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Data from Novel Mouse Models of Bladder Cancer Identify a Prognostic Signature Associated with Risk of Disease Progression

Posted on 2023-03-31 - 05:04
Abstract

To study the progression of bladder cancer from non–muscle-invasive to muscle-invasive disease, we have developed a novel toolkit that uses complementary approaches to achieve gene recombination in specific cell populations in the bladder urothelium in vivo, thereby allowing us to generate a new series of genetically engineered mouse models (GEMM) of bladder cancer. One method is based on the delivery of adenoviruses that express Cre recombinase in selected cell types in the urothelium, and a second uses transgenic drivers in which activation of inducible Cre alleles can be limited to the bladder urothelium by intravesicular delivery of tamoxifen. Using both approaches, targeted deletion of the Pten and p53 tumor suppressor genes specifically in basal urothelial cells gave rise to muscle-invasive bladder tumors. Furthermore, preinvasive lesions arising in basal cells displayed upregulation of molecular pathways related to bladder tumorigenesis, including proinflammatory pathways. Cross-species analyses comparing a mouse gene signature of early bladder cancer with a human signature of bladder cancer progression identified a conserved 28-gene signature of early bladder cancer that is associated with poor prognosis for human bladder cancer and that outperforms comparable gene signatures. These findings demonstrate the relevance of these GEMMs for studying the biology of human bladder cancer and introduce a prognostic gene signature that may help to stratify patients at risk for progression to potentially lethal muscle-invasive disease.

Significance:

Analyses of bladder cancer progression in a new series of genetically engineered mouse models has identified a gene signature of poor prognosis in human bladder cancer.

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Cancer Research

AUTHORS (15)

  • Soonbum Park
    Lijie Rong
    Tomasz B. Owczarek
    Matteo Di Bernardo
    Rivka L. Shoulson
    Chee-Wai Chua
    Jaime Y. Kim
    Amir Lankarani
    Prithi Chakrapani
    Talal Syed
    James M. McKiernan
    David B. Solit
    Michael M. Shen
    Hikmat A. Al-Ahmadie
    Cory Abate-Shen
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