Inferring Loss-of-Heterozygosity from Unpaired Tumors Using High-Density Oligonucleotide SNP Arrays

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Abstract
Loss of heterozygosity (LOH) of chromosomal regions bearing tumor suppressors is a key event in the evolution of epithelial and mesenchymal tumors. Identification of these regions usually relies on genotyping tumor and counterpart normal DNA and noting regions where heterozygous alleles in the normal DNA become homozygous in the tumor. However, paired normal samples for tumors and cell lines are often not available. With the advent of oligonucleotide arrays that simultaneously assay thousands of single-nucleotide polymorphism (SNP) markers, genotyping can now be done at high enough resolution to allow identification of LOH events by the absence of heterozygous loci, without comparison to normal controls. Here we describe a hidden Markov model-based method to identify LOH from unpaired tumor samples, taking into account SNP intermarker distances, SNP-specific heterozygosity rates, and the haplotype structure of the human genome. When we applied the method to data genotyped on 100 K arrays, we correctly identified 99% of SNP markers as either retention or loss. We also correctly identified 81% of the regions of LOH, including 98% of regions greater than 3 megabases. By integrating copy number analysis into the method, we were able to distinguish LOH from allelic imbalance. Application of this method to data from a set of prostate samples without paired normals identified known regions of prevalent LOH. We have developed a method for analyzing high-density oligonucleotide SNP array data to accurately identify of regions of LOH and retention in tumors without the need for paired normal samples. A key event in the generation of many cancers is loss of heterozygosity (LOH) of chromosomal regions containing tumor suppressor genes, whereby one parent's version of the tumor suppressor is lost. As we develop a better understanding of the molecular mechanisms that generate different cancers, a description of the LOH events underlying these cancers is forming an important part of their classification. Generally, detection of LOH relies on comparison of the tumor's genome to the normal genome of the individual. Unfortunately, for many tumors, including most experimental models of cancer, the normal genome is not available. Therefore, the authors have developed a hidden Markov model-based method that evaluates the probability of LOH at all sites throughout the genome, based on high-resolution genotyping of only the tumor. They were able to achieve high levels of accuracy, specifically by taking into account the haplotype block structure of the genome. Application of this method to a set of 34 prostate cancer samples allowed the authors to identify the locations of the known and suspected tumor suppressor genes that are targeted by LOH.