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Regulation of Metabolism by LSR (part II)

ABSTRACT: Our aim is to identify the LSR-driven metabolomics profile of breast cancer cells in lean and obesogenic environments. Breast cancer cell models with high or undetectable levels of LSR, including drug resistance models, were cultured in lean and obesogenic environments and comprehensive metabolomics profiling, including lipidomics-focused sub-analyses were performed. The metabolomics analyses using both approaches will help us determine if LSR enhances aggressive breast cancer phenotypes via modulation of cellular bioenergetic metabolism, ultimately contributing to poor patient outcome.

ORGANISM(S): Human Homo Sapiens

TISSUE(S): Cells

DISEASE(S): Cancer

SUBMITTER: Susan Sumner

PROVIDER: ST000529 | MetabolomicsWorkbench | Fri Dec 30 00:00:00 GMT 2016

REPOSITORIES: MetabolomicsWorkbench

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Project description:Background: Human breast cancer is a heterogeneous disease consisting of multiple molecular subtypes. Genetically engineered mouse models (GEMMs) are useful resources for studying breast cancers in vivo under genetically controlled and immune competent conditions. Identifying murine models with conserved human tumor features will facilitate etiology determinations, highlight the effects of mutations on pathway activation, and improve preclinical drug validation. Results: Transcriptomic profiles of 27 murine models of mammary carcinoma and normal mammary tissue were determined using gene expression microarrays. Hierarchical clustering analysis identified 17 distinct murine subtypes (classes). Across species analyses using three independent human breast cancer datasets identified eight murine classes that represent specific human breast cancer subtypes. Multiple models were associated with human basal-like tumors including TgC3(1)-Tag, TgWap-Myc, and Trp53-/-. Interestingly, the TgWAPCre-Etv6 model mimicked the HER2-enriched subtype, a group of human tumors without a murine counterpart in previous comparative studies. Gene signature analysis identified hundreds of commonly expressed pathways between linked mouse and human subtypes, highlighting potentially common genetic drivers of tumorigenesis and candidate pathways for therapeutic intervention. Conclusion: This study consolidates murine models of breast carcinoma into the largest comprehensive transcriptomic dataset to date to identify human-mouse disease subtype counterparts. This approach illustrates the value of comparisons between species to identify murine models that faithfully mimic the human condition and indicates that multiple GEMMs are needed to represent the diversity of human breast cancers. These trans-species associations should guide model selection during preclinical study design to ensure appropriate representatives of the human disease subtypes are used. Keywords: breast cancer, comparative genomics, genetically engineered mouse models, and molecular pathway signatures reference x sample

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Regulation of Metabolism by LSR (part II)

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