Optimizing combination of chemotherapy and checkpoint inhibitors of oncology treatment by offline deep reinforcement learning
Principal Investigator
Name
Yao Yao
Degrees
Ph.D.
Institution
City University of Hong Kong
Position Title
Ph.D. student
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1133
Initial CDAS Request Approval
Dec 27, 2022
Title
Optimizing combination of chemotherapy and checkpoint inhibitors of oncology treatment by offline deep reinforcement learning
Summary
The invention and application of checkpoint inhibitors reforms the therapy of oncology for that they sharply increase the overall survival of many kinds of cancer. However, the checkpoint inhibitor can cause verse events which is similar to the autoimmunity and fail when tumor is out of control. Then the treatment will finally turn to traditional chemotherapy. Besides, there are mounting evidence showing that the chemotherapy can modulate the immune microenvironment and promote the effect of checkpoint inhibitors. As a result, the combination effect of chemotherapy and checkpoint inhibitors is worthy for further research, especially based on real data and by advanced algorithms, for example, offline deep reinforcement learning.
Aims
Analyze the interplay of chemotherapy and checkpoint inhibitors of their effects on tumor growth controlling.
Propose suggestions on combined chemotherapy and checkpoint inhibitors therapy.
Collaborators
Dr. Zhang Qingpeng, School of Data Science, City University of Hong Kong, Hong Kong, China.