Multi-omic serum biomarkers for prognosis of disease progression in prostate cancer

Abstract

Background Predicting 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. Methods Pre-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. Results Among 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. Conclusions In 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.

The Role of Cancer-Associated Fibroblasts in Prostate Cancer Tumorigenesis

“…TNC re-expression may also take place during PCa bone metastasis, since it has been shown that bone metastatic PCa cells interact with TNC through α9β1 integrin when cultured in osteomimetic surfaces containing this glycoprotein [170]. In addition, in a multi-omics approach study, TNC protein was detected in the circulation prior to radical prostatectomy and its combination with three other serum markers and metabolites could predict BCR-free survival with high accuracy [171]. Re-expression of TNC in the bone could be a factor attracting disseminated PCa cells in this site, supporting the idea that cancer cells tend to metastasize in microenvironments similar to the primary tumor.…”

Stroma transcriptomic and proteomic profile of prostate cancer metastasis xenograft models reveals conservation of bone microenvironment signatures

“…Genomic amplification in TNC gene associates with highly aggressive neuroendocrine PCa occurrence [37]. In a multi-omics approach study, TNC protein was one of the panel of four markers detected in preoperative serum samples and collectively predict biochemical relapse events with high accuracy [38].…”

“…Osteoblastic bone metastasis stromal seven-gene signature was induced in the mouse- may suggest that it would be relevant to examine TNC mRNA and protein expression in human bone metastasis or ideally matched primary-metastasis cases in order to understand the kinetic of TNC in terms of disease progression. Given that TNC expression was found elevated from 0% in BPH stroma to 47% in tumor-associated stroma [24], its detection in circulation [38], its immunomodulatory role [40], indicates TNC as a promising drug target and disease determining factor. TNC clinical progression predictive value is performing best in earlier stage, low risk PCa, while our data show that in high risk PCa low number of TNC producing cells were associated with poor prognosis possibly due to changes in tissue remodeling and thus variable TNC levels.…”

Current Status of Metabolomic Biomarker Discovery: Impact of Study Design and Demographic Characteristics

“…Technological advancements have enabled novel strategies to discover individual biomarkers as well as panels of markers derived from different -omics platforms [5][6][7][8][9][10][11][12][13][14]. These technologies harvest substantial volumes of data at multiple biological levels and sift the resulting large data sets with informatics tools including artificial intelligence (AI).…”