Abstract
BackgroundPredicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease. The objective of our study was to identify prostate cancer prognostic markers in patients ‘sera using a multi-omics discovery platform.MethodsPre-surgical serum samples collected from a longitudinal, racially diverse, prostate cancer patient cohort (N = 382) were examined. Linear Regression and Bayesian computational approaches integrated with multi-omics, were used to select markers to predict biochemical recurrence (BCR). BCR-free survival was modeled using unadjusted Kaplan–Meier estimation curves and multivariable Cox proportional hazards analysis, adjusted for key pathologic variables. Receiver operating characteristic (ROC) curve statistics were used to examine the predictive value of markers in discriminating BCR events from non-events. The findings were further validated by creating a training set (N = 267) and testing set (N = 115) from the cohort.ResultsAmong 382 patients, 72 (19%) experienced a BCR event in a median follow-up time of 6.9 years. Two proteins—Tenascin C (TNC) and Apolipoprotein A1V (Apo-AIV), one metabolite—1-Methyladenosine (1-MA) and one phospholipid molecular species phosphatidic acid (PA) 18:0-22:0 showed a cumulative predictive performance of AUC = 0.78 [OR (95% CI) = 6.56 (2.98–14.40), P < 0.05], in differentiating patients with and without BCR event. In the validation set all four metabolites consistently reproduced an equivalent performance with high negative predictive value (NPV; > 80%) for BCR. The combination of pTstage and Gleason score with the analytes, further increased the sensitivity [AUC = 0.89, 95% (CI) = 4.45–32.05, P < 0.05], with an increased NPV (0.96) and OR (12.4) for BCR. The panel of markers combined with the pathological parameters demonstrated a more accurate prediction of BCR than the pathological parameters alone in prostate cancer.ConclusionsIn this study, a panel of serum analytes were identified that complemented pathologic patient features in predicting prostate cancer progression. This panel offers a new opportunity to complement current prognostic markers and to monitor the potential impact of primary treatment versus surveillance on patient oncological outcome.
Highlights
Predicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease
30–40% of patients treated with radical prostatectomy (RP) for clinically localized prostate cancer will experience disease progression indicated by rising post-surgery serum prostate specific antigen (PSA) levels
Study design and participants In this retrospective cohort study, patients enrolled in both the Center for Prostate Disease Research (CPDR) biospecimen databank and the multi-center national clinical database, were eligible. Both databases have been approved by the institutional review boards (IRB) at the Walter Reed National Military Medical Center (WRNMMC) and the Uniformed Services University of the Health Sciences (USUHS) in Bethesda, Maryland, respectively
Summary
Predicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease. Discovery of early biomarkers for prostate cancer progression are crucial to predict the risk of relapse and to temper active monitoring using PSA. Used prognostic markers such as diagnostic PSA, biopsy grade and clinical stage have limited value in predicting which patients will develop metastatic prostate cancer. Intensive efforts have led to the development of new biomarkers for early detection and prognosis of prostate cancer. These biomarkers include pre-diagnostic urine-based assays (PCA3, T2-ERG, Exosome DX, Select MDx and Prostarix), serum-based assays for PSA derivatives, and diagnostic biopsy tissue-based assays (Oncotype DX, Prolaris, Decipher and ProMark assays) [2,3,4,5,6,7,8,9,10]. While most of the pre-treatment assays rely on biopsy tissue, the rise in post-treatment serum PSA and detection of metastasis using imaging modalities remains the “gold standard” for monitoring disease progression
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