When/Where:   E2-215 at 12pm on January 22nd, 2020

Presenter:   Ioannis “Yianni” Anastopoulos  (UC Santa Cruz)

Title: Generalizing Drug Response from Cell Lines to Patients

Abstract:
Cancer treatment poses a unique challenge in part due to the heterogeneous nature of the disease. It is estimated that a staggering 75% of cancer patients do not respond to any type of cancer treatment. Preclinical models, such as cell lines and patient-derived xenografts, have been used to better understand the genes and pathways that contribute to tumorigenesis, as well as to identify markers of treatment response. For my thesis project, I am leveraging deep learning techniques to generalize drug response prediction from preclinical models to patients.

Standard machine learning methods have modest prediction accuracy and are challenged by the ever-increasing dimensionality of available data. Deep learning solves many shortcomings of previous methods and has been used successfully to advance drug design, drug-target profiling, and drug repositioning. In my preliminary results, I have shown that a deep learning model incorporating transcriptome and drug structure information achieves competitive drug response prediction performance on the Cancer Cell Line Encyclopedia (CCLE) dataset. I plan to extend this work to enable generalization to future drug compounds.