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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 | Year: 2016

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.


Tran T.P.,Sanford Burnham Institute for Medical Research | Ong E.,Salgomed, Inc. | Hodges A.P.,Sanford Burnham Institute for Medical Research | Paternostro G.,Sanford Burnham Institute for Medical Research | And 3 more authors.
BMC Systems Biology | Year: 2014

Background: Many kinase inhibitors have been approved as cancer therapies. Recently, libraries of kinase inhibitors have been extensively profiled, thus providing a map of the strength of action of each compound on a large number of its targets. These profiled libraries define drug-kinase networks that can predict the effectiveness of untested drugs and elucidate the roles of specific kinases in different cellular systems. Predictions of drug effectiveness based on a comprehensive network model of cellular signalling are difficult, due to our partial knowledge of the complex biological processes downstream of the targeted kinases.Results: We have developed the Kinase Inhibitors Elastic Net (KIEN) method, which integrates information contained in drug-kinase networks with in vitro screening. The method uses the in vitro cell response of single drugs and drug pair combinations as a training set to build linear and nonlinear regression models. Besides predicting the effectiveness of untested drugs, the KIEN method identifies sets of kinases that are statistically associated to drug sensitivity in a given cell line. We compared different versions of the method, which is based on a regression technique known as elastic net. Data from two-drug combinations led to predictive models, and we found that predictivity can be improved by applying logarithmic transformation to the data. The method was applied to the A549 lung cancer cell line, and we identified specific kinases known to have an important role in this type of cancer (TGFBR2, EGFR, PHKG1 and CDK4). A pathway enrichment analysis of the set of kinases identified by the method showed that axon guidance, activation of Rac, and semaphorin interactions pathways are associated to a selective response to therapeutic intervention in this cell line.Conclusions: We have proposed an integrated experimental and computational methodology, called KIEN, that identifies the role of specific kinases in the drug response of a given cell line. The method will facilitate the design of new kinase inhibitors and the development of therapeutic interventions with combinations of many inhibitors. © 2014 Tran et al.; licensee BioMed Central Ltd.


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.


Grant
Agency: National Science Foundation | Branch: | Program: STTR | Phase: Phase I | 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.


Grant
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.


Grant
Agency: Department of Health and Human Services | Branch: | Program: STTR | Phase: Phase I | Award Amount: 209.74K | Year: 2012

DESCRIPTION (provided by applicant): Search algorithms for drug combinations: Extending approved cancer therapies. 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, a new approach for discovering effective drug combinations is needed. This project is based on the hypothesis that search algorithms can quickly explore the experimental space of combinations to find efficient therapies. Thus far, advanced mathematical algorithms and high-throughput screening have not been integrated into commercially available platforms able to explore multi-drug combinations. Our goal is to develop an integrated computational and experimental platform implementing in vitro search algorithms for drug combinations. We will start with algorithms commonly used in engineering and physics that iteratively search for optimal therapeutic interventions. In the implementation of the algorithms, we will integrate biological information from genomics and gene expression data, and predictions from explicit signaling networks models to improve convergence and performance. We will focus on an approved EGFR inhibitor, and we will validate our approach by including this drug in combinations with up to three other compounds from libraries of approved drugs and of kinase inhibitors. The proposed platform will be able to optimize combinations of drugs for selective killing of cancer cell lines and limited toxicity on normal cells lines. Protein kinases are central to cellular signaling and are highly investigated as targets of therapeutic agents because they contain activating mutations in many cancers. The specific pattern of oncogenic kinase mutations varies among cancers and individual patients, creating opportunities for selective action and personalization of combinations. PUBLIC HEALTH RELEVANCE: This research uses search algorithms and state-of-the-art drug screening to discover distinct combinations of an FDA-approved EGFR inhibitor and other drugs that target specific proteins (kinases) in cancer cells. These kinases actin a complex network of interactions that make it difficult to predict the effect of combining drugs. If successful, this study will uncover specific combinations of drugs that are more effective at selectively killing cancer cells than a single drug acting alone.


PubMed | Salgomed, Inc.
Type: Journal Article | Journal: Journal of computational biology : a journal of computational molecular cell biology | Year: 2015

A key aim of systems biology is the reconstruction of molecular networks. We do not yet, however, have networks that integrate information from all datasets available for a particular clinical condition. This is in part due to the limited scalability, in terms of required computational time and power, of existing algorithms. Network reconstruction methods should also be scalable in the sense of allowing scientists from different backgrounds to efficiently integrate additional data. We present a network model of acute myeloid leukemia (AML). In the current version (AML 2.1), we have used gene expression data (both microarray and RNA-seq) from 5 different studies comprising a total of 771 AML samples and a protein-protein interactions dataset. Our scalable network reconstruction method is in part based on the well-known property of gene expression correlation among interacting molecules. The difficulty of distinguishing between direct and indirect interactions is addressed by optimizing the coefficient of variation of gene expression, using a validated gold-standard dataset of direct interactions. Computational time is much reduced compared to other network reconstruction methods. A key feature is the study of the reproducibility of interactions found in independent clinical datasets. An analysis of the most significant clusters, and of the network properties (intraset efficiency, degree, betweenness centrality, and PageRank) of common AML mutations demonstrated the biological significance of the network. A statistical analysis of the response of blast cells from 11 AML patients to a library of kinase inhibitors provided an experimental validation of the network. A combination of network and experimental data identified CDK1, CDK2, CDK4, and CDK6 and other kinases as potential therapeutic targets in AML.


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.
PLoS ONE | Year: 2014

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.


PubMed | Salgomed, Inc., Michigan State University and Sanford Burnham Institute for Medical Research
Type: Journal Article | Journal: PloS one | Year: 2015

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 GSK3 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.


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