33405, France. sebastien.benzekry@inria.fr.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Benzekry
authors.affiliation: Department of Oncology, Faculty of Medicine & Dentistry, University of Alberta, 116 St and 85 Ave, Edmonton, AB, T6G 2R3, Canada. jtus@phys.ualberta.ca.|Department of Physics, University of Alberta, 116 St and 85 Ave, Edmonton, AB, T6G 2R3, Canada. jtus@phys.ualberta.ca.
authors.forename: Jack A
authors.initials: JA
authors.lastname: Tuszynski
authors.affiliation: Newman-Lakka Institute, Floating Hospital for Children at Tufts Medical Center, 75 Kneeland St, Boston, MA, 02111, USA. erietman@gmail.org.
authors.forename: Edward A
authors.initials: EA
authors.lastname: Rietman
authors.affiliation: Newman-Lakka Institute, Floating Hospital for Children at Tufts Medical Center, 75 Kneeland St, Boston, MA, 02111, USA. glakkaklement@tuftsmedicalcenter.org.|Department of Pediatric Hematology Oncology, Floating Hospital for Children at Tufts Medical Center, 755 Washington St, Boston, MA, 02116, USA. glakkaklement@tuftsmedicalcenter.org.|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, Box 14, Boston, MA, 02111, USA. glakkaklement@tuftsmedicalcenter.org.
authors.forename: Giannoula
authors.initials: G
authors.lastname: Lakka Klement
mesh_terms: D000465:Algorithms; D002986:Clinical Trials as Topic; D019295:Computational Biology; D003198:Computer Simulation; D015973:Gene Expression Regulation, Leukemic; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000378:metabolism / Q000628:therapy; D008962:Models, Theoretical; D009369:Neoplasms / Q000235:genetics / Q000628:therapy*; D011336:Probability; D025941:Protein Interaction Mapping; D015398:Signal Transduction