Integrating construction and spatial imaging of organoid for tumor metabolic heterogeneity and interactions

Abstract The metabolism of tumors is a complex network process involving multiple metabolic species and exhibits metabolic spatial heterogeneity, which poses challenges for the development of effective therapeutic strategies. Patient‐derived tumor organoids, recently developed, hold significant potential in elucidating novel aspects of cancer biology, expediting the discovery of oncology drugs, and personalizing cancer treatment. However, tracing metabolic reactions in the tumor organoid network presents a technological challenge. In this study, we propose methodologies that utilize enzymatically derived tissue debris (TD) to generate the biobank patient‐derived lung cancer organoids (LCOs) that accurately replicate the histological characteristics, stemness of lung cancer stem cells, cellular heterogeneity, and mutational patterns observed in their respective original tumors. Importantly, our LCO model can be integrated with mass spectrometry imaging to analyze tumor metabolic heterogeneity and the spatial distributions of fingerprint lipids associated with extracellular matrix homeostasis including lysophosphatidic acid (LPA), phosphatidic acid (PA), phosphatidylserine (PS), phosphatidylglycerol (PG), diacylglycerol (DAG). Furthermore, we demonstrate the effectiveness of LCOs in evaluating personalized treatment by establishing a connection between their mutational profiles and reactions to specific drugs. Our findings highlight that LCOs from TD retain crucial metabolic characteristics of lung cancer and can be readily utilized to investigate individualized treatment strategies.