Genomic Determinants of Protein Abundance Variation in Colorectal Cancer Cells

Abstract

SummaryAssessing the impact of genomic alterations on protein networks is fundamental in identifying the mechanisms that shape cancer heterogeneity. We have used isobaric labeling to characterize the proteomic landscapes of 50 colorectal cancer cell lines and to decipher the functional consequences of somatic genomic variants. The robust quantification of over 9,000 proteins and 11,000 phosphopeptides on average enabled the de novo construction of a functional protein correlation network, which ultimately exposed the collateral effects of mutations on protein complexes. CRISPR-cas9 deletion of key chromatin modifiers confirmed that the consequences of genomic alterations can propagate through protein interactions in a transcript-independent manner. Lastly, we leveraged the quantified proteome to perform unsupervised classification of the cell lines and to build predictive models of drug response in colorectal cancer. Overall, we provide a deep integrative view of the functional network and the molecular structure underlying the heterogeneity of colorectal cancer cells.

The Nucleosome Remodelling and Deacetylation complex suppresses transcriptional noise during lineage commitment

“…This genetic dissection has revealed that NuRD component proteins display a large degree of inter-dependence for protein stability, as has been reported for other multiprotein complexes (Roumeliotis et al, 2017). Mta1 and Mta2 both contain two distinct RBBP interaction domains, while Mta3 lacks the C-terminal most RBBP interaction domain (Millard et al, 2016).…”

Multi-omics characterization of interaction-mediated control of human protein abundance levels

“…Zhang et al, 2014) . In addition we compiled existing protein/gene expression and copy-number data for cancer cell lines from Lapek et al (BRCA) (Lapek et al, 2017) , Roumeliotis et al (COREAD) (Roumeliotis et al, 2017) and Lawrence et al (BRCA) (Lawrence et al, 2015) . In total, 368 cancer samples (294 tumours and 74 cell lines) were compiled in our study with matched gene expression, copy-number and protein abundance ( Figure 1A ).…”

“…These results are in-line with pulse chase degradation measurements showing that several complex subunits have a two-state degradation profile, that is compatible with a model in which they are expressed above the required levels and have a higher degradation rate when unbound from the complex (McShane et al, 2016) . The attenuation of changes at the protein level also justifies why protein complex subunits show higher correlation of protein abundances than the corresponding mRNA levels (Ryan, Kennedy, Bajrami, Matallanas, & Lord, 2017;Wang et al, 2017) and why correlation analysis can be used to identify cancer specific interaction networks (Lapek et al, 2017;Roumeliotis et al, 2017) .…”

An integrated landscape of protein expression in human cancer

“…Mass spectrometry (MS) is the main proteomics technology, capable of providing systemwide measurements of protein expression, post-translational modifications and/or protein-protein interactions, among other pieces of information 7,8 . Accordingly, cancer cell lines proteomes have been characterised in a number of MS-based studies [9][10][11][12][13][14][15][16][17] . However, due to technological limitations, these and other currently publicly available proteomics datasets are usually smaller in scope in comparison to analogous genomics and transcriptomics studies.…”