Toshie Saito1, Yue Wei2, Li Wen3, Chaitanya Srinivasan4,5, Benjamin O Wolthers6, Cheng-Yu Tsai1, Marian H Harris7, Kristen Stevenson8, Craig Byersdorfer9, Judy-April Oparaji10, Christian Fernandez11, Amitava Mukherjee12, Maisam Abu-El-Haija13,14, Sameer Agnihotri15, Kjeld Schmiegelow6, Megan R Showalter16, Paul W Fogle16, Scott McCulloch16, Kevin Contrepois17,18, Lewis B Silverman19,20, Ying Ding2, Sohail Z Husain21. 1. Department of Pediatrics, Stanford University, 750 Welch Road, Palo Alto, CA, 94304, USA. 2. Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA. 3. Department of Gastroenterology and Shanghai Key Laboratory of Pancreatic Disease, Shanghai General Hospital, Shanghai Jiao Tong University, Shanghai, China. 4. Department of Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. 5. Department of Pediatrics, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA. 6. Department of Pediatrics and Adolescent Medicine, University Hospital Rigshospitalet, Copenhagen, Denmark. 7. Department of Pathology, Boston Children's Hospital, Boston, MA, USA. 8. Department of Data Sciences at Dana-Farber Cancer Institute, Boston, MA, USA. 9. Department of Pediatrics, Division of Blood and Marrow Transplant and Cellular Therapies, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. 10. Pediatrics, Guthrie Ambulatory Health Center, Fort Drum, NY, USA. 11. Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA. 12. Department of Radiation Oncology, University of Pittsburgh, Pittsburgh, PA, USA. 13. Division of Gastroenterology, Hepatology and Nutrition, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA. 14. Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA. 15. School of Medicine, Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, USA. 16. Metabolon, Inc., Durham, NC, USA. 17. Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA. 18. Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA. 19. Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. 20. Division of Pediatric Hematology-Oncology, Boston Children's Hospital, Boston, MA, USA. 21. Department of Pediatrics, Stanford University, 750 Welch Road, Palo Alto, CA, 94304, USA. sohail.husain@stanford.edu.
Abstract
INTRODUCTION: Acute lymphoblastic leukemia (ALL) is among the most common cancers in children. With improvements in combination chemotherapy regimens, the overall survival has increased to over 90%. However, the current challenge is to mitigate adverse events resulting from the complex therapy. Several chemotherapies intercept cancer metabolism, but little is known about their collective role in altering host metabolism. OBJECTIVES: We profiled the metabolomic changes in plasma of ALL patients initial- and post- induction therapy. METHODS: We exploited a biorepository of non-fasted plasma samples derived from the Dana Farber Cancer Institute ALL Consortium; these samples were obtained from 50 ALL patients initial- and post-induction therapy. Plasma metabolites and complex lipids were analyzed by high resolution tandem mass spectrometry and differential mobility tandem mass spectrometry. Data were analyzed using a covariate-adjusted regression model with multiplicity adjustment. Pathway enrichment analysis and co-expression network analysis were performed to identify unique clusters of molecules. RESULTS: More than 1200 metabolites and complex lipids were identified in the total of global metabolomics and lipidomics platforms. Over 20% of those molecules were significantly altered. In the pathway enrichment analysis, lipids, particularly phosphatidylethanolamines (PEs), were identified. Network analysis indicated that the bioactive fatty acids, docosahexaenoic acid (DHA)-containing (22:6) triacylglycerols (TAGs), were decreased in the post-induction therapy. CONCLUSION: Metabolomic profiling in ALL patients revealed a large number of alterations following induction chemotherapy. In particular, lipid metabolism was substantially altered. The changes in metabolites and complex lipids following induction therapy could provide insight into the adverse events experienced by ALL patients.
INTRODUCTION: Acute lymphoblastic leukemia (ALL) is among the most common cancers in children. With improvements in combination chemotherapy regimens, the overall survival has increased to over 90%. However, the current challenge is to mitigate adverse events resulting from the complex therapy. Several chemotherapies intercept cancer metabolism, but little is known about their collective role in altering host metabolism. OBJECTIVES: We profiled the metabolomic changes in plasma of ALL patients initial- and post- induction therapy. METHODS: We exploited a biorepository of non-fasted plasma samples derived from the Dana Farber Cancer Institute ALL Consortium; these samples were obtained from 50 ALL patients initial- and post-induction therapy. Plasma metabolites and complex lipids were analyzed by high resolution tandem mass spectrometry and differential mobility tandem mass spectrometry. Data were analyzed using a covariate-adjusted regression model with multiplicity adjustment. Pathway enrichment analysis and co-expression network analysis were performed to identify unique clusters of molecules. RESULTS: More than 1200 metabolites and complex lipids were identified in the total of global metabolomics and lipidomics platforms. Over 20% of those molecules were significantly altered. In the pathway enrichment analysis, lipids, particularly phosphatidylethanolamines (PEs), were identified. Network analysis indicated that the bioactive fatty acids, docosahexaenoic acid (DHA)-containing (22:6) triacylglycerols (TAGs), were decreased in the post-induction therapy. CONCLUSION: Metabolomic profiling in ALL patients revealed a large number of alterations following induction chemotherapy. In particular, lipid metabolism was substantially altered. The changes in metabolites and complex lipids following induction therapy could provide insight into the adverse events experienced by ALL patients.
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