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. The use of the PGS to adjust PSA allows identification of aggressive versus low-risk prostate cancer and can potentially reduce unnecessary biopsies, said the lead investigator of a large genome-wide association study (GWAS) presented at the 2022 American Association for Cancer Research annual meeting.
PGS accounting 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 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.
“I think 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 a 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,” stated lead investigator Linda Kachuri, PhD, Postdoctoral Scholar, Department of Epidemiology & Biostatistics, University of California, San Francisco, at a press conference.
Although PSA testing is widely used to diagnose and manage prostate cancer, its use is controversial because of poor sensitivity and specificity. Many men are biopsied unnecessarily and a high PSA can lead to overtreatment of prostate cancer. Other factors can elevate PSA and cause suspicion of prostate cancer when none is present, including older age, infection, and benign prostatic hyperplasia.
The large GWAS included approximately 95,000 men from the United States, England, and Sweden. The analysis identified 128 PSA-related variants not related to cancer, including 82 not previously recognized.
Based on these data, the investigators developed a PGS that accounted for the variants’ contributions to PSA values. The score was individualized for each patient and represented the sum of genotypes across the 128 variants, 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) prevention studies of men who were free of prostate cancer at enrollment.
They found that the PGS score explained 7.3% of variation in PSA values in PCPT and 8.8% of the variation in SELECT. Moreover, the PGS was not associated with prostate cancer in either of the large prevention trials, confirming that the score reflected benign PSA variation.
Then they substituted individual PGS values for participants’ measured PSA values to reclassify patients. This led to estimating that 19.6% of negative biopsies could have potentially 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.
The investigators also evaluated the utility of PGS to identify aggressive prostate cancer. The results showed that the adjusted PSA values outperformed measured PSA levels, and also validated PGS for identifying aggressive disease in both the PCPT and SELECT studies. The best prediction tool turned out to be a combination of the PGS score and the genetically adjusted PSA measure.
During a press conference, Dr Kachuri was asked whether these results were clinically applicable.
“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,” she replied.