The development of radiation predictive assays has been a central question in radiation research for more than fifty years. Recent developments in genomic profiling (DNA microarrays) have led to promises that predictors or classifiers will be developed to predict the classification and prognosis of cancer. We have developed for the first time a radiation classifier that predicts radiation sensitivity based on gene expression profiles derived from DNA microarrays. One of our goals is to improve the accuracy of our classifier which is currently 60%. This would allow us to eventually validate this new approach in a clinical trial. To do this we will expand the database of cell lines in the classifier to include all 60 cancer cell lines from the NCI. The design of our classifier involves two steps: a gene filtering step, where predictive genes are identified, and an evaluation process, where the predictor is tested. The gene filtering step is performed using significance of microarrays analysis (SAM), and the evaluation is performed using leave-one-out cross-validation. Our novel approach to gene identification consistently selected three known and one unknown genes as predictive of radiation response. In this proposal we plan to validate the results of this in silico analysis, by quantitative RT-PCR. Validation of these genes would not only confirm the validity of our analysis, but would establish three new genes that may be involved in radiation response. Finally, we have hypothesized that RbAp48, one of the genes identified in our analysis, plays a direct role in determining radiation phenotype. We will overexpress (transfection) and/or downregulate (antisense oligos),RbAp48 in cell lines and determine whether the radiation response is altered. In summary, our proposal may significantly advance radiation oncology research in several areas. Improvement in the accuracy of the classifier will potentially allow for the first time the identification of patients with radiocurable tumors, prior to the delivery of therapy. Validation of the predictive genes, will possibly identify several novel molecular pathways in radiation response.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Clinical Investigator Award (CIA) (K08)
Project #
5K08CA108926-02
Application #
6916581
Study Section
Subcommittee G - Education (NCI)
Program Officer
Ojeifo, John O
Project Start
2004-07-01
Project End
2009-06-30
Budget Start
2005-07-01
Budget End
2006-06-30
Support Year
2
Fiscal Year
2005
Total Cost
$131,307
Indirect Cost
Name
H. Lee Moffitt Cancer Center & Research Institute
Department
Type
DUNS #
139301956
City
Tampa
State
FL
Country
United States
Zip Code
33612
Ahmed, Kamran A; Chinnaiyan, Prakash; Fulp, William J et al. (2015) The radiosensitivity index predicts for overall survival in glioblastoma. Oncotarget 6:34414-22
Eschrich, Steven; Zhang, Hongling; Zhao, Haiyan et al. (2009) Systems biology modeling of the radiation sensitivity network: a biomarker discovery platform. Int J Radiat Oncol Biol Phys 75:497-505
Eschrich, Steven A; Pramana, Jimmy; Zhang, Hongling et al. (2009) A gene expression model of intrinsic tumor radiosensitivity: prediction of response and prognosis after chemoradiation. Int J Radiat Oncol Biol Phys 75:489-96
Torres-Roca, Javier F; DeSilvio, Michelle; Mora, Linda B et al. (2007) Activated STAT3 as a correlate of distant metastasis in prostate cancer: a secondary analysis of Radiation Therapy Oncology Group 86-10. Urology 69:505-9
Scuto, Anna; Zhang, Hongling; Zhao, Haiyan et al. (2007) RbAp48 regulates cytoskeletal organization and morphology by increasing K-Ras activity and signaling through mitogen-activated protein kinase. Cancer Res 67:10317-24
Torres-Roca, Javier F; Cantor, Alan B; Shukla, Sonia et al. (2006) Treatment of intermediate-risk prostate cancer with brachytherapy without supplemental pelvic radiotherapy: a review of the H. Lee Moffitt Cancer Center experience. Urol Oncol 24:384-90
Torres-Roca, Javier F (2006) The role of external-beam radiation therapy in the treatment of clinically localized prostate cancer. Cancer Control 13:188-93
Torres-Roca, Javier F; Eschrich, Steven; Zhao, Haiyan et al. (2005) Prediction of radiation sensitivity using a gene expression classifier. Cancer Res 65:7169-76