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Full-Text Articles in Medicine and Health Sciences
Building A Statistical Model For Predicting Cancer Genes, Ivan P. Gorlov, Christopher J. Logothetis, Shenying Fang, Olga Y. Gorlova, Christopher Amos
Building A Statistical Model For Predicting Cancer Genes, Ivan P. Gorlov, Christopher J. Logothetis, Shenying Fang, Olga Y. Gorlova, Christopher Amos
Dartmouth Scholarship
More than 400 cancer genes have been identified in the human genome. The list is not yet complete. Statistical models predicting cancer genes may help with identification of novel cancer gene candidates. We used known prostate cancer (PCa) genes (identified through KnowledgeNet) as a training set to build a binary logistic regression model identifying PCa genes. Internal and external validation of the model was conducted using a validation set (also from KnowledgeNet), permutations, and external data on genes with recurrent prostate tumor mutations. We evaluated a set of 33 gene characteristics as predictors. Sixteen of the original 33 predictors were …
Improved Tumor Contrast Achieved By Single Time Point Dual-Reporter Fluorescence Imaging, Kenneth M. Tichauer, Kimberley S. Samkoe, Kristian J. Sexton, Jason R. Gunn, Tayyaba Hasan, Brian W. Pogue
Improved Tumor Contrast Achieved By Single Time Point Dual-Reporter Fluorescence Imaging, Kenneth M. Tichauer, Kimberley S. Samkoe, Kristian J. Sexton, Jason R. Gunn, Tayyaba Hasan, Brian W. Pogue
Dartmouth Scholarship
In this study, we demonstrate a method to quantify biomarker expression that uses an exogenous dual-reporter imaging approach to improve tumor signal detection. The uptake of two fluorophores, one nonspecific and one targeted to the epidermal growth factor receptor (EGFR), were imaged at 1 h in three types of xenograft tumors spanning a range of EGFR expression levels (n = 6 in each group). Using this dual-reporter imaging methodology, tumor contrast-to-noise ratio was amplified by >6 times at 1 h postinjection and >2 times at 24 h. Furthermore, by as early as 20 min postinjection, the dual-reporter imaging signal …
Ovarian Cancer Progression Is Controlled By Phenotypic Changes In Dendritic Cells, Uciane K. Scarlett, Melanie R. Rutkowski, Adam M. Rauwerdink, Jennifer Fields, Ximena Escovar-Fadul, Jason Baird, Juan R. Cubillos-Ruiz, Ana C. Jacobs, Jorge L. Gonzalez, John Weaver, Steven Fiering, Jose R. Conejo-Garcia
Ovarian Cancer Progression Is Controlled By Phenotypic Changes In Dendritic Cells, Uciane K. Scarlett, Melanie R. Rutkowski, Adam M. Rauwerdink, Jennifer Fields, Ximena Escovar-Fadul, Jason Baird, Juan R. Cubillos-Ruiz, Ana C. Jacobs, Jorge L. Gonzalez, John Weaver, Steven Fiering, Jose R. Conejo-Garcia
Dartmouth Scholarship
We characterized the initiation and evolution of the immune response against a new inducible p53-dependent model of aggressive ovarian carcinoma that recapitulates the leukocyte infiltrates and cytokine milieu of advanced human tumors. Unlike other models that initiate tumors before the development of a mature immune system, we detect measurable antitumor immunity from very early stages, which is driven by infiltrating dendritic cells (DCs) and prevents steady tumor growth for prolonged periods. Coinciding with a phenotypic switch in expanding DC infiltrates, tumors aggressively progress to terminal disease in a comparatively short time. Notably, tumor cells remain immunogenic at advanced stages, but …