Impact of deleterious passenger mutations on cancer progression

Abstract

Cancer progression is driven by the accumulation of a small number of genetic alterations. However, these few driver alterations reside in a cancer genome alongside tens of thousands of additional mutations termed passengers. Passengers are widely believed to have no role in cancer, yet many passengers fall within protein-coding genes and other functional elements that can have potentially deleterious effects on cancer cells. Here we investigate the potential of moderately deleterious passengers to accumulate and alter the course of neoplastic progression. Our approach combines evolutionary simulations of cancer progression with an analysis of cancer sequencing data. From simulations, we find that passengers accumulate and largely evade natural selection during progression. Although individually weak, the collective burden of passengers alters the course of progression, leading to several oncological phenomena that are hard to explain with a traditional driver-centric view. We then tested the predictions of our model using cancer genomics data and confirmed that many passengers are likely damaging and have largely evaded negative selection. Finally, we use our model to explore cancer treatments that exploit the load of passengers by either (i) increasing the mutation rate or (ii) exacerbating their deleterious effects. Though both approaches lead to cancer regression, the latter is a more effective therapy. Our results suggest a unique framework for understanding cancer progression as a balance of driver and passenger mutations.

Fig. 1.

Dynamics of cancer progression. (A) Our evolutionary model: individual cancer cells stochastically divide (potentially acquiring new drivers/passengers) and die. A new driver increases the birth rate by s_d, whereas a passenger decreases it by s_p (Eq. 1). Drivers arise rarely, but have large effects, while passengers are common, but have small individual effects. (B) Simulated cancer progression using a Gompertz death rate; despite identical parameters, trajectories exhibit markedly different behavior, sometimes regressing to extinction or having long periods of dormancy. (C) The number of accumulated passengers increases with mutation rate and depends, nonmonotonically, on passenger strength.

Fig. 2.

Mechanisms of passenger accumulation. (A) Spurts of population growth, caused by the acquisition of a new driver, are interspersed with a gradual decline due to passenger accumulation. (B) Passengers accumulate both steadily between the arrival of drivers and by hitchhiking during clonal expansions. (C) Each subclone, containing a unique number of passengers (shown by color), grows and declines stochastically, eventually to extinction. In between drivers, the population becomes heterogeneous. A new driver will promotes only one clone, creating a clonal population. Afterward, new mutations on top of the previous hitchhikers restore heterogeneity.

Fig. 3.

Moderately deleterious passengers alter cancer progression and mostly evade selection. (A) Passengers of intermediate fitness effect s_p prolong the time to cancer and accumulate in large, highly variable quantities (red solid, mean; dotted, ±1 SD). Moderately deleterious passengers affect cancer only if they are strong or frequent enough to be comparable to the effects of drivers, yet weak enough to avoid selection (SI Appendix, SI Text). Experimentally observed fitness effects of random point mutations in YFP in yeast ranged from 0.007 to 0.028 (green shading) (14). (B) Population dynamics did not change noticeably when passengers were drawn from various distributions of fitness distributions (SI Appendix, SI Text). (C) Passenger fixation probability declined only moderately with increasing deleteriousness.

Fig. 4.

Characterization of missense mutations in cancer sequencing data. (A) Mutations were assayed using the ΔPSIC score of PolyPhen, which estimates the damaging effect of a new mutation, given known homologs; mutations with high ΔPSIC scores are most likely damaging (43). Passengers have large ΔPSIC, close to random mutations, suggesting that they are deleterious. (B) Deleterious passenger phenotypes were observed in all subsets of passengers studied, arguing that these results cannot be explained by recessive phenotypes, or lack of expression, or database biases.

Fig. 5.

Deleterious passengers can be exploited for treatment. (A) Cancers grown to 10⁶ cells are treated by increasing the mutation rate (green) or deleterious effect of passengers (magenta). Both strategies lead to reduction in cancer size. (B) Much smaller increase of the deleterious effect of passengers is sufficient to prevent 5-y relapse.