European Conference on Mathematical and Theoretical Biology (ECMTB 2026)

Personalizing Cancer Therapy by Modeling the Evolutionary Dynamics of the Tumor Immune Microenvironment

16 Jul 2026, 17:20

20m

01.18 - SZ (University of Graz)

Contributed Talk Mathematical Oncology Contributed Talks

Speaker

Qingpeng Zhang (The University of Hong Kong)

Description

In this talk, we present our research on using mathematical modeling and machine learning to characterize the evolutionary dynamics of the tumor immune microenvironment (TIME) for personalized cancer therapy. We developed various ODE-based TIME models, integrating genomic and transcriptomic data. These models, enhanced by deep reinforcement learning (DRL), optimize therapy regimens, including intermittent androgen deprivation therapy (IADT) for prostate cancer and personalized schedules for immune checkpoint inhibitors (ICIs) and chemotherapy. Our findings demonstrate the potential of these approaches to improve patient outcomes by tailoring treatments to individual tumor dynamics.

Bibliography

@article{yao_optimized_2024,
title = {Optimized patient-specific immune checkpoint inhibitor therapies for cancer treatment based on tumor immune microenvironment modeling},
volume = {25},
copyright = {https://creativecommons.org/licenses/by-nc/4.0/},
issn = {1467-5463, 1477-4054},
url = {https://academic.oup.com/bib/article/doi/10.1093/bib/bbae547/7841508},
doi = {10.1093/bib/bbae547},
language = {en},
number = {6},
urldate = {2026-05-10},
journal = {Briefings in Bioinformatics},
author = {Yao, Yao and Chen, Youhua Frank and Zhang, Qingpeng},
month = sep,
year = {2024},
pages = {bbae547},
}