Abstract
Inferring the rate at which a clonal population grows, or its fitness, is important for many biomedical applications. For example, measuring the fitness of mutated cells in a patient with cancer may provide important information about prognosis and treatment. Similarly, measuring the fitness of new viral strains that emerge during a pandemic can inform how to plan an effective response. In previous work, the lineage trees constructed from individuals randomly sampled from the population at the final time-point have been used to infer the fitness and the times at which the mutation providing the fitness advantage arose in a diverse set of systems, such as blood cancers [1], [2] and the influenza virus [3]. However, it is not clear to what extent the inferred values depend on the exact biological details assumed in the models used for the inference. In this paper we show that coalescent statistics of lineage trees are invariant to changes in key parameters underlying the expansion, such as the distribution of the number of progenies produced by each individual and heterogeneity in the expansion rate. In addition, we show that competition between drift and selection imply that the fitness of the mutated population and when the mutation occurred can be inferred without knowledge of the mutation rate per generation even though the population size itself cannot be inferred. Lastly, we show that our results also generalize to cases where multiple competing mutations result in multiple distinct subclones with different values of fitness. Taken together, our results show that inferring fitness from lineage trees is robust to most model assumptions.
Competing Interest Statement
The authors have declared no competing interest.
