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"Systems Biology"[Title/Abstract] AND ("2005/01/01"[PDAT] : "3000"[PDAT])

These pubmed results were generated on 2016/05/18

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

'RE:fine drugs': an interactive dashboard to access drug repurposing opportunities.

Database (Oxford). 2016;2016

Authors: Moosavinasab S, Patterson J, Strouse R, Rastegar-Mojarad M, Regan K, Payne PR, Huang Y, Lin SM

Abstract
The process of discovering new drugs has been extremely costly and slow in the last decades despite enormous investment in pharmaceutical research. Drug repurposing enables researchers to speed up the process of discovering other conditions that existing drugs can effectively treat, with low cost and fast FDA approval. Here, we introduce 'RE:fine Drugs', a freely available interactive website for integrated search and discovery of drug repurposing candidates from GWAS and PheWAS repurposing datasets constructed using previously reported methods in Nature Biotechnology. 'RE:fine Drugs' demonstrates the possibilities to identify and prioritize novelty of candidates for drug repurposing based on the theory of transitive Drug-Gene-Disease triads. This public website provides a starting point for research, industry, clinical and regulatory communities to accelerate the investigation and validation of new therapeutic use of old drugs.Database URL: http://drug-repurposing.nationwidechildrens.org.

PMID: 27189611 [PubMed - as supplied by publisher]

Learning disease relationships from clinical drug trials.

J Am Med Inform Assoc. 2016 May 17;

Authors: Haslam B, Perez-Breva L

Abstract
OBJECTIVE: Our objective is to test the limits of the assumption that better learning from data in medicine requires more granular data. We hypothesize that clinical trial metadata contains latent scientific, clinical, and regulatory expert knowledge that can be accessed to draw conclusions about the underlying biology of diseases. We seek to demonstrate that this latent information can be uncovered from the whole body of clinical trials.
MATERIALS AND METHODS: We extract free-text metadata from 93 654 clinical drug trials and introduce a representation that allows us to compare different trials. We then construct a network of diseases using only the trial metadata. We view each trial as the summation of expert knowledge of biological mechanisms and medical evidence linking a disease to a drug believed to modulate the pathways of that disease. Our network representation allows us to visualize disease relationships based on this underlying information.
RESULTS: Our disease network shows surprising agreement with another disease network based on genetic data and on the Medical Subject Headings (MeSH) taxonomy, yet also contains unique disease similarities.
DISCUSSION AND CONCLUSION: The agreement of our results with other sources indicates that our premise regarding latent expert knowledge holds. The disease relationships unique to our network may be used to generate hypotheses for future biological and clinical research as well as drug repurposing and design. Our results provide an example of using experimental data on humans to generate biologically useful information and point to a set of new and promising strategies to link clinical outcomes data back to biological research.

PMID: 27189012 [PubMed - as supplied by publisher]

Enhancing the enrichment of pharmacophore-based target prediction for the polypharmacological profiles of drugs.

J Chem Inf Model. 2016 May 17;

Authors: Wang X, Pan C, Gong J, Liu X, Li H

Abstract
PharmMapper is a web server for drug targets identification by reversed pharmacophore matching the query compound against an annotated pharmacophore model database, which provides a computational polypharmacology prediction approach for drug repurposing and side effects risk evaluation. But due to the inherent non-discriminative feature of the simple fit scores used for prediction results ranking, the signal/noise ratio of the prediction results is high, posing a challenge for predictive reliability. In this paper, we improved the predictive accuracy of PharmMapper by generating a ligand-target pairwise fit scores matrix from profiling all the annotated pharmacophore models against corresponding ligands in the original complex structures that were used to extract these pharmacophore models. The matrix reflects the noise baseline of fit scores distribution of the background database, thus enables estimating the probability of finding a given target by random with the calculated ligand-pharmacophore fit score. Two retrospective tests were performed and confirmed the probability-based ranking score outperformed the simple fit score in terms of identification of both known drug targets and adverse drug reactions (ADRs) related off-targets.

PMID: 27187084 [PubMed - as supplied by publisher]

Using reverse docking for target identification and its applications for drug discovery.

Expert Opin Drug Discov. 2016 May 17;

Authors: Lee A, Lee K, Kim D

Abstract
Introduction In contrast to traditional molecular docking, inverse or reverse docking is used for identifying receptors for a given ligand among a large number of receptors. Reverse docking can be used to discover new targets for existing drugs and natural compounds, explain polypharmacology and the molecular mechanism of a substance, find alternative indications of drugs through drug repositioning, and detecting adverse drug reactions and drug toxicity. Areas covered In this review, the authors examine how reverse docking methods have evolved over the past fifteen years and how they have been used for target identification and related applications for drug discovery. They discuss various aspects of target databases, reverse docking tools and servers. Expert opinion There are several issues related to reverse docking methods such as target structure dataset construction, computational efficiency, how to include receptor flexibility, and most importantly, how to properly normalize the docking scores. In order for reverse docking to become a truly useful tool for the drug discovery, these issues need to be adequately resolved.

PMID: 27186904 [PubMed - as supplied by publisher]

Rhomboid Enhancer Activity Defines a Subset of Drosophila Neural Precursors Required for Proper Feeding, Growth and Viability.

PLoS One. 2015;10(8):e0134915

Authors: Gresser AL, Gutzwiller LM, Gauck MK, Hartenstein V, Cook TA, Gebelein B

Abstract
Organismal growth regulation requires the interaction of multiple metabolic, hormonal and neuronal pathways. While the molecular basis for many of these are well characterized, less is known about the developmental origins of growth regulatory structures and the mechanisms governing control of feeding and satiety. For these reasons, new tools and approaches are needed to link the specification and maturation of discrete cell populations with their subsequent regulatory roles. In this study, we characterize a rhomboid enhancer element that selectively labels four Drosophila embryonic neural precursors. These precursors give rise to the hypopharyngeal sensory organ of the peripheral nervous system and a subset of neurons in the deutocerebral region of the embryonic central nervous system. Post embryogenesis, the rhomboid enhancer is active in a subset of cells within the larval pharyngeal epithelium. Enhancer-targeted toxin expression alters the morphology of the sense organ and results in impaired larval growth, developmental delay, defective anterior spiracle eversion and lethality. Limiting the duration of toxin expression reveals differences in the critical periods for these effects. Embryonic expression causes developmental defects and partially penetrant pre-pupal lethality. Survivors of embryonic expression, however, ultimately become viable adults. In contrast, post-embryonic toxin expression results in fully penetrant lethality. To better define the larval growth defect, we used a variety of assays to demonstrate that toxin-targeted larvae are capable of locating, ingesting and clearing food and they exhibit normal food search behaviors. Strikingly, however, following food exposure these larvae show a rapid decrease in consumption suggesting a satiety-like phenomenon that correlates with the period of impaired larval growth. Together, these data suggest a critical role for these enhancer-defined lineages in regulating feeding, growth and viability.

PMID: 26252385 [PubMed - indexed for MEDLINE]

Editorial.

Cell Mol Biol (Noisy-le-grand). 2016;62(5):1

Authors: Farooqi AA

Abstract
To optimize treatment, we need to understand biology of different diseases in much more detail with emphasis on morphological, proteomic, genetic and epigenetic grounds. Keeping in view the facts and stimulating developments in molecular pathology, it is worthwhile to present an up-date on this topic.It is becoming progressively more understandable that exciting fields of pharmacogenomics and pharmacogenetics have revolutionized field of medicine. Better understanding of underlying mechanisms of different diseases has provided us with better ways to treat illnesses. There cannot be a distinct definition of 'discipline' of pathology, mainly because investigation of human disease encompasses all the scientific disciplines of biomedical research. Sen et al reported that hyperbarıc oxygen (HBO) administration affected the endocrinological functions of fat tissue. Observation of significant increases in leptin, visfatin and IL-10 levels, leads to the consideration that in near future HBO administration may be applied as treatment for obesity, DM, eating disorders and obesity related diseases...

PMID: 27188861 [PubMed - as supplied by publisher]

Hepatotoxicity of targeted therapy for cancer.

Expert Opin Drug Metab Toxicol. 2016 May 17;

Authors: Lee KW, Chan SL

Abstract
Molecular targeted agents (MTA) have become the mainstay of treatment for many cancers in the past decade. Hepatotoxicity of varying forms and severity is common with the use of MTA and risk factors have been identified. Hepatotoxicity can result from direct hepatocellular, cholestatic or steatotic injury, through immunogenic pathways or reactivation of viral hepatitis. Selecting an appropriate starting dose for special populations, arranging viral hepatitis screening prior to initiation of relevant MTA and knowing the standards for liver function test (LFT) monitoring are crucial to preventing morbidity and mortality from drug-induced liver injury (DILI) and minimising discontinuation of MTA. This is a comprehensive review on how to interpret LFTs in the light of MTA use, mechanisms of DILI, identification of high risk groups, and measures for preventing and managing hepatotoxicity.
INTRODUCTION: Understanding the mechanism of DILI with MTA, and how to avoid and manage these toxicities is essential for minimising inferior cancer treatment outcomes. An organised and comprehensive overview of MTA-associated hepatotoxicity is lacking; this review aims to fill the gap. Areas covered: A literature review was performed based on published case reports and relevant studies or articles pertaining to the topics on PubMed. Food and Drug Administration drug information documents and search on the US National Library of Medicine LiverTox database was performed for all relevant MTA. Expert opinion: MTA-associated hepatotoxicity is common but rarely fatal. The pattern of hepatotoxicity is predominantly idiosyncratic. Pharmacogenomics show potential in predicting patients at risk of poorly metabolising or developing immunoallergic responses to MTA, but prospective data is scant. Preventing reactivation of viral hepatitis using anti-viral drugs, and avoidance of drug combinations at high risk of negative interactions are the most readily preventable measures for DILI.

PMID: 27187715 [PubMed - as supplied by publisher]

Clinical Zheng-hou Pharmacology: the Missing Link between Pharmacogenomics and Personalized Medicine?

Curr Vasc Pharmacol. 2015;13(4):423-32

Authors: Yu YN, Liu J, Zhang L, Wang Z, Duan DD, Wang YY

Abstract
In Chinese medicine, Zheng-hou, instead of disease, is used to define complex medical problems in clinical practice. In the postgenomics era, it becomes particularly compelling to review the application of Zheng-hou in characterizing complex clinical problems independent of disease or syndrome. While disease or syndrome describes a pathological phenotype or phenotypes, Zheng-hou spells the pathological phenome. Clinical Zheng-hou pharmacology (CZP) is an emerging clinical discipline that aims to leverage breakthroughs in the genome-wide solutions for complex medical problems through a combination of the current "omics" technology and the knowledge of Chinese medicine. The concept of CZP suggests that systematic and standard studies of multiple phenotypes will be important because of the collaborative cross between diversified external and internal factors at different levels both in vitro and in vivo. In this paper, we discuss the novel phenomic approaches to the understanding of Zheng-hou and the link of pharmacogenomics to personalized medicine through CZP, or pharmacophenomics. CZP enables ever-finer mapping of Zheng-hou and detection of dynamic variations in most current omics platforms. Although major challenges still remain in identifying and effectively investigating the diversity of Zheng-hou, CZP is expected to pave new paths to the systemic understanding of medical problems. While still at early stages in the clinical phenome domain, there remains great promise that CZP can help us realize the application of personalized medicine and contribute to rational holistic diagnosis and treatment.

PMID: 25360846 [PubMed - indexed for MEDLINE]

Autoantigen Microarray for High-throughput Autoantibody Profiling in Systemic Lupus Erythematosus.

