Major efforts to sequence cancer genomes are now occurring throughout the
world. Though the emerging data from these studies are illuminating, their
reconciliation with epidemiologic and clinical observations poses a major
challenge. In the current study, we provide a novel mathematical model that
begins to address this challenge. We model tumors as a discrete time branching
process that starts with a single driver mutation and proceeds as each new
driver mutation leads to a slightly increased rate of clonal expansion. Using
the model, we observe tremendous variation in the rate of tumor development -
providing an understanding of the heterogeneity in tumor sizes and development
times that have been observed by epidemiologists and clinicians. Furthermore,
the model provides a simple formula for the number of driver mutations as a
function of the total number of mutations in the tumor. Finally, when applied
to recent experimental data, the model allows us to calculate, for the first
time, the actual selective advantage provided by typical somatic mutations in
human tumors in situ. This selective advantage is surprisingly small, 0.005 +-
0.0005, and has major implications for experimental cancer research
