Proteogenomics Sheds Light on Tumors

In the first integrated proteogenomic analysis of human cancer, a team of researchers analyzed proteomes of colorectal tumors and identified protein signatures of genetic mutations, potentially leading to advances in diagnosis and treatment.

The Clinical Proteomic Tumor Analysis Consortium, led by researchers at Vanderbilt University in Nashville, TN, used mass spectrometry to gather proteomic data on 95 colorectal tumor samples previously characterized by The Cancer Genome Atlas (TCGA). They found that abnormalities in the genes and messenger RNA of tumor samples did not necessarily correlate with abnormal proteins (Nature 2014 July 20 [Epub ahead of print]).

“People have analyzed messenger RNA expression patterns as signatures of cancer biology and different subtypes of cancer,” says the study's senior author, Daniel Liebler, PhD, director of the Jim Ayers Institute for Precancer Detection and Diagnosis at the Vanderbilt-Ingram Cancer Center. “But what we found is that the messenger RNA actually doesn't predict what the protein will do across a collection of tumors, suggesting that protein-level expression might be a better way of characterizing subtypes of tumors.”

Liebler's team found five proteomic subtypes of colorectal cancer by analyzing protein levels in the TCGA samples. Significantly, one of the TCGA subtypes split into two proteomic subtypes, only one of which was associated with poor prognosis.

“These proteomic subtypes have very different driving biology,” explains Liebler. “Although we need another study to prove the association with poor outcomes, this finding shows that protein-level subtyping of tumors is going to be at least as valuable as messenger RNA transcription subtyping.”

Protein-level analysis could also lead to new diagnostic tests to identify subsets of colorectal cancer, he adds.

“Protein-level measurements are already quite compatible with what is currently done to measure certain types of biomarkers and proteins associated with breast cancer, such as HER2 and estrogen receptor,” notes Liebler. “Protein data that identify biologically or clinically useful subtypes have the potential to translate directly to clinical diagnostics in a way that has proven to be more difficult for RNA-based measurements.”

The research team discovered that 17 chromosomal regions of significant focal amplification identified by TCGA were not necessarily associated with protein abundance, as had been assumed.

“We were very surprised to find that the proteins expressed from these loci were only elevated in four of the 17 cases and had strong effects on the expression of other genes,” says Liebler. The finding suggests that proteomic measurements may help researchers zero in on the most impactful genetic abnormalities as potential therapeutic targets.

“Proteomics guides us to a subset of these amplified regions,” says Liebler. “It helps us prioritize among many genomic alterations to identify the ones that look like they will have the biggest effects because they manifest very strongly at the protein level.”

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