Assistant Professor of Biomedical Engineering Oregon Health & Science University Portland, Oregon
Jayne Koskinas Ted Giovanis Foundation for Health and Policy Partnership
Seeking to understand the cellular processes leading to drug restistance.
A cross-disciplinary approach is applied to identify new targets to prevent resistance to anti-cancer therapies.
This collaborative study will accelerate discoveries that will inform strategies to improve patient outcomes.
Drug resistance is a key impediment to breast cancer therapy. While genetic mechanisms of drug resistance have been a focus of many studies, the ability of a cell to dynamically evade drugs through non-genetic means is an often-overlooked mechanism for why drugs may not be effective.
Cellular plasticity–the ability to interconvert between different functional states–gives rise to dynamic tumor heterogeneity through the generation of biologically and genetically distinct tumor cell subpopulations with diverse properties, including susceptibility to therapy.
Drs. Heiser and Nie believe that cell state plasticity contributes to drug resistance. In this project co-funded by BCRF and Jayne Koskinas Ted Giovanis Foundation for Health and Policy, they will employ an integrated experimental and modeling approach that can be used to identify rational approaches to overcome plasticity-induced resistance.
The goal of this collaborative project is to identify non-genetic mechanisms of drug resistance that can inform more effective treatments for patients with breast cancer.
Dr. Heiser received her BA in molecular biology at UC Berkeley and her PhD in neuroscience from the University of Pittsburgh. She is currently an Assistant Professor in the Department of Biomedical Engineering at OHSU. Her laboratory is focused on identifying predictors of drug response and resistance, using novel imaging techniques to identify phenotypic changes associated with molecular aberrations and therapeutic response, and studying the influence of the microenvironment on cancer cells. In all of this work, she uses both computational and experimental techniques, with a key goal of closing the gap between these approaches.