It is of fundamental importance to understand the key mechanisms that govern the progression of cancer and elucidate the often-unknown factors that account for treatment failures. Although they fail to cure most patients with common metastatic solid cancers (like breast and lung), immunotherapies have had a significant impact in a minority of late-stage lung cancer and melanoma patients. While these potentially curative cancer therapies are being rapidly developed and tested, a major barrier is the lack of quantitative models to describe and evaluate their efficacy. This project proposes to explore clinically relevant math and in-silico models of cancer cell dynamics for personalized immunotherapy. We will focus on two distinct, yet strongly interconnected, approaches of cancer therapy: (1) adoptive-cell transfer, in which in-vitro engineered and personalized tumor- infiltrating T-cells are transfused to suppress tumor growth; and (2) checkpoint inhibitors that boost anti-tumor activities of effector immune cells. Very recently, a wealth of immune-related biomarker data has become available?their close integration with mechanistic, mathematical models would unleash their explanatory and predictive power in treatment response and outcome. Here, Project 3 will take advantage of these biomarker data to infer and quantify key parameters that govern cancer-immune interactions. Specifically, Aim 1 will develop a quantitative mathematical framework based on the dynamical systems approach to provide practical guidance for clinical assessment of the efficacy of adoptive cell transfer approach.
Aim 2 will optimize therapeutic strategies for checkpoint inhibitors and their potential combinations, while Aim 3 will evaluate and identify immune-related biomarkers for melanoma cancer by closely integrating computational modeling with single-cell sequencing data from animal models and clinical trials. This design will use a theoretical framework to assess and compare the efficacies of different combinations, as well as to provide guidance on the minimum efficacy and optimal dosage schedule of checkpoint inhibitors required to achieve positive clinical outcomes. This proposal will develop clinically relevant math and in-silico models that will facilitate the way novel cancer immunotherapeutic strategies are conceived, tested, and understood. Owing to their innate flexibility, these in- silico models also can be readily incorporated with the specific cancer profile on the cancer-cell level, and thus enable informed treatment decisions and predict treatment outcomes in a personalized fashion. The ultimate goal is to use these in-silico and mathematical models to interpret lab and clinical results and to guide design principles of future lab experiments and clinical trials, all with an eye toward model-informed personalized immunotherapy.
