Significance of KRAS, NRAS, BRAF and PIK3CA mutations in metastatic colorectal cancer patients receiving Bevacizumab: a single institution experience.

KRAS mutation is an important predictive and prognostic factor for patients receiving anti-EGFR therapy. An expanded KRAS, NRAS, BRAF, PIK3CA mutation analysis provides additional prognostic information, but its role in predicting bevacizumab efficacy is unclear. The aim of our study was to evaluate the incidence of KRAS, NRAS, BRAF and PIK3CA mutations in metastatic colorectal cancer patients receiving first line oxaliplatin based chemotherapy with or without bevacizumab and to evaluate their prognostic and predictive significance. 55 patients with the first-time diagnosed CRC receiving FOLFOX ± bevacizumab were involved in the study. Tumour blocks were tested for KRAS mutations in exons 2, 3 and 4, NRAS mutations in exons 2, 3 and 4, BRAF mutation in exon 15 and PIK3CA mutations in exons 9 and 20. The association between mutations and clinico-pathological factors, treatment outcomes and survival was analyzed. KRAS mutations were detected in 67.3% of the patients, BRAF in 1.8%, PIK3CA in 5.5% and there were no NRAS mutations. A significant association between the high CA 19-9 level and KRAS mutation was detected (mean CA 19-9 levels were 276 and 87 kIU/l, respectively, = 0.019). There was a significantly higher response rate in the KRAS, NRAS, BRAF and PIK3CA wild type cohort receiving bevacizumab compared to any gene mutant type (100 and 60%, respectively, = 0.030). The univariate Cox regression analysis did not confirm KRAS and other tested mutations as prognostic factors for PFS or OS. Our study revealed higher KRAS and lower NRAS, BRAF and PIK3CA mutation rates in the Lithuanian population than those reported in the literature. KRAS mutation was associated with the high CA 19-9 level and mucinous histology type, but did not show any predictive or prognostic significance. The expanded KRAS, NRAS, BRAF and PIK3CA mutation analysis provided additional significant predictive information.