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Molecular cytogenetic alterations, specifically monosomy 3, are strongly correlated with metastases and death in uveal melanoma (UVM). Although FISH can be used for the identification of monosomy 3, a subset of UVM exhibit only partial loss of chromosome 3 potentially missed with chromosome enumeration probes. Moreover, often limited material available can compromise detection of additional alterations with potential clinical relevance. Microarray analysis is an alternative method for the analysis of such specimens affording whole genome interrogation. In the current study we performed microarray to detect genomic changes in DNA from FFPET and FZT UVM samples (clinical trial NCT00952939). Of the available cases, 23.5% yielded DNA of sufficient quantity and quality to obtain interpretable microarray results, representing 28 patients. All but one case demonstrated significant abnormalities. Gains of 8q, consistent with an apparent i(8)(q10) karyotype, were the most common finding, seen in 21 patients. Monosomy 3 was detected by microarray in 15 cases. A single case showed partial loss of 3 (3q11.2q25.31), the only clinically significant abnormality detected in that case. All cases lacking chromosome 3 abnormalities showed a copy gain of 6p sharing a small 28.18 Mb region of overlap at 6p25.2p21.33. Additional findings included TERT and NEDD9 amplifications, and CDKN2A/B and LUM gene deletions. SNP analysis revealed three cases with unique regions of copy neutral LOH involving 5p15.33q35.3, 15q11.2q21.1 and one case with whole chromosome LOH for chromosomes 3, 4 and 6. The latter was detected in a FZT specimen, while the paired FFPET sample lacked evidence of LOH and showed monosomy 3, trisomy 4 and whole arm gains and losses of 6p and 6q, respectively, suggesting the presence of tumor heterogeneity. Microarray analysis has identified new recurrent abnormalities associated with UVM with potential relevance to UVM biology, diagnosis and prognosis. Conflict of Interest: Roger Schultz is an employee of Perkin Elmer.

Cancer samples pose a challenging problem for analysis using microarray technology. The basic array CGH (aCGH) approach can confidently detect changes in copy number when a sample is measured against a normal control. However, the necessary normalization pre-processing step will essentially wash away the information associated with hyperdiploid samples. This can be an issue especially with cancer samples. Additionally, mosaic samples, due to different mixture of cells in the sample, can also severely complicate the analysis and interoperation of samples with microarray technology. One of the recently proposed approaches to simultaneously deal with these issues is called the Allele Specific Copy Number Analysis of Tumors (ASCAT) algorithm (1). The approach takes advantage of the SNP information from SNP microarrays to combine the change in total signal strength (log-R value) with the B-Allele Frequency (BAF) to create an optimization function that has local minimas at values corresponding to possible sample ploidy and percent normal contamination. Here we demonstrate an efficient implementation of the ASCAT algorithm on processing cancer samples generated by The Cancer Genome Atlas (TCGA) project. Using Affymetrix and Illumina SNP array data, results are compared between the different platforms as well as with and without application of the ASCAT algorithm. Characterization of a percentage of the cells as normal contaminants within the samples using the ASCAT algorithm reveals many more gains and losses in the aberration profile of the tumors than in samples processed without ASCAT. ASCAT also identified that a large percentage of samples contain high ploidy numbers. With the sample ploidy and extent of normal contamination identified using ASCAT, clearer and more accurate calls can be made for cancer samples. 1. Van Loo P, Nordgard SH, Lingjaerde OC, et al. Allele-specific copy number analysis of tumors. Proc Natl Acad Sci USA 2010;107:16910-16915.