Publikation
High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification
Wissenschaftlicher Artikel/Review - 18.09.2019
Zhu Yi, Christiansen Ailsa, Fritz Christine, Rupp Niels J, Poyet Cédric, Rushing Elisabeth, Weller Michael, Roth Patrick, Haralambieva Eugenia, Hofer Silvia, Chen Chen, Jochum Wolfram, Gao Xiaofei, Teng Xiaodong, Chen Lirong, Zhong Qing, Wild Peter J, Aebersold Ruedi, Ljubicic Jelena, Rutishauser Dorothea, Schmid Michael, Weiss Tobias, Zhang Qiushi, Sun Rui, Wang Bo, Yi Xiao, Wu Zhicheng, Gao Huanhuan, Cai Xue, Ruan Guan, Zhu Tiansheng, Xu Chao, Lou Sai, Yu Xiaoyan, Gillet Ludovic, Blattmann Peter, Saba Karim, Fankhauser Christian D, Guo Tiannan
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Kurzbeschreibung/Zielsetzung
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 between 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.