Student Presentation -- Chieh-Hsiang Yang
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Ph.D. Dissertation Defense, Wednesday December 13, 2017 -- Modeling Endometrial Malignancy to Facilitate the Development of Genomic-guided Cancer Treatments

HCI Research South auditorium , 1:00 pm

Speaker: Chieh-Hsiang Yang. Advisor: Dr. Margit Janat-Amsbury


Abstract:

The incidence rate of endometrial cancer (EC) has doubled within the last two decades going along with a drop in average diagnostic age and a statistically significant reduction in survival rate. The exploration of four genomic clusters by The Cancer Genome Atlas (TCGA) Network provides molecular insights and opportunities to refine current management strategies. However, the advancement of genomic-guided treatments is hampered by the lack of appropriate disease models to obtain prospective validation of the relationship between cluster-association and treatment response. Work in this dissertation attempts to develop predictive, clinically relevant tools for therapeutic evaluation and integration of genomic-guided treatments. The first part describes the development of an estrogen induced endometrial hyperplasia (EH) mouse model. EH is an antecedent lesion to EC and currently only diagnosed by endometrial sampling. Histological analysis of murine endometrial tissues demonstrates that this model closely resembles disease progression, hormonal receptors status, and common genetic aberrations as seen in patients. The second section describes the establishment and characterization of EC patient-derived xenografts (EC-PDXs), which authentically recapitulate patient histology, genomic features, metastasis, as well as treatment outcomes. The last section aims to validate the predictive capabilities of utilizing genomic classification and cluster affiliation in the refinement of current treatment strategies conducting a PDX clinical trial. Our results demonstrate that selecting therapeutics based on affiliation to genomic clusters indeed results in improved treatment response and accuracy of prediction, and genomic similarities across various cancer types can be used to direct treatment. In conclusion, this is the first study that successfully demonstrates the use of genomic classification to enable the prediction of drug response for EC, and thus may facilitate to refine current management in a more precise and personalized way. Our models will also serve as advanced tools to study tumorigenesis and drug response.