Special issue of MCP: Multiomics studies of cancer

MCP cover imageAs high-throughput analytical tools improve, allowing researchers to collect more and more data, the challenge becomes how to interpret it all. When transcriptomic, genomic, metabolomic and proteomic analyses are layered together, parsing out a signal can be a monumental task. The field needs new analytical strategies, and new user-friendly software, to condense a dizzying array of data types into coherent, interpretable findings that can inform clinical decisions. 

Researchers surfing this multiomics wave report a plethora of new tools and approaches this month in a special issue of the journal Molecular & Cellular Proteomics devoted to integrating multiple omics. The issue, edited by Bernhard Kuster of the Technical University of Munich and Bing Zhang of the Baylor College of Medicine, includes a robust section combining genomic and proteomic approaches to understanding cancer. Many of these studies use data from the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium and The Cancer Genome Atlas. Each of these projects collects deep proteomic and genomic data from many patients with defined cancer types and then makes the data widely available for bioinformatics analyses.

Below are some highlights.

Leveraging proteomics to interpret clinical data

Zhan et al. combined multiomics with clinical outcomes and pathology-lab images to yield histopathological markers, such as cell density or size, that might be prognostic — a boon, since images of biopsies can be taken at many clinics, while omics approaches are less widely available.

From CNV to cellular phenotype

Ma et al. investigated how copy-number variations, common in cancer, affect the cellular phenotypes through protein and phosphoprotein abundance. They discovered new genome regions that, when altered, affect the abundance of important cancer-associated proteins.

Song et al. asked similar questions, integrating transcriptome, phosphoproteome and proteome information about advanced ovarian cancer to understand how copy-number variation or methylation at a given locus can reverberate through the cell.

Sousa et al. investigated how, in cancers with duplicated or deleted sections of genome that might be expected to produce much more or less protein, protein–protein interactions buffer stability and final protein levels.

New substrates, new mechanisms, new datasets

Liu et al. used independent component analysis, a machine-learning approach, to find patterns in breast cancer proteogenomic data sets that might point to new cellular mechanisms of disease.

Chen et al. introduce a new iteration of the Cancer Proteome Atlas, a repository of reverse-phase protein array data from some 8,000 patients’ tumor samples. The new platform, version 3.0, allows users to integrate the available protein array data with other omics data.

Arshad et al. combined protein and phosphopeptide abundance across tumor samples to identify new kinase substrates and match modifications that regulate kinase activity to the substrates they select for.