1. Generation of stable PDX derived cell lines using conditional reprogramming
- Author
-
Alexandra Borodovsky, Travis J. McQuiston, Daniel Stetson, Ambar Ahmed, David Whitston, Jingwen Zhang, Michael Grondine, Deborah Lawson, Sharon S. Challberg, Michael Zinda, Brian A. Pollok, Brian A. Dougherty, and Celina M. D’Cruz
- Subjects
Patient derived xenograft ,Conditional reprogramming ,Cell line models ,Drug discovery ,Oncology ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Efforts to develop effective cancer therapeutics have been hindered by a lack of clinically predictive preclinical models which recapitulate this complex disease. Patient derived xenograft (PDX) models have emerged as valuable tools for translational research but have several practical limitations including lack of sustained growth in vitro. In this study, we utilized Conditional Reprogramming (CR) cell technology- a novel cell culture system facilitating the generation of stable cultures from patient biopsies- to establish PDX-derived cell lines which maintain the characteristics of the parental PDX tumor. Human lung and ovarian PDX tumors were successfully propagated using CR technology to create stable explant cell lines (CR-PDX). These CR-PDX cell lines maintained parental driver mutations and allele frequency without clonal drift. Purified CR-PDX cell lines were amenable to high throughput chemosensitivity screening and in vitro genetic knockdown studies. Additionally, re-implanted CR-PDX cells proliferated to form tumors that retained the growth kinetics, histology, and drug responses of the parental PDX tumor. CR technology can be used to generate and expand stable cell lines from PDX tumors without compromising fundamental biological properties of the model. It offers the ability to expand PDX cells in vitro for subsequent 2D screening assays as well as for use in vivo to reduce variability, animal usage and study costs. The methods and data detailed here provide a platform to generate physiologically relevant and predictive preclinical models to enhance drug discovery efforts.
- Published
- 2017
- Full Text
- View/download PDF