Personal response to immune checkpoint inhibitors of patients with advanced melanoma explained by a computational model of cellular immunity, tumor growth, and drug

Abstract

Immune checkpoint inhibitors, such as pembrolizumab, are transforming clinical oncology. Yet, insufficient overall response rate, and accelerated tumor growth rate in some patients, highlight the need for identifying potential responders. To construct a computational model, identifying response predictors, and enabling immunotherapy personalization. The combined dynamics of cellular immunity, pembrolizumab, and the melanoma cancer were modeled by a set of ordinary differential equations. The model relies on a scheme of T memory stem cells, progressively differentiating into effector CD8+ T cells, and additionally includes T cell exhaustion, reinvigoration and senescence. Clinical data of a pembrolizumab-treated patient with advanced melanoma (Patient O’) were used for model calibration and simulations. Virtual patient populations, varying in one parameter or more, were generated for retrieving clinical studies. Simulations captured the major features of Patient O’s disease, displaying a good fit to her clinical data. A temporary increase in tumor burden, as implied by the clinical data, was obtained only when assuming aberrant self-renewal rates. Variation in effector T cell cytotoxicity was sufficient for simulating dynamics that vary from rapid progression to complete cure, while variation in tumor immunogenicity has a delayed and limited effect on response. Simulations of a-specific clinical trial were in good agreement with the clinical results, demonstrating positive correlations between response to pembrolizumab and the ratio of reinvigoration to baseline tumor load. These results were obtained by assuming inter-patient variation in the toxicity of effector CD8+ T cells, and in their intrinsic division rate, as well as by assuming that the intrinsic division rate of cancer cells is correlated with the baseline tumor burden. In conclusion, hyperprogression can result from lower patient-specific effector cytotoxicity, a temporary increase in tumor load is unlikely to result from real tumor growth, and the ratio of reinvigoration to tumor load can predict personal response to pembrolizumab. Upon further validation, the model can serve for immunotherapy personalization.

Accelerating the Development of Personalized Cancer Immunotherapy by Integrating Molecular Patients’ Profiles with Dynamic Mathematical Models

“…The inter-patient variation in the values of the T cell toxicity parameter explains the rich variation in response, including a pattern roughly resembling HPD. Virtual clinical trials (see above) in the Perlstein and colleagues' model 77 successfully retrieve real-life clinical trial results, showing that the ratio of reinvigoration rate to baseline tumor load can serve to cluster patients according to the predicted quality of their response. 79 Such endeavors demonstrate how multiple personal model parameters, which are impossible to estimate in the REVIEW clinical setting, can be combined into one quantifiable measure helping to classify responsive/nonresponsive patients.…”

“…from different organization levels in one framework, facilitating the personalization process in a scale-insensitive way, to ultimately determine personal drug-disease-host dynamics and predict personal responses to ICBs. The understanding, emerging from the analysis of the mathematical models, that relatively small changes in personal parameters can significantly affect a patient's response, as suggested, for example, for T cell functionality, 77 forces one to revisit the basics of drug development. The "one-size-fits-all" paradigm is still the central pillar in drug development, in which the performance of an investigational new drug is evaluated by the response of hundreds, or even thousands, of patients to one specific treatment protocol.…”

“…model for cancer immunotherapy by the immune checkpoint blocker pembrolizumab, and its simulation of tumor load in an advanced melanoma patient from a hospital registry 77. Immunity is represented by T cell subsets (tori; increasing shading represents increased maturity).…”

Toward Systems Biomarkers of Response to Immune Checkpoint Blockers

“…Based on a system of three ordinary differential equations to describe the interaction between tumor cells, Treg cells, and cytotoxic T cells, this model could explain why, in response to ICBs, the tumor can worsen before starting regressing. Other multi-cellular models have been used to derive in silico patients to test different possible dynamics of treatment response ( 111 , 112 ), that could be compared with longitudinal measurements of tumor load from PET/CT imaging ( 112 ). Longitudinal data are often limited to non-invasive imaging and, in a few cases, to transcriptomics, IHC, TCR, and genome sequencing data ( 113 , 114 ) for a limited number of time points due to invasiveness of biopsies.…”

A New Method for Optimizing Sepsis Therapy by Nivolumab and Meropenem Combination: Importance of Early Intervention and CTL Reinvigoration Rate as a Response Marker

“…This enables in silico simulations of the system's behavior under different administration schedules of the drug(s), hence predicting the patient response to each application regimen. Models of this kind have proven useful for this purpose in a wide range of medical fields, including cancer immunotherapy by ICBs ( 29 , 30 ). In sepsis, previous mathematical modeling has focused mainly on the shift of equilibrium between the pro- and anti-inflammatory signaling cascades, not considering the immunosuppressive arm ( 31 , 32 ).…”