Optimized patient-specific immune checkpoint inhibitors therapy for cancer treatment based on tumor immune microenvironment modeling

Abstract

ABSTRACTObjectiveEnhancing patient response to immune checkpoint inhibitors (ICIs) is crucial in cancer immunotherapy. We aim to create a data-driven mathematical model of the tumor immune microenvironment (TIME) and utilize deep reinforcement learning (DRL) to optimize patient-specific ICI therapy combined with chemotherapy (ICC).MethodsUsing patients’ genomic and transcriptomic data, we develop an ordinary differential equations (ODEs)-based TIME model to characterize interactions among chemotherapy, ICIs, immune cells, and cancer cells. A DRL algorithm is trained to determine the personalized optimal ICC therapy.ResultsNumerical experiments with real-world data demonstrates that the proposed TIME model can predict ICI therapy response. The DRL-derived personalized ICC therapy outperforms predefined fixed schedules. For tumors with extremely low CD8+T cell infiltration (“extremely cold tumors”), DRL recommends high-dosage chemotherapy alone. For tumors with higher CD8+T cell infiltration (“cold” and “hot tumors”), an appropriate chemotherapy dosage induces CD8+T cell proliferation, enhancing ICI therapy outcomes. Specifically, for “hot tumors,” chemotherapy and ICI are administered simultaneously, while for “cold tumors,” a mid-dosage of chemotherapy makes the TIME “hotter” before ICI administration. However, a number of “cold tumors” with rapid resistant cancer cell growth, ICC eventually fails.ConclusionThis study highlights the potential of utilizing real-world clinical data and DRL to develop personalized optimal ICC by understanding the complex biological dynamics of a patient’s TIME. Our ODE-based TIME model offers a theoretical framework for determining the best use of ICI, and the proposed DRL model may guide personalized ICC schedules.SIGNIFICANCE STATEMENTOur research presents a novel data-driven approach to personalized cancer treatment by combining artificial intelligence and mathematical models of the tumor’s surrounding environment, known as the tumor immune microenvironment (TIME). This innovative method allows for the optimization of patient-specific immune checkpoint inhibitors and combined chemotherapy therapy. By utilizing deep reinforcement learning, our approach can adapt and improve treatment strategies for individual patients, ultimately maximizing the effectiveness of cancer therapies. This pioneering work has the potential to significantly enhance clinical decision-making and improve patient outcomes, paving the way for personalized cancer immunotherapy.