In Depth Proteomic Analysis of Prostate Cancer Biopsies Gabor Toth (Presenter) Hungarian Academy of Sciences Presenter Bio: My name is Gábor Tóth, I graduated as a chemical engineer at the Budapest University of Technology and Economics. I am at the beginning of my professional scientific career as I proceeded with starting my Ph.D. programme from July 2018 on. I have now been a student researcher at the MS Proteomics Group of the Research Centre for Natural Sciences, Hungarian Academy of Sciences for four years and despite being a chemical engineer I am dedicated to life science research. My Ph.D. program is related to clinical proteomics, the main aim is to discover possible new biomarkers for prostate cancer. I am completely overwhelmed with the depth of natural sciences, I have always loved solving problems and searching for the reasons behind the events, therefore it is a great pleasure to step on this path.

Authors: Gábor Tóth (1,2), Oliver Ozohanics (1), András Ács (1,3), Ágnes Révész (1), Károly Vékey (1), László Drahos (1), Lilla Turiák (1)
(1) MS Proteomics Research Group, Research Centre for Natural Sciences, Hungarian Academy of Sciences, Magyar tudósok körútja 2., H-1117 Budapest, Hungary (2) Budapest University of Technology and Economics, Faculty of Chemical Technology and Biotechnology, Műegyetem rkp. 3., H-1111 Budapest, Hungary (3) Semmelweis University, PhD School of Pharmaceutical Sciences, Üllői út 26., H-1085 Budapest, Hungary

Cancer research is among the most studied areas of science and prostate cancer (PCa) is one of the most common types of cancer among men. In this work we describe a detailed proteomics analysis of PCa tissue microarrays (TMAs) with our novel methodology. It is based on surface proteolytic digestion and proved to be capable of quantifying over 500 proteins from a single 1.5 mm diameter TMA core. We have compared the protein composition of tissues with various grades and stages of cancer. Samples from healthy and cancerous tissues were clearly distinguished and a good correlation with cancer grade was found. A well balanced study was carried out and over 100 proteins showed statistically significant abundance changes between various groups. During STRING evaluation up-regulation eg. in KEGG ribosome pathway and mRNA splicing could be observed.

Introduction

Cancer research is among the most studied areas of science and prostate cancer (PCa) is one of the most common types of cancer among men. Tissue samples, especially biopsies are often used in mass spectrometry-based biomarker research as they have a great potential in understanding biochemical mechanisms underlying diseases such as cancer. Tissue microarrays (TMA) consist of several biopsies (generally 1.5 mm in diameter) of different patients placed on a glass slide.

The aim of our work was to develop and apply advanced nanoLC MS(MS) techniques to reliably identify specific proteins and cancer associated protein pathways from the surface of PCa TMAs (ca. 10 µg tissue).

Methods

A previously published surface proteolytic digestion method [1] was adapted and used with slight modifications for the sample preparation of the TMA cores and it was followed by reversed phase nanoHPLC-MS(MS) measurements. For the data evaluation Scaffold, MaxQuant label-free quantitation and for protein pathway analysis STRING software were used. Principle component analysis was used to differentiate between sample groups using XLStat software.

Results

Surface digestion resulted in 5 times more protein identifications than bulk digestion of the same TMA spot and allowed quantitation of over 500 proteins in healthy and cancerous prostate TMA cores. Label-free quantitation showed that biological variability among all samples was ca. 3 times larger than the technical reproducibility.

We compared healthy and cancerous prostate tissues; and also tissues with various grades and stages of cancer. Healthy and cancerous tissues were distinguished to a high extent, and we found a correlation of proteomic patterns in cancer progression with cancer grade, but not with cancer stage. This way in the further studies cancer grade stands for the basis of comparison.

Following the initial pilot study, a well balanced full-scale study with ca. 20-30 samples of each grade was carried out and over 200 proteins showed statistically significant abundance changes between various groups. During bioinformatics evaluation, high rate of up-regulation (increased abundance) of proteins e.g. in the KEGG ribosome pathway and in mRNA splicing was observed in accordance with cancer progression.

Conclusions & Discussion

The presented TMA surface proteolytic digestion methodology is extremely efficient and opens a new way towards an alternative means of diagnostics supplementing histological analysis. We have identified ca. 200 proteins which showed statistically significant changes in abundance between healthy and cancerous tissue samples. Several protein pathways were identified which change significantly and have already been presented to have crucial effects in prostate cancer progression. Besides, several unique proteins were identified some of which pose the potential as new biomarkers for diagnostics. A further in-depth characterization is in progress in order to find specific proteins and pathways for possible diagnostics use.

1. Turiák, L., et al., Workflow for Combined Proteomics and Glycomics Profiling from Histological Tissues. Analytical Chemistry, 2014. 86(19): p. 9670-9678.

TG appreciates the financial support through the Young Investigator Grant. LT, LD, and KV are grateful for funding from the National Research Development and Innovation Office (NKFIH PD-121187, NKFIH K-109006, and NKFIH K-119459). LT and ÁR are grateful for support from the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.