The use of a polygenic score (PGS) based on noncancer genetic variations in prostate-specific antigen (PSA) values helped to refine PSA screening in a large group of men without prostate cancer at baseline in a large genome-wide study. The use of the PGS to adjust PSA allowed for the identification of aggressive versus low-risk prostate cancer and may reduce unnecessary biopsies, according to Linda Kachuri, MPH, PhD, Postdoctoral Scholar, Department of Epidemiology and Biostatistics, University of California, San Francisco, the lead investigator of the study that was presented at the 2022 American Association for Cancer Research meeting.
A PGS that accounts for noncancerous variations in PSA values explained 7.3% to 8.8% of the variation in baseline PSA values in 2 large prostate cancer prevention studies. Correcting PSA values for noncancerous variations would have led to almost 20% fewer negative biopsies in men without cancer and to 15.7% fewer biopsies in men with low-risk disease. PGS-adjusted PSA values were more strongly associated with aggressive prostate cancer than unadjusted values.
“Our findings are exciting because we’re able to show that we can use these genetic discoveries that are coming out of genome-wide association studies to potentially improve the detection of prostate cancer and hopefully try to make PSA a more useful and accurate screening biomarker. This is only the first step. It’s absolutely important to validate these findings in additional patient populations,” Dr Kachuri stated at a press conference at the meeting.
Although PSA testing is widely used to diagnose and manage prostate cancer, its use is controversial because of poor sensitivity and specificity. Many men have a biopsy unnecessarily, and a high PSA can lead to overtreatment of prostate cancer. Factors other than cancer can elevate PSA levels, including older age, infection, and benign prostatic hyperplasia.
The large genome-wide association study included approximately 95,000 men from the United States, England, and Sweden. The analysis identified 128 PSA-related variants that are not related to cancer, including 82 variants that were not previously recognized.
Based on these data, the researchers developed a PGS that accounted for the variants’ contributions to PSA values. The score was individualized for each patient and represented the sum of the genotypes across the 128 variants, which were weighted to reflect the variants’ effect on PSA levels. A personalized adjustment factor was applied to a patient’s PSA value to account for the patient’s unique PSA profile.
Next, they validated the PGS by adjusting the PSA values of men who participated in the Prostate Cancer Prevention Trial (PCPT; N = 5725) and the Selenium and Vitamin E Cancer Prevention Trial (SELECT; N = 25,917), which included men who did not have prostate cancer at enrollment.
The PGS score explained 7.3% of the variations in PSA values in the PCPT study and 8.8% of the variation in the SELECT study. Moreover, the PGS was not associated with prostate cancer in either of the large prevention trials, which confirms that the score reflected benign PSA variation.
The researchers substituted the individual PGS values for the participants’ measured PSA values to reclassify the patients. This led to an estimation that 19.6% of negative biopsies could have been avoided if PGS scores were used. In a separate analysis, the PGS was applied to men who had indolent, low-grade prostate cancer. The results suggested that 15.7% of biopsies could have been avoided in those men.
“This is another indication that genetically adjusted PSA could potentially be useful for reducing overdiagnosis of prostate cancer,” Dr Kachuri emphasized.
They also evaluated the use of PGS to identify aggressive prostate cancer. The results showed that the adjusted PSA values outperformed the measured PSA levels and validated the use of the PGS for identifying aggressive disease in the PCPT and SELECT studies. The best prediction tool was a combination of the PGS score and the genetically adjusted PSA measure.
When asked whether these results were clinically applicable, Dr Kachuri replied, “The reason I’m optimistic about the translation of this is because the complicated part is calculating the genetic risk score. But the implementation is straightforward, because we’re still using PSA, which is a biomarker that people are very familiar with and clinicians are familiar with.”