
Quantitative Profiling of Single Formalin Fixed Tumour Sections: proteomics for translational research
Author(s) -
Christopher S. Hughes,
Melissa K. McConechy,
Dawn R. Cochrane,
Tayyebeh M. Nazeran,
Anthony N. Karnezis,
David G. Huntsman,
Gregg B. Morin
Publication year - 2016
Publication title -
scientific reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.24
H-Index - 213
ISSN - 2045-2322
DOI - 10.1038/srep34949
Subject(s) - proteome , proteomics , computational biology , biomarker discovery , biology , transcriptome , gene expression profiling , serous ovarian cancer , bioinformatics , ovarian cancer , cancer , gene , gene expression , genetics
Although re-sequencing of gene panels and mRNA expression profiling are now firmly established in clinical laboratories, in-depth proteome analysis has remained a niche technology, better suited for studying model systems rather than challenging materials such as clinical trial samples. To address this limitation, we have developed a novel and optimized platform called SP3-Clinical Tissue Proteomics (SP3-CTP) for in-depth proteome profiling of practical quantities of tumour tissues, including formalin fixed and paraffin embedded (FFPE). Using single 10 μm scrolls of clinical tumour blocks, we performed in-depth quantitative analyses of individual sections from ovarian tumours covering the high-grade serous, clear cell, and endometrioid histotypes. This examination enabled the generation of a novel high-resolution proteome map of ovarian cancer histotypes from clinical tissues. Comparison of the obtained proteome data with large-scale genome and transcriptome analyses validated the observed proteome biology for previously validated hallmarks of this disease, and also identified novel protein features. A tissue microarray analysis validated cystathionine gamma-lyase (CTH) as a novel clear cell carcinoma feature with potential clinical relevance. In addition to providing a milestone in the understanding of ovarian cancer biology, these results show that in-depth proteomic analysis of clinically annotated FFPE materials can be effectively used as a biomarker discovery tool and perhaps ultimately as a diagnostic approach.