Optimization of combination chemotherapy based on the calculation of network entropy for protein-protein interactions in breast cancer cell lines View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2015-12

AUTHORS

Nicolas Carels, Tatiana Martins Tilli, Jack A. Tuszynski

ABSTRACT

In this report, we show how entropy computation can be applied to the characterization of a protein-protein interaction networks to assist the selection of personalized chemotherapeutic strategy for cancer treatment. With seven malignant (BT-20, BT-474, MDA-MB-231, MDA-MB-468, MCF-7, T-47D, ZR-75-1) and one healthy (MCF10A) cell lines, we combined interactome and transcriptome data as well as Shanon entropy computation to classify drugs according to their inhibitory potential and to identify the top-5 protein targets best suited for personalized chemotherapy. We have investigated breast cancer cell lines and found that the entropy of their protein interaction networks is negatively correlated with their sensitivity to target-specific drugs of high potency. This sensitivity is defined as half cell growth inhibition (GI50) with respect to drug administration. By contrast, we found no correlation for drugs that are either of low potency or with no specific molecular targets (cytotoxic). As a result, drugs can be divided into target specific and generally cytotoxic according to the GI50 they produce in malignant cell lines. By extrapolation, we predict that the inactivation of the top-5 up-regulated protein hubs by specific drugs will reduce the protein network entropy by ~2 %, on average, which is expected to substantially increase the benefit of a personalized chemo-therapeutic strategy for patient survival. We propose several novel drug combinations using only the approved drugs for the inactivation of the target identified in this study with the purpose of increasing patient survival and lowering the deleterious side effects of cancer chemotherapy. More... »

PAGES

6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1140/epjnbp/s40366-015-0023-3

DOI

http://dx.doi.org/10.1140/epjnbp/s40366-015-0023-3

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https://app.dimensions.ai/details/publication/pub.1038654420


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