Abstract C054: Topographical investigation of metabolites in excised squares (TIMES2): Mapping in vivo of metabolic heterogeneity in pancreatic tumors
Authors: Peter Yu, Robert Banh, Stephen Martis, Douglas Biancur, Albert Sohn, Elaine Lin, Keisuke Yamamoto, Benjamin Greenbaum, Alec Kimmelman
Journal: Cancer Research (2024)

Abstract Background: Pancreatic cancer is a lethal malignancy characterized by complex intratumoral metabolic reprogramming and intercellular nutrient sharing between cells in the tumor microenvironment (TME) that promote pancreatic cancer progression. However, this crosstalk, as well as regional variation in perfusion and oxygenation, can lead to metabolic heterogeneity that has not been appreciated by metabolomics of whole tumors. Here we quantify amino acids and tricarboxylic acid cycle (TCA) intermediates using a novel methodology that allows us to portray global tumor metabolite heterogeneity in a tumor. Methods: Human PaTu-8902 or murine HY19636 (from female KPC mice p48-Cre+, KRASLSL-G12D/+, Trp53lox/+) pancreatic cancer cell lines were orthotopically injected into pancreata of NCr nude mice (n=3) or C57BL/6 mice (n=2). Mice were euthanized after 3-5 weeks and tumors were harvested. Tumor slices were further sectioned into 1mm x 1mm x 1mm cubes using a custom-made multisectional slicing device and each cube location was recorded. Each cube was extracted using methanol, water, and chloroform with labelled amino acid standards, derivitized, and resolved using gas chromatography-mass spectrometry (DB-35MS column with Agilent 7890B gas chromatograph coupled to a single quadrupole 5977B mass spectrometer). 22 metabolites (15 amino acids, 5 TCA intermediates, lactate, and pyruvate) were identified by unique fragments and retention time compared to known standards. Peaks were picked using OpenChrom and analyzed using MATLAB. Data was analyzed using Graphpad Prism. Principal Component Analysis (PCA) was visualized using Python on a Jupyter notebook. Results: Both orthotopic human and murine pancreatic tumors demonstrated striking levels of intratumoral metabolite heterogeneity. Glycine, glutamine, and proline were the amino acids with the highest coefficient of variance, while leucine, isoleucine, and serine had the lowest coefficient of variance. α-ketoglutarate and succinate were the TCA intermediates with highest coefficient of variance. Lactate had the lowest coefficient of variance among all examined metabolites. Spatial mapping of each metabolite demonstrated distinct regions with varying abundance levels of metabolites. PCA demonstrated 75% of variance was carried by PC1 and 10% carried by PC2. Conclusions: This study reveals insights into the degree of intratumoral heterogeneity present in pancreatic tumors that illustrate the difficulty of in vivo metabolomics analysis and suggest that high-resolution (single cell) metabolomics techniques will be critical to study metabolism in the complex TME. Citation Format: Peter Yu, Robert Banh, Stephen Martis, Douglas Biancur, Albert Sohn, Elaine Lin, Keisuke Yamamoto, Benjamin Greenbaum, Alec Kimmelman. Topographical investigation of metabolites in excised squares (TIMES2): Mapping in vivo of metabolic heterogeneity in pancreatic tumors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr C054.