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Leskov I.,Massachusetts Institute of Technology | Pallasch C.P.,Massachusetts Institute of Technology | Pallasch C.P.,University of Cologne | Drake A.,Massachusetts Institute of Technology | And 10 more authors.
Oncogene | Year: 2013

Although numerous mouse models of B-cell malignancy have been developed via the enforced expression of defined oncogenic lesions, the feasibility of generating lineage-defined human B-cell malignancies using mice reconstituted with modified human hematopoietic stem cells (HSCs) remains unclear. In fact, whether human cells can be transformed as readily as murine cells by simple oncogene combinations is a subject of considerable debate. Here, we describe the development of humanized mouse model of MYC/BCL2-driven 'double-hit' lymphoma. By engrafting human HSCs transduced with the oncogene combination into immunodeficient mice, we generate a fatal B malignancy with complete penetrance. This humanized-MYC/BCL2-model (hMB) accurately recapitulates the histopathological and clinical aspects of steroid-, chemotherapy- and rituximab-resistant human 'double-hit' lymphomas that involve the MYC and BCL2 loci. Notably, this model can serve as a platform for the evaluation of antibody-based therapeutics. As a proof of principle, we used this model to show that the anti-CD52 antibody alemtuzumab effectively eliminates lymphoma cells from the spleen, liver and peripheral blood, but not from the brain. The hMB humanized mouse model underscores the synergy of MYC and BCL2 in 'double-hit' lymphomas in human patients. Additionally, our findings highlight the utility of humanized mouse models in interrogating therapeutic approaches, particularly human-specific monoclonal antibodies. © 2013 Macmillan Publishers Limited All rights reserved. Source


Lee J.,Ut Md Anderson Cancer Center Houston | Ji Y.,Ut Md Anderson Cancer Center Houston | Liang S.,Ut Md Anderson Cancer Center Houston | Cai G.,Ut Md Anderson Cancer Center Houston | Muller P.,University of Texas at Austin
Cancer Informatics | Year: 2011

Motivation: RNA-Seq is a novel technology that provides read counts of RNA fragments in each gene, including the mapped positions of each read within each gene. Besides many other applications it can be used to detect differentially expressed genes. Most published methods collapse the position-level read data into a single gene-specific expression measurement. Statistical inference proceeds by modeling these gene-level expression measurements. Results: We present a Bayesian method of calling differential expression (BM-DE) that directly models the position-level read counts. We demonstrate the potential advantage of the BM-DE method compared to existing approaches that rely on gene-level aggregate data. An important additional feature of the proposed approach is that BM-DE can be used to analyze RNA-Seq data from experiments without biological replicates. This becomes possible since the approach works with multiple position-level read counts for each gene. We demonstrate the importance of modeling for position-level read counts with a yeast data set and a simulation study. © the author(s), publisher and licensee Libertas Academica Ltd. Source

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