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Abstract:
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Cancer growth, just like phylogeny and human development, requires genome replication, which inevitably produces "passenger" (neutral) replication errors. In practice, DNA methylation patterns from just a few CpG sites can convey information about cancer cell ancestry when sampled from related lineages. We take a genealogical approach to infer tumor growth patterns using variation in DNA methylation patterns. We apply approximate Bayesian computation (ABC), a simulation-based method, to model tumor growth under a variety of evolutionary scenarios, finding models that fit observed DNA methylation patterns. Applying the ABC approach to data from two separate genomic regions in a collection of 12 colon tumors, we find evidence that cancer trees are consistent with Gompertzian growth (rapid initial clonal expansion), which is the growth pattern of most tumors.
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