Checkpoint-blockade immunotherapy enables the immune system to recognize tumor cells that were previously invisible due to immune escape, but these therapies lead to heterogeneous patient outcomes. Focusing on colorectal cancer, in which two subtypes have markedly different responses to immunotherapy, we query the relationship between a tumor’s mutagenic landscape and therapeutic outcomes. First, we model neoantigen evolution in growing tumors using a stochastic branching-process model and label each neoantigen by its predicted immunogenicity, giving each in-silico tumor a unique pre-treatment mutational landscape. Next, we use a dynamical systems model of tumor-immune interactions under checkpoint-blockade therapy, parameterized using clinical trial data, to simulate immunotherapy. We relate therapeutic outcomes to the heterogeneity of tumor mutational landscape, finding that a strong clonal neoantigen appears crucial for a successful response. Additionally, the minimal neoantigen quality across all neoantigens contributing to response dynamics is one of the strongest predictors of durable response. Checkpoint-blockade immunotherapy can result in heterogenous clinical outcomes. Here, the authors develop a computational framework by combining a stochastic model of mutational evolution during development of colorectal cancer with a dynamical systems model of immunotherapy treatment to predict response.
Sholokhova et al. (Sat,) studied this question.