Gadolinium is a paramagnetic relaxation enhancement (PRE) agent that accelerates the relaxation of metabolite nuclei. In this study, we noted the ability of gadolinium to improve the sensitivity of two-dimensional, non-uniform sampled NMR spectral data collected from metabolomics samples. In time-equivalent experiments, the addition of gadolinium increased the mean signal intensity measurement and the signal-to-noise ratio for metabolite resonances in both standard and plasma samples. Gadolinium led to highly linear intensity measurements that correlated with metabolite concentrations. In the presence of gadolinium, we were able to detect a broad array of metabolites with a lower limit of detection and quantification in the low micromolar range. We also observed an increase in the repeatability of intensity measurements upon the addition of gadolinium. The results of this study suggest that the addition of a gadolinium-based PRE agent to metabolite samples can improve NMR-based metabolomics.

Background: Discovery of oncogenic mutations as targets for cancer therapy revolutionized treatment of GIST and other cancers. However, nearly all patients ultimately progress, which emphasizes the need for development of new tools to assess cancer prognosis and factors associated with benefit of cancer therapies. Altered metabolism is a hallmark of cancer, enabling tumors to proliferate, survive and metastasize. By measuring the complete set of metabolites in an individual (metabolome) it is possible to identify biomarkers that correlate with disease status, prognosis, and therapeutic response. Methods: We performed untargeted NMR and MS-based machine learning metabolomic analysis (Olaris, Waltham, MA) of serial plasma samples collected at baseline and during experimental systemic therapies in 39 patients with advanced/metastatic GIST. Results were compared to clinical outcomes. Results: In serial plasma samples from 39 patients with advanced/metastatic GIST using untargeted NMR and MS-based machine learning (ML) metabolomic analysis, we identified metabolic signatures to build Biomarker-of-Response (BoR) ML models that could accurately differentiate patients with response, intrinsic and adaptive resistance to experimental systemic therapies. The BoR ML model also correlated with tumor growth or tumor reduction in patients with response, intrinsic and adaptive resistance. Finally, we identified metabolic pathways associated with response and resistance. Conclusions: Comprehensive metabolomic profiling of serially collected plasma is feasible and detects metabolic signatures associated with therapeutic response in advanced GIST. Citation Format: Chandrashekhar Honrao, Srihari Raghavendra Rao, Nathalie Teissier, S. Greg Call, Elizabeth M. ODay, Filip Janku. Plasma based metabolic profiling in metastatic gastrointestinal stromal tumors (GIST) [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 LB031.