Genomics Proteomics Bioinformatics. 2015 Aug;13(4):210-8

Authors: Zhu H, Luo H, Yan M, Zuo X, Li QZ

Abstract
Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by the production of autoantibodies to a broad range of self-antigens. Profiling the autoantibody repertoire using array-based technology has emerged as a powerful tool for the identification of biomarkers in SLE and other autoimmune diseases. Proteomic microarray has the capacity to hold large number of self-antigens on a solid surface and serve as a high-throughput screening method for the determination of autoantibody specificities. The autoantigen arrays carrying a wide variety of self-antigens, such as cell nuclear components (nucleic acids and associated proteins), cytoplasmic proteins, phospholipid proteins, cell matrix proteins, mucosal/secreted proteins, glomeruli, and other tissue-specific proteins, have been used for screening of autoantibody specificities associated with different manifestations of SLE. Arrays containing synthetic peptides and molecular modified proteins are also being utilized for identification of autoantibodies targeting to special antigenic epitopes. Different isotypes of autoantibodies, including IgG, IgM, IgA, and IgE, as well as other Ig subtypes, can be detected simultaneously with multi-color labeled secondary antibodies. Serum and plasma are the most common biologic materials for autoantibody detection, but other body fluids such as cerebrospinal fluid, synovial fluid, and saliva can also be a source of autoantibody detection. Proteomic microarray as a multiplexed high-throughput screening platform is playing an increasingly-important role in autoantibody diagnostics. In this article, we highlight the use of autoantigen microarrays for autoantibody exploration in SLE.

PMID: 26415621 [PubMed - indexed for MEDLINE]

Molecular cytopathology for thyroid nodules: A review of methodology and test performance.

Cancer Cytopathol. 2016 Jan;124(1):14-27

Authors: Nishino M

Abstract
Advances in the molecular characterization of thyroid cancers have fueled the development of genetic and gene expression-based tests for thyroid fine-needle aspirations. Collectively, these tests are designed to improve the diagnostic certainty of thyroid cytology. This review summarizes the early published experience with the commercially available versions of these tests: the Afirma Gene Expression Classifier, ThyGenX (formerly miRInform)/ThyraMIR, and ThyroSeq. Key differences in testing approaches and issues regarding test performance and interpretation are also discussed.

PMID: 26348024 [PubMed - indexed for MEDLINE]

RNA-Seq: Improving Our Understanding of Retinal Biology and Disease.

Cold Spring Harb Perspect Med. 2015;5(9):a017152

Authors: Farkas MH, Au ED, Sousa ME, Pierce EA

Abstract
Over the past several years, rapid technological advances have allowed for a dramatic increase in our knowledge and understanding of the transcriptional landscape, because of the ability to study gene expression in greater depth and with more detail than previously possible. To this end, RNA-Seq has quickly become one of the most widely used methods for studying transcriptomes of tissues and individual cells. Unlike previously favored analysis methods, RNA-Seq is extremely high-throughput, and is not dependent on an annotated transcriptome, laying the foundation for novel genetic discovery. Additionally, RNA-Seq derived transcriptomes provide a basis for widening the scope of research to identify potential targets in the treatment of retinal disease.

PMID: 25722474 [PubMed - indexed for MEDLINE]

Argo: enabling the development of bespoke workflows and services for disease annotation.

Database (Oxford). 2016;2016

Authors: Batista-Navarro R, Carter J, Ananiadou S

Abstract
Argo (http://argo.nactem.ac.uk) is a generic text mining workbench that can cater to a variety of use cases, including the semi-automatic annotation of literature. It enables its technical users to build their own customised text mining solutions by providing a wide array of interoperable and configurable elementary components that can be seamlessly integrated into processing workflows. With Argo's graphical annotation interface, domain experts can then make use of the workflows' automatically generated output to curate information of interest.With the continuously rising need to understand the aetiology of diseases as well as the demand for their informed diagnosis and personalised treatment, the curation of disease-relevant information from medical and clinical documents has become an indispensable scientific activity. In the Fifth BioCreative Challenge Evaluation Workshop (BioCreative V), there was substantial interest in the mining of literature for disease-relevant information. Apart from a panel discussion focussed on disease annotations, the chemical-disease relations (CDR) track was also organised to foster the sharing and advancement of disease annotation tools and resources.This article presents the application of Argo's capabilities to the literature-based annotation of diseases. As part of our participation in BioCreative V's User Interactive Track (IAT), we demonstrated and evaluated Argo's suitability to the semi-automatic curation of chronic obstructive pulmonary disease (COPD) phenotypes. Furthermore, the workbench facilitated the development of some of the CDR track's top-performing web services for normalising disease mentions against the Medical Subject Headings (MeSH) database. In this work, we highlight Argo's support for developing various types of bespoke workflows ranging from ones which enabled us to easily incorporate information from various databases, to those which train and apply machine learning-based concept recognition models, through to user-interactive ones which allow human curators to manually provide their corrections to automatically generated annotations. Our participation in the BioCreative V challenges shows Argo's potential as an enabling technology for curating disease and phenotypic information from literature.Database URL: http://argo.nactem.ac.uk.

