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

Content Provider	Springer Nature : BioMed Central
Author	Kiebish, Michael A. Cullen, Jennifer Mishra, Prachi Ali, Amina Milliman, Eric Rodrigues, Leonardo O. Chen, Emily Y. Tolstikov, Vladimir Zhang, Lixia Panagopoulos, Kiki Shah, Punit Chen, Yongmei Petrovics, Gyorgy Rosner, Inger L. Sesterhenn, Isabell A. McLeod, David G. Granger, Elder Sarangarajan, Rangaprasad Akmaev, Viatcheslav Srinivasan, Alagarsamy Srivastava, Shiv Narain, Niven R. Dobi, Albert
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.
Related Links	https://translational-medicine.biomedcentral.com/counter/pdf/10.1186/s12967-019-02185-y.pdf
Ending Page	10
Page Count	10
Starting Page	1
File Format	HTM / HTML
ISSN	14795876
DOI	10.1186/s12967-019-02185-y
Journal	Journal of Translational Medicine
Issue Number	1
Volume Number	18
Language	English
Publisher	BioMed Central
Publisher Date	2020-01-07
Access Restriction	Open
Subject Keyword	Biomedicine Medicine Public Health Bayesian networks Biochemical recurrence Biomarkers Metabolomics Prostate cancer Medicine/Public Health
Content Type	Text
Resource Type	Article
Subject	Biochemistry, Genetics and Molecular Biology Medicine
Journal Impact Factor	6.1/2023
5-Year Journal Impact Factor	6.3/2023

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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