Validation of a genomic classifier for predicting biochemical failure following postoperative radiation therapy in high-risk prostate cancer.

Authors

null

Robert Benjamin Den

Department of Radiation Oncology, Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA

Robert Benjamin Den , Felix Yi-Chung Feng , Timothy Norman Showalter , Mark Vikas Mishra , Edouard John Trabulsi , Costas D. Lallas , Leonard G. Gomella , Ruth C. Birbe , Peter McCue , Mercedeh Ghadessi , Karen E. Knudsen , Adam Dicker

Organizations

Department of Radiation Oncology, Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, University of Michigan Health System, Ann Arbor, MI, Department of Radiation Oncology, University of Virginia, Charlottesvile, VA, Department of Urology, Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, Jefferson Medical College and Kimmel Cancer Center, Philadelphia, PA, Thomas Jefferson University Hospital, Philadelphia, PA, Department of Pathology, Thomas Jefferson University, Philadelphia, PA, GenomeDx Biosciences, Inc., Vancouver, BC, Canada, The Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA

Research Funding

No funding sources reported

Background: Radiation therapy (RT) is commonly offered in the post radical prostatectomy (RP) setting, however response varies. We hypothesized that the genomic classifier ([GC] Decipher) score would predict biochemical failure (BF) and distant metastasis (DM) in men receiving post−RP RT. Methods: Under an institutional review board approved protocol, 223 men who underwent post−RP RT at the Kimmel Cancer Center of Thomas Jefferson University for pT3 or margin positive disease from 1990 to 2009 were identified. RNA was extracted from 143 patients with paraffin−embedded specimens and expression quantified from the highest Gleason grade tumor focus using a high−density oligonucleotide microarray. Excluding men who received neo−adjuvant therapy, 139 patients remained for GC calculation. Area under the receiver operating curve (AUC), decision curves, cumulative incidence accounting for competing risks, and multivariable Cox regression analyses were used to assess GC for predicting BF and DM after RT in comparison to nomograms. Results: The AUC of CAPRA-S was 0.67 (95% CI 0.58−0.77) and 0.65 (95% CI 0.44−0.86) for BF and DM, respectively. Integration of GC improved AUC to 0.75 (95% CI 0.66−0.84) and 0.77 (95% CI 0.64−0.91) for BF and DM, respectively. Cumulative incidence of BF at 8 years post-RT was 21%, 48%, and 81% for low (less than 0.4), intermediate (0.4 to 0.6), and high (more than 0.6) GC, respectively (p<0.00001). In multivariable analysis, patients who received RT early (pre−RT prostate-specific antigen [PSA] less than 1 ng/mL) had a BF benefit with a significantly reduced hazard ratio (HR) of 0.32 (95% CI 0.11−0.96, p<0.042). Patients with high GC had an HR of 14.73 for BF (95% CI 4.90−44.31, p<0.00001). Earlier PSA recurrence was observed in patients with high GC score that received salvage compared to adjuvant RT with median BF survival post-RT of 4.67 versus 8.78 years (p<0.04). This held true after adjusting for CAPRA-S score. Conclusions: This is the first validation of the GC in the post−RP RT setting. GC improved risk stratification above clinical classifiers. Patients with high GC received significant benefit from early RT intervention. For those patients with high pre-RT PSA and high GC, exploration of intensified therapy is warranted.

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Abstract Details

Meeting

2014 Genitourinary Cancers Symposium

Session Type

Poster Session

Session Title

General Poster Session A: Prostate Cancer

Track

Prostate Cancer

Sub Track

Prostate Cancer

Citation

J Clin Oncol 32, 2014 (suppl 4; abstr 10)

DOI

10.1200/jco.2014.32.4_suppl.10

Abstract #

10

Poster Bd #

A2

Abstract Disclosures