Mutations in epidermal growth factor receptor and K-ras in Chinese patients with colorectal cancer

BMC Med Genet. 2010 Feb 26:11:34. doi: 10.1186/1471-2350-11-34.

Abstract

Background: Mutations of EGFR and K-ras are biomarkers for predicting the efficacy of targeting agents in non-small-cell lung cancer (NSCLC) and colorectal cancer (CRC). Data on the gene mutation status of EGFR and K-ras in Chinese patients with CRC are limited.

Methods: EGFR mutations in exon 18-21 and K-ras mutations in exon 1 and 2 were detected in tumor samples from 101 Chinese patients with CRC by polymerase chain reaction and Sanger sequencing. [corrected] The relationship between patients' characteristics and survival time and gene mutation status were analyzed using the Statistical Package for the Social Sciences.

Results: Only two samples (2.0%) had EGFR mutations in exon 18 or 21, and 33 of 101 samples (32.7%) had K-ras mutations in codon 12, 13, 45, 69, or 80. Univariate analysis suggested that differentiation might be correlated with K-ras mutations (p = 0.05), which was confirmed by a logistic regression model (p = 0.04). The median overall survival (OS) and median survival after metastasis were 44.0 and 18.0 months, respectively, in the mutant K-ras group, and 53.3 and 19.0 months, respectively, in the wild K-ras group. K-ras mutation was not an independent prognostic factor for OS or survival after metastasis (p = 0.79 and 0.78, respectively).

Conclusions: In Chinese patients with CRC, EGFR mutations were rare, and K-ras mutations were similar to those of Europeans. New mutations in codons 45, 69, and 80 were found in the Chinese population. Poor differentiation was an independent factor related to K-ras mutations.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Asian People / genetics*
  • Base Sequence
  • China
  • Colorectal Neoplasms / genetics*
  • Colorectal Neoplasms / pathology
  • Colorectal Neoplasms / therapy
  • Female
  • Genes, erbB-1 / genetics*
  • Genes, ras / genetics*
  • Humans
  • Male
  • Middle Aged
  • Multivariate Analysis
  • Mutation*
  • Survival Analysis