bioRxiv ScienceSearch

Biology subjects

Goodspeed, A.

Publications and source records attributed to Goodspeed, A..

2 recordsLinked to original sources

Low MSH2 protein levels identify muscle-invasive bladder cancer resistant to cisplatin

BackgroundThe response to first-line, platinum-based treatment of muscle-invasive bladder cancer has not improved in three decades.\n\nObjectiveThe objective of this study is to identify genes that predict cisplatin resistance in bladder cancer.\n\nDesignWe performed a whole-genome, CRISPR-based screen in a bladder cancer cell line treated with cisplatin to identify genes that mediate response to cisplatin. Targeted validation was performed in vitro across two bladder cancer cell lines. The top gene candidate was validated in a publicly available bladder cancer dataset containing 340 bladder cancer patients with treatment, protein, and survival information.\n\nResults and limitationsThe cisplatin resistance screen suggested the mismatch repair pathway through the loss of MSH2 and MLH1 contribute to cisplatin resistance. Bladder cancer cells depleted of MSH2 are resistant to cisplatin in vitro, in part due to a reduction in apoptosis. These cells maintain sensitivity to the cisplatin-analog, oxaliplatin. Bladder tumors with low protein levels of MSH2 have poorer overall survival when treated with cisplatin- or carboplatin-based therapy.\n\nConclusionsWe generated in vitro and clinical support that bladder cancer cell lines and tumors with low levels of MSH2 are more resistant to cisplatin-based therapy. Further studies are warranted to determine the ability of MSH2 protein levels to serve as a prospective biomarker of chemotherapy response in bladder cancer.\n\nPatient summaryWe report the first evidence that the protein level of MSH2 may contribute to chemotherapy resistance observed in bladder cancer. MSH2 levels has the potential to serve as a biomarker of treatment response.

cancer biology

GSEA-InContext: Identifying novel and common patterns in expression experiments

MotivationGene Set Enrichment Analysis (GSEA) is routinely used to analyze and interpret coordinate changes in transcriptomics experiments. For an experiment where less than seven samples per condition are compared, GSEA employs a competitive null hypothesis to test significance. A gene set enrichment score is tested against a null distribution of enrichment scores generated from permuted gene sets, where genes are randomly selected from the input experiment. Looking across a variety of biological conditions, however, genes are not randomly distributed with many showing consistent patterns of up- or down-regulation. As a result, common patterns of positively and negatively enriched gene sets are observed across experiments. Placing a single experiment into the context of a relevant set of background experiments allows us to identify both the common and experiment-specific patterns of gene set enrichment.\n\nResultsWe compiled a compendium of 442 small molecule transcriptomic experiments and used GSEA to characterize common patterns of positively and negatively enriched gene sets. To identify experiment-specific gene set enrichment, we developed the GSEA-InContext method that accounts for gene expression patterns within a user-defined background set of experiments to identify statistically significantly enriched gene sets. We evaluated GSEA-InContext on experiments using small molecules with known targets and show that it successfully prioritizes gene sets that are specific to each experiment, thus providing valuable insights that complement standard GSEA analysis.\n\nAvailability and ImplementationGSEA-InContext is implemented in Python. Code, the background expression compendium, and results are available at: https://github.com/CostelloLab/GSEA-InContext

bioinformatics