Deep learning analysis of PBMC chromatin images revealed that patients whose profiles shifted toward healthy states after proton radiation therapy were less likely to experience disease recurrence.
Does deep learning-based analysis of PBMC chromatin images predict patient trajectories and disease recurrence in cancer patients undergoing proton radiation therapy?
Deep learning analysis of PBMC chromatin images offers a novel, non-invasive biomarker for monitoring patient response and predicting recurrence in cancer patients undergoing proton radiation therapy.
Abstract Introduction: The development of non-invasive, simple, and accurate methods to predict patient response to cancer therapy remains an open challenge. Proton radiation therapy (PRT) is increasingly used for hard-to-reach tumors or those in sensitive areas. However, it remains more expensive than other radiation therapies and while considered safer than conventional radiation therapy, its short- and long-term side effects are still not well explored. Therefore, developing an early measure for patient response is a critical research direction. Here we sought to test whether chromatin images of peripheral blood mononuclear cells (PBMCs) contain sufficient information to track patients’ trajectories during and after PRT. Methods: We collected blood samples at five timepoints (before, during, at the end of, and twice after PRT) from 150 patients across various cancers including Central Nervous System and Head Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 5468.
Schlüter et al. (2026) studied this question. Deep learning analysis of PBMC chromatin images revealed that patients whose profiles shifted toward healthy states after proton radiation therapy were less likely to experience disease recurrence.