Optimisation of pembrolizumab therapy for de novo metastatic MSI-H/dMMR colorectal cancer using data-driven delay integro-differential equations

Colorectal cancer (CRC), the third most commonly diagnosed cancer worldwide, presents a growing public health concern, with 20% of new diagnoses involving de novo metastatic disease and up to 80% of these patients presenting with unresectable metastatic lesions. Microsatel-lite instability-high (MSI-H) CRC and deficient mismatch repair (dMMR) CRC constitute 15% of all CRC, and 4% of metastatic CRC, and, while less responsive to conventional chemotherapy, exhibit notable sensitivity to immunotherapy, especially programmed cell death protein 1 (PD-1) checkpoint inhibitors such as pembrolizumab. Despite this, there is a significant need to optimise immunotherapeutic regimens to maximise clinical efficacy and patient quality of life whilst minimising financial burden. In this work, we adapt our mechanistic model for locally advanced MSI-H/dMMR CRC to de novo metastatic MSI-H/dMMR CRC (dnmMCRC), deriving model parameters from pharmacokinetic, bioanalytical, and radiographic studies, as well as bulk RNA-sequencing data deconvolution from the TCGA COADREAD and GSE26571 datasets. We finally optimised treatment with pembrolizumab to balance efficacy, efficiency, and toxicity in dnmMCRC, comparing against currently FDA-approved regimens, analysing factors influencing treatment success and comparing immune dynamics to those in locally advanced disease.