A high throughput approach to delineate protein complexes enriched in Prostate Adenocarcinoma formalin-fixed paraffin-embedded tissue samples

JUAN BIZZOTTO; PABLO SANCHIS; SERGIO NEMIROVSKY; GERALDINE GUERON; PIA VALACCO; CARLOS SCORTICATI; ELBA VAZQUEZ; PIA VALACCO; CARLOS SCORTICATI; ELBA VAZQUEZ; SOFIA LAGE VICKERS; OSVALDO MAZZA; JAVIER COTIGNOLA; SOFIA LAGE VICKERS; OSVALDO MAZZA; JAVIER COTIGNOLA; JUAN BIZZOTTO; PABLO SANCHIS; SERGIO NEMIROVSKY; GERALDINE GUERON

Congreso; Prostate Cancer Foundation Scientific Retreat 2020; 2020

Formalin-fixed, paraffin-embedded (FFPE) tissues are highly valuable resources for translationalproteomics studies. Although it is well known that Prostate Cancer (PCa) is a progressivedisease involving multiple gene alterations, little is known at the proteome level. Most of thefunctional information of the cancer-associated genes relies in the proteome, an exceptionallycomplex biological system involving several proteins that function through dynamic protein-protein interactions and post-translational modifications.To identify differential protein complexes associated with PCa we carried out an in-depthproteomics analysis. PCa and Benign Prostatic Hyperplasia (BPH) samples were obtained fromthe Hospital de Clínicas ?José de San Martín?, Buenos Aires, Argentina (with written informedconsent and institutional review board approval). Proteins were obtained using phase-transfersurfactant-aided extraction/tryptic digestion of formalin-fixed, paraffin-embedded (FFPE) tissuesections mounted on microscope slides. Samples were subjected to mass spectrometry analysis(ESI-MS/MS). We found 109 proteins enriched in PCa compared with BPH samplesWe took a departure from conventional analysis approaches utilizing biological networks. Weutilized CORUM, a database of human protein complexes formed by physically interactingproteins, to identify protein complexes differentially expressed in PCa and BPH tissues. Theseprotein complexes (PC) may be regarded as units of biological function, hence suitable forcontextualizing proteomics data. In this regard, using PC as a cluster vector, we calculated aProteomics Signature Profile (PSP) for each sample based on the hit rates (H) of their reportedproteins (H= number of proteins found in the sample / number of proteins in the clustervector) , against the cluster vector.Our results show 5 differentially expressed protein complexes in PCa compared with BPH(P