The impact of deleterious passenger mutations on cancer progression

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.

💡 Research Summary

The paper challenges the conventional driver‑centric view of tumor evolution by systematically investigating the role of mildly deleterious passenger mutations. Using an agent‑based stochastic model, individual cancer cells undergo division, death, and acquisition of two mutation classes: advantageous drivers that increase proliferative fitness and weakly harmful passengers that slightly reduce it. Although each passenger confers only a minute fitness cost, their high mutation rate leads to rapid accumulation. Simulations reveal that passengers largely escape purifying selection because the selective disadvantage they impose is modest compared to the stochastic fluctuations inherent in expanding cell populations. As the passenger load grows, the collective burden begins to dominate tumor dynamics, slowing growth, extending latency periods, and in extreme cases causing regression. These phenomena provide a natural explanation for clinical observations such as prolonged indolent phases and variable responses to therapy that are difficult to reconcile with a model that considers only driver events.

To validate the model’s predictions, the authors mined large‑scale cancer genomics datasets (TCGA, ICGC) across multiple tissue types. They annotated nonsynonymous somatic mutations with functional impact predictors (SIFT, PolyPhen‑2, CADD) and identified a substantial fraction of passengers that are likely damaging. The ratio of nonsynonymous to synonymous mutations (dN/dS) for these putative deleterious passengers hovers around unity, indicating that they have not been efficiently removed by negative selection. Moreover, tumors with higher inferred passenger burdens exhibit poorer overall survival and reduced sensitivity to standard chemotherapies, supporting the hypothesis that passenger load imposes a hidden fitness penalty on cancer cells.

Building on these insights, the authors explored two therapeutic strategies that exploit the passenger load. The first strategy artificially raises the mutation rate (e.g., through mutagenic chemotherapy or radiation), thereby accelerating the influx of additional passengers. While this approach can trigger tumor shrinkage, the concurrent increase in driver mutation probability raises the risk of relapse. The second strategy amplifies the deleterious effect of existing passengers without increasing mutation frequency. By targeting cellular processes that buffer protein misfolding, metabolic stress, or DNA repair fidelity, the marginal fitness cost of each passenger is magnified, pushing the tumor over a “mutational load threshold” that leads to collapse. Simulations show that this load‑enhancement approach not only induces regression but also yields a markedly lower relapse rate compared with mutation‑rate escalation.

Overall, the study proposes a new evolutionary framework in which cancer progression is a balance between driver acquisition and passenger accumulation. It demonstrates that passengers are not merely neutral background noise; they constitute a quantifiable, exploitable vulnerability. The work suggests that future precision oncology could incorporate passenger‑load metrics into prognostic models and that therapeutic regimens designed to tip the balance toward passenger‑induced fitness collapse may achieve more durable responses.