Abstract 3595: A panel of serum proteins, metabolite and lipid for prognosing prostate cancer progression

Autor: Kiki Panagopoulos, David G. McLeod, Alagarsamy Srinivasan, Niven R. Narain, Vladimir Tolstikov, Emily Y. Chen, Albert Dobi, Gyorgy Petrovics, Jeonifer Garren, Michael A. Kiebish, Elder Granger, Isabell A. Sesterhenn, Eric J. Milliman, Shiv Srivastava, Leonardo O. Rodrigues, Amina Ali, Inger L. Rosner, Fei Gao, Rangaprasad Sarangarajan, Lixia Xang, Viatcheslav R. Akmaev, Yongmei Chen, Jennifer Cullen
Rok vydání: 2018
Předmět:
Zdroj: Cancer Research. 78:3595-3595
ISSN: 1538-7445
0008-5472
DOI: 10.1158/1538-7445.am2018-3595
Popis: Background: Predicting the clinical course of prostate cancer is challenging due to the wide biological spectrum of the disease. The limited prognostic value of pretreatment PSA, grade, and clinical stage prompted discoveries of early biomarkers for predicting the clinical course of the disease. Along these lines new biomarkers have been developed including pre-diagnostic urine-based tests, serum-based assays for PSA derivatives, and diagnostic biopsy tissue-based assays. Although these advancements continue to improve early diagnosis and prognosis there is a need for further developments to complement current prognostic approaches. While most of the prostate cancer detection and prognostic approaches have been developed by using one type of analyte, e.g. mRNA/DNA or proteins, or metabolites, some also include combination of multiple types of analytes. Indeed, development of prognostic panels that include multiple type of analytes requires sensitive and reproducible detection methods and advanced bioinformatic platforms. The objective of our study was to discover prostate cancer prognostic markers employing an advanced multi-analyte discovery platform. Methods: Pre-surgery serum samples were evaluated among a longitudinally followed (median 10 years), racially diverse prostate cancer patient group (N=385) by mass spectrometry, integrating proteomic, metabolomic and lipidomic (multi-omics) data to differentiate disease progression-free patients (N=310) from patients with disease progression (N=75) by using the Bayesian computational approach. Results: Integrated disease progression data with multi-omics profiles have identified the combined predictive performance of two proteins, a metabolite and a phospholipid molecular species with a cumulative performance of AUC= 0.782 for differentiating patient groups with disease progression-free survival from the group of patients with progression. This panel demonstrated a 0.94 negative predictive value with a modest positive predictive value of 0.34. The odds ratio of disease progression was 6.56 (2.98, 14.40) with the panel of markers. Conclusions: We have identified a panel of multi-analytes with promising performance in predicting disease progression-free survival of prostate cancer patients by evaluating pre-surgery serum samples. This panel offers new opportunities complementing current prognostic markers with potential impact on primary treatment and follow up strategies. Citation Format: Michael Kiebish, Jennifer Cullen, Amina Ali, Leonardo O. Rodrigues, Emily Y. Chen, Eric Milliman, Lixia Xang, Vladimir Tolstikov, Fei Gao, Kiki Panagopoulos, Jeonifer Garren, Yongmei Chen, Gyorgy Petrovics, Inger L. Rosner, Isabell A. Sesterhenn, David McLeod, Elder Granger, Rangaprasad Sarangarajan, Alagarsamy Srinivasan, Viatcheslav Akmaev, Albert Dobi, Niven Narain, Shiv Srivastava. A panel of serum proteins, metabolite and lipid for prognosing prostate cancer progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3595.
Databáze: OpenAIRE
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