Project description:Combinations of anticancer agents may have synergistic anti-tumor effects, but enhanced toxicity often limit their clinical use. The risk that combinations of two or more drugs will cause adverse effects that are more severe than drugs used as monotherpies can be hypothesized from comprehensive analysis of each compound’s activity. We generated microarray gene expression data following a single dose of agents administered individually with that of the agents administered in a combination. The key objective of this initiative is to generate and make publicly available key high-content gene expression data sets for mechanistic hypothesis generation for several anticancer drug combinations. The expectation is that availability of tissue-based genomic information that are derived from target tissues will facilitate the generation and testing of mechanistic hypotheses. The view is that availability of these data sets for bioinformaticians and other scientists will contribute to analysis of these data and evaluation of the approach.

2018-09-11 | GSE119129 | GEO

Transcriptomic profiles of tissues from rats treated with drug combinations [Study 3]

2018-09-11 | GSE119133 | GEO

Transcriptomic profiles of tissues from rats treated with drug combinations [Study 1]

2018-09-11 | GSE119122 | GEO

Transcriptomic profiles of tissues from rats treated with anticancer drug combinations.

Project description:To achieve therapeutic goals, many cancer chemotherapeutics are used at doses close to their maximally tolerated doses. Thus, it may be expected that when therapies are combined at therapeutic doses, toxicity profiles may change. In many ways, prediction of synergistic toxicities for drug combinations is similar to predicting synergistic efficacy, and is dependent upon building hypotheses from molecular mechanisms of drug toxicity. The key objective of this initiative was to generate and make publicly available key high-content data sets for mechanistic hypothesis generation as it pertains to a unique toxicity profile of a drug pair for several anticancer drug combinations. The expectation is that tissue-based genomic information that are derived from target tissues will also facilitate the generation and testing of mechanistic hypotheses. The view is that availability of these data sets for bioinformaticians and other scientists will contribute to analysis of these data and evaluation of the approach.

| S-EPMC6326153 | biostudies-literature

Transcriptomic profiles of tissues from rats treated with drug combinations

Project description:This SuperSeries is composed of the SubSeries listed below.

2018-09-11 | GSE119135 | GEO

Emergent gene expression responses to drug combinations predict higher-order drug interactions

Project description:Effective design of combination therapies requires understanding the changes in cell physiology resulting from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations.

2019-10-02 | GSE138256 | GEO

Emergent gene expression responses to drug combinations predict higher-order drug interactions

Project description:Emergent gene expression responses to drug combinations predict higher-order drug interactions

| PRJNA575231 | ENA