Abstract
The molecular landscape of human cancers involves multiple omics layers of complexity, from genome to proteome and beyond. Cancer proteogenomics involves the integration of protein expression patterns with somatic DNA alterations. Recently, advances in mass spectrometry-based proteomic profiling technologies have enabled the generation of combined proteomic and multi-omic data for thousands of human tumors across dozens of studies. These data in the public domain can be utilized to give us a more complete picture of cancer-specific pathways and processes and identify gene candidates for therapeutic targeting. Many proteogenomic studies are ongoing involving various cancer types according to tissue or cell of origin, including studies to predict response to therapy. In addition, pan-cancer analyses across multiple studies can identify molecular commonalities, differences, and emergent themes across tumor lineages. Data integration can determine which gene alterations at the transcriptome level are translated to the protein level. A wealth of knowledge and analytical approaches developed historically to integrate gene transcription with genomic data can be readily applied to proteogenomic analyses. Here is provided an overview of higher-level analyses of proteogenomic datasets. Such analyses include defining proteomic subtypes of cancer, exploring the impact of somatic mutations and epigenetic modifications on protein expression, cataloging proteomic correlates of more aggressive disease or drug response, and identifying enriched pathways.
