A pharmacogenomic method for individualized prediction of drug sensitivity

Cohen, Adam L.; Soldi, Raffaella; Zhang, Haiyu; Gustafson, Adam M.; Wilcox, R. Bruce; Welm, Bryan E.; Chang, Jeffrey T.; Johnson, W. Evan; Spira, Avrum; Jeffrey, Stefanie S.; Bild, Andrea

doi:10.1038/msb.2011.47

Cited by 41 publications

(51 citation statements)

References 53 publications

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“…42 In this paper, we developed a novel method to identify drug RMs and elucidated the potential pharmacological mechanisms of different compounds individually (BA, JA or UA) and in combinations (BJ or JU) in treating cerebral ischemia-reperfusion injury. In contrast to conventional studies on drug response that mainly focused on genes and pathways, [43][44][45] our method focused on modules and analyzed both their topological structures and functions, which provided a holistic view of the overall differences and similarities in the pharmacological effects of the five compounds at a systems level. All of the compounds contained more RM D s than RM E s, indicating that several modules vanished after drug intervention.…”

Section: Discussionmentioning

confidence: 99%

Entropy-based divergent and convergent modular pattern reveals additive and synergistic anticerebral ischemia mechanisms

Zhang

et al. 2016

Exp Biol Med (Maywood)

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Module-based network analysis of diverse pharmacological mechanisms is critical to systematically understand combination therapies and disease outcomes. We first constructed drug-target ischemic networks in baicalin, jasminoidin, ursodeoxycholic acid, and their combinations baicalin and jasminoidin as well as jasminoidin and ursodeoxycholic acid groups and identified modules using the entropy-based clustering algorithm. The modules 11, 7, 4, 8 and 3 were identified as baicalin, jasminoidin, ursodeoxycholic acid, baicalin and jasminoidin and jasminoidin and ursodeoxycholic acid-emerged responsive modules, while 12, 8, 15, 17 and 9 were identified as disappeared responsive modules based on variation of topological similarity, respectively. No overlapping differential biological processes were enriched between baicalin and jasminoidin and jasminoidin and ursodeoxycholic acid pure emerged responsive modules, but two were enriched by their co-disappeared responsive modules including nucleotide-excision repair and epithelial structure maintenance. We found an additive effect of baicalin and jasminoidin in a divergent pattern and a synergistic effect of jasminoidin and ursodeoxycholic acid in a convergent pattern on ''central hit strategy'' of regulating inflammation against cerebral ischemia. The proposed module-based approach may provide us a holistic view to understand multiple pharmacological mechanisms associated with differential phenotypes from the standpoint of modular pharmacology.

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Section: Discussionmentioning

confidence: 99%

Entropy-based divergent and convergent modular pattern reveals additive and synergistic anticerebral ischemia mechanisms

Zhang

et al. 2016

Exp Biol Med (Maywood)

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“…The feasibility of this approach has been demonstrated in several studies, including using gene expression signatures of disease to identify the tripeptide GHK as an agent that reverses an emphysema lung tissue signature,38 and the antiseizure medication valproic acid as a novel therapy for triple-negative breast cancer that has been validated in a xenograph mode 39. This approach has also been successful in identifying potential novel therapeutics for lung cancer, including the identification of cimetidine as a potential treatment for lung adenocarcinoma,40 and the tricyclic imipramine and the antihistamine promethazine as predicated therapies for small cell lung cancer 41.…”

Section: Using the Transcriptome To Develop Biomarkers For Lung Cancementioning

confidence: 99%

Translating the transcriptome into tools for the early detection and prevention of lung cancer: Figure 1

Gesthalter

Vick

Steiling

et al. 2015

Thorax

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Despite advances in the management of lung cancer, this disease remains a significant global health burden with survival rates that have not significantly improved in decades. The mortality reduction achieved by low-dose helical CT (LDCT) screening of select high-risk patients is challenged by the high false positive rate of this screening modality and the potential for morbidity associated with follow-up diagnostic evaluation in patients with high risk for iatrogenic complications. The diagnostic dilemma of the indeterminate nodule incidentally identified on diagnostic or screening CT has created a need for reliable biomarkers capable of distinguishing benign from malignant disease. Furthermore, there is an urgent need to develop molecular biomarkers to supplement clinical risk models in order to identify patients at highest risk for having an early stage lung cancer that may derive the greatest benefit from LDCT screening, as well as identifying patients at high-risk for developing lung cancer that may be candidates for emerging chemopreventive strategies. Evolving bioinformatic techniques and the application of these algorithms to analyse the transcriptomic changes associated with lung cancer promise translational discoveries that can bridge these large clinical gaps. The identification of lung cancer associated transcriptomic alterations in readily accessible tissue sampling sites offers the potential to develop early diagnostic and risk stratification strategies applicable to large populations. This review summarises the challenges associated with the early detection, screening and chemoprevention of lung cancer with an emphasis on how genomic information encapsulated by the transcriptome can facilitate future innovations in these clinical settings.

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“…By querying disease signatures against drug signatures, it is possible to identify existing compounds that cause a more "healthy-like" pattern of gene expression and are predicted therefore to potentially serve as new treatments for that disease. As an example, the Connectivity Map has been leveraged to predict valproic acid, an anti-seizure medication, as a potential therapy for triple-negative breast cancer (49). The ability of valproic acid to inhibit triplenegative breast cancer was subsequently validated in an in vivo tumor-specific xenograft model (49).…”

Section: Transatlantic Airway Conferencementioning

confidence: 99%

“…As an example, the Connectivity Map has been leveraged to predict valproic acid, an anti-seizure medication, as a potential therapy for triple-negative breast cancer (49). The ability of valproic acid to inhibit triplenegative breast cancer was subsequently validated in an in vivo tumor-specific xenograft model (49). The Connectivity Map has also been used to identify topiramate, another anti-seizure medication, as a potential therapy for inflammatory bowel disease, a prediction that was validated in a rat model of this disease (50).…”

Section: Transatlantic Airway Conferencementioning

confidence: 99%

Personalized Management of Chronic Obstructive Pulmonary Disease via Transcriptomic Profiling of the Airway and Lung

Steiling¹,

Lenburg²,

Spira³

2013

Annals ATS

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Chronic obstructive pulmonary disease (COPD) is a clinically heterogeneous disease composed of variable degrees of airflow obstruction, emphysematous destruction, and small airway wall thickening. The natural history of this disease, although generally characterized by continued decline in lung function, is also highly variable. Novel transcriptomic approaches to study the airway and lung tissue in COPD hold the potential to improve our understanding of the molecular mechanisms underlying this heterogeneity and identify molecular subtypes of disease that have similar clinical manifestations. This new understanding can be leveraged to develop targeted COPD therapies and ultimately personalize treatment of COPD based on each patient's specific molecular subphenotype.

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A pharmacogenomic method for individualized prediction of drug sensitivity

Abstract: Using valproic acid as an example, the authors demonstrate that drug response signatures derived from genome-wide expression data can identify individuals likely to respond to a drug, and propose that this method could select optimal populations for clinical trials of new therapies.

Cited by 41 publications

References 53 publications

Entropy-based divergent and convergent modular pattern reveals additive and synergistic anticerebral ischemia mechanisms

Entropy-based divergent and convergent modular pattern reveals additive and synergistic anticerebral ischemia mechanisms

Translating the transcriptome into tools for the early detection and prevention of lung cancer: Figure 1

Personalized Management of Chronic Obstructive Pulmonary Disease via Transcriptomic Profiling of the Airway and Lung

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