Flexible and Accurate Detection of Genomic Copy-Number Changes from aCGH

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Abstract
Genomic DNA copy-number alterations (CNAs) are associated with complex diseases, including cancer: CNAs are indeed related to tumoral grade, metastasis, and patient survival. CNAs discovered from array-based comparative genomic hybridization (aCGH) data have been instrumental in identifying disease-related genes and potential therapeutic targets. To be immediately useful in both clinical and basic research scenarios, aCGH data analysis requires accurate methods that do not impose unrealistic biological assumptions and that provide direct answers to the key question, “What is the probability that this gene/region has CNAs?” Current approaches fail, however, to meet these requirements. Here, we introduce reversible jump aCGH (RJaCGH), a new method for identifying CNAs from aCGH; we use a nonhomogeneous hidden Markov model fitted via reversible jump Markov chain Monte Carlo; and we incorporate model uncertainty through Bayesian model averaging. RJaCGH provides an estimate of the probability that a gene/region has CNAs while incorporating interprobe distance and the capability to analyze data on a chromosome or genome-wide basis. RJaCGH outperforms alternative methods, and the performance difference is even larger with noisy data and highly variable interprobe distance, both commonly found features in aCGH data. Furthermore, our probabilistic method allows us to identify minimal common regions of CNAs among samples and can be extended to incorporate expression data. In summary, we provide a rigorous statistical framework for locating genes and chromosomal regions with CNAs with potential applications to cancer and other complex human diseases. As a consequence of problems during cell division, the number of copies of a gene in a chromosome can either increase or decrease. These copy-number alterations (CNAs) can play a crucial role in the emergence of complex multigenic diseases. For example, in cancer, amplification of oncogenes can drive tumor activation, and CNAs are associated with metastasis development and patient survival. Studies on the relationship between CNAs and disease have been recently fueled by the widespread use of array-based comparative genomic hybridization (aCGH), a technique with much finer resolution than previous experimental approaches. Detection of CNAs from these data depends on methods of analysis that do not impose biologically unrealistic assumptions and that provide direct answers to fundamental research questions. We have developed a statistical method, using a Bayesian approach, that returns estimates of the probabilities of CNAs from aCGH data, the most direct and valuable answer to the key biological question: “What is the probability that this gene/region has an altered copy number?” The output of the method can therefore be immediately used in different settings from clinical to basic research scenarios, and is applicable over a wide variety of aCGH technologies.