Abstract 5565: Multi-omic profiling of prostate cancer evolution in 39 patients
Authors: Zhong Qing , Guo Tiannan , Toussaint Nora , Wagner Ulrich , Charmpi Konstantina , Calzone Laurence , Beyer Andreas , Aebersold Ruedi , Wild Peter J. 
Published: July 2017

Abstract
The pathological molecular events occurring during prostate cancer (PCa) progression have not been comprehensively investigated. It remains elusive how genomic aberration is transcribed into transcriptomic abnormalities that eventually influence proteins in the progression of PCa. We aimed to study the biological information flow along the central dogma and to identify critical molecular mechanisms in PCa evolution.
We performed comprehensive, multi-omics analyses of 105 prostate samples, consisting of both benign prostatic hyperplasia regions and malignant tumors from 39 PCa patients. Patients were from different risk groups, including 12 low-grade (Gleason score &lt; 7), 17 intermediate (Gleason score =7), and 10 high-grade (Gleason score &gt; 7) PCas. The omics data comprised exome sequencing, copy number variation (CNV) analysis, RNAseq, quantitative proteomics, and tissue morphology. Furthermore, peripheral blood samples from each patient were genomically profiled as reference.
We detected 806 somatic gene mutations from the 105 tissue samples, out of which 18 genes were mutated in three samples and 129 genes were mutated in two samples. More mutations were detected in patients with higher Gleason scores. We also found the copy numbers of 914 genes to be substantially up- or down-regulated in at least one sample. For RNAseq, we determined genes with either strictly monotonically increasing or decreasing expression from normal to intermediate and high-grade PCa per patient and per patient group, based on the regularized log-transformed read counts. Moreover, we collected tissue punches and analyzed each punch in technical duplicates by PCT-SWATH and used OpenSWATH software for data interpretation. We obtained precise quantification for several thousand proteins. The differential analysis revealed increasing and decreasing patterns of proteins of interest. Furthermore, we used network smoothing to determine overlapping molecular aberrations by comparing shortlisted DNA-mRNA-protein pathways.
By integrating proteogenomics with additional omic information, we demonstrated that molecular changes in PCa at genomic level are buffered at transcriptional level, and further condensed to a few modular changes in protein networks. We also found high correlation between pathway activities with tumor progression and heterogeneity. Our multi-omic profiling further revealed PCa evolution by using molecular alterations at different layers with associated mutant allele frequencies and uncovered several protein modules as key events in the progression of prostate cancer.
Citation Format: Qing Zhong, Tiannan Guo, Nora Toussaint, Ulrich Wagner, Konstantina Charmpi, Laurence Calzone, Andreas Beyer, Ruedi Aebersold, Peter J. Wild. Multi-omic profiling of prostate cancer evolution in 39 patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5565. doi:10.1158/1538-7445.AM2017-5565