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Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.

ABSTRACT: 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 control 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 tumor 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 lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) based on the clinical data from The Cancer Genome Atlas database (TCGA). Thus, network entropy increases in parallel 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 the 100% patient survival rate 5 years after diagnosis. Large standard deviations of subnetwork entropies and variations in target numbers per patient among tumor types characterize tumor heterogeneity.

SUBMITTER: Conforte AJ

PROVIDER: S-EPMC6816034 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.

Conforte Alessandra J AJ Tuszynski Jack Adam JA da Silva Fabricio Alves Barbosa FAB Carels Nicolas N

Frontiers in genetics 20191021

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. ...[more]

PMID: 31695721

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Project description:Shannon entropy H and related measures are increasingly used in molecular ecology and population genetics because (1) unlike measures based on heterozygosity or allele number, these measures weigh alleles in proportion to their population fraction, thus capturing a previously-ignored aspect of allele frequency distributions that may be important in many applications; (2) these measures connect directly to the rich predictive mathematics of information theory; (3) Shannon entropy is completely additive and has an explicitly hierarchical nature; and (4) Shannon entropy-based differentiation measures obey strong monotonicity properties that heterozygosity-based measures lack. We derive simple new expressions for the expected values of the Shannon entropy of the equilibrium allele distribution at a neutral locus in a single isolated population under two models of mutation: the infinite allele model and the stepwise mutation model. Surprisingly, this complex stochastic system for each model has an entropy expressable as a simple combination of well-known mathematical functions. Moreover, entropy- and heterozygosity-based measures for each model are linked by simple relationships that are shown by simulations to be approximately valid even far from equilibrium. We also identify a bridge between the two models of mutation. We apply our approach to subdivided populations which follow the finite island model, obtaining the Shannon entropy of the equilibrium allele distributions of the subpopulations and of the total population. We also derive the expected mutual information and normalized mutual information ("Shannon differentiation") between subpopulations at equilibrium, and identify the model parameters that determine them. We apply our measures to data from the common starling (Sturnus vulgaris) in Australia. Our measures provide a test for neutrality that is robust to violations of equilibrium assumptions, as verified on real world data from starlings.

Project description:Personalized Medicine has the potential to improve health outcomes and reduce the cost of care; however its adoption has been slow in Canada. Bridgepoint Health is a complex continuous care provider striving to reduce the burden of polypharmacy in chronic patients. The main goal of the study was to explore the feasibility of utilizing personalized medicine in the treatment of chronic complex patients as a preliminary institutional health technology assessment. We analyzed stroke treatment optimization as a clinical indication that could serve as a "proof of concept" for the widespread implementation of pharmacogenetics. The objectives of the study were three-fold:1. Review current practice in medication administration for stroke treatment at Bridgepoint Health2. Critically analyze evidence that pharmacogenetic testing could (or could not) enhance drug selection and treatment efficacy for stroke patients;3. Assess the cost-benefit potential of a pharmacogenetic intervention for stroke.Review current practice in medication administration for stroke treatment at Bridgepoint HealthCritically analyze evidence that pharmacogenetic testing could (or could not) enhance drug selection and treatment efficacy for stroke patients;Assess the cost-benefit potential of a pharmacogenetic intervention for stroke.We conducted a review of stroke treatment practices at Bridgepoint Health, scanned the literature for drug-gene and drug-outcome interactions, and evaluated the potential consequences of pharmacogenetic testing using the ACCE model.There is a substantial body of evidence suggesting that pharmacogenetic stratification of stroke treatment can improve patient outcomes in the long-term, and provide substantial efficiencies for the healthcare system in the short-term. Specifically, pharmacogenetic stratification of antiplatelet and anticoagulant therapies for stroke patients may have a major impact on the risk of disease recurrence, and thus should be explored further for clinical application. Bridgepoint Health, and other healthcare institutions taking this path, should consider launching pilot projects to assess the practical impact of pharmacogenetics to optimize treatment for chronic continuous care.

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Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.

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Signaling Complexity Measured by Shannon Entropy and Its Application in Personalized Medicine.

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