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Chemical genomics identifies small-molecule MCL1 repressors and BCL-xL as a predictor of MCL1 dependency.

ABSTRACT: MCL1, which encodes the antiapoptotic protein MCL1, is among the most frequently amplified genes in human cancer. A chemical genomic screen identified compounds, including anthracyclines, that decreased MCL1 expression. Genomic profiling indicated that these compounds were global transcriptional repressors that preferentially affect MCL1 due to its short mRNA half-life. Transcriptional repressors and MCL1 shRNAs induced apoptosis in the same cancer cell lines and could be rescued by physiological levels of ectopic MCL1 expression. Repression of MCL1 released the proapoptotic protein BAK from MCL1, and Bak deficiency conferred resistance to transcriptional repressors. A computational model, validated in vivo, indicated that high BCL-xL expression confers resistance to MCL1 repression, thereby identifying a patient-selection strategy for the clinical development of MCL1 inhibitors.

SUBMITTER: Wei G

PROVIDER: S-EPMC3685408 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature

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Chemical genomics identifies small-molecule MCL1 repressors and BCL-xL as a predictor of MCL1 dependency.

Wei Guo G Margolin Adam A AA Haery Leila L Brown Emily E Cucolo Lisa L Julian Bina B Shehata Shyemaa S Kung Andrew L AL Beroukhim Rameen R Golub Todd R TR

Cancer cell 20120401 4

MCL1, which encodes the antiapoptotic protein MCL1, is among the most frequently amplified genes in human cancer. A chemical genomic screen identified compounds, including anthracyclines, that decreased MCL1 expression. Genomic profiling indicated that these compounds were global transcriptional repressors that preferentially affect MCL1 due to its short mRNA half-life. Transcriptional repressors and MCL1 shRNAs induced apoptosis in the same cancer cell lines and could be rescued by physiologica ...[more]

PMID: 22516262

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Project description:Exposure to wildfire smoke continues to be a growing threat to public health, yet the chemical components in wildfire smoke that primarily drive toxicity and associated disease are largely unknown. This study utilized a suite of computational approaches to identify groups of chemicals induced by variable biomass burn conditions that were associated with biological responses in the mouse lung, including pulmonary immune response and injury markers. Smoke condensate samples were collected and characterized, resulting in chemical distribution information for 86 constituents across ten different exposures. Mixtures-relevant statistical methods included (i) a chemical clustering and data-reduction method, weighted chemical co-expression network analysis (WCCNA), (ii) a quantile g-computation approach to address the joint effect of multiple chemicals in different groupings, and (iii) a correlation analysis to compare mixtures modeling results against individual chemical relationships. Seven chemical groups were identified using WCCNA based on co-occurrence showing both positive and negative relationships with biological responses. A group containing methoxyphenols (e.g., coniferyl aldehyde, eugenol, guaiacol, and vanillin) displayed highly significant, negative relationships with several biological responses, including cytokines and lung injury markers. This group was further shown through quantile g-computation methods to associate with reduced biological responses. Specifically, mixtures modeling based on all chemicals excluding those in the methoxyphenol group demonstrated more significant, positive relationships with several biological responses; whereas mixtures modeling based on just those in the methoxyphenol group demonstrated significant negative relationships with several biological responses, suggesting potential protective effects. Mixtures-based analyses also identified other groups consisting of inorganic elements and ionic constituents showing positive relationships with several biological responses, including markers of inflammation. Many of the effects identified through mixtures modeling in this analysis were not captured through individual chemical analyses. Together, this study demonstrates the utility of mixtures-based approaches to identify potential drivers and inhibitors of toxicity relevant to wildfire exposures.

Dataset Information

Chemical genomics identifies small-molecule MCL1 repressors and BCL-xL as a predictor of MCL1 dependency.

Publications

Chemical genomics identifies small-molecule MCL1 repressors and BCL-xL as a predictor of MCL1 dependency.

Similar Datasets

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