Cysteinyl Peptide Capture for Shotgun Proteomics: Global Assessment of Chemoselective Fractionation

The complexity of cell and tissue proteomes presents one of the most significant technical challenges in proteomic biomarker discovery. Multidimensional liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based shotgun proteomics can be coupled with selective enrichment of cysteinyl peptides (Cys-peptides) to reduce sample complexity and increase proteome coverage. Here we evaluated the impact of Cys-peptide enrichment on global proteomic inventories. We employed a new cleavable thiolreactive biotinylating probe, N-(2-(2-(2-(2-(3-(1-hydroxy-2-oxo-2-phenylethyl)phenoxy)acetamido)ethoxy)ethoxy)ethyl)-5-(2-oxohexahydro-1H-thieno[3,4-d]imidazol-4-yl)pentanamide (IBB), to capture Cyspeptides after digestion. Treatment of tryptic digests with the IBB reagent followed by streptavidin capture and mild alkaline hydrolysis releases a highly purified population of Cys-peptides with a residual S-carboxymethyl tag. Isoelectric focusing (IEF) followed by LC-MS/MS of Cys-peptides significantly expanded proteome coverage in Saccharomyces cerevisiae (yeast) and in human colon carcinoma RKO cells. IBB-based fractionation enhanced detection of Cys-proteins in direct proportion to their cysteine content. The degree of enrichment typically was 2-8-fold but ranged up to almost 20-fold for a few proteins. Published copy number annotation for the yeast proteome enabled benchmarking of MS/MS spectral count data to yeast protein abundance and revealed selective enrichment of cysteine-rich

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (∼80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis

Colorectal cancer cell line proteomes are representative of primary tumors and predict drug sensitivity

Proteomics holds promise for individualizing cancer treatment. We analyzed to what extent the proteomic landscape of human colorectal cancer (CRC) is maintained in established CRC cell lines and the utility of proteomics for predicting therapeutic responses. Proteomic and transcriptomic analyses were performed on 44 CRC cell lines, compared against primary CRCs (n=95) and normal tissues (n=60), and integrated with genomic and drug sensitivity data. Cell lines mirrored the proteomic aberrations of primary tumors, in particular for intrinsic programs. Tumor relationships of protein expression with DNA copy number aberrations and signatures of post-transcriptional regulation were recapitulated in cell lines. The 5 proteomic subtypes previously identified in tumors were represented among cell lines. Nonetheless, systematic differences between cell line and tumor proteomes were apparent, attributable to stroma, extrinsic signaling, and growth conditions. Contribution of tumor stroma obscured signatures of DNA mismatch repair identified in cell lines with a hypermutation phenotype. Global proteomic data showed improved utility for predicting both known drug-target relationships and overall drug sensitivity as compared with genomic or transcriptomic measurements. Inhibition of targetable proteins associated with drug responses further identified corresponding synergistic or antagonistic drug combinations. Our data provide evidence for CRC proteomic subtype-specific drug responses. Proteomes of established CRC cell line are representative of primary tumors. Proteomic data tend to exhibit improved prediction of drug sensitivity as compared with genomic and transcriptomic profiles. Our integrative proteogenomic analysis highlights the potential of proteome profiling to inform personalized cancer medicine