Abstract 2191: Quantitative cell-level spatial analysis of tumor metabolism
Authors: Tiffany M. Heaster, Bennett A. Landman, Melissa C. Skala
Journal: Cancer Research (2018)

Abstract Sub-populations of tumor cells exhibit distinct responses to treatment. This cell-level heterogeneity can lead to growth of resistant populations, and tumor recurrence. Many cancer therapies alter tumor cell metabolism, and the metabolic microenvironment can further contribute to tumor heterogeneity and drug response. However, the relationship between the spatial diversity of tumor cell metabolism and treatment response requires further characterization. Two-photon fluorescence lifetime microscopy (FLIM) monitors cellular metabolism by measuring NAD(P)H and FAD autofluorescence, and enables single-cell resolution of metabolic heterogeneity within 3D samples. Previous studies have shown that FLIM predicts treatment response of 2D and 3D cultures in vitro, and in vivo tumors. Additionally, these studies demonstrated responsive cells have lower post-treatment NAD(P)H mean lifetime (NAD(P)H τm) than non-responsive cells. Thus, these published FLIM data were used to develop an algorithm to quantify the spatial distribution of cellular metabolism in response to treatment. Density-based clustering analysis defined sub-populations in organoids based on NAD(P)H τm. Accuracy of cluster analysis classification was first evaluated on 2D co-culture images of two cell lines plated at varying proportions. Following this validation, metabolic sub-populations and their spatial distributions were characterized in control and drug-treated 3D tumor organoids of human squamous cell carcinoma (FaDu). Descriptive spatial metrics were developed using graph-based network analysis integrated with standard image analysis to establish local (sub-population level) and global (organoid level) spatial relationships. High classification accuracy (94.7%) of cell lines was achieved in 2D breast carcinoma co-cultures. In all FaDu 3D organoids, metabolic sub-populations were largely segregated into clusters of identical metabolic phenotype, with ≥78% of cell neighbors belonging to the same sub-population. Clustering density was greater in populations with low NAD(P)H τm (responsive), exhibiting shorter intercellular distances than high NAD(P)H τm (non-responsive) populations (p &amp;lt;0.05). Additionally, responsive and non-responsive cell sub-populations displayed similar organization relative to both adjacent sub-populations and organoid centers. Geographically weighted principal component analysis demonstrates variability of each metabolic and spatial parameter as a function of cell location. This analysis is under refinement for use with in vivo FaDu xenograft images. This algorithm to quantify spatial distributions of metabolic sub-populations within tumors could enhance our understanding of tumor progression and therapeutic resistance. Citation Format: Tiffany M. Heaster, Bennett A. Landman, Melissa C. Skala. Quantitative cell-level spatial analysis of tumor metabolism [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2191.