  Cancer progression is driven by a small number of genetic alterations
accumulating in a neoplasm. These few driver alterations reside in a cancer
genome alongside tens of thousands of other mutations that are widely believed
to have no role in cancer and termed passengers. Many passengers, however, fall
within protein coding genes and other functional elements and can possibly have
deleterious effects on cancer cells. Here we investigate a potential of mildly
deleterious passengers to accumulate and alter the course of neoplastic
progression. Our approach combines evolutionary simulations of cancer
progression with the analysis of cancer sequencing data. In our simulations,
individual cells stochastically divide, acquire advantageous driver and
deleterious passenger mutations, or die. Surprisingly, despite selection
against them, passengers accumulate and largely evade selection during
progression. Although individually weak, the collective burden of passengers
alters the course of progression leading to several phenomena observed in
oncology that cannot be explained by a traditional driver-centric view. We
tested predictions of the model using cancer genomic data. We find that many
passenger mutations are likely to be damaging and that, in agreement with the
model, they have largely evaded purifying selection. Finally, we used our model
to explore cancer treatments that exploit the load of passengers by either 1)
increasing the mutation rate; or 2) exacerbating their deleterious effects.
While both approaches lead to cancer regression, the later leads to less
frequent relapse. Our results suggest a new framework for understanding cancer
progression as a balance of driver and passenger mutations.
