Elevated Levels of Guanine Nucleotide Exchange Factors in Circulating Tumor Cells Predict Poor Outcome for Colorectal Cancer Patients

They are potential targets for cancer therapy due to their role in many signaling pathways, particularly cell proliferation.

Investigators at the University of California, San Diego (USA) determined the levels of GEFs - GIV/Girdin, Daple, NUCB1, and NUCB2 in circulating tumor cells isolated from the peripheral blood of patients with metastatic colorectal cancer.

They reported in the February 26, 2016, online edition of the journal Scientific Reports that elevated expression of each GEF was associated with a shorter PFS. The GEFs were stronger prognostic markers than two other markers of cancer progression, S100A4 and MACC1, and clustering of all GEFs together improved the prognostic accuracy of the individual family members.

PFS was significantly lower in the high-GEFs versus the low-GEFs groups. Since nucleotide exchange is the rate-limiting step in cyclical activation of G-proteins, the poor prognosis conferred by these GEFs in CTCs implied that hyperactivation of G-protein signaling by these GEFs was an important event during metastatic progression, and may be more frequently encountered than mutations in G-proteins and/or GPCRs (G protein–coupled receptors).

"We found that elevated expression of each GEF is associated with a shorter, progression-free survival in patients with metastatic colorectal cancer," said senior author Dr. Pradipta Ghosh, associate professor of medicine at the University of California, San Diego. "The GEFs fared better as prognostic markers than two well-known markers of cancer progression and the clustering of all GEFs together improved the predictive accuracy of each individual family member. Our work shows the prognostic impact of elevated expressions of individual and clustered GEFs on survival and the benefit of transcriptome analysis of G protein regulatory proteins in cancer biology. The next step will be to carry this technology into the clinic where it can be directly applied to deciphering a patient's state of cancer and how best to treat."

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