Optimized patient-specific immune checkpoint inhibitor therapies for cancer treatment based on tumor immune microenvironment modeling

Yao Yao, Youhua Frank Chen, Qingpeng Zhang*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Citation (Scopus)
14 Downloads (CityUHK Scholars)

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.
Original languageEnglish
Article numberbbae547
Number of pages10
JournalBriefings in Bioinformatics
Volume25
Issue number6
Online published25 Oct 2024
DOIs
Publication statusPublished - 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/

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