Prostate cancer remains the most commonly diagnosed malignancy in men in the United States and a leading cause of cancer related mortality. Nomograms and multi-variable tables identify men at high risk for recurrence following definitive local therapy but provide little insight as to the specific biology driving aggressive disease. Recent findings have highlighted the role for genetic translocations in lethal disease and molecular-based prognostic and pathway signatures have the potential to guide multi-modality therapy and improve the potential for cure. Our proposal will validate established RNA-, DNA-, and microRNA prognostic and pathway signatures in both a retrospective and prospective cohort of men with high-risk prostate cancer. We will first comprehensively assay a retrospective cohort of 200 men who have had prostatectomy, have frozen and paraffin embedded tissue available, and have mature clinical follow up. This cohort will be processed through a centralized system including the CALGB pathology coordinating office, assayed in laboratories with established expertise in comprehensive RNA-, DNA-, and microRNA analysis, and results will be analyzed in the CALGB Statistical Center. This cohort will provide the initial validation of prognostic and predictive signatures and determine how these signatures relate to ETS-family member and RAF translocations. The most informative signatures will be re-derived by the statistical center, re-applied to the retrospective dataset to confirm reproducibility of the computational models, and then applied to a samples from CALGB 90203, a prospective, randomized phase III trial that will randomize 750 men between immediate surgery or neoadjuvant docetaxel and androgen deprivation therapy followed by surgery. In addition, computational investigators will perform integrative analysis of RNA-, DNA-, and microRNA data so as to distinguish driver pathways responsible for the lethal phenotype from bystander pathways that are correlated with aggressiveness but not causative. Finally, promising, validated signatures will be adapted to paraffin-embedded tissue so as to develop clinically deployable bioassays. Our proposal leverages two outstanding cohorts, the scientific and computational expertise in five independent laboratories, the established capabilities of industrial partners, and the statistical rigor of the CALGB to validate prognostic and pathway signatures in high-risk prostate cancer. All data will be shared through GEO and other emerging databases so as to ensure that along with the insight resulting from our own analysis, our data will be an outstanding resource for the larger investigative community interested in the validation of prognostic signatures in high-risk prostate cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01CA157703-05
Application #
8917870
Study Section
Special Emphasis Panel (ZCA1-SRLB-4 (J1))
Program Officer
Mckee, Tawnya C
Project Start
2011-09-26
Project End
2016-08-31
Budget Start
2015-09-01
Budget End
2016-08-31
Support Year
5
Fiscal Year
2015
Total Cost
$454,043
Indirect Cost
$44,361
Name
Duke University
Department
Biostatistics & Other Math Sci
Type
Schools of Medicine
DUNS #
044387793
City
Durham
State
NC
Country
United States
Zip Code
27705
Pi, Lira; Halabi, Susan (2018) Combined Performance of Screening and Variable Selection Methods in Ultra-High Dimensional Data in Predicting Time-To-Event Outcomes. Diagn Progn Res 2:
Beltran, Himisha; Wyatt, Alexander W; Chedgy, Edmund C et al. (2017) Impact of Therapy on Genomics and Transcriptomics in High-Risk Prostate Cancer Treated with Neoadjuvant Docetaxel and Androgen Deprivation Therapy. Clin Cancer Res 23:6802-6811
Lin, Chen-Yen; Halabi, Susan (2017) A Simple Method for Deriving the Confidence Regions for the Penalized Cox's Model via the Minimand Perturbation. Commun Stat Theory Methods 46:4791-4808
Kattan, Michael W; Hess, Kenneth R; Amin, Mahul B et al. (2016) American Joint Committee on Cancer acceptance criteria for inclusion of risk models for individualized prognosis in the practice of precision medicine. CA Cancer J Clin 66:370-4
Zheng, Jie; Harris, Marcelline R; Masci, Anna Maria et al. (2016) The Ontology of Biological and Clinical Statistics (OBCS) for standardized and reproducible statistical analysis. J Biomed Semantics 7:53
Kim, Sangjin; Halabi, Susan (2016) High Dimensional Variable Selection with Error Control. Biomed Res Int 2016:8209453
Sartor, Oliver; Halabi, Susan (2015) Independent data monitoring committees: an update and overview. Urol Oncol 33:143-8
Potthoff, Richard F; Halabi, Susan (2015) A novel test to compare two treatments based on endpoints involving both nonfatal and fatal?events. Pharm Stat 14:273-83
Moser, Barry Kurt; Halabi, Susan (2015) Sample Size Requirements and Study Duration for Testing Main Effects and Interactions in Completely Randomized Factorial Designs When Time to Event is the Outcome. Commun Stat Theory Methods 44:275-285
Kim, Hyung L; Halabi, Susan; Li, Ping et al. (2015) A Molecular Model for Predicting Overall Survival in Patients with Metastatic Clear Cell Renal Carcinoma: Results from CALGB 90206 (Alliance). EBioMedicine 2:1814-20

Showing the most recent 10 out of 24 publications