114650-132

Quantifying copy number variations in cell-free DNA for potential clinical utility from a large prostate cancer cohort.

Subcategory: 
Category: 
Genitourinary (Prostate) Cancer
Session Type and Session Title: 
General Poster Session, Genitourinary (Prostate) Cancer
Abstract Number: 
5072
Citation: 
J Clin Oncol 31, 2013 (suppl; abstr 5072)
Author(s): 
Ekkehard Schütz, Mohammad R Akbari, Julia Beck, Howard B. Urnovitz, William Zhang, William M. Mitchell, Robert Nam, Steven Narod; Chronix Biomedical, Göttingen, Germany; Women’s College Research Institute, Women’s College Hospital, University of Toronto, Toronto, ON, Canada; Department of Pathology, Vanderbilt University, Nashville, TN; Odette Cancer Centre, Sunnybrook Health Sciences Centre; University of Toronto, Toronto, ON, Canada; Women’s College Research Institute, Women's College Research Institute, University of Toronto, Toronto, ON, Canada

Abstracts that were granted an exception in accordance with ASCO's Conflict of Interest Policy are designated with a caret symbol (^).

Abstract Disclosures

Abstract: 

Background: Prostate cancer (PrCa) is the most frequent non-dermatological malignancy in the male population. Genomic instability resulting in copy number variation (CNV) is a hallmark of malignant transformation. CNV traces from tumors in cell-free DNA (cfDNA) of prostate cancer patients may be identified through massive parallel sequencing (MPS) of serum DNA. These CNV traces may be biomarkers of cancer with clinical applications for screening and follow-up. Methods: DNA was extracted from serum of 205 PrCa patients (Gleason 2 to10), 207 age matched male controls (HC), 10 men with benign hyperplasia (BPH) and 10 with prostatitis (PiS). DNA was amplified using random primers, tagged with a unique molecular identifier per sample, sequenced on a SOLiD system and aligned to the human genome (Build 37). Hits were counted in sliding 100kbp intervals and normalized. Using a random-resampling procedure, genomic regions showing copy number variations in cfDNA that distinguish PrCa from HC were selected. A model using 20 cfDNA regions was cross-validated and used as cfDNA biomarker. Receiver operator characteristics (ROC) curves were calculated for assessment of diagnostic performance by means of area under the curve (AUC). Results: To assess whether CNVs in cfDNA are indicative of PrCa, the number of regions with significant CNV deviation was counted in a first subset of 82 PrCa. Using only the number of regions as measure resulted in an AUC of 0.81 (0.7 – 0.9, p<0.001). Therefore, all samples were used to select regions (n=80) in random resampling (50/50). These regions were used to define a highly significant 20-regions model using five rounds of 10-fold cross-validation (AUC: 0.85±0.7; p< 10-7). This final model discriminated between PrCa and HC with an AUC of 0.92 (0.87 – 0.95) reaching a calculated accuracy of 83%. Both BPH and PiS could be distinguished from PrCa using the cfDNA CNV biomarker with a predicted accuracy of 90%. Conclusions: MPS revealed that only a limited number of chromosomal regions showing CNVs are necessary to achieve statistical separation between prostate cancer and controls. This technique may prove to be clinically useful for screening and follow up of men with prostate cancer.