Predicting cancer evolution: quantifying immune selection and other selective pressures

Tumors are heterogeneous populations of cancer cells that evolve and adapt, with the fittest clones surviving and, possibly, leading to metastasis. Certain mutations can confer fitness advantage to cancer cells, for instance, by altering function of crucial proteins. Mutations can also lead to fitness disadvantage, notably by giving rise to neoantigens – mutated protein peptides that may become foreign enough to be recognized by patient’s own immune system. Here we develop a biophysically grounded model to quantify immunogenicity of tumor neoantigens. We use the model to define the fitness of tumor clones as a balance between negative selection due to immune recognition and positive selection resulting from oncogenic mutations. We apply the model on various cohorts to determine the selective pressures that govern the evolution under different therapy and endogenous immune activity scenarios. In particular, we investigate how pancreatic cancers – in general a lowly mutated, poorly immunogenic cancer, largely presumed to not be subject to immunoediting – evolve over years. With the fitness model, we show that the tumors of rare long-term survivors show signatures of evolution under strong negative selection due to immune recognition. Their tumors have relatively less clones and are deprived of highly immunogenic neoantigens. Importantly, the fitness model predicts the clonal composition of recurrent tumors of the patients. Thus, we submit longitudinal evidence that the human immune system naturally edits neoantigens. Furthermore, we present a model that describes how tumor cell populations evolve under immune pressure over time, with implications for design of cancer treatments.