Abstract
When a cancer patient develops a new tumor it is necessary to determine if it is a recurrence (metastasis) of the original cancer, or an entirely new occurrence of the disease. This is accomplished by assessing the histo-pathology of the lesions. However, there are many clinical scenarios in which this pathological diagnosis is difficult. Since each tumor is characterized by a distinct pattern of somatic mutations, a more definitive diagnosis is possible in principle in these difficult clinical scenarios by comparing the two patterns. In this article we develop and evaluate a statistical strategy for this comparison when the data are derived from array copy number data, designed to identify all of the somatic allelic gains and losses across the genome. First a segmentation algorithm is used to estimate the regions of allelic gain and loss. The correlation in these patterns between the two tumors is assessed, and this is complemented with more precise quantitative comparisons of each plausibly clonal mutation within individual chromosome arms. The results are combined to determine a likelihood ratio to distinguish clonal tumor pairs (metastases) from independent second primaries. Our data analyses show that in many cases a strong clonal signal emerges. Sensitivity analyses show that most of the diagnoses are robust when the data are of high quality.