The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics

PLoS Comput Biol. 2021 Feb 12;17(2):e1008702. doi: 10.1371/journal.pcbi.1008702. eCollection 2021 Feb.


Intratumour heterogeneity is increasingly recognized as a frequent problem for cancer treatment as it allows for the evolution of resistance against treatment. While cancer genotyping becomes more and more established and allows to determine the genetic heterogeneity, less is known about the phenotypic heterogeneity among cancer cells. We investigate how phenotypic differences can impact the efficiency of therapy options that select on this diversity, compared to therapy options that are independent of the phenotype. We employ the ecological concept of trait distributions and characterize the cancer cell population as a collection of subpopulations that differ in their growth rate. We show in a deterministic model that growth rate-dependent treatment types alter the trait distribution of the cell population, resulting in a delayed relapse compared to a growth rate-independent treatment. Whether the cancer cell population goes extinct or relapse occurs is determined by stochastic dynamics, which we investigate using a stochastic model. Again, we find that relapse is delayed for the growth rate-dependent treatment type, albeit an increased relapse probability, suggesting that slowly growing subpopulations are shielded from extinction. Sequential application of growth rate-dependent and growth rate-independent treatment types can largely increase treatment efficiency and delay relapse. Interestingly, even longer intervals between decisions to change the treatment type may achieve close-to-optimal efficiencies and relapse times. Monitoring patients at regular check-ups may thus provide the temporally resolved guidance to tailor treatments to the changing cancer cell trait distribution and allow clinicians to cope with this dynamic heterogeneity.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cell Proliferation
  • Computer Simulation
  • Humans
  • Immunotherapy
  • Models, Genetic
  • Models, Statistical
  • Neoplasm Recurrence, Local*
  • Neoplasms / metabolism
  • Neoplasms / pathology*
  • Phenotype
  • Population Dynamics
  • Stochastic Processes
  • Treatment Outcome

Grants and funding

G.C., M.B., and A.T. are funded by Deutsche Forschungsgemeinschaft through the "Clinician Scientist Program in Evolutionary Medicine” (Project number 413490537, G.C. and M.B. are funded by Deutsche Krebshilfe, project number 70113252 and Deutsche José Carreras Leukämie-Stiftung (DJCLS, German Jose Carreras leukemia foundation) project number DJCLS 22R/2019; S.S. and A.T. are funded by Deutsche Forschungsgemeinschaft through the Research Training Group “Translational Evolutionary Research” (Project number 400993799, The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.