Research Papers:

Personalized anticancer therapy selection using molecular landscape topology and thermodynamics

Edward A. Rietman, Jacob G. Scott, Jack A. Tuszynski and Giannoula Lakka Klement _

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Oncotarget. 2017; 8:18735-18745. https://doi.org/10.18632/oncotarget.12932

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Edward A. Rietman1, Jacob G. Scott2,3, Jack A. Tuszynski4,5, Giannoula Lakka Klement6,7,8

1BINDS lab, College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA

2Wolfson Center for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, UK

3Department of Integrated Mathematical Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA

4Department of Oncology, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada

5Department of Physics, University of Alberta, Edmonton, Alberta, Canada

6Molecular Oncology Research Institute, Tufts Medical Center, Boston, MA, USA

7Pediatric Hematology Oncology, Floating Hospital for Children at Tufts Medical Center, Boston, MA, USA

8Sackler School of Graduate Biomedical Sciences at Tufts University, Boston, MA, USA

Correspondence to:

Giannoula Lakka Klement, email: [email protected]

Keywords: precision medicine, targeted agents, glioma, topology, thermodynamic measures

Received: May 23, 2016     Accepted: October 12, 2016     Published: October 26, 2016


Personalized anticancer therapy requires continuous consolidation of emerging bioinformatics data into meaningful and accurate information streams. The use of novel mathematical and physical approaches, namely topology and thermodynamics can enable merging differing data types for improved accuracy in selecting therapeutic targets. We describe a method that uses chemical thermodynamics and two topology measures to link RNA-seq data from individual patients with academically curated protein-protein interaction networks to select clinically relevant targets for treatment of low-grade glioma (LGG). We show that while these three histologically distinct tumor types (astrocytoma, oligoastrocytoma, and oligodendroglioma) may share potential therapeutic targets, the majority of patients would benefit from more individualized therapies. The method involves computing Gibbs free energy of the protein-protein interaction network and applying a topological filtration on the energy landscape to produce a subnetwork known as persistent homology. We then determine the most likely best target for therapeutic intervention using a topological measure of the network known as Betti number. We describe the algorithm and discuss its application to several patients.

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