  Cancer progression is an example of a rapid adaptive process where evolving
new traits is essential for survival and requires a high mutation rate.
Precancerous cells acquire a few key mutations that drive rapid population
growth and carcinogenesis. Cancer genomics demonstrates that these few 'driver'
mutations occur alongside thousands of random 'passenger' mutations-a natural
consequence of cancer's elevated mutation rate. Some passengers can be
deleterious to cancer cells, yet have been largely ignored in cancer research.
In population genetics, however, the accumulation of mildly deleterious
mutations has been shown to cause population meltdown. Here we develop a
stochastic population model where beneficial drivers engage in a tug-of-war
with frequent mildly deleterious passengers. These passengers present a barrier
to cancer progression that is described by a critical population size, below
which most lesions fail to progress, and a critical mutation rate, above which
cancers meltdown. We find support for the model in cancer age-incidence and
cancer genomics data that also allow us to estimate the fitness advantage of
drivers and fitness costs of passengers. We identify two regimes of adaptive
evolutionary dynamics and use these regimes to rationalize successes and
failures of different treatment strategies. We find that a tumor's load of
deleterious passengers can explain previously paradoxical treatment outcomes
and suggest that it could potentially serve as a biomarker of response to
mutagenic therapies. Collective deleterious effect of passengers is currently
an unexploited therapeutic target. We discuss how their effects might be
exacerbated by both current and future therapies.
