ESTRO 2025 - Abstract Book

S3894

Radiobiology - Microenvironment

ESTRO 2025

4. Coombs CC, Zehir A, Devlin SM, et al. Therapy-related clonal hematopoiesis in patients with non-hematologic cancers is common and associated with adverse clinical outcomes. Cell Stem Cell 2017;21:374-382 e4.

2202

Digital Poster (R)evolutionary Approach to Predicting Individual Response to Radiation Therapy in Breast Cancer Naheel Khatri 1,2 , Raafat Chalar 1 , Yujie Xiao 3 , Jowana Obeid 1 , Alexander Stessin 4 , Mehdi Damaghi 5,1 1 Stony Brook Cancer Center, Stony Brook Medicine, Stony Brook, USA. 2 Biomedical Informatics, Stony Brook Medicine, Stony Brook, USA. 3 Applied Mathematics and Statistics, Stony Brook, Stony Brook, USA. 4 Radiation Oncology, Stony Brook Medicine, Stony Brook, USA. 5 Pathology, Stony Brook Medicine, Stony Brook, USA Purpose/Objective: Despite increasing incorporation of gene panel testing into radiotherapeutic decision-making, there is no established method to tailor prescribed radiation regimens to individual tumor radiosensitivity. We propose a novel approach to assessing evolutionary determinants of radioresistance from patient-derived tumor samples. We hypothesized that pre-existing resistance to oxidative damage in tumor microenvironment (TME) can define a cell's evolutionary trajectory and its radiosensitivity. In contrast with the current genetic panels, we use a panel catered toward heterogeneity within the tumor and the TME niches, which may harbor radioresistant populations from collateral TME mechanisms of resistance. The aim is to develop an AI-based algorithm using these eco-evolutionary results to individualize radiation dose in patients, thus minimizing the percentage of non-responders. Material/Methods: 3D spheroids of MCF7, T47D, and MDA-MB231 breast cancer cell lines as well as MCF10A as control were grown and collected and radiated. The spheroids were collected at 6 hours, 24 hours, and 1 week after irradiation. Matrix assisted laser desorption/ionization (MALDI) data was acquired in negative and positive ionization mode for spatial lipidomics and metabolomics analysis. Additionally, spatial single cell multiplexed immunofluorescence was performed on the sequential cut to MALDI using markers associated with hypoxia, acidosis, ROS, and epithelial-to mesenchymal transition. Then patient derived organoids (PDO) of breast cancer were grown and irradiated and analyzed as described above. We tracked back the discovered features in spheroids in responder and responder PDOs. Results: Based on a previously validated oxygen diffusion model, spheroids were categorized according to size, with those <240 micrometers in diameter designated as O-spheroids (oxygenated) and those ≥ 240 micrometers designated as H-spheroids (hypoxic core). Spheroid growth curves were developed by measuring the largest projected area z stack. Across all breast cancer cell lines, while the irradiated O-spheroid growth curves plateaued upon irradiation, H-spheroids grew at the same rate as the non-irradiated controls. Habitats and cell phenotypes were defined using spatial multi-omics data from MALDI and multiplexed-IF, respectively. We found differential distributions of habitats in radiated spheroids as determined by the cluster size and abundance spatio-temporally. This growth difference between the O-spheroids and the H-spheroids was correlated to a switch in the sphingolipid metabolism pathway. Clustering of the multiplexed-IF data within each habitat was used for TME niche delineation. Conclusion: Using principles of ecology and evolution, we have developed a novel approach to identifying radiosensitivity markers. Detecting these cell states and ecological niches in patients’ pre-treatment biopsies can inform a more individualized approach to radiotherapy.

Keywords: Radioresistance, Eco-evolution, Spatial Multiomics