Search

Two-dimensional (2D) graphitic carbon nitride (g-C

Introduction The desmoplastic Tumour MicroEnvironment (TME) in Pancreatic Ductal Adenocarcinoma (PDA) plays a key role in drug delivery, metabolism and resistance; drug interventions in turn regulate tumour metabolism. Mass Spectrometry Imaging (MSI) techniques may provide information about regional tumour metabolic profile and drug delivery to enhance our understanding of TME heterogeneity and its impact on drug efficacy and resistance. Material and methods Multimodal MSI acquisition of the tissue distribution of gemcitabine, the ATR inhibitor AZD6738, their metabolites and the endogenous metabolome was performed in a KPC GEMM mouse model of PDA. Targeted analysis of the compounds and their metabolites were used to evaluate drug delivery. Small molecule quantitation including haem and metabolic markers such as ATP/ADP/AMP, were used together with H and E and staining for known molecular biomarkers including Pan-CK and αSMA to characterise tissue architecture. Untargeted analyses including statistical identification of discriminative or colocalised metabolites were used to identify de novo endogenous metabolite biomarkers and cellular phenotypes driving tumour heterogeneity. Results and discussions MSI revealed significant intratumoural heterogeneity of drug delivery and drug metabolism. Highest delivery of the parent compounds (AZD6738 and dFdC) were found to colocalise with haem in areas confirmed histologically to be necrotic and haemorrhagic. dFdC metabolism appeared related to TME metabolic heterogeneity. The active and inactive metabolites of gemcitabine (including dFdCTP and dFdU) demonstrated differential distribution, both from the parent compounds and each other in the tissue. Unsupervised clustering based segmentation and colocalisation analysis of the MSI metabolomic data enabled identification and characterisation of these distinct tissue regions based on similarities in their metabolic profile. Conclusion We have shown that MSI allows spatial resolution of drug delivery, metabolism and that MSI-based metabolomics analysis enables detection of greater tumour heterogeneity than visible by traditional pathology methods such as H and E. Combining this with biomarker information from Imaging Mass Cytometry (IMC) may enable identification of the cell types and phenotypes responsible for the differential metabolic effects observed with combination therapies. Innovative, information-rich technologies such as IMC and MSI may drive greater understanding of the impact of tumour heterogeneity on drug efficacy in vivo and ultimately in patients.