PNNL - Prostate_Biomarker_PNNL_2019

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

  • Organism: Homo sapiens
  • Instrument: TSQ Vantage
  • SpikeIn: Yes
  • Keywords: biochemical recurrence, biomarkers, early detection, metastasis, prostate cancer, proteomics
  • Lab head: Tao Liu
Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease, while the cancer is still organ-confined and treatable, is a key challenge. Using highly sensitive, antibody-independent targeted mass spectrometry assays, we quantitatively evaluated 52 candidate biomarkers, selected from existing PCa genomics data sets and known PCa driver genes, at protein level in primary tumors from PCa patients treated with radical prostatectomy (RP) at a single military institution, across three study outcomes: (i) development of metastasis ≥ 1-year post-RP, (ii) biochemical recurrence at any time point ≥ 1-year post-RP, (iii) no evidence of disease progression after follow-up ≥10 years post-RP. Sixteen proteins that significantly changed in abundance in an initial set of 105 samples were evaluated in the entire cohort (n=338). A five-protein classifier which combined the proteomic markers FOLH1, PSA, TGFβ1, SPARC, and CAMKK2 with existing clinical and pathology standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving Area Under the Curve of 0.92 (0.86, 0.99, p=0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention, compared to low risk patients who could be managed through active surveillance.
Created on 5/11/20, 4:35 PM