Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Jan 7;18(1):10.
doi: 10.1186/s12967-019-02185-y.

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

Michael A Kiebish  1 , Jennifer Cullen  2   3 , Prachi Mishra  2   3 , Amina Ali  3 , Eric Milliman  1 , Leonardo O Rodrigues  1 , Emily Y Chen  1 , Vladimir Tolstikov  1 , Lixia Zhang  1 , Kiki Panagopoulos  1 , Punit Shah  1 , Yongmei Chen  2   3 , Gyorgy Petrovics  2   3 , Inger L Rosner  3 , Isabell A Sesterhenn  4 , David G McLeod  3 , Elder Granger  1 , Rangaprasad Sarangarajan  1 , Viatcheslav Akmaev  1 , Alagarsamy Srinivasan  2 , Shiv Srivastava  3 , Niven R Narain  1 , Albert Dobi  5   6
Affiliations

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

Michael A Kiebish et al. J Transl Med. .

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.

Keywords: Bayesian networks; Biochemical recurrence; Biomarkers; Metabolomics; Prostate cancer.

PubMed Disclaimer

Conflict of interest statement

MAK, NRN, RS, VA, LOR, LZ, SS, AD, and JC are co-inventors on patent application “Markers for the Diagnosis of Prostate Cancer”. Other authors declare that they have no competing interests.

MAK, EM, LOR, EYC, VT, LZ, KP, PS, EG, RS, VA, and NRN are current or former employees of BERG, LLC and have stock options. NRN is co-founder of BERG, LLC.

Figures

Fig. 1
Fig. 1
Conceptual schematic describes approach for integrating multi-omics and artificial intelligence for biomarker discovery from presurgical serum for prognosis of prostate cancer progression
Fig. 2
Fig. 2
Volcano plots depict the top analytes from serum samples of prostate cancer patients with BCR, run through different mass spectrometry platforms for abundance of a metabolites, b proteins, c structural lipids and d signaling lipids. Each dot in the plot represents an analyte. The X-axis represents the fold change of the analytes (log2 scale); the Y-axis representing the P-values in − log10 scale. The color bar represents the number of analytes that had a fold-change and P-value in that range. For metabolites: 17 had an unadjusted P ≤ 0.05. For proteins: 13 had an unadjusted P ≤ 0.05. For structural lipids: 56 had an unadjusted P ≤ 0.05. For signaling lipids: 2 had an unadjusted P ≤ 0.05
Fig. 3
Fig. 3
Plots represent specific metabolites and proteins individually with a significant differential abundance selected to form a combinatorial panel for diagnosis or prognosis of prostate cancer. a Tenascin C, b Apolipoprotein A-IV, c 1-methyladenosine and d PA-18:0-22:0 were the four chosen analytes. The normalized abundance of the analytes is represented in boxplots. Each dot represents a patient measurement for the stated analyte
Fig. 4
Fig. 4
ROC curve analysis of the marker panel alone compared to marker panel plus “standard of care” pathology variables. a The analysis demonstrates cumulative sensitivity and specificity of four markers with an AUC = 0.78, OR (CI) = 6.56 (2.98, 14.40), selected as ideal biomarkers for a prognostic test. b ROC analysis shows the combined sensitivity and specificity of four markers along with the pathological/clinical features increasing the AUC = 0.89 and OR (CI) = 12.47 (4.85, 32.05) representing an enhanced prognostic test. 1-MA 1-methyladenosine, APOA-IV apolipoprotein A-IV, AUC area under curve, GLS gleason score, OR odds ratio, TNC Tenascin C
See this image and copyright information in PMC

References

    1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. Cancer J Clin. 2019;69(1):7–34. doi: 10.3322/caac.21551. - DOI - PubMed
    1. Cooperberg MR, Davicioni E, Crisan A, Jenkins RB, Ghadessi M, Karnes RJ. Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. Eur Urol. 2015;67(2):326–333. doi: 10.1016/j.eururo.2014.05.039. - DOI - PMC - PubMed
    1. Cooperberg MR, Simko JP, Cowan JE, Reid JE, Djalilvand A, Bhatnagar S, et al. Validation of a cell-cycle progression gene panel to improve risk stratification in a contemporary prostatectomy cohort. J Clin Oncol. 2013;31(11):1428–1434. doi: 10.1200/JCO.2012.46.4396. - DOI - PubMed
    1. Cullen J, Rosner IL, Brand TC, Zhang N, Tsiatis AC, Moncur J, et al. A Biopsy-based 17-gene genomic prostate score predicts recurrence after radical prostatectomy and adverse surgical pathology in a racially diverse population of men with clinically low- and intermediate-risk prostate cancer. Eur Urol. 2015;68(1):123–131. doi: 10.1016/j.eururo.2014.11.030. - DOI - PubMed
    1. Klein EA, Cooperberg MR, Magi-Galluzzi C, Simko JP, Falzarano SM, Maddala T, et al. A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling. Eur Urol. 2014;66(3):550–560. doi: 10.1016/j.eururo.2014.05.004. - DOI - PubMed

Publication types