Formalin-fixed, paraffin-embedded (FFPE), biobanked tissue samples offer an invaluable resource for clinical and biomarker research. Here, we developed a pressure cycling technology (PCT)-SWATH mass spectrometry workflow to analyze FFPE tissue proteomes and applied it to the stratification of prostate cancer (PCa) and diffuse large B-cell lymphoma (DLBCL) samples. We show that the proteome patterns of FFPE PCa tissue samples and their analogous fresh-frozen (FF) counterparts have a high degree of similarity and we confirmed multiple proteins consistently regulated in PCa tissues in an independent sample cohort. We further demonstrate temporal stability of proteome patterns from FFPE samples that were stored Abbreviations BPH, benign prostatic hyperplasia; CRYAB, crystallin alpha Bbetween 1 and 15 years in a biobank and show a high degree of the proteome pattern similarity between two types of histological regions in small FFPE samples, that is, punched tissue biopsies and thin tissue sections of micrometer thickness, despite the existence of a certain degree of biological variations. Applying the method to two independent DLBCL cohorts, we identified myeloperoxidase, a peroxidase enzyme, as a novel prognostic marker. In summary, this study presents a robust proteomic method to analyze bulk and biopsy FFPE tissues and reports the first systematic comparison of proteome maps generated from FFPE and FF samples. Our data demonstrate the practicality and superiority of FFPE over FF samples for proteome in biomarker discovery. Promising biomarker candidates for PCa and DLBCL have been discovered.
Application of pressure cycling technology and Sequential Windowed Acquisition of all THeoretical mass spectrometry allows quantifying the degree of intra-tumor heterogeneity of protein expression in prostate tumors. The data show that protein intra-tumor heterogeneity, if not characterized, may distort protein biomarker suitability in tumor tissues.
PURPOSE: To externally validate recently published prostate cancer risk calculators (PCa-RCs) incorporating multiparametric magnetic resonance imaging (mpMRI) for the prediction of clinically significant prostate cancer (csPCa) and compare their performance to mpMRI-naïve PCa-RCs. MATE-RIAL AND METHODS: Men without previous PCa diagnosis undergoing transperineal template saturation prostate biopsy with fusion-guided targeted biopsy between 11/2014 and 03/2018 in our academic tertiary referral center were identified. Any Gleason pattern 4 was defined to be csPCa. Predictors (age, PSA, DRE, prostate volume, family history, previous prostate biopsy and highest region of interest according to PIRADS) were retrospectively collected. Four mpMRI-PCa-RCs and two mpMRI-naïve PCa-RCs were evaluated for their discrimination, calibration and clinical net benefit using a ROC analysis, calibration plots and a decision curve analysis, respectively. RESULTS: Out of 468 men, 193 (41%) were diagnosed with csPCa. Three mpMRI-PCa-RCs showed similar discrimination with area-underneaththe-receiver-operating-characteristic-curves (AUC) from 0.83 to 0.85, which was significantly higher than the other PCa-RCs (AUCs: 0.69-0.74). Calibration-in-the-large showed minimal deviation from the true amount of csPCa by 2% for two mpMRI-PCa-RCs, while the other PCa-RCs showed worse calibration (11-27%). A clinical net benefit could only be observed for three mpMRI-PCa-RCs at biopsy thresholds 15%, while none of the six investigated PCa-RCs demonstrated clinical utility against a biopsy all strategy at thresholds <15%. CONCLUSIONS: Performance of the mpMRI-PCa-RCs varies, but they generally outperform mpMRI-naïve PCa-RCs in regard to discrimination, calibration and clinical usefulness. External validation in other biopsy settings is highly encouraged.
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