Development and Optimization of a Subtraction-Normalized Immunocyte Profiling Signature for Prostate Cancer Active Surveillance Risk Stratification.

Purpose: Less invasive decision support tools are desperately needed to identify occult high-risk disease in men with prostate cancer (PCa) on active surveillance (AS). For a variety of reasons, many men on AS with low- or intermediate-risk disease forgo the necessary repeat surveillance biopsies needed to identify potentially higher-risk PCa. Here, we describe the development of a blood-based immunocyte transcriptomic signature to identify men harboring occult aggressive PCa. We then validate it on a biopsy-positive population with the goal of identifying men who should not be on AS and confirm those men with indolent disease who can safely remain on AS. This model uses subtraction-normalized immunocyte transcriptomic profiles to risk-stratify men with PCa who could be candidates for AS.
Materials and Methods: Men were eligible for enrollment in the study if they were determined by their physician to have a risk profile that warranted prostate biopsy. Both training (n = 1017) and validation cohort (n = 1198) populations had blood samples drawn coincident to their prostate biopsy. Purified CD2 ⁺ and CD14 ⁺ immune cells were obtained from peripheral blood mononuclear cells, and RNA was extracted and sequenced. To avoid overfitting and unnecessary complexity, a regularized regression model was built on the training cohort to predict PCa aggressiveness based on the National Comprehensive Cancer Network PCa guidelines. This model was then validated on an independent cohort of biopsy-positive men only, using National Comprehensive Cancer Network unfavorable intermediate risk and worse as an aggressiveness outcome, identifying patients who were not appropriate for AS.
Results: The best final model for the AS setting was obtained by combining an immunocyte transcriptomic profile based on 2 cell types with PSA density and age, reaching an AUC of 0.73 (95% CI: 0.69-0.77). The model significantly outperforms ( P < .001) PSA density as a biomarker, which has an AUC of 0.69 (95% CI: 0.65-0.73). This model yields an individualized patient risk score with 90% negative predictive value and 50% positive predictive value.
Conclusions: While further validation in an intended-use cohort is needed, the immunocyte transcriptomic model offers a promising tool for risk stratification of individual patients who are being considered for AS.