Genetic Abnormalities in Benign or Malignant Tissues Predict Relapse of Prostate Cancer
More efficient and accurate technique reported in The American Journal of Pathology
Philadelphia, PA, May 7, 2012 – While active monitoring of serum prostate specific antigen (PSA) levels in men over 50 has greatly improved early detection of prostate cancer, prediction of clinical outcomes after diagnosis remains a major challenge. Researchers from the University of Pittsburgh School of Medicine have found that a genetic abnormality known as copy number variation (CNV) in prostate cancer tumors, as well as in the benign prostate tissues adjacent to the tumor and in the blood of patients with prostate cancer, can predict whether a patient will experience a relapse, and the nature of the relapse — aggressive or indolent. Their report is published in the June issue of The American Journal of Pathology.
Copy number variations are large areas of the genome with either duplicated or missing sections of DNA. “Our analysis indicates that CNV occurred in both cancer and non-cancer tissues, and CNV of these tissues predicts prostate cancer progression,” says lead investigator Jian-Hua Luo, MD, PhD, associate professor in the Divisions of Molecular and Cellular Pathology, and Anatomic Molecular Pathology, Department of Pathology, University of Pittsburgh School of Medicine. “Prediction models of prostate cancer relapse, or of the rate of PSA level increase after surgery, were generated from specific CNV patterns in tumor or benign prostate tissues adjacent to cancer samples.”
To detect the abnormalities, scientists conducted a comprehensive genome analysis on 238 samples obtained from men undergoing radical prostatectomy: 104 prostate tumor samples, 85 blood samples from patients with prostate cancer, and 49 samples of benign prostate tissues adjacent to a tumor. A third of the samples were from patients exhibiting recurrence with a PSA level increasing at a rapid rate, doubling in less than four months (rapid increases are associated with lethal prostate cancer); a third from patients exhibiting recurrence with a PSA level increasing at a slow rate, doubling time greater than 15 months; and a third with no relapse more than five years after surgery. Three commercially available prostate cancer cell lines were also tested to validate the results.
Deletions of large segments of specific chromosomes occurred with high frequency, whereas amplification of other chromosomes occurred in only a subset of prostate cancer samples. Similar amplification and deletion of the same regions also occurred in benign prostate tissue samples adjacent to the cancer. Prostate cancer patients’ blood was found to contain significant CNVs. Most were not unique and overlapped with those of prostate cancer samples.
Using gene-specific CNV from tumor, the model correctly predicted 73% of cases for relapse and 75% of cases for short PSA doubling time. The CNV model from tissue adjacent to the prostate tumor correctly predicted 67% of cases for relapse and 77% of cases for short PSA doubling time. Using median-size CNV from blood, the genome model correctly predicted 81% of the cases for relapse and 69% of the cases for short PSA doubling time.
Dr. Luo notes that there are several potential clinical applications using CNV tests. “For a patient diagnosed with prostate cancer, CNV analysis done on blood or normal tissues would eliminate the need for additional invasive procedures to decide a treatment mode. For a patient already having a radical prostatectomy, CNV analysis on the tumor or blood sample may help to decide whether additional treatment is warranted to prevent relapse. Despite some limitations, including the need for high quality genome DNA, CNV analysis on the genome of blood, normal prostate, or tumor tissues holds promise to become a more efficient and accurate way to predict the behavior of prostate cancer.”
# # #
Notes for editors“Genome Abnormalities Precede Prostate Cancer and Predict Clinical Relapse,” by Y.P. Yu, C. Song, G. Tseng, B. Ren, W. Laframboise, G. Michalopoulos, J. Nelson, and J-H. Luo (DOI: http://dx.doi.org/10.1016/j.ajpath.2012.03.008). It will appear in The American Journal of Pathology, Volume 180, Issue 6 (June 2012) published by Elsevier.
Full text of the article is available to credentialed journalists upon request; contact David Sampson at +1 215 239 3171 or email@example.com. Journalists wishing to interview the authors may contact Jian-Hua Luo, MD, PhD, at +1 412 648 8791 or firstname.lastname@example.org.
About The American Journal of Pathology
The American Journal of Pathology ( http://ajp.amjpathol.org), official journal of the American Society for Investigative Pathology, seeks to publish high-quality, original papers on the cellular and molecular biology of disease. The editors accept manuscripts that advance basic and translational knowledge of the pathogenesis, classification, diagnosis, and mechanisms of disease, without preference for a specific analytic method. High priority is given to studies on human disease and relevant experimental models using cellular, molecular, animal, biological, chemical, and immunological approaches in conjunction with morphology.
The leading global forum for reporting quality original research on cellular and molecular mechanisms of disease, The American Journal of Pathology is the most highly cited journal in Pathology with an Impact Factor of 5.224 according to Thomson Reuters Journal Citation Reports® 2010.
Elsevier is a global information analytics company that helps institutions and professionals progress science, advance healthcare and improve performance for the benefit of humanity. Elsevier provides digital solutions and tools in the areas of strategic research management, R&D performance, clinical decision support, and professional education; including ScienceDirect, Scopus, ClinicalKey and Sherpath. Elsevier publishes over 2,500 digitized journals, including The Lancet and Cell, more than 35,000 e-book titles and many iconic reference works, including Gray's Anatomy. Elsevier is part of RELX Group, a global provider of information and analytics for professionals and business customers across industries. www.elsevier.com