A Genomic Strategy to Guide Use of Chemotherapeutics in Solid Tumors
Abstract
Using in vitro drug sensitivity data coupled with Affymetrix microarray data, we developed gene expression signatures that predict sensitivity to individual chemotherapeutic drugs. Each signature was validated with response data from an independent set of cell line studies. We further show that many of these signatures can accurately predict clinical response in individuals treated with these drugs. Notably, signatures developed to predict response to individual agents, when combined, could also predict response to multidrug regimens. Finally, we integrated the chemotherapy response signatures with signatures of oncogenic pathway deregulation to identify new therapeutic strategies that make use of all available drugs. The development of gene expression profiles that can predict response to commonly used cytotoxic agents provides opportunities to better use these drugs, including using them in combination with existing targeted therapies.
*** Please note that several changes have been made to posted data and supplemental information as indicated below. ***
Authors
Anil Potti, Holly K Dressman, Andrea Bild, Richard F Riedel, Gina Chan, Robyn Sayer, Janiel Cragun, Hope Cottrill, Michael J Kelley, Rebecca Petersen, David Harpole, Jeffrey Marks, Andrew Berchuck, Geoffrey S Ginsburg, Phillip Febbo, Johnathan Lancaster & Joseph R Nevins
Contact
- Joseph Nevins (j.nevins@duke.edu)
- Anil Potti (anil.potti@duke.edu)
Datasets
- Chang breast tumor data (n=24) (see clarification)
- Lung tumor and cell line data
- PI3K pathway data
- Docetaxel ovarian tumor data (n=14)
- Adria ALL data (n=95) (see clarification)
- Topotecan ovarian tumor data (n=48)
- Duke (CODEX) breast tumor data (n=171)
- MDACC breast tumor data (n=51)
- Paclitaxel ovarian tumor data (n=36)