as a marker for recurrent loss-of-heterozygosity in cancer

Identifying regions of loss-of-heterozygosity (LOH) in a tumor sample is a challenging problem. State-of-the-art computational approaches can infer LOH from single-nucleotide polymorphism (SNP) array data, but call-ing precise boundaries is complicated by normal-cell contamination and markers that are homozygous in the germline and therefore non-informative. More recently, the focus has shifted to pinpointing the loci recurrent-ly affected by LOH events across multiple tumors. Recurrent LOH regions often harbor genes important for tumor suppression. Here, we propose a method that infers LOH rates across an entire sample set on an SNP-by-SNP basis. Our method achieves this by leveraging the straightforward principle that, by definition, LOH depletes heterozygotes, thereby disrupting Hardy–Weinberg equilibrium. We apply a statistical test for such LOH-influenced disruptions, and derive a maximum-likelihood estimator for the LOH rate based on the observed number of heterozygotes. This accounts for LOH in both its hemizygous deletion and copy-neutral forms, and does not make use of matched normal genotypes. Power simulations show high levels of sensi-tivity for the statistical test, and application to a control normal-tissue data set demonstrates a low false-dis-covery rate. We apply the method to three large publicly available tumor SNP array data sets, where it is able to localize tumor-suppressor gene targets of the LOH events. Inferred LOH rates are quite concordant across platforms/laboratories and between cell lines and tumors, but in a tumor type-dependent fashion. Finally, we produce rate estimates that are generally higher than previously published, and provide evidence that the latter are likely underestimates