Profile Characterization of Biogenic Amines in Glioblastoma Patients Undergoing Standard-of-Care Treatment

Author:

Aboud Orwa123ORCID,Liu Yin124,Dahabiyeh Lina56ORCID,Abuaisheh Ahmad7,Li Fangzhou8910ORCID,Aboubechara John Paul1ORCID,Riess Jonathan311,Bloch Orin2,Hodeify Rawad12ORCID,Tagkopoulos Ilias8910,Fiehn Oliver5ORCID

Affiliation:

1. Department of Neurology, University of California, Davis, Sacramento, CA 95817, USA

2. Department of Neurological Surgery, University of California, Davis, Sacramento, CA 95817, USA

3. Comprehensive Cancer Center, University of California Davis, Sacramento, CA 95817, USA

4. Department of Ophthalmology, University of California, Davis, Sacramento, CA 95817, USA

5. West Coast Metabolomics Center, University of California Davis, Davis, CA 95616, USA

6. Department of Pharmaceutical Sciences, School of Pharmacy, The University of Jordan, Amman 11942, Jordan

7. School of Medicine, Al Balqa Applied University, Al-Salt 19117, Jordan

8. Department of Computer Science, University of California, Davis, Sacramento, CA 95616, USA

9. Genome Center, University of California, Davis, Sacramento, CA 95616, USA

10. USDA/NSF AI Institute for Next Generation Food Systems (AIFS), Davis, CA 95616, USA

11. Department of Internal Medicine, Division of Hematology and Oncology, University of California, Davis, Sacramento, CA 95817, USA

12. Department of Biotechnology, School of Arts and Sciences, American University of Ras Al Khaimah, Ras Al-Khaimah 10021, United Arab Emirates

Abstract

Introduction: Biogenic amines play important roles throughout cellular metabolism. This study explores a role of biogenic amines in glioblastoma pathogenesis. Here, we characterize the plasma levels of biogenic amines in glioblastoma patients undergoing standard-of-care treatment. Methods: We examined 138 plasma samples from 36 patients with isocitrate dehydrogenase (IDH) wild-type glioblastoma at multiple stages of treatment. Untargeted gas chromatography–time of flight mass spectrometry (GC-TOF MS) was used to measure metabolite levels. Machine learning approaches were then used to develop a predictive tool based on these datasets. Results: Surgery was associated with increased levels of 12 metabolites and decreased levels of 11 metabolites. Chemoradiation was associated with increased levels of three metabolites and decreased levels of three other metabolites. Ensemble learning models, specifically random forest (RF) and AdaBoost (AB), accurately classified treatment phases with high accuracy (RF: 0.81 ± 0.04, AB: 0.78 ± 0.05). The metabolites sorbitol and N-methylisoleucine were identified as important predictive features and confirmed via SHAP. Conclusion: To our knowledge, this is the first study to describe plasma biogenic amine signatures throughout the treatment of patients with glioblastoma. A larger study is needed to confirm these results with hopes of developing a diagnostic algorithm.

Funder

National Cancer Institute

National Institutes of Health

USDA-NIFA

Publisher

MDPI AG

Subject

General Biochemistry, Genetics and Molecular Biology,Medicine (miscellaneous)

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