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Full-Text Articles in Medicine and Health Sciences
Personalizing Radiation Treatment Delivery In The Management Of Breast Cancer, Kamran A. Ahmed, G. Daniel Grass, Amber G. Orman, Casey Liveringhouse, Michael E. Montejo, Hatem H. Soliman, Heather S. Han, Brian J. Czerniecki, Javier F. Torres-Roca, Roberto Diaz
Personalizing Radiation Treatment Delivery In The Management Of Breast Cancer, Kamran A. Ahmed, G. Daniel Grass, Amber G. Orman, Casey Liveringhouse, Michael E. Montejo, Hatem H. Soliman, Heather S. Han, Brian J. Czerniecki, Javier F. Torres-Roca, Roberto Diaz
Oncologic Sciences Faculty Publications
Long-term data establishes the efficacy of radiotherapy in the adjuvant management of breast cancer. New dose and fractionation schemas have evolved and are available, each with unique risks and rewards. Current efforts are ongoing to tailor radiotherapy to the unique biology of breast cancer. In this review, we discuss our efforts to personalize radiotherapy dosing using genomic data and the implications for future clinical trials. We also explore immune mechanisms that may contribute to a tumor’s unique radiation sensitivity or resistance.
Sb Driver Analysis: A Sleeping Beauty Cancer Driver Analysis Framework For Identifying And Prioritizing Experimentally Actionable Oncogenes And Tumor Suppressors, Justin Y. Newberg, Michael A. Black, Nancy A. Jenkins, Neal G. Copeland, Karen M. Mann, Michael B. Mann
Sb Driver Analysis: A Sleeping Beauty Cancer Driver Analysis Framework For Identifying And Prioritizing Experimentally Actionable Oncogenes And Tumor Suppressors, Justin Y. Newberg, Michael A. Black, Nancy A. Jenkins, Neal G. Copeland, Karen M. Mann, Michael B. Mann
Oncologic Sciences Faculty Publications
Cancer driver prioritization for functional analysis of potential actionable therapeutic targets is a significant challenge. Meta-analyses of mutated genes across different human cancer types for driver prioritization has reaffirmed the role of major players in cancer, including KRAS, TP53 and EGFR, but has had limited success in prioritizing genes with non-recurrent mutations in specific cancer types. Sleeping Beauty (SB) insertional mutagenesis is a powerful experimental gene discovery framework to define driver genes in mouse models of human cancers. Meta-analyses of SB datasets across multiple tumor types is a potentially informative approach to prioritize drivers, and complements efforts …