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Szedlak A.,Michigan State University | Smith N.,Salgomed, Inc. | Liu L.,Arizona State University | Paternostro G.,Sanford Burnham Institute for Medical Research | Piermarocchi C.,Michigan State University
PLoS Computational Biology

The diverse, specialized genes present in today’s lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins’ binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the topological properties of an acute myeloid leukemia GRN and a general human GRN are strongly coupled with its genes’ evolutionary properties. Slowly evolving (“cold”), old genes tend to interact with each other, as do rapidly evolving (“hot”), young genes. This naturally causes genes to segregate into community structures with relatively homogeneous evolutionary histories. We argue that gene duplication placed old, cold genes and communities at the center of the networks, and young, hot genes and communities at the periphery. We demonstrate this with single-node centrality measures and two new measures of efficiency, the set efficiency and the interset efficiency. We conclude that these methods for studying the relationships between a GRN’s community structures and its genes’ evolutionary properties provide new perspectives for understanding evolutionary genetics. © 2016 Szedlak et al. Source

Kang Y.,Sanford Burnham Institute for Medical Research | Tierney M.,Sanford Burnham Institute for Medical Research | Ong E.,Salgomed, Inc. | Zhang L.,Sanford Burnham Institute for Medical Research | And 3 more authors.

Cell-based therapies to treat skeletal muscle disease are limited by the poor survival of donor myoblasts, due in part to acute hypoxic stress. After confirming that the microenvironment of transplanted myoblasts is hypoxic, we screened a kinase inhibitor library in vitro and identified five kinase inhibitors that protected myoblasts from cell death or growth arrest in hypoxic conditions. A systematic, combinatorial study of these compounds further improved myoblast viability, showing both synergistic and additive effects. Pathway and target analysis revealed CDK5, CDK2, CDC2, WEE1, and GSK3β as the main target kinases. In particular, CDK5 was the center of the target kinase network. Using our recently developed statistical method based on elastic net regression we computationally validated the key role of CDK5 in cell protection against hypoxia. This method provided a list of potential kinase targets with a quantitative measure of their optimal amount of relative inhibition. A modified version of the method was also able to predict the effect of combinations using single-drug response data. This work is the first step towards a broadly applicable system-level strategy for the pharmacology of hypoxic damage. © 2015 Kang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Source

Agency: NSF | Branch: Standard Grant | Program: | Phase: | Award Amount: 225.00K | Year: 2014

This Small Business Technology Transfer (STTR) Phase I project proposes to develop an integrated computational and experimental platform based on the hypothesis that the efficacy of anticancer drugs could be greatly improved when used in combination with other drugs. The proposed platform will implement in vitro search algorithms for combinations of drugs acting on cancer metabolism. Combination drug therapy is commonly used to enhance efficacy and overcome drug resistance in cancer, but at present the choice of drugs is based on empirical clinical experience alone. Testing multi-drug therapies in a systematic way is hampered by the large number of possible choices for drugs and doses. Therefore, an innovative approach for discovering effective drug combinations is needed. The proposed project is an innovative solution based on a highly interdisciplinary approach that combines drug discovery, systems biology, and advanced mathematical and algorithmic methods.

The broader impact/commercial potential of this project, if successful, will be to produce novel and effective therapeutic strategies for the treatment of cancer. Despite the fact that until recently research investment was increasing steadily, the number of drugs approved annually by the FDA remains disappointingly low. Further, currently, there are no effective systematic methods for the discovery and optimization of combinatorial therapies. The plan is to use drugs that are either on the market or in the pipeline. The commercial strategy of the company is to discover specific fixed-dose combinations for cancer therapeutics as a service for pharmaceutical companies, or to generate intellectual property for licensing. In the long term, the company plans to provide a service for physicians by creating personalized drug combinations based on patient data.

Kang Y.,Sanford Burnham Institute for Medical Research | Hodges A.,Sanford Burnham Institute for Medical Research | Ong E.,Salgomed, Inc. | Roberts W.,University of California at San Diego | And 2 more authors.

The BCR-ABL translocation is found in chronic myeloid leukemia (CML) and in Ph+ acute lymphoblastic leukemia (ALL) patients. Although imatinib and its analogues have been used as front-line therapy to target this mutation and control the disease for over a decade, resistance to the therapy is still observed and most patients are not cured but need to continue the therapy indefinitely. It is therefore of great importance to find new therapies, possibly as drug combinations, which can overcome drug resistance. In this study, we identified eleven candidate anti-leukemic drugs that might be combined with imatinib, using three approaches: a kinase inhibitor library screen, a gene expression correlation analysis, and literature analysis. We then used an experimental search algorithm to efficiently explore the large space of possible drug and dose combinations and identified drug combinations that selectively kill a BCR-ABL+ leukemic cell line (K562) over a normal fibroblast cell line (IMR-90). Only six iterations of the algorithm were needed to identify very selective drug combinations. The efficacy of the top forty-nine combinations was further confirmed using Ph+ and Ph- ALL patient cells, including imatinib-resistant cells. Collectively, the drug combinations and methods we describe might be a first step towards more effective interventions for leukemia patients, especially those with the BCR-ABL translocation. © 2014 Kang et al. Source

Methods that incorporate drug libraries and in vitro measurements to predict the response of cells to previously untested drug combinations containing two or more compounds to identify drug combinations for use as a pharmaceutical in the treatment of cancers and other disease using regression analysis of cell-based assays.

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