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

Abstract

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 is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used 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. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic 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.

Proteomic Analysis of Prostate Cancer FFPE Samples Reveals Markers of Disease Progression and Aggressiveness

“…Proteomic studies in tissue, cell lines, blood, urine, seminal plasma, and exosomes have identified many potential biomarkers for PCa diagnosis, metastasis, aggressiveness, and resistance to treatment. These studies have utilized both untargeted gel-based and gel-free approaches, including two-dimensional gel electrophoresis coupled to mass spectrometry (2-DE-MS) [ 23 , 24 ], liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), [ 25 , 26 , 27 ] and capillary electrophoresis coupled to mass spectrometry (CE-MS) [ 28 , 29 ], with LC-MS/MS being the most commonly used approach, as well as targeted mass spectrometry-based approaches [ 30 ]. The reproducibility of these findings remains challenging due to the heterogeneity of cancer itself and the various techniques used, but the data generated through these attempts is a great contribution to personalized approaches after more targeted investigations [ 19 ].…”

IL‐17F facilitates prostate cancer cell malignant phenotypes via activation of the PI3K/AKT signalling pathway

“…Although treatment strategies for PCa have been improved, the annual incidence of PCa has increased by 14% since 1990 (Ishizaki et al., 2012). Patients of early‐stage PCa often have a good prognosis after comprehensive treatment, but due to recurrence, metastasis and castration‐resistance, the 5‐year survival rate of patients with advanced‐stage PCa is only 29% (Gao et al., 2020). Unfortunately, although numerous studies have been implemented, the pathogenesis of tumorigenesis and progression of PCa has not been well elucidated yet.…”

Biomarkers for the Detection and Risk Stratification of Aggressive Prostate Cancer

“…A classifier was created using differentially expressed proteins in formalin-fixed parafilm-embedded tissue specimens. This classifier comprised a panel of five proteins (i.e., FOLH1, KLK3, TGFB1, SPARC, and CAMKK2) that are capable of stratifying PCa patients according to the risk of aggressiveness [ 108 ]. In a recent study, seven differentially expressed proteins (i.e., HSPA9 and HSPE1, NPM1, VCAN, SERBP1, MRPL3, and UQCRH) were successfully used as indicators of PCa progression and aggressiveness [ 109 , 110 ].…”