PMID: 27189607 [PubMed - as supplied by publisher]

Identifying a biomarker network for corticosteroid resistance in asthma from bronchoalveolar lavage samples.

Mol Biol Rep. 2016 May 17;

Authors: Vargas JE, Porto BN, Puga R, Stein RT, Pitrez PM

Abstract
Corticosteroid resistance (CR) is a major barrier to the effective treatment of severe asthma. Hence, a better understanding of the molecular mechanisms involved in this condition is a priority. Network analysis is an emerging strategy to explore this complex heterogeneous disorder at system level to identify a small own network for CR in asthma. Gene expression profile of GSE7368 from bronchoalveolar lavage (BAL) of CR in subjects with asthma was downloaded from the gene expression omnibus (GEO) database and compared to BAL of corticosteroid-sensitive (CS) patients. DEGs were identified by the Limma package in R language. In addition, DEGs were mapped to STRING to acquire protein-protein interaction (PPI) pairs. Topological properties of PPI network were calculated by Centiscape, ClusterOne and BINGO. Subsequently, text-mining tools were applied to design one own cell signalling for CR in asthma. Thirty-five PPI networks were obtained; including a major network consisted of 370 nodes, connected by 777 edges. After topological analysis, a minor PPI network composed by 48 nodes was indentified, which is composed by most relevant nodes of major PPI network. In this subnetwork, several receptors (EGFR, EGR1, ESR2, PGR), transcription factors (MYC, JAK), cytokines (IL8, IL6, IL1B), one chemokine (CXCL1), one kinase (SRC) and one cyclooxygenase (PTGS2) were described to be associated with inflammatory environment and steroid resistance in asthma. We suggest a biomarker network composed by 48 nodes that could be potentially explored with diagnostic or therapeutic use.

PMID: 27188427 [PubMed - as supplied by publisher]

On the unsupervised analysis of domain-specific Chinese texts.

Proc Natl Acad Sci U S A. 2016 May 16;

Authors: Deng K, Bol PK, Li KJ, Liu JS

Abstract
With the growing availability of digitized text data both publicly and privately, there is a great need for effective computational tools to automatically extract information from texts. Because the Chinese language differs most significantly from alphabet-based languages in not specifying word boundaries, most existing Chinese text-mining methods require a prespecified vocabulary and/or a large relevant training corpus, which may not be available in some applications. We introduce an unsupervised method, top-down word discovery and segmentation (TopWORDS), for simultaneously discovering and segmenting words and phrases from large volumes of unstructured Chinese texts, and propose ways to order discovered words and conduct higher-level context analyses. TopWORDS is particularly useful for mining online and domain-specific texts where the underlying vocabulary is unknown or the texts of interest differ significantly from available training corpora. When outputs from TopWORDS are fed into context analysis tools such as topic modeling, word embedding, and association pattern finding, the results are as good as or better than that from using outputs of a supervised segmentation method.

PMID: 27185919 [PubMed - as supplied by publisher]

Exploring mechanisms of Panax notoginseng saponins in treating coronary heart disease by integrating gene interaction network and functional enrichment analysis.

Chin J Integr Med. 2016 May 16;

Authors: Yu G, Wang J

Abstract
OBJECTIVE: To investigate the mechanisms of Panax notoginseng saponins (PNS) in treating coronary heart disease (CHD) by integrating gene interaction network and functional enrichment analysis.
METHODS: Text mining was used to get CHD and PNS associated genes. Gene-gene interaction networks of CHD and PNS were built by the GeneMANIA Cytoscape plugin. Advanced Network Merge Cytoscape plugin was used to analyze the two networks. Their functions were analyzed by gene functional enrichment analysis via DAVID Bioinformatics. Joint subnetwork of CHD network and PNS network was identified by network analysis.
RESULTS: The 11 genes of the joint subnetwork were the direct targets of PNS in CHD network and enriched in cytokine-cytokine receptor interaction pathway. PNS could affect other 85 genes by the gene-gene interaction of joint subnetwork and these genes were enriched in other 7 pathways. The direct mechanisms of PNS in treating CHD by targeting cytokines to relieve the inflammation and the indirect mechanisms of PNS in treating CHD by affecting other 7 pathways through the interaction of joint subnetwork of PNS and CHD network. The genes in the 7 pathways could be potential targets for the immunologic adjuvant, anticoagulant, hypolipidemic, anti-platelet and anti-hypertrophic activities of PNS.
CONCLUSION: The key mechanisms of PNS in treating CHD could be anticoagulant and hypolipidemic which are indicated by analyzing biological functions of hubs in the merged network.

