Deep learning-based analysis reveals patient-level proton radiation therapy trajectories using single-cell PBMC chromatin images

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 & Neck cancers, and 50 healthy volunteers. PBMCs were isolated, stained with DAPI to label the DNA, and imaged with a confocal microscope. We applied machine learning methods to single-cell crops of the PBMCs to: 1) classify healthy vs. cancer patients, 2) derive patient-level similarity-to-healthy scores, and 3) predict patient trajectories. To account for the possibility that variation in PBMC proportions might be a key difference between healthy and cancer patients, we adopted a multiple-instance learning (MIL) approach. MIL is a form of weakly-supervised learning that automatically discovers which features and which cells are important within a collection of cells. Results: By comparing chromatin images of PBMCs from cancer patients and healthy volunteers using our MIL framework, we identified cancer-specific alterations in PBMC chromatin architecture induced by tumor-derived signals in the bloodstream. Longitudinal tracking across five time points revealed three distinct patient subgroups. Patients whose PBMC profiles shifted toward greater similarity to healthy volunteers after therapy were less likely to experience disease recurrence. Furthermore, our MIL framework enabled prediction of patients' likelihood of returning to a healthy state after therapy, based solely on pre-treatment PBMC chromatin images, within the largest cancer type population in our study, Head & Neck cancer. Conclusion: In summary, we demonstrated that simple chromatin images derived from liquid biopsies can serve as a non-invasive, easily obtained, and inexpensive biomarker for monitoring patient trajectories during PRT. This motivates further investigation of the use of PBMC chromatin images in the context of cancer screening and treatment monitoring, and more broadly in other disease contexts where PBMCs have been previously studied as potential biomarkers.