This study reports on the development of a novel serum protein panel of three prostate cancer biomarkers, Filamin A, Filamin B and Keratin-19 (FLNA, FLNB and KRT19) using multivariate models for disease screening and prognosis. ELISA and IPMRM (LC-MS/MS) based assays were developed and analytically validated by quantitative measurements of the biomarkers in serum. Retrospectively collected and clinically annotated serum samples with PSA values and Gleason scores were analyzed from subjects who underwent prostate biopsy, and showed no evidence of cancer with or without indication of prostatic hyperplasia, or had a definitive pathology diagnosis of prostatic adenocarcinoma. Probit linear regression models were used to combine the analytes into score functions to address the following clinical questions: does the biomarker test augment PSA for population screening? Can aggressive disease be differentiated from lower risk disease, and can the panel discriminate between prostate cancer and benign prostate hyperplasia? Modelling of the data showed that the new prostate biomarkers and PSA in combination were better than PSA alone in identifying prostate cancer, improved the prediction of high and low risk disease, and improved prediction of cancer versus benign prostate hyperplasia.

Aim:A novel strategy for prostate cancer (PrCa) biomarker discovery is described.Materials & methods:In vitro perturbation biology, proteomics and Bayesian causal analysis identified biomarkers that were validated in in vitro models and clinical specimens.Results:Filamin-B (FLNB) and Keratin-19 were identified as biomarkers. Filamin-A (FLNA) was found to be causally linked to FLNB. Characterization of the biomarkers in a panel of cells revealed differential mRNA expression and regulation. Moreover, FLNA and FLNB were detected in the conditioned media of cells. Last, in patients without PrCa, FLNA and FLNB blood levels were positively correlated, while in patients with adenocarcinoma the relationship is dysregulated.Conclusion:These data support the strategy and the potential use of the biomarkers for PrCa.

Background: Prostate cancer (PrCa) is a leading cause of cancer deaths in males in the US. Current tests of prostate-specific antigen (PSA) screening and the diagnostic prostate biopsy are often inconclusive. Many patients with a PSA positive blood test often undergo invasive repeat biopsy procedures. Additionally, there is a need for diagnostic tests that differentiate between low and high-risk cancers. This study reports on the development of a novel serum protein panel of three PrCa biomarkers, Filamin A, Filamin B and Keratin-19 (FLNA, FLNB and KRT19) using multivariate models for disease screening and prognosis. Methods: ELISA and IPMRM (LC-MS/MS) based assays were developed and analytically validated by quantitative measurements of the biomarkers in serum. Retrospectively collected and clinically annotated serum samples with PSA values and Gleason scores (GS) were analyzed from 503 subjects who underwent prostate biopsy, and showed no evidence of cancer with or without indication of prostatic hyperplasia, or had a definitive pathology diagnosis of prostatic adenocarcinoma. Probit linear regression models were used to combine the analytes into score functions to address the following clinical questions: does the biomarker test augment PSA for population screening? Can aggressive disease be differentiated from lower risk disease, and can the panel discriminate between benign prostate hyperplasia (BPH) and PrCa? Results: Table 1: Berg PrCa Panel AUC summary for the four clinical indications. Conclusion: As shown in Table 1, modelling of the data demonstrated that the new PrCa biomarkers and PSA in combination were better than PSA alone in identifying PrCa, improved the prediction of high and low risk disease, and improved prediction of BPH versus PrCa. Citation Format: Shobha Ravipaty, Wenfang Wu, Aditee Dalvi, Nikunj Tanna, Joe Andreazi, Tracey Friss, Allison Klotz, Chenchen Liao, Jeonifer Garren, Sally Schofield, Eleftherios P. Diamandis, Eric A. Klein, Albert Dobi, Shiv Srivastava, Poornima Tekumalla, Michael A. Kiebish, Vivek K. Vishnudas, Rangaprasad Sarangarajan, Niven R. Narain, Viatcheslav Akmaev. Clinical validation of a serum protein panel (FLNA, FLNB and KRT19) for diagnosis and prognosis of prostate cancer [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 3826. doi:10.1158/1538-7445.AM2017-3826

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