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

Abstract

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 (ϳ80%). 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. Massively parallel sequencing (MPS)1 of cancer genomes has demonstrated enormous complexity, and it is often unclear which somatic mutations drive tumor biology and which are nonfunctional passenger mutations that passively accumulate. RNA sequencing is frequently used to determine which nucleotide variants are transcribed and therefore have the potential for biological function. However, many mutations detected at the DNA level are not observed at the mRNA level, and their observation is dependent upon expression of the stability of the mRNA (1). Mutation detection at the peptide level clearly increases the confidence that any given variant is

Proteogenomics connects somatic mutations to signalling in breast cancer

“…1b, Supplementary Table 5), the number of genomic and transcriptomic variants that were confirmed as peptides by MS was low (Supplementary Discussion). Sparse detection of individual genomic variants by peptide sequencing has been noted in our previous studies 16 and reflects limited coverage at the single amino-acid level with current technology. However quantitative MS analysis of multiple peptides for each protein is used to reliably infer overall protein levels.…”

“…1a). The database was constructed using the QUILTS software package 16 leveraging RefSeq gene models based on whole exome and RNA-seq data generated from portions of the same tumors and matched germline DNA (Fig. 1a, Supplementary Table 5).…”

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer

“…These results demonstrate the ability to confidently detect, identify and validate genome-level predictions at the protein level. More novel peptides would likely be observed if there was sufficient sample for more extensive fractionation and/or replicate analyses (Ruggles et al, 2015); these limitations preclude any conclusions about the biological significance of unobserved events. For example, only 2 mutant p53 peptides were identified despite the presence of p53 mutations in all tumors examined.…”

Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line

“…23,41,42 However, SAAVs mostly derive from genomic nsSNPs that occur in coding regions, although they may be epigenetically controlled at some level. 43 The codon frequency usage in all coding sequences shows bias for gene expression based on the predicted Web site (http://www.kazusa.or.jp/codon/).…”