A modular approach for integrative analysis of large-scale gene-expression and drug-response data

2008 ◽  
Vol 26 (5) ◽  
pp. 531-539 ◽  
Author(s):  
Zoltán Kutalik ◽  
Jacques S Beckmann ◽  
Sven Bergmann
Keyword(s):  
Gene Expression ◽  
Large Scale ◽  
Drug Response ◽  
Integrative Analysis ◽  
Modular Approach ◽  
Response Data

scholarly journals Graph Convolutional Network for Drug Response Prediction Using Gene Expression Data

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 772
Author(s):  
Seonghun Kim ◽  
Seockhun Bae ◽  
Yinhua Piao ◽  
Kyuri Jo
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Large Scale ◽  
Drug Response ◽  
Response Prediction ◽  
Biological Data ◽  
Expression Data ◽  
Convolutional Network ◽  
Essential Information ◽  
Protein Protein Interaction

Genomic profiles of cancer patients such as gene expression have become a major source to predict responses to drugs in the era of personalized medicine. As large-scale drug screening data with cancer cell lines are available, a number of computational methods have been developed for drug response prediction. However, few methods incorporate both gene expression data and the biological network, which can harbor essential information about the underlying process of the drug response. We proposed an analysis framework called DrugGCN for prediction of Drug response using a Graph Convolutional Network (GCN). DrugGCN first generates a gene graph by combining a Protein-Protein Interaction (PPI) network and gene expression data with feature selection of drug-related genes, and the GCN model detects the local features such as subnetworks of genes that contribute to the drug response by localized filtering. We demonstrated the effectiveness of DrugGCN using biological data showing its high prediction accuracy among the competing methods.

scholarly journals Linking drug target and pathway activation for effective therapy using multi-task learning

2017 ◽  
Author(s):  
Mi Yang ◽  
Jaak Simm ◽  
Chi Chung Lam ◽  
Pooya Zakeri ◽  
Gerard J.P. van Westen ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Drug Targets ◽  
Large Scale ◽  
Drug Response ◽  
Drug Repurposing ◽  
Machine Learning Algorithms ◽  
Response Data ◽  
Combination Strategies ◽  
Pathway Activation ◽  
Protein X

ABSTRACTDespite the abundance of large-scale molecular and drug-response data, the insights gained about the mechanisms underlying treatment efficacy in cancer has been in general limited. Machine learning algorithms applied to those datasets most often are used to provide predictions without interpretation, or reveal single drug-gene association and fail to derive robust insights. We propose to use Macau, a bayesian multitask multi-relational algorithm to generalize from individual drugs and genes and explore the interactions between the drug targets and signaling pathways’ activation. A typical insight would be: “Activation of pathway Y will confer sensitivity to any drug targeting protein X”. We applied our methodology to the Genomics of Drug Sensitivity in Cancer (GDSC) screening, using gene expression of 990 cancer cell lines, activity scores of 11 signaling pathways derived from the tool PROGENy as cell line input and 228 nominal targets for 265 drugs as drug input. These interactions can guide a tissue-specific combination treatment strategy, for example suggesting to modulate a certain pathway to maximize the drug response for a given tissue. We confirmed in literature drug combination strategies derived from our result for brain, skin and stomach tissues. Such an analysis of interactions across tissues might help target discovery, drug repurposing and patient stratification strategies.

scholarly journals Assessment of pharmacogenomic agreement

2016 ◽  
Author(s):  
Zhaleh Safikhani ◽  
Nehme El-Hachem ◽  
Rene Quevedo ◽  
Petr Smirnov ◽  
Anna Goldenberg ◽  
...  
Keyword(s):  
Cell Line ◽  
Reasonable Agreement ◽  
Large Scale ◽  
Drug Response ◽  
Drug Sensitivity ◽  
Cancer Cell Line ◽  
Genomic Data ◽  
Clinical Settings ◽  
Response Data ◽  
Pharmacological Data

AbstractIn 2013 we published an analysis demonstrating that drug response data and gene-drug associations reported in two independent large-scale pharmacogenomic screens, Genomics of Drug Sensitivity in Cancer1(GDSC) and Cancer Cell Line Encyclopedia2(CCLE), were inconsistent3. The GDSC and CCLE investigators recently reported that their respective studies exhibit reasonable agreement and yield similar molecular predictors of drug response4, seemingly contradicting our previous findings3. Reanalyzing the authors’ published methods and results, we found that their analysis failed to account for variability in the genomic data and more importantly compared different drug sensitivity measures from each study, which substantially deviate from our more stringent consistency assessment. Our comparison of the most updated genomic and pharmacological data from the GDSC and CCLE confirms our published findings that the measures of drug response reported by these two groups are not consistent5. We believe that a principled approach to assess the reproducibility of drug sensitivity predictors is necessary before envisioning their translation into clinical settings.

scholarly journals PharmacoDB: an integrative database for miningin vitrodrug screening studies

2017 ◽  
Author(s):  
Petr Smirnov ◽  
Victor Kofia ◽  
Alexander Maru ◽  
Mark Freeman ◽  
Chantal Ho ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Large Scale ◽  
Drug Response ◽  
Chemical Compounds ◽  
Integrative Analysis ◽  
Multiple Drug ◽  
List Type ◽  
Pharmacogenomic Studies

ABSTRACTRecent pharmacogenomic studies profiled large panels of cancer cell lines against hundreds of approved drugs and experimental chemical compounds. The overarching goal of these screens is to measure sensitivity of cell lines to chemical perturbation, correlate these measures to genomic features, and thereby develop novel predictors of drug response. However, leveraging this valuable data is challenging due to the lack of standards for annotating cell lines and chemical compounds, and quantifying drug response. Moreover, it has been recently shown that the complexity and complementarity of the experimental protocols used in the field result in high levels of technical and biological variation in thein vitropharmacological profiles. There is therefore a need for new tools to facilitate rigorous comparison and integrative analysis of large-scale drug screening datasets. To address this issue, we have developed PharmacoDB (pharmacodb.pmgenomics.ca), a database integrating the largest pharmacogenomic studies published to date. Here, we describe how the curation of cell line and chemical compound identifiers maximizes the overlap between datasets and how users can leverage such data to compare and extract robust drug phenotypes. PharmacoDB provides a unique resource to mine a compendium of curated pharmacogenomic datasets that are otherwise disparate and difficult to integrate.Key pointsCuration of cell line and drug identifiers in the largest pharmacogenomic studies published to dateUniform processing of drug sensitivity data to reduce heterogeneity across studiesMultiple drug response summary metrics enabling visual comparison and integrative analysis

scholarly journals Large-Scale Labeling and Assessment of Sex Bias in Publicly Available Expression Data

2020 ◽  
Author(s):  
Emily Flynn ◽  
Annie Chang ◽  
Russ B. Altman
Keyword(s):  
Gene Expression ◽  
Cell Line ◽  
Large Scale ◽  
Drug Response ◽  
Drug Exposure ◽  
Adverse Drug Events ◽  
Human Cancer ◽  
Sampling Bias ◽  
Sex Bias ◽  
Expression Data

ABSTRACTWomen are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we infer sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio. Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of single sex studies, split between female-only and male-only (33.3% vs 18.4% in human and 31.0% vs 30.4% in mouse respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies respectively. Our expression-based sex labels allow us to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2-5%). We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.

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