What Role Should Genomic Classifier Testing Play in Prostate Cancer?

Genomic classifier (GC) tests do not appear to consistently influence risk classification or treatment decisions for patients with newly diagnosed prostate cancer considering first-line treatment, according to results from a systematic review.

Among 10 studies reporting risk reclassification after GC testing with one of three available tests — Decipher, Prolaris, or the Oncotype DX Genomic Prostate Score (GPS), very low- or low-risk patients with prostate cancer were more likely to have their risk levels classified as the same or lower (GPS: 100% to 88.1%; Decipher: 87.2% to 82.9%; Prolaris: 76.9%) in low risk-of-bias observational studies, reported Amir Alishahi Tabriz, MD, PhD, MPH, of the Moffitt Cancer Center in Tampa, Florida, and colleagues.

However, a randomized trial (the ENACT trial) showed that GC testing with GPS reclassified 34.5% of very low-risk and 29.4% of low-risk patients to a higher risk category, they noted in the Annals of Internal Medicine.

In addition, 14 studies assessed the impact of GC tests on treatment intensity, with 12 observational studies indicating that GC testing led to higher rates of recommending active surveillance (AS) after diagnosis, with a relative change to AS ranging from 7.5% to 61.8%, and two randomized analyses from the ENACT trial showing that patients’ preference for active treatment (prostatectomy or radiation) “modestly” increased when assigned to receive GPS testing.

On the other hand, urologists’ preference for those treatments substantially increased, from 11.4% to 29.3%, for patients who received GPS testing compared with those who did not (15.3% to 14.1%), resulting in 2.55 higher odds of urologists preferring active treatment for those assigned to GPS compared with those who were not.

“[A]lthough GC tests may affect risk reclassification and treatment choice, differences between observational studies and randomized trials, across type of GC test, and patient characteristics complicate understanding of the role of these tests for patient care,” Tabriz and colleagues concluded.

“Our finding that reclassification rates varied widely across studies leaves unanswered questions about who might benefit the most from pursuing GC test evaluation,” they noted. “The reason for this variation may be attributable to use of different baseline clinical risk classification systems as comparators, clinical differences across patient populations that received the tests, variations in test performance over time or across test types, or inconsistencies in test interpretation.”

The authors pointed out that the evidence regarding the utility of GC testing among racial and ethnic groups was limited, and that apart from the ENACT trial, only two studies examined the effect of GC testing on risk reclassification, and only one investigated the impact of GPS testing on AS selection in Black men.

“Some research suggests that a subset of Black men may have genomically aggressive tumors that traditional clinical risk classifiers could miss,” they wrote, adding that more studies are needed to assess the impact of integrating these tests into the risk classification and clinical evaluation of Black men with prostate cancer.

In an editorial accompanying the study, Syed Arsalan Ahmed Naqvi, MD, and Irbaz Bin Riaz, MD, MBI, PhD, both of the Mayo Clinic in Phoenix, noted that evidence for GCs as predictive biomarkers has been limited and primarily derived from observational studies or retrospective analyses, “which are prone to bias and variability, undermining their robustness and generalizability.”

They added that pivotal trials that provided the evidence that GCs could be used as predictive biomarkers for guiding adjuvant chemotherapy decisions for hormone receptor-positive, HER2-negative, early-stage breast cancer were conducted over a period of more than a decade, while their use in prostate cancer is in its early stages.

“Ultimately, integrating GCs with AI-driven multimodal approaches and testing them prospectively in randomized controlled trials holds the promise to address current gaps in clinical utility while keeping pace with the rapidly evolving landscape of precision oncology,” they wrote.

Nineteen studies were included for data abstraction in this systematic review, two of which were analyses of the ENACT randomized trial, and the remaining 17 were observational studies. Decipher was used in four studies, GPS in 10, and Prolaris in five. With one exception, all studies were conducted in the U.S.

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