Projects per year
Abstract
Enhancing 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). Using patients’ genomic and transcriptomic data, we develop an ordinary differential equations (ODEs)–based TIME dynamic evolutionary model to characterize interactions among chemotherapy, ICIs, immune cells, and tumor cells. A DRL agent is trained to determine the personalized optimal ICC therapy. Numerical experiments with real-world data demonstrate 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’), the DRL agent 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, in several ‘cold tumors’ with rapid resistant tumor cell growth, ICC eventually fails. This study highlights the potential of utilizing real-world clinical data and DRL algorithm to develop personalized optimal ICC by understanding the complex biological dynamics of a patient’s TIME. Our ODE–based TIME dynamic evolutionary model offers a theoretical framework for determining the best use of ICI, and the proposed DRL agent may guide personalized ICC schedules.
©TheAuthor(s) 2024. Published by Oxford University Press.
©TheAuthor(s) 2024. Published by Oxford University Press.
Original language | English |
---|---|
Article number | bbae547 |
Number of pages | 10 |
Journal | Briefings in Bioinformatics |
Volume | 25 |
Issue number | 6 |
Online published | 25 Oct 2024 |
DOIs | |
Publication status | Published - Nov 2024 |
Funding
This research is supported by the General Research Fund of the Research Grants Council of the Hong Kong Special Administrative Region (grant no. 11218221).
Research Keywords
- tumor immune microenvironment
- immune checkpoint inhibitors
- personalized medicine
- deep reinforcement learning
Publisher's Copyright Statement
- This full text is made available under CC-BY-NC 4.0. https://creativecommons.org/licenses/by-nc/4.0/
Fingerprint
Dive into the research topics of 'Optimized patient-specific immune checkpoint inhibitor therapies for cancer treatment based on tumor immune microenvironment modeling'. Together they form a unique fingerprint.Projects
- 1 Finished
-
GRF: Optimizing Interventions for Changing HIV Risk Behaviors via Temporal Link Prediction in MSM Social Networks
ZHANG, Q. (Principal Investigator / Project Coordinator), GAO, S. (Co-Investigator), Lau, J.T.-F. (Co-Investigator), LI, X. (Co-Investigator) & Tang, W. (Co-Investigator)
1/01/22 → 15/11/23
Project: Research