Investigators who analysed 95 human colorectal tumour samples have determined how gene alterations identified in previous analyses of the same samples are expressed at the protein level.

The integration of proteomic and genomic data, or proteogenomics, provides a more comprehensive view of the biological features that drive cancer than genomic analysis alone and may help identify the most important targets for cancer detection and intervention.

The results of this study appeared online in Nature.

The colorectal study produced several key findings:

• Measurements of messenger RNA abundance did not reliably predict protein abundance. The investigators were not surprised by this discordance, because many regulatory controls lie between RNA and protein expression. However, it did demonstrate that RNA analyses do not necessarily give a correct indication of protein levels.
• Most of the focal amplifications (increased amounts of certain chromosome segments) observed in the earlier genomic analyses of the same tumours did not result in corresponding elevations in protein level. Proteomic analyses identified a few amplifications that had dramatic effects on protein levels and may represent potentially important targets for diagnosis or therapeutic intervention.
• Proteomics identified five colon cancer subtypes, including classifications that could not be derived from genomic data. Protein expression signatures for one of the subtypes indicated molecular characteristics associated with highly aggressive tumours with poor clinical outcome.

These findings, by Clinical Proteomic Tumor Analysis Consortium (CPTAC) investigators, including Henry Rodriguez, Ph.D., director of the Office of Cancer Clinical Proteomics Research, NCI, and Daniel C. Liebler, Ph.D., Vanderbilt University School of Medicine, Nashville, were made possible because of genomic analyses that were done on the same tumours in 2012 by The Cancer Genome Atlas (TCGA) Research Network.

The CPTAC study provided a clear demonstration of how proteomics can be used to help understand how genomic abnormalities drive cancer.

Source: National Cancer Institute