PMID: 27184904 [PubMed - as supplied by publisher]

Celastrol targets IRAKs to block Toll-like receptor 4-mediated nuclear factor-κB activation.

J Integr Med. 2016 May;14(3):203-8

Authors: Shen YF, Zhang X, Wang Y, Cao FF, Uzan G, Peng B, Zhang DH

Abstract
OBJECTIVE: Celastrol has been established as a nuclear factor-κB (NF-κB) activation inhibitor; however, the exact mechanism behind this action is still unknown. Using text-mining technology, the authors predicted that interleukin-1 receptor-associated kinases (IRAKs) are potential celastrol targets, and hypothesized that targeting IRAKs might be one way that celastrol inhibits NF-κB. This is because IRAKs are key molecules for some crucial pathways to activate NF-κB (e.g., the interleukin-1 receptor (IL-1R)/Toll-like receptor (TLR) superfamily).
METHODS: The human hepatocellular cell line (HepG2) treated with palmitic acid (PA) was used as a model for stimulating TLR4/NF-κB activation, in order to observe the potential effects of celastrol in IRAK regulation and NF-κB inhibition. The transfection of small interfering RNA was used for down-regulating TLR4, IRAK1 and IRAK4, and the Western blot method was used to detect changes in the protein expressions.
RESULTS: The results showed that celastrol could effectively inhibit PA-caused TLR4-dependent NF-κB activation in the HepG2 cells; PA also activated IRAKs, which were inhibited by celastrol. Knocking down IRAKs abolished PA-caused NF-κB activation.
CONCLUSION: The results for the first time show that targeting IRAKs is one way in which celastrol inhibits NF-κB activation.

PMID: 27181127 [PubMed - in process]

Text mining patents for biomedical knowledge.

Drug Discov Today. 2016 May 11;

Authors: Rodriguez-Esteban R, Bundschus M

Abstract
Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery.

PMID: 27179985 [PubMed - as supplied by publisher]

Genetic testing to guide warfarin dosing: Impact of direct oral anticoagulants.

Clin Pharmacol Ther. 2016 May 14;

Authors: Lentz SR

PMID: 27178490 [PubMed - as supplied by publisher]

Therapeutic Application of Pharmacogenomics in Oncology.

AAPS J. 2016 May 13;

Authors: Zhang Y, Somtakoune SD, Cheung C, Listiawan M, Feng X

Abstract
Personalizing cancer treatment has been proved to be difficult for healthcare providers due to the nature of chemotherapies which includes narrow therapeutic indices, severe and potential life-threatening toxicities, and variable response rates and efficacies. Studies in pharmacogenomics (PGx) may help guide clinicians to personalize treatment for cancer patients. Implementing PGx in cancer treatment may offer choices to anticipate differences in drug response, resistance, efficacy, and toxicity within chemotherapeutic agents and targeted immune biologic agents. This can be used to achieve optimization of treatment regimens based on patients' variability. Many of the cancer treatment agents are biologics targeting specific antigens expressed on cancer cells, or blocking stimulators and signal transduction pathways of tumor growth, or enhance anticancer immune responses. It is now crucial for clinicians to understand the important association of clinically important biomarker polymorphisms with the clinical benefits of cancer therapies. By identifying specific PGx biomarker polymorphisms present in cancer cells, physicians can select and tailor a patient's treatment based on his or her genetic profile. PGx-guided cancer treatment may have the ability to improve the survival of patients while avoiding the unnecessary cost due to unresponsive treatment and toxicities of that patients experience.

PMID: 27178043 [PubMed - as supplied by publisher]