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Cancer Prevention Research

Year: 2009  |  Volume: 2  |  Issue: 7  |  Page No.: 617 - 624

Novel Susceptibility Loci for Second Primary Tumors/Recurrence in Head and Neck Cancer Patients: Large-Scale Evaluation of Genetic Variants

X Wu, M. R Spitz, J. J Lee, S. M Lippman, Y Ye, H Yang, F. R Khuri, E Kim, J Gu, R Lotan and W. K. Hong

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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