Inferring a Tumor Progression Model for Neuroblastoma From Genomic Data

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Abstract
Purpose: The knowledge of the key genomic events that are causal to cancer development and progression not only is invaluable for our understanding of cancer biology but also may have a direct clinical impact. The task of deciphering a model of tumor progression by requiring that it explains (or at least does not contradict) known clinical and molecular evidence can be very demanding, particularly for cancers with complex patterns of clinical and molecular evidence. Materials and Methods: We formalize the process of model inference and show how a progression model for neuroblastoma (NB) can be inferred from genomic data. The core idea of our method is to translate the model of clonal cancer evolution to mathematical testable rules of inheritance. Seventy-eight NB samples in stages 1, 4S, and 4 were analyzed with array-based comparative genomic hybridization. Results: The pattern of recurrent genomic alterations in NB is strongly stage dependent and it is possible to identify traces of tumor progression in this type of data. Conclusion: A tumor progression model for neuroblastoma is inferred, which is in agreement with clinical evidence, explains part of the heterogeneity of the clinical behavior observed for NB, and is compatible with existing empirical models of NB progression.