Genome abnormalities precede prostate cancer and predict clinical relapse, "Beyond the Abstract," by Jianhua Luo, MD, PhD

BERKELEY, CA (UroToday.com) -

Genome copy number profiling to predict prostate cancer clinical outcomes

Prostate cancer is the most frequent malignancy in men and one of the most lethal cancers in the U.S.^{[1, 2]} However, only a small fraction of prostate cancer patients die from the disease. Currently, three options are available for the treatment of prostate cancer: Watchful waiting, surgical removal of prostate gland and seminal vesicles, or combination of radiation and hormonal therapies. These treatment modalities are based on the assumption that some of the prostate cancers are slow developing and not life threatening, while others are not. Even for prostate cancer that relapses, the speed of prostate specific antigen rise in the blood is a crucial determinant of whether a prostate cancer will be lethal. A fast prostate specific antigen rise (<4 months) signals a dramatic higher mortality rate of prostate cancer.^{[3, 4, 5, 6]} As a result, accurate prediction of prostate cancer behavior is crucial in choosing appropriate therapy.

Prediction of prostate cancer clinical outcome remains a major challenge after diagnosis. In our recent report,^[7] we performed a genome-wide copy number analysis on samples of prostate cancer, benign prostate tissues adjacent to tumor, and blood from prostate cancer patients to evaluate whether copy number variation (CNV) of genomes predicts the occurrence of prostate cancer, prostate cancer relapse, and the kinetics of relapse. Two hundred and forty-one samples, including 104 T, 49 matched AT, 85 matched B and 3 cell lines were analyzed through Affymetrix SNP 6.0 chips. Using gene-specific CNV from prostate cancer, the genome model correctly predicted 73% (ROC p=0.003) cases for relapse and 75% (p<0.001) for short prostate specific antigen doubling time (less than 4 months post-surgical treatment). Interestingly, the gene-specific CNV model from benign prostate tissue adjacent to cancer correctly predicted 67% (p=0.041) cases for relapse and 77% (p=0.015) for short prostate specific antigen doubling time. Using median size of CNV from blood samples of prostate cancer patients, the genome model correctly predicted 81% (p<0.001) cases for relapse and 69% (p=0.001) for short prostate specific antigen doubling time. Using median size of CNV from prostate cancer samples, the genome model correctly predicted 75% (p<0.001) cases for relapse and 80% (p<0.001) for short prostate specific antigen doubling time. For the first time, our analysis indicates that genome abnormalities of either benign or malignant tissues are predictive of clinical outcome of a malignancy. In addition, our analysis showed that 86% of benign prostate tissues adjacent to cancer had strong resemblance to prostate cancer in terms of CNV profiles.

There are several salient potentials for clinical application using our CNV tests: For a patient being diagnosed with prostate cancer, CNV analysis done on the blood or perhaps other normal tissues from the patient would eliminate the need for additional invasive procedure to decide a treatment mode. For a patient already having a radical prostatectomy, the CNV analysis on tumor or blood sample may help to decide whether additional treatment is warranted to prevent relapse. When morphology becomes indeterminate in a biopsy sample, the gene-specific CNV field effect in benign prostate tissues may help to obtain a firmer diagnosis. The main limitation of the genome CNV analysis for clinical test is its requirement of high quality genome DNA. Formalin-fixed paraffin-embedded tissues may not be suitable. When gene specific CNV prediction is performed, a training set containing samples with known outcome is required for the prediction (while there is no need of training set when size of CNV analysis is performed). Despite these limitations, CNV analysis on the genome of blood, normal prostate, or tumor tissues of prostate cancer patients holds promise to become a more efficient and accurate way to predict the behavior of prostate cancer. 

References:

1. Jemal, A., et al., Global cancer statistics. CA Cancer J Clin, 2012.
2. Siegel, R., D. Naishadham, and A. Jemal, Cancer statistics, 2012. CA Cancer J Clin, 2012. 62(1): p. 10-29.
3. Lee, W.R., et al., Observations of pretreatment prostate-specific antigen doubling time in 107 patients referred for definitive radiotherapy. Int J Radiat Oncol Biol Phys, 1995. 31(1): p. 21-4.
4. Hanks, G.E., et al., Pretreatment prostate-specific antigen doubling times: clinical utility of this predictor of prostate cancer behavior. Int J Radiat Oncol Biol Phys, 1996. 34(3): p. 549-53.
5. Gohji, K., et al., Detection of prostate carcinoma using prostate specific antigen, its density, and the density of the transition zone in Japanese men with intermediate serum prostate specific antigen concentrations. Cancer, 1997. 79(10): p. 1969-76.
6. Lee, W.R., G.E. Hanks, and A. Hanlon, Increasing prostate-specific antigen profile following definitive radiation therapy for localized prostate cancer: clinical observations. J Clin Oncol, 1997. 15(1): p. 230-8.
7. Yu, Y.P., et al., Genome abnormalities precede prostate cancer and predict clinical relapse. Am J Pathol, 2012. 180(6): p. 2240-8. 

Written by:

Jianhua Luo, MD, PhD as part of Beyond the Abstract on UroToday.com. This initiative offers a method of publishing for the professional urology community. Authors are given an opportunity to expand on the circumstances, limitations etc... of their research by referencing the published abstract.

Professor of Pathology
Director of High Throughput Genome Center
University of Pittsburgh School of Medicine
 

Genome abnormalities precede prostate cancer and predict clinical relapse - Abstract

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