May 17, 2020
Journal Article

Proteomic Tissue-based Classifier for Early Prediction of Prostate Cancer Progression

Abstract

Although approximately 40% of screen-detected prostate cancers are indolent, advanced-stage prostate cancer is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease, when cancer is still organ-confined and treatable, is a key challenge. Toward this end, in this study 52 candidate biomarkers were selected from existing prostate cancer genomics data sets, including known prostate cancer driver genes. Candidate biomarkers were quantitatively evaluated at the protein level using highly sensitive, antibody-independent, and multiplexed targeted mass spectrometry assays (high-pressure, high-resolution separations coupled with intelligent selection and multiplexing coupled to selected reaction monitoring, PRISM-SRM). All assays were performed on tumor tissue obtained from primary tumors from prostate cancer patients under going radical prostatectomy at a single military institution (n=338) bwteen 1996 and 2017. Patients were compared across three study outcomes: (i) development of metastasis = 1 year post-radical prostactomy (RP), versus (ii) biochemical recurrence at any time point = 1 year post-RP, versus (iii) no evidence of disease progression after a minimum follow-up =10 years post-RP. Biomarkers with significant performance included known prostate markers FOLH1 (also known as prostatate specific membrane antigen, PSMA), PSA, TGFß1, and SPARC (also known as Osteonectin) with significantly different expression levels across the three study outcomes. A five-protein classifier, which also included CAMKK2, was developed by combining the classifier with existing clinical and pathology standard of care (SOC) variables. The new classifer demonstrated improvement in Area Under the Curve (AUC) statistics for predicting distant metastasis, achieving an AUC of 0.92 (0.86, 0.99, p=0.001) and a negative predictive value of 92% in the training and testing analysis. This classifer has the potential to improve risk assessment, and to identify pateints who are likely to develop an aggressive metastatic prostate cancer that will require early intervention, as well as patients with low risk who could be managed thorugh active surveillance.

Revised: July 20, 2020 | Published: May 17, 2020

Citation

Gao Y., Y. Wang, Y. Chen, H. Wang, D. Young, T. Shi, and Y. Song, et al. 2020. Proteomic Tissue-based Classifier for Early Prediction of Prostate Cancer Progression. Cancers 12, no. 5:1268. PNNL-SA-149705. doi:10.3390/cancers12051268