Loss of 18q predicts poor survival of patients with squamous cell carcinoma of the head and neck

Genes Chromosomes Cancer. 1998 Apr;21(4):333-9. doi: 10.1002/(sici)1098-2264(199804)21:4<333::aid-gcc7>3.0.co;2-#.

Abstract

Tumor suppressor genes play an important role in normal growth regulation. Loss or inactivation of these genes has been implicated in the development of squamous cell cancer and progression of neoplasia. Previous studies in our laboratories have implicated chromosome 18 long-arm deletions as a possible marker of progression in head and neck squamous cell cancer (HNSCC). To test this hypothesis, we evaluated DNA from 67 HNSCC patients for loss of heterozygosity (LOH) at 18q loci, and for association of LOH with survival. Tumor and normal DNA were extracted from fresh tissue and paraffin blocks and were amplified by PCR using primers for three microsatellite repeat polymorphisms in 18q (D18S336, D18S34, and MBP). A total of 27 (40%) patients had LOH of 18q, and these patients had a statistically significantly poorer two-year survival compared to those without 18q LOH (30% vs. 63%; P = 0.008). In a Cox proportional hazards model in which time from diagnosis to death was the outcome variable, patients with 18q LOH had an unadjusted relative risk (RR) of death of 2.46 (P = 0.005). When 18q LOH was placed in a multivariate model controlling for possible confounders in the study, the RR for death was still elevated (RR = 2.10; P = 0.025). The observation of a prognostic association between 18q LOH and poor patient survival suggests that loss of an 18q tumor suppressor gene or genes is important in the progression of HNSCC.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Carcinoma, Squamous Cell / genetics*
  • Chromosome Deletion*
  • Chromosomes, Human, Pair 18 / genetics*
  • Female
  • Head and Neck Neoplasms / genetics*
  • Humans
  • Loss of Heterozygosity*
  • Male
  • Multivariate Analysis
  • Prognosis
  • Proportional Hazards Models
  • Survival Analysis