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Cancer Prev Res (Phila). 2009 Jul;2(7):617-24. doi: 10.1158/1940-6207.CAPR-09-0025.

Novel susceptibility loci for second primary tumors/recurrence in head and neck cancer patients: large-scale evaluation of genetic variants.

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  • 1Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA.

Abstract

This study was aimed to identify novel susceptibility variants for second primary tumor (SPT) or recurrence in curatively treated early-stage head and neck squamous cell carcinoma (HNSCC) patients. We constructed a custom chip containing a comprehensive panel of 9,645 chromosomal and mitochondrial single nucleotide polymorphisms (SNP) representing 998 cancer-related genes selected by a systematic prioritization schema. Using this chip, we genotyped 150 early-stage HNSCC patients with and 300 matched patients without SPT/recurrence from a prospectively conducted randomized trial and assessed the association of these SNPs with risk of SPT/recurrence. Individually, six chromosomal SNPs and seven mitochondrial SNPs were significantly associated with risk of SPT/recurrence after adjustment for multiple comparisons. A strong gene-dosage effect was observed when these SNPs were combined, as evidenced by a progressively increasing SPT/recurrence risk as the number of unfavorable genotypes increased (P for trend < 1.00 x 10(-20)). Several polygenic analyses suggest an important role of interconnected functional network and gene-gene interaction in modulating SPT/recurrence. Furthermore, incorporation of these genetic markers into a multivariate model improved significantly the discriminatory ability over the models containing only clinical and epidemiologic variables. This is the first large-scale systematic evaluation of germ-line genetic variants for their roles in HNSCC SPT/recurrence. The study identified several promising susceptibility loci and showed the cumulative effect of multiple risk loci in HNSCC SPT/recurrence. Furthermore, this study underscores the importance of incorporating germ-line genetic variation data with clinical and risk factor data in constructing prediction models for clinical outcomes.

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PMID:
19584075
[PubMed - indexed for MEDLINE]
PMCID:
PMC2964280
Free PMC Article

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