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Loss of Heterozygosity of Chromosome 3 Detected with Single Nucleotide Polymorphisms Is Superior to Monosomy 3 for Predicting Metastasis in Uveal Melanoma

Authors' Affiliations: ¹ Departments of Ophthalmology and Visual Sciences and ² Human Genetics, Washington University, St. Louis, Missouri and ³ Tumori Foundation, San Francisco, California

Requests for reprints: J. William Harbour, Departments of Ophthalmology and Visual Sciences, 660 South Euclid Avenue, Box 8096, St. Louis, Missouri. Phone: 314-362-3315; Fax: 314-747-5073; E-mail: harbour{at}wustl.edu.

Purpose: Loss of chromosome 3 is strongly associated with metastasis in uveal melanoma and has been proposed as the basis for clinical prognostic testing. It is not known whether techniques that identify loss of heterozygosity for chromosome 3 predict metastasis more accurately than those that detect only numerical loss of chromosome 3 (monosomy 3).

Experimental Design: Fifty-three uveal melanomas were analyzed by 28 single nucleotide polymorphisms (SNP) across chromosome 3. SNP was compared with fluorescence in situ hybridization (FISH) and array-based comparative genomic hybridization (aCGH) for metastasis prediction by sensitivity, specificity, and Kaplan-Meier survival analysis, using our validated gene expression-based classifier as a reference standard.

Results: By Kaplan-Meier analysis, only the gene expression-based classifier (P = 0.001) and SNP-based detection of loss of heterozygosity for chromosome 3 (P = 0.04) were significantly associated with metastasis. Sensitivity and specificity were 95.2% and 80.8%, respectively, for SNP, 77.8% and 64.7%, respectively, for FISH, and 85.0% and 72.0%, respectively, for aCGH. Isodisomy 3 was identified by SNP but undetected by aCGH and FISH in three tumors.

Conclusions: Prognostic tests based on SNP platforms, which detect both chromosomal homologues and their subregions, may be superior to techniques that only detect changes in chromosome number. These observations could have important implications for efforts to detect genetic alterations in cancer genomes with CGH-based approaches.

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