Original Research ARTICLE
Signaling complexity measured by Shannon entropy and its application in personalized medicine
- 1Centro de Desenvolvimento Tecnológico em Saúde (CDTS), Fiocruz, Brazil
- 2Scientific Computing Program, Laboratory of Computational Modeling of Biological Systems, Oswaldo Cruz Foundation, Brazil
- 3Department of Mechanical and Aerospace Engineering, Polytechnic University of Turin, Italy
- 4Experimental Oncology, Department of Oncology, University of Alberta, Canada
- 5Department of Physics, Faculty of Science, University of Alberta, Canada
- 6Oswaldo Cruz Foundation (Fiocruz), Brazil
Traditional approaches to cancer therapy seek common molecular targets in tumors from different patients. However, molecular profiles differ between patients and most tumors exhibit inherent heterogeneity. Hence, imprecise targeting commonly results in side effects, reduced efficacy, and drug resistance. By contrast, personalized medicine aims to establish a molecular diagnosis specific to each patient, which is currently feasible due to the progress achieved with high throughput technologies. In this report, we explored data from human RNA-seq and protein-protein interaction (PPI) networks using bioinformatics to investigate the relationship between tumor entropy and aggressiveness. To compare PPI subnetworks of different sizes, we calculated the Shannon entropy associated with vertex connections of differentially expressed genes comparing tumor samples with their paired non-malignant tissues. We found that the inhibition of up-regulated connectivity hubs led to a higher reduction of subnetwork entropy compared to that obtained with the inhibition of targets selected at random. Furthermore, these hubs were described to be participating in malignant processes. We also found a significant negative correlation between subnetwork entropies of tumors and the respective 5-year survival rates of the corresponding cancer types. This correlation was also observed considering patients with LUSC and LUAD based on the clinical data from the cancer genome atlas database (TCGA). Thus, network entropy increases together with tumor aggressiveness but does not correlate with PPI subnetwork size. This correlation is consistent with previous reports and allowed us to assess the number of hubs to be inhibited for therapy to be effective, in the context of precision medicine, by reference to 100% of patient survival rate in the five years after diagnosis. Large standard deviations of subnetwork entropies and variations in target numbers per patient among tumor types characterized tumor heterogeneity.
Keywords: molecular target, precision medicine, RNA-Seq, Interactome, chemotherapy
Received: 20 Mar 2019;
Accepted: 05 Sep 2019.
Copyright: © 2019 Conforte, Tuszynski, Silva and Carels. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: PhD. Nicolas Carels, Oswaldo Cruz Foundation (Fiocruz), Rio de Janeiro, Brazil, email@example.com