Cescon, D. | bioRxiv Science

bioRxiv2018

Metabolic adaptations underlie epigenetic vulnerabilities in chemoresistant breast cancer.

Cancer cell survival upon cytotoxic drug exposure leads to changes in cell identity, dictated by the epigenome. Several metabolites serve as substrates or co-factors to chromatin-modifying enzymes, suggesting that metabolic changes can underlie change in cell fate. Here, we show that progression of triple-negative breast cancer (TNBC) to taxane-resistance is characterized by altered methionine metabolism and S-adenosylmethionine (SAM) availability, giving rise to DNA hypomethylation in regions enriched for transposable elements (TE). Compensatory redistribution of H3K27me3 forming Large Organized Chromatin domains of lysine (K) modification (LOCK) prevents expression of TE in taxane-resistant cells. Pharmacological inhibition of EZH2, the H3K27me3 methyltransferase, alleviates TE repression, leading to the accumulation of dsRNA and activation of the interferon viral mimicry-response, specifically inhibiting the growth of taxane-resistant TNBC. Together, our work delineates a role for metabolic adaptations in redefining the epigenome of taxane-resistant TNBC cells and underlies an epigenetic vulnerability toward pharmacological inhibition of EZH2.

genomics↗

bioRxiv2017

Gene isoforms as expression-based biomarkers predictive of drug response in vitro

BackgroundOne of the main challenges in precision medicine is the identification of molecular features associated to drug response to provide clinicians with tools to select the best therapy for each individual cancer patient. The recent adoption of next-generation sequencing technologies enables accurate profiling of not only gene expression but also alternatively-spliced transcripts in large-scale pharmacogenomic studies. Given that altered mRNA splicing has been shown to be prominent in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery.\n\nMethodsTo address the lack of reproducibility of drug sensitivity measurements across studies, we developed a meta-analytical framework combining the pharmacological data generated within the Cancer Cell Line Encyclopedia (CCLE) and the Genomics of Drug Sensitivity in Cancer (GDSC). Predictive models are fitted with CCLE RNA-seq data as predictor variables, controlled for tissue type, and combined GDSC and CCLE drug sensitivity values as dependent variables.\n\nResultsWe first validated the biomarkers identified from GDSC and CCLE using an existing pharmacogenomic dataset of 70 breast cancer cell lines. We further selected four drugs with the most promising biomarkers to test whether their predictive value is robust to change in pharmacological assay. We successfully validated 10 isoform-based biomarkers predictive of drug response in breast cancer, including TGFA-001 for the MEK tyrosine kinase inhibitor (TKI) AZD6244, DUOX-001 for the EGFR inhibitor erlotinib, and CPEB4-001 transcript expression associated with lack of sensitivity to paclitaxel.\n\nConclusionThe results of our meta-analysis of pharmacogenomic data suggest that isoforms represent a rich resource for biomarkers predictive of response to chemo- and targeted therapies. Our study also showed that the validation rate for this type of biomarkers is low (<50%) for most drugs, supporting the requirements for independent datasets to identify reproducible predictors of response to anticancer drugs.

bioinformatics↗