Abstract Introduction Mapping the metabolic landscapes of prostate cancer (PCa) with near single-cell precision and understanding their treatment responses can uncover new biomarkers and oncometabolites. This could pave the way for metabolically informed approaches to personalized treatment decisions. Methods and experimental procedures Organoids technology and spatial metabolomics by imaging mass spectrometry were employed to generate the first of its kind PCa Atlas of metabolic states. Briefly, patient-derived xenograft organoids (PDXOs) and patient derived organoids (PDOs) models were used to model different stages of the disease: androgen-dependent (AD) state, castration resistant (CR), and neuroendocrine/double negative stages. Organoids were treated with 24 metabolic enzyme-targeting compounds (metabolic perturbators) to reveal their metabolic dependencies. Sublethal concentrations of the perturbators were used on the organoids for two analyses: metabolomic and lipidomic data with spatial metabolomics and RNA sequencing. ChatGPT was emplyed to improve text clarity. Summary of the new, unpublished data Spatial metabolomics detected an average of 35. 000 pixels/slide, at the near-single-cell resolution. 144 and 705 ions were detected for the metabolomic and lipidomic analysis, respectively. Different metabolic perturbations induced different metabolic states, visualized using UMAP. For example, 2-deoxy-glucose (2-DG) induced a cluster separation for the control sample with differential analysis revealing metabolites enrichment according to the treatment (e. g. glucose, glucose 6-P, erythrose 4-P) in both AD and CR models. Palmitate supplementation, on the other hand, elicited clustering for the lipidomic UMAP, with enrichment of oleic, linoleic, and palmitoleic acid, confirming method reliability. A multiplexing approach was implemented to increment the throughput: organoids were labeled with live dyes, and the colors used were assigned a metabolic perturbation. MSI readout proved the feasibility of the multiplexing approach with the dye not altering the metabolic readout. Transcriptomic data clustered according to the models; at a second level, within each model, metabolic perturbations clustering could be observed. Perturbations of the electron transport chain (ETC) induced the most evident transcriptomic separation in all the models. The mitochondria are the hub of metabolic processes, and their morphology was investigated at the transmission electron microscope (TEM). Organoid treatments revealed a shift in their morphology from a canonical to a condensed phenotype or even swollen/disrupted, depending on the target of the metabolic perturbation. Statement of the conclusions In conclusion, the initial experiments demonstrated the reliability and feasibility of creating the first Prostate Cancer Atlas of metabolic states. Utilizing this atlas could reveal new cancer metabolite markers and steer the development of personalized treatments guided by metabolic insights. Citation Format: Andrea Brunello, Jeany Delafiori, Mohammed Shahraz, Louis McConnell, Federico La Manna, Mariia Naumenko, Marianna Rapsomaniki, Theodore Alexandrov, Marianna Kuithof-de Julio. Unveiling the metabolic profiles of prostate cancer to anticipate patient response to treatment abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Innovations in Prostate Cancer Research and Treatment; 2026 Jan 20-22; Philadelphia PA. Philadelphia (PA): AACR; Cancer Res 2026;86 (2Suppl): Abstract nr PR028.
Brunello et al. (Tue,) studied this question.