Abstract 4645: A serum multiomics signature for enhancing prostate cancer diagnosis and prognosis
Authors: Kiebish Michael A. , Cullen Jennifer , Dobi Albert , Ali Amina , Rodrigues Leonardo O. , Sun Yezhou , Pawar Aniruddha , Dalvi Aditee , Young Denise , Vishnudas Vivek K. , Sedarsky Jason , Petrovics Gyorgy , Chen Emily , Akmaev Viatcheslav , Rosner Inger L. , McLeod David , Sesterhenn Isabell A. , Sarangarajan Rangaprasad , Srinivasan Alagarsamy , Grainger Elder , Narain Niven R. , Srivastava Shiv 
Published: July 2017

Abstract
Introduction and Objective: Diagnosis of prostate cancer (CaP) has relied on prostate specific antigen (PSA) level and digital rectal examination (DRE) followed by prostate biopsies. These modalities have the potential to yield false-positive and false-negative results for CaP. These challenges prompted efforts to develop more specific body fluid based assays including PCA3, TMPRSS2:ERG, K4csore and PHI tests. Further, emerging data on significant racial differences of common CaP driver genes, e.g., PTEN and ERG in CaP can lead to significant limitations in biomarker performance. Thus, the goal of our study was to discover CaP serum markers with equal performance among African American (AA) and Caucasian American (CA) men. We employed proteomics, signal- and structural lipidomics and metabolomics platforms to discover serum biomarkers and evaluate their utility for diagnosis and prognosis of CaP in AA and CA men.
Methods: Sera from 700 individuals were analyzed, which included AA and CA CaP patients stratified for ERG oncoprotein expression by immunohistochemistry (N=495). Sera from age-matched healthy control men were also included (N=205) in this study. Quantitative global profiles of lipidome, proteome and metabolome were analyzed by high resolution MS-based technologies. Random forest and Interrogative Biology® analytical platforms were used to identify analytes differentiating healthy from CaP cases including clinical-pathologic data.
Results: The unbiased global profiling and integration of the data and clinical-pathologic features have led to the identification of molecular fingerprints differentiating cancer patients from healthy controls. Specifically, three analytes in serum metabolome showed robust separation between both AA and CA CaP and control groups. Elevated levels of nicotinamide and eicosenoic acid and decreased levels of a decanoylcarnitate alone have indicated strong separation between cases and controls. Further inclusion of additional 9 analytes provided an optimal multi-omics panel for distinguishing the combined cohort of AA and CA cases vs. healthy controls.
Conclusions: The findings presented here support that an integrated multiomics approach has the potential to define serum marker panels for diagnosis of CaP in the context of racial diversity and molecular annotation (e.g., ERG) of CaP. These promising data are undergoing validation in additional patient cohorts.
Citation Format: Michael A. Kiebish, Jennifer Cullen, Albert Dobi, Amina Ali, Leonardo O. Rodrigues, Yezhou Sun, Aniruddha Pawar, Aditee Dalvi, Denise Young, Vivek K. Vishnudas, Jason Sedarsky, Gyorgy Petrovics, Emily Chen, Viatcheslav Akmaev, Inger L. Rosner, David McLeod, Isabell A. Sesterhenn, Rangaprasad Sarangarajan, Alagarsamy Srinivasan, Elder Grainger, Niven R. Narain, Shiv Srivastava. A serum multiomics signature for enhancing prostate cancer diagnosis and prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4645. doi:10.1158/1538-7445.AM2017-4645