Agent-based modeling for personalized prediction of an experimental immune response to immunotherapeutic antibodies.

ABSTRACT: Targeting immune checkpoint pathways to evoke an immune response against tumors has revolutionized clinical oncology over the last decade. Antibodies that block the PD-1/PD-L1 pathway have demonstrated effective antitumor activity in cancer patients and are approved for treatment of several different types of cancer. However, many patients do not experience durable beneficial clinical responses. The ability to predict response to immunotherapy is a clinical need with immediate implications on the optimization of oncologic treatments. In this work we developed and tested the ability of an Agent-Based Model (ABM) to predict the ex vivo immune response of memory T cells to anti-PD-L1 blocking antibody, based on personalized immune-phenotypes. We performed mixed lymphocyte reaction (MLR) exper

SUBMITTER: Matalon O 

PROVIDER: S-EPMC12148075 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature