Presentation Title

Global Onco-GPS

Start Date

November 2016

End Date

November 2016

Location

MSE 113

Type of Presentation

Oral Talk

Abstract

Cancer is a complex disease that can often be difficult to characterize and treat. Current classification and treatment focus on the site of origin of the tumor stemming from the belief that tumors from the same tissue will be more similar to each other than tumors from different tissues; however, the emergence of subtypes of cancers has complicated this view. Treating difficult cases as equivalent to average cases in the same site of origin can be detrimental to a patient as there is no time to waste on ineffective treatments. In order to solve this problem my project examines a new, state centric way to classify and treat cancer based on detailed genetic information from cell lines and computational analysis. I will explain the significance of this method through the characterization of states for cancers that show transcriptional activation of downstream pathways of KRAS. I will prove this is a reliable method by first annotating the defined states through the association of each state with genomic datasets such as mutations, drug sensitivity, gene expression, and protein expression. Next I will perform literature search to prove that some of the results have already been shown to be viable through other techniques. Finally I will show how this method can be used to discover new biomarkers and treatments for cancer not based on the site of origin, but instead the state the cancer belongs to.

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Nov 12th, 11:45 AM Nov 12th, 12:00 PM

Global Onco-GPS

MSE 113

Cancer is a complex disease that can often be difficult to characterize and treat. Current classification and treatment focus on the site of origin of the tumor stemming from the belief that tumors from the same tissue will be more similar to each other than tumors from different tissues; however, the emergence of subtypes of cancers has complicated this view. Treating difficult cases as equivalent to average cases in the same site of origin can be detrimental to a patient as there is no time to waste on ineffective treatments. In order to solve this problem my project examines a new, state centric way to classify and treat cancer based on detailed genetic information from cell lines and computational analysis. I will explain the significance of this method through the characterization of states for cancers that show transcriptional activation of downstream pathways of KRAS. I will prove this is a reliable method by first annotating the defined states through the association of each state with genomic datasets such as mutations, drug sensitivity, gene expression, and protein expression. Next I will perform literature search to prove that some of the results have already been shown to be viable through other techniques. Finally I will show how this method can be used to discover new biomarkers and treatments for cancer not based on the site of origin, but instead the state the cancer belongs to.