Convergent network effects along the axis of gene expression during prostate cancer progression

Abstract

36Tumor-specific genomic aberrations are routinely determined by high throughput genomic 37 measurements. However, it is unclear how complex genome alterations affect molecular networks 38 through changing protein levels, and consequently biochemical states of tumor tissues. Here, we 39 investigated how tumor heterogeneity evolves during prostate cancer progression. In this study, we 40 performed proteogenomic analyses of 105 prostate samples, consisting of both benign prostatic 41 hyperplasia regions and malignant tumors, from 39 prostate cancer (PCa) patients. Exome sequencing, 42 copy number analysis, RNA sequencing and quantitative proteomic data were integrated using a network-43 based approach and related to clinical and histopathological features. In general, the number and 44 magnitude of alterations (DNA, RNA and protein) correlated with histopathological tumor grades. 45Although common sets of proteins were affected in high-grade tumors, the extent to which these proteins 46 changed their concentrations varied considerably across tumors. Our multi-layer network integration 47 identified a sub-network consisting of nine genes whose activity positively correlated with increasingly 48 aggressive tumor phenotypes. Importantly, although the effects on individual gene members were barely 49 detectable, together the perturbation of this sub-network was predictive for recurrence-free survival time. 50The multi-omics profiling of multiple tumor sites from the same patients revealed cases of likely shared 51 clonal origins as well as the occasional co-existence of multiple clonally independent tumors in the same 52 prostate. Overall, this study revealed molecular networks with remarkably convergent alterations across 53 tumor sites and patients, but it also exposed a diversity of network effects: we could not identify a single 54 sub-network that was perturbed in all high-grade tumor regions. 55 56 57

Comparison of Approaches to Transcriptomic Analysis in Multi-Sampled Tumors

“…Cohort 2 consists of tumor samples from 39 patients diagnosed with intermediate to high-risk prostate cancer and subsequently treated by radical prostatectomy [19]. These tumor samples were acquired from tissue punches of radical prostatectomy tissue, guided by a pathologist's determination of low grade or high grade cancer regions.…”

“…These tumor samples were acquired from tissue punches of radical prostatectomy tissue, guided by a pathologist's determination of low grade or high grade cancer regions. The manuscript by Ciampi et al [19] describes this cohort and each tumor region.…”

“…Foci in cohort 1 were microdissected based on distinct histologic features, such as gland architecture, proximity to other tumor cells, and immunochemistry against common prostate cancer markers such as anti-ERG and anti-PTEN. Cohort 2 was comprised of 66 tumor punches from either high-grade or low-grade tumors from prostatectomy samples assessed without imaging, with 1-2 foci per patient [19]. Regions sampled in cohort 2 were determined solely on tumor aggressivity.…”

Convergent network effects along the axis of gene expression during prostate cancer progression

“…Exome and RNA sequencing data were submitted to the Sequence Read Archive (SRA) at NCBI under accession numbers PRJNA577801 (exome-seq) [ 119 ] and PRJNA579899 (RNA-seq) [ 120 ], respectively. The SWATH proteomics data were deposited in PRIDE.…”

“…The SWATH proteomics data were deposited in PRIDE. The project accession code is PXD004589 [ 121 ]. The published datasets of the two PCa cohorts (TCGA and MSKCC) analyzed during the current study can be downloaded from cBioPortal [ 122 , 123 ] while the third (Aarhus) is available at the NCBI GEO repository under the accession number GSE46602 [ 124 ].…”