Proteogenomics: Key Driver for Clinical Discovery and Personalized Medicine

Abstract

Proteogenomics is a multi-omics research field that has the aim to efficiently integrate genomics, transcriptomics and proteomics. With this approach it is possible to identify new patient-specific proteoforms that may have implications in disease development, specifically in cancer. Understanding the impact of a large number of mutations detected at the genomics level is needed to assess the effects at the proteome level. Proteogenomics data integration would help in identifying molecular changes that are persistent across multiple molecular layers and enable better interpretation of molecular mechanisms of disease, such as the causal relationship between single nucleotide polymorphisms (SNPs) and the expression of transcripts and translation of proteins compared to mainstream proteomics approaches. Identifying patient-specific protein forms and getting a better picture of molecular mechanisms of disease opens the avenue for precision and personalized medicine. Proteogenomics is, however, a challenging interdisciplinary science that requires the understanding of sample preparation, data acquisition and processing for genomics, transcriptomics and proteomics. This chapter aims to guide the reader through the technology and bioinformatics aspects of these multi-omics approaches, illustrated with proteogenomics applications having clinical or biological relevance.

Integrated proteogenomic approach identifying a protein signature of COPD and a new splice variant of SORBS1

“…Here, we report on the first explorative study using an integrative proteogenomic approach to study pathogenetic changes in Stage IV COPD (n=10) compared with control (n=8) lung tissue (all ex-smokers, table 1). With this proteogenomics approach,5 we integrated mass spectrometry-based proteomic and RNA-sequencing data of polyadenylated transcripts of the same frozen lung tissue samples that were stored at −80°C, of which consecutive slides (10×10 µm) were cut and used for RNA and protein isolation (detailed methods in online supplementary files). The most important step in this integrated approach was the prediction of the protein sequence variants present in each sample based on the RNA-sequencing data, creating sample-specific protein reference databases.…”

Multicenter reverse-phase protein array data integration

“…Moreover, if parallel signaling pathways are activated in the tumor, the patient will show resistance to the administered targeted therapy. An emerging consensus view has therefore identified the need to include proteomic analyses in drug development pipelines and molecular screening programs [7][8][9][10][11] . However, successful implementation of this requires robust, reproducible, and affordable high-throughput proteomics technologies.…”

Connecting Proteomics to Next‐Generation Sequencing: Proteogenomics and Its Current Applications in Biology

“…As discussed, proteogenomics can assist in resolving mis‐annotation, discrepancy and missing information in the genomes. Similar strategies and workflows can be applied in the context of precision medicine as proteogenomics may be used to identify patient‐specific proteoforms that are essential in disease development . Due to space constraints, we focus our discussion on cancer, which is one of the best‐studied diseases using proteogenomic methodology.…”