High-throughput proteomic analysis of FFPE tissue samples facilitates tumor stratification.

Yi Zhu,Peter Blattmann,Peter J Wild,Huanhuan Gao,Xiaodong Teng,Xiao Yi,Tiansheng Zhu,Qiushi Zhang,Christine Fritz,Jelena Ljubicic,Zhicheng Wu,Karim Saba,Ruedi Aebersold,Michael B Schmid,Xue Cai,Chao Xu,Lirong Chen,Tiannan Guo,Chen Chen,Xiaoyan Yu,Eugenia Haralambieva,Guan Ruan,Rui Sun,Qing Zhong,Niels J Rupp,Sai Lou,Ludovic Gillet,Dorothea Rutishauser,Tobias Weiss,Patrick Roth,Michael Weller,Wolfram Jochum,Xiaofei Gao,Bo Wang,Ailsa J Christiansen ,Silvia Höfer ,Cédric Poyet ,Elisabeth J Rushing ,Christian Daniel Fankhauser

doi:10.1002/1878-0261.12570

Abstract

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.

Highlights

Quantitative molecular profiling of phenotypically well-annotated clinical sample cohorts using genomic, transcriptomic, or metabolomic techniques, followed by the statistical association of molecular and phenotypic data has been a powerful approach for the development of biomarkers, guiding classification, stratification, and therapy, with regard to cancer patients (Ritchie et al, 2015; Sawyers, 2008)
We showed that the proteome map derived from FFPE samples correlate well with corresponding maps generated from their analogous fresh frozen (FF) samples and that the same biomarker panel can be identified from both sample types, even if the samples have been stored for 4–8 years in their respective format
We established a workflow for highthroughput proteomic analysis of large number of FFPE tissue samples

Summary

Introduction

Quantitative molecular profiling of phenotypically well-annotated clinical sample cohorts using genomic, transcriptomic, or metabolomic techniques, followed by the statistical association of molecular and phenotypic data has been a powerful approach for the development of biomarkers, guiding classification, stratification, and therapy, with regard to cancer patients (Ritchie et al, 2015; Sawyers, 2008). Various techniques and evaluation studies have been reported for genomic (Martelotto et al, 2017; Van Allen et al, 2014), transcriptomic (von Ahlfen et al, 2007; Li et al, 2014), proteomic and protein (Giusti and Lucacchini, 2013; Gustafsson et al, 2015; Hood et al, 2005; Ostasiewicz et al, 2010) from FFPE samples

Methods

Results

Conclusion