Abstract 4694: (R)evolutionary approach to predicting individual response to radiation therapy in breast cancer

Abstract 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. 3D spheroids of several breast cancer cell lines were grown and collected at multiple time points after 10 Gy of radiation. 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 we grew patient derived organoids (PDO) of breast cancer and irradiated them and analyzed them as described above. 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 microns designated as H-spheroids (hypoxic core). While the irradiated O-spheroid growth curves plateaued upon irradiation, H-spheroids grew at the same rate as the non-irradiated controls. To investigate this phenomenon, 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. We discovered a switch in the sphingolipid metabolism pathway. 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. Citation Format: Naheel Khatri, Raafat Chalar, Saiful Samad, Alexander Stessin, Mehdi Damaghi. (R)evolutionary approach to predicting individual response to radiation therapy in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4694.