Proteogenomic characterization of human colon and rectal cancer

By Bing Zhang, Jing Wang, Xiaojing Wang, Jing Zhu, Qi Liu, Zhiao Shi, Matthew Chambers, L. Zimmerman, Kent Shaddox, Sangtae Kim and 56 more. Nature, 2014

1,339 citations recorded by the source corpus.

Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA ‘microsatellite instability/CpG island methylation phenotype’ transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis- and trans-effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.

Subfields: Molecular Biology, Pathology and Forensic Medicine, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/23d7a81d6b8a58f6742f995caf60de34359fda09

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Citations in the corpus

The Consensus Molecular Subtypes of Colorectal Cancer (2015, methodology): “is supported by grants from the Dutch Cancer Society (UVA2011-4969, UVA2014-7245), Worldwide Cancer Research (14-1164), the Maag Lever Darm Stichting (MLDS-CDG 14-03) and the European Research Council (ERG-StG 638193).”
Quantitative Proteomics of the Cancer Cell Line Encyclopedia (2020, positive): “Although the proteome of MSI colorectal cancer has been analyzed recently in a panel of ten cell lines and in the TCGA tumor data (Halvey et al., 2013; Liu and Zhang, 2016; Zhang et al., 2014), the CCLE contained MSI samples of multiple tissue lineages.”
Multidimensional proteomics for cell biology (2015, background): “…protein properties — for example, protein degradation, synthesis and turnover rates 3–6 — are examined either in parallel or sequentially with other protein properties, such as subcellular localization, total protein abundance, tissue distribution 7,8 and protein isoforms or variants 4,9–11 .”
A deep proteome and transcriptome abundance atlas of 29 healthy human tissues (2018, background): “Broader scale analyses have more recently become possible owing to advances in proteome and transcriptome profiling technologies, but these have mostly focussed on a single (disease) tissue or the cell-type resolved analysis of protein expression in single tissues (Zhang et al. 2014; Mertins et al. ”
Deep Learning based multi-omics integration robustly predicts survival in liver cancer (2017, methodology): “This approachwas used tonormalize samples fromRNA-Seq data previously (40).”
A novel community driven software for functional enrichment analysis of extracellular vesicles data (2017, background): “Advances in high-throughput techniques including next generation sequencing, RNA sequencing and proteomics have spurred enormous volume of data [1 – 3].”
Trans-Proteomic Pipeline, a standardized data processing pipeline for large-scale reproducible proteomics informatics (2015, methodology): “The use of robust tools such as the TPP provides the groundwork for the development of these datasets that enable clinically relevant biological proteomics tobecollected,interpreted,consolidate,andultimatelyshared [49,50].”