Assay offers precision in assigning prostate cancer risk categories


Combining a prostate cancer gene assay with standard risk-stratification factors substantially increased information to guide decision making about active surveillance, a validation study of the assay showed.

San Diego-Combining a prostate cancer gene assay with standard risk-stratification factors substantially increased information to guide decision making about active surveillance, a validation study of the assay showed.

Standard risk-stratification criteria indicated that 5% to 10% of men had an 80% probability of organ-confined disease. The proportion increased to almost 25% with the addition of the Oncotype DX Genomic Prostate Score (GPS) (Genomic Health, Inc., Redwood City, CA).

“The GPS adds independent predictive information beyond all standard clinical and pathological data,” said first author Matthew R. Cooperberg, MD, MPH, associate professor of urology at the University of California, San Francisco. “The GPS assesses underlying biology from very small biopsy tumor volumes, helping address tumor heterogeneity and biopsy undersampling to more accurately predict overall disease aggressiveness.

“Incorporation of the GPS enables more accurate identification of a larger population of patients who can more confidently choose active surveillance as an initial management strategy.”

The GPS demonstrated precision for assigning patients to higher- and lower-risk categories, he added.

The development process for the GPS addressed two key limitations of current approaches to risk stratification and identification of men who are suitable for active surveillance: biopsy undersampling and tumor heterogeneity. Moreover, development of standardized quantitative methods facilitates reliable gene expression measurement in small biopsy tumor volumes, Dr. Cooperberg said.

The GPS comprises a 17-gene panel that combines information related to stromal response, cellular organization, androgen signaling, and proliferation, as well as a group of reference genes. Previous studies have shown that the assay can accurately assess gene expression in prostate cancer specimens as small as 1 mm.

At the AUA annual meeting in San Diego, Dr. Cooperberg reported findings from an assay validation study involving 395 patients with low- and intermediate-risk prostate cancer treated by radical prostatectomy. The primary objective was the extent of agreement between GPS findings from core needle biopsies and the final pathology results based on prostatectomy specimens.

The results showed that every 20-unit increase in the 100-point GPS increased the odds ratio for high-grade disease by 2.48 and the likelihood of pT3 disease by 2.20 (p<.001 for both evaluations). Dr. Cooperberg said a 20-unit change is analogous to a comparison of the top and bottom patient quartiles.

Independent predictor of high-grade/high-stage PCa

Multivariable models that incorporated all known information about a tumor specimen showed that the GPS remained an independent predictor of high-grade and/or high-stage disease (OR, 1.85-2.13, p=.003 to p<.001).

Investigators also evaluated the additive information provided by the GPS when combined with two standard risk-stratification systems: the Cancer of the Prostate Risk Assessment (CAPRA) and the National Comprehensive Cancer Network (NCCN) risk-assessment system.

When added to the CAPRA, the GPS led to at least a 5% shift in risk in 49% of patients. The change was toward more favorable status in 26% of cases and toward less favorable status in 23%.

By themselves, the CAPRA and NCCN risk-stratification schemes predicted that 5% to 10% of men had at least an 80% likelihood of organ-confined disease. When the GPS results were added to either risk-stratification system, the proportion increased to 24% to 26%.

Dr. Cooperberg is a consultant/adviser for Genomic Health, which provided funding for the study. Several of Dr. Cooperberg’s co-authors are consultant/advisers, employees, and/or board members/officers/trustees and/or have an investment interest in Genomic Health.UT

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