Abstract 2342: Multidimensional metabolomic stratification of ER+/HER2- compared to ER-/HER2- breast tumors
Authors: Valerie Bussberg, Vladimir Tolstikov, Guisong Wang, Punit Shah, Rick Searfoss, Leigh Fantacone-Campbell, Jeffrey A. Hooke, Brenda Deyarmin, Rebecca N. Zingmark, Stella Somiari, Jianfang Liu, Leonid Kvecher, Bradley Mostoller, Lori Sturtz, Praveen-Kumar Raj-Kumar, Elder Granger, Linda Vahdat, Mary L. Cutler, Chas Bountra, Rangaprasad Sarangarajan, Hai Hu, Albert J. Kovatich, Michael A. Kiebish, Niven R. Narain, Craig D. Shriver
Journal: Cancer Research (2021)

Abstract Introduction: In the United States, breast cancer represents the leading cancer diagnosis among women and can readily be classified as a metabolic disease based on its distinct metabolic activity within the tumor microenvironment. Compared to other omics technologies, extensive lipidomic and metabolomic studies are lacking. Here in, we evaluated a cohort of 109 tumors characterized as ER+/HER2- and ER-/HER2- based on immunohistochemistry (IHC) and performed comprehensive structural lipidomic, signaling lipidomic, and global metabolomic analyses for an extensive characterization of the biophysical, signaling, and metabolic interplay between these tumors. Methods: Clinical IHC subtyping of core biopsies was used to select a cohort of patients with ER+/HER2- or ER-/HER2- primary tumors from flash-frozen surgical samples. The positive/negative status of ER/PR/HER2 was defined using updated ASCO 2020 guidelines. Ki-67 status was determined using the 2011 St. Gallen's International Expert Consensus recommendations. ER low (1-10%) cases were excluded from this analysis. Structural lipidomic analysis was employed through the use of MS/MSALL high resolution shotgun lipidomics using a SCIEX 5600+ TripleTOF micro LC approach characterizing 23 lipid classes and over 1200 molecular species. Signaling lipids were analyzed using a SCIEX 6600 TripleTOF microLC platform characterizing 106 lipid analytes across octadecanoid, eicosanoid and docosanoid species. Metabolomics analysis was performed using LECO PEGASUS GC TOF, SCIEX 5500 HILIC LC MS/MS analysis, and SCIEX 6600 High resolution RP-LC-MS analysis detecting 450 metabolite Metabolomics data was further interpreted using MetaboAnalyst software. Results/Conclusions: Compared to their ER+ counterparts, ER-/HER2- tumors exhibited a significant decrease in triacylglycerides, and a corresponding increase in cholesterol ester, phosphatidylcholine, phosphatidylethanolamine, and phosphatidylglycerol species demonstrating a signature of biophysical and metabolic rewiring with alterations in Kennedy pathway lipid shuttling. One signaling lipid was decreased and six were increased (predominantly arachidonic species) in ER-/HER2- tumors compared to ER+/HER2- ones. Metabolomic analysis revealed distinct alterations in cysteine/methionine, arginine/proline, purine, butanoate, and tryptophan metabolism. Utilizing a multidimensional metabolic integration approach, we identified distinct biophysical, signaling, and biochemical alterations in ER+/HER2- compared to ER-/HER2- breast tumors, which may impact selection of therapy and outcome in the future. Citation Format: Valerie Bussberg, Vladimir Tolstikov, Guisong Wang, Punit Shah, Rick Searfoss, Leigh Fantacone-Campbell, Jeffrey A. Hooke, Brenda Deyarmin, Rebecca N. Zingmark, Stella Somiari, Jianfang Liu, Leonid Kvecher, Bradley Mostoller, Lori Sturtz, Praveen-Kumar Raj-Kumar, Elder Granger, Linda Vahdat, Mary L. Cutler, Chas Bountra, Rangaprasad Sarangarajan, Hai Hu, Albert J. Kovatich, Michael A. Kiebish, Niven R. Narain, Craig D. Shriver. Multidimensional metabolomic stratification of ER+/HER2- compared to ER-/HER2- breast tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2342.