Z Bannur1, L K Teh2, T Hennesy3, W R W Rosli4, N Mohamad5, A Nasir5, R Ankathil6, Z A Zakaria7, A Baba8, M Z Salleh9. 1. Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA Malaysia, Selangor, Malaysia. 2. Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA Malaysia, Selangor, Malaysia; Faculty of Pharmacy, Universiti Teknologi MARA Malaysia, Selangor, Malaysia. Electronic address: tehlaykek@gmail.com. 3. Agilent Technologies, 1 Yishun Ave 7, 768923 Singapore. 4. Faculty of Pharmacy, Universiti Teknologi MARA Malaysia, Selangor, Malaysia. 5. Department of Paediatrics, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia. 6. Human Genome Centre, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia. 7. Department of Biomedical Science, Faculty of Medicine and Health Sciences, University Putra Malasia, 43400 UPM Serdang, Selangor, Malaysia. 8. Department of Medicine, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia. 9. Integrative Pharmacogenomics Institute (iPROMISE), Universiti Teknologi MARA Malaysia, Selangor, Malaysia; Faculty of Pharmacy, Universiti Teknologi MARA Malaysia, Selangor, Malaysia. Electronic address: zakisalleh.mzs@gmail.com.
Abstract
BACKGROUND: Acute lymphoblastic leukaemia (ALL) has posed challenges to the clinician due to variable patients' responses and late diagnosis. With the advance in metabolomics, early detection and personalised treatment are possible. METHODS: Metabolomic profile of 21 ALL patients treated with 6-mercaptopurine and 10 healthy volunteers were analysed using liquid chromatography/mass spectrometry quadrupole-time of flight (LC/MS Q-TOF). Principal components analysis (PCA), recursive analysis, clustering and pathway analysis were performed using MassHunter Qualitative and Mass Profiler Professional (MPP) software. RESULTS: Several metabolites were found to be expressed differently in patients treated with 6-mercaptopurine. Interestingly, 13 metabolites were significantly differently expressed [p-value <0.01 (unpaired t-test) and 2-fold change] in 19% of the patients who had relapses in their treatment. Down-regulated metabolites in relapsed patients were 1-tetrahexanoyl-2-(8-[3]-ladderane-octanyl)-sn-GPEtn, GPEtn (18:1(9Z)/0:0), GPCho(O-6:0/O-6:0), GPCho(O-2:0/O-1:0), methyl 8-[2-(2-formyl-vinyl)-3-hydroxy-5-oxo-cyclopentyl]-octanoate and plasma free amino acids (PFAA). Characterizing the subjects according to their ITPA 94C>A genotypes reveal differential expression of metabolites. CONCLUSIONS: Our research contributes to identification of metabolites that could be used to monitor disease progress of patients and allow targeted therapy for ALL at different stages, especially in preventing complication of relapse.
BACKGROUND:Acute lymphoblastic leukaemia (ALL) has posed challenges to the clinician due to variable patients' responses and late diagnosis. With the advance in metabolomics, early detection and personalised treatment are possible. METHODS: Metabolomic profile of 21 ALL patients treated with 6-mercaptopurine and 10 healthy volunteers were analysed using liquid chromatography/mass spectrometry quadrupole-time of flight (LC/MS Q-TOF). Principal components analysis (PCA), recursive analysis, clustering and pathway analysis were performed using MassHunter Qualitative and Mass Profiler Professional (MPP) software. RESULTS: Several metabolites were found to be expressed differently in patients treated with 6-mercaptopurine