Abbreviated Name

Aggressive PCa Biomarkers

Lead Investigator

Liu, Tao — Pacific Northwest National Laboratory

Coordinating Investigator

Liu, Tao — Pacific Northwest National Laboratory

Involved Investigators

• Liu, Tao — Pacific Northwest National Laboratory
• Chesnut, Gregory — Center for Prostate Disease Research Uniformed Services University of the Health Sciences and the Walter Reed National Military Medical Center
• Feng, Ziding — Fred Hutchinson Cancer Center

Abstact

In this study, we focus on the identification of reliable early molecular markers capable of accurately predicting future aggressive disease progression. Our objective is to develop a panel of protein biomarkers, detectable in radical prostatectomy (RP) samples from men with organ-confined PCa, that would improve the ability to stratify patients for risk of progression, defined as either biochemical recurrence (BCR) after prostatectomy or distant metastasis (DM). The feasibility of generating protein expression data for low abundance proteins from formalin-fixed parafilm-embedded (FFPE) specimens from primary prostate tumors is demonstrated using an antibody-independent targeted proteomics analysis (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing-selected reaction monitoring (PRISM-SRM)) developed by our team. Differential protein abundance is then used to identify proteins associated with PCa aggressiveness. The predictive accuracy of a proteomic classifier in predicting local and distant cancer progression is validated in a cohort of men with long-term follow-up data and detailed clinical annotation. The addition of the proteomic classifier to the traditional, Standard of Care (SOC) variables is examined using training and testing analysis to determine the ability of the classifier to improve performance in the study cohort.

Aims

1. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we will use targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. 2. Protein classifiers will be identified and evaluated with or without combining with existing clinical and pathological standard of care variables to demonstrate significant improvement in predicting distant metastasis or biochemical recurrence, in a training/testing analysis. 3. We will validate the findings in another independent cohort.

Analytic Method

The application of our antibody-independent PRISM-SRM method, which utilizes offline chromatographic separation and “intelligent” fraction selection via monitoring the heavy isotope-labeled peptide internal standards, allows for much higher sample loading (e.g., 70-fold in the current study), highly effective peptide enrichment, and significantly reduced sample complexity that provided much higher sensitivity and is thus well suited for the detection of protein biomarker candidates in a broad concentration range. Using synthetic peptides with and without heavy isotope labeling of C-terminal lysine or arginine, highly sensitive, precise, and multiplex PRISM-SRM assays were developed in our laboratory using procedures we previously established.

Outcome

In this study, we focus on the identification of reliable early molecular markers capable of accurately predicting future aggressive disease progression. Our objective is to develop a panel of protein biomarkers, detectable in radical prostatectomy (RP) samples from men with organ-confined PCa, that would improve the ability to stratify patients for risk of progression, defined as either biochemical recurrence (BCR) after prostatectomy or distant metastasis (DM). The feasibility of generating protein expression data for low abundance proteins from formalin-fixed parafilm-embedded (FFPE) specimens from primary prostate tumors is demonstrated using an antibody-independent targeted proteomics analysis (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing-selected reaction monitoring (PRISM-SRM)) developed by our team. Differential protein abundance is then used to identify proteins associated with PCa aggressiveness. The predictive accuracy of a proteomic classifier in predicting local and distant cancer progression is validated in a cohort of men with long-term follow-up data and detailed clinical annotation. The addition of the proteomic classifier to the traditional, Standard of Care (SOC) variables is examined using training and testing analysis to determine the ability of the classifier to improve performance in the study cohort.

Publications

• No publications available at this time for this protocol.

Biomarkers

• CAMKK2
• EGFR
• FOLH1
• NCOA2
• PSA
• SPARC
• TGFB1

Data Collections

• No data collections available at this time for this protocol.

Start Date

Sep 15 2015

Estimated Finish Date

Sep 15 2022

Protocol ID

478

Protocol Type

Collaboration

Field of Research

Proteomics

Collaborative Group

Prostate and Urologic Cancers Research Group

Cancer Types

• Malignant neoplasm of prostate