Motivation: Targeted therapeutics have the potential for efficacy against tumors with minimal effects on normal tissues. However, predicting effective drugs from molecular signatures remains a challenge. Here, we present Drug Mechanism Enrichment Analysis (DMEA), a method that uses a transcriptomic signature to predict drug mechanism(s) of action to which a tumor cell may be sensitive or resistant. The method derives its power by aggregating data from many drugs with a shared mechanism of action. Results: We first tested the sensitivity of DMEA using synthetic data. We next validated that DMEA recapitulated known sensitivities to HMGCR, EGFR, and RAF inhibitors while also identifying drug mechanisms for resistant cancers. Finally, we predicted tissue-dependent drug sensitivity for tumors with high and low expression of the cystine/glutamate antiporter xCT. Collectively, DMEA is a novel bioinformatic tool that uses molecular signatures to predict targeted therapeutics sharing a common mechanism of action. Availability and implementation: DMEA is freely available to download as an R package at: https://github.com/BelindaBGarana/DMEA.
Background There is a pressing need for improved methods to identify effective therapeutics for diseases. Many computational approaches have been developed to repurpose existing drugs to meet this need. However, these tools often output long lists of candidate drugs that are difficult to interpret, and individual drug candidates may suffer from unknown off-target effects. We reasoned that an approach which aggregates information from multiple drugs that share a common mechanism of action (MOA) would increase on-target signal compared to evaluating drugs on an individual basis. In this study, we present drug mechanism enrichment analysis (DMEA), an adaptation of gene set enrichment analysis (GSEA), which groups drugs with shared MOAs to improve the prioritization of drug repurposing candidates. Results First, we tested DMEA on simulated data and showed that it can sensitively and robustly identify an enriched drug MOA. Next, we used DMEA on three types of rank-ordered drug lists: (1) perturbagen signatures based on gene expression data, (2) drug sensitivity scores based on high-throughput cancer cell line screening, and (3) molecular classification scores of intrinsic and acquired drug resistance. In each case, DMEA detected the expected MOA as well as other relevant MOAs. Furthermore, the rankings of MOAs generated by DMEA were better than the original single-drug rankings in all tested data sets. Finally, in a drug discovery experiment, we identified potential senescence-inducing and senolytic drug MOAs for primary human mammary epithelial cells and then experimentally validated the senolytic effects of EGFR inhibitors. Conclusions DMEA is a versatile bioinformatic tool that can improve the prioritization of candidates for drug repurposing. By grouping drugs with a shared MOA, DMEA increases on-target signal and reduces off-target effects compared to analysis of individual drugs. DMEA is publicly available as both a web application and an R package at https://belindabgarana.github.io/DMEA.
Commercially available bite blocks used for invasive imaging procedures have design limitations, including bulky profile, being made of hard plastic that may damage surrounding tissue, and tendency to dislodge. We designed a novel bite block to address these limitations and evaluated this bite block in 50 patients undergoing diagnostic or intraprocedural transesophageal echocardiography examinations. Nine of 11 (82%) imagers who used the redesigned bite block preferred it over the standard bite block used at our institution. The novel bite block is an alternative device to standard bite blocks that was redesigned to protect both the patient and probe.
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