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Utilization of the E?-Myc mouse to model heterogeneity of therapeutic response.

ABSTRACT: Human aggressive B-cell non-Hodgkin lymphomas (NHL) encompass the continuum between Burkitt lymphoma and diffuse large B-cell lymphoma (DLBCL), and display considerable clinical and biologic heterogeneity, most notably related to therapy response. We previously showed that lymphomas arising in the E?-Myc transgenic mouse are heterogeneous, mirroring genomic differences between Burkitt lymphoma and DLBCL. Given clinical heterogeneity in NHL and the need to develop strategies to match therapeutics with discrete forms of disease, we investigated the extent to which genomic variation in the E?-Myc model predicts response to therapy. We used genomic analyses to classify E?-Myc lymphomas, link E?-Myc lymphomas with NHL subtypes, and identify lymphomas with predicted resistance to conventional and NF-?B-targeted therapies. Experimental evaluation of these predictions links genomic profiles with distinct outcomes to conventional and targeted therapies in the E?-Myc model, and establishes a framework to test novel targeted therapies or combination therapies in specific genomically defined lymphoma subgroups. In turn, this will rationally inform the design of new treatment options for aggressive human NHL.

SUBMITTER: Rempel RE

PROVIDER: S-EPMC4258467 | biostudies-literature | 2014 Dec

REPOSITORIES: biostudies-literature

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Utilization of the Eμ-Myc mouse to model heterogeneity of therapeutic response.

Rempel Rachel E RE Jiang Xiaolei X Fullerton Paul P Tan Tuan Zea TZ Ye Jieru J Lau Jieying Amelia JA Mori Seiichi S Chi Jen-Tsan JT Nevins Joseph R JR Friedman Daphne R DR

Molecular cancer therapeutics 20141027 12

Human aggressive B-cell non-Hodgkin lymphomas (NHL) encompass the continuum between Burkitt lymphoma and diffuse large B-cell lymphoma (DLBCL), and display considerable clinical and biologic heterogeneity, most notably related to therapy response. We previously showed that lymphomas arising in the Eμ-Myc transgenic mouse are heterogeneous, mirroring genomic differences between Burkitt lymphoma and DLBCL. Given clinical heterogeneity in NHL and the need to develop strategies to match therapeutics ...[more]

PMID: 25349303

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Project description:BackgroundBreast cancer is a complex and heterogeneous disease with distinct subtypes and molecular profiles corresponding to different clinical outcomes. Mouse models of breast cancer are widely used, but their relevance in capturing the heterogeneity of human disease is unclear. Previous studies have shown the heterogeneity at the gene expression level for the MMTV-Myc model, but have only speculated on the underlying genetics.MethodsTumors from the microacinar, squamous, and EMT histological subtypes of the MMTV-Myc mouse model of breast cancer underwent whole genome sequencing. The genomic data obtained were then integrated with previously obtained matched sample gene expression data and extended to additional samples of each histological subtype, totaling 42 gene expression samples. High correlation was observed between genetic copy number events and resulting gene expression by both Spearman's rank correlation coefficient and the Kendall rank correlation coefficient. These same genetic events are conserved in humans and are indicative of poor overall survival by Kaplan-Meier analysis. A supervised machine learning algorithm trained on METABRIC gene expression data was used to predict the analogous human breast cancer intrinsic subtype from mouse gene expression data.ResultsHerein, we examine three common histological subtypes of the MMTV-Myc model through whole genome sequencing and have integrated these results with gene expression data. Significantly, key genomic alterations driving cell signaling pathways were well conserved within histological subtypes. Genomic changes included frequent, co-occurring mutations in KIT and RARA in the microacinar histological subtype as well as SCRIB mutations in the EMT subtype. EMT tumors additionally displayed strong KRAS activation signatures downstream of genetic activating events primarily ascribed to KRAS activating mutations, but also FGFR2 amplification. Analogous genetic events in human breast cancer showed stark decreases in overall survival. In further analyzing transcriptional heterogeneity of the MMTV-Myc model, we report a supervised machine learning model that classifies MMTV-Myc histological subtypes and other mouse models as being representative of different human intrinsic breast cancer subtypes.ConclusionsWe conclude the well-established MMTV-Myc mouse model presents further opportunities for investigation of human breast cancer heterogeneity.

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Utilization of the E?-Myc mouse to model heterogeneity of therapeutic response.

Publications

Utilization of the Eμ-Myc mouse to model heterogeneity of therapeutic response.

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