Bladder cancer, the fourth most common cancer among U.S. men, causes significant morbidity and mortality in the United States and across the world. A better understanding of how bladder cancer initiates and progresses is critical to its prevention, early detection, and treatment. The field effect in cancer is a biological process in which early changes in microscopically normal tissues and cells initiate cancer and precede the development of microscopically recognizable precursor lesions and carcinoma. We have developed a whole-organ histologic and genomic mapping (WOHGM) approach to identify genes and biological pathways whose modifications are associated with the evolution of bladder cance...

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Bladder cancer, the fourth most common cancer among U.S. men, causes significant morbidity and mortality in the United States and across the world. A better understanding of how bladder cancer initiates and progresses is critical to its prevention, early detection, and treatment. The field effect in cancer is a biological process in which early changes in microscopically normal tissues and cells initiate cancer and precede the development of microscopically recognizable precursor lesions and carcinoma. We have developed a whole-organ histologic and genomic mapping (WOHGM) approach to identify genes and biological pathways whose modifications are associated with the evolution of bladder cancer from occult mucosal field effects to invasive disease. Despite the initial success of the WOHGM approach, the development of computational methods for analyzing and integrating spatially correlated multiplatform genomic data is still in its infancy, resulting in inefficient analysis and lost biological insights into the integrated mechanisms of cancer development. To address these pressing analytical needs and challenges, we propose to develop novel statistical and computational methods to identify the field effects of bladder cancer through integrative modeling of spatially resolved multiplatform genomics data. The identified field-effect genes will be validated experimentally. We will also develop user-friendly software to implement the proposed computational methods and facilitate their use and dissemination in the scientific community. The completion of the proposed project will not only yield a suite of innovative and powerful computational and analytic methods for integrating spatially-resolved multiplatform genomics data but also identify field-effect genes and pathways, leading to a better understanding of bladder cancer evolution and progression, new biomarkers for early detection, and novel therapeutic targets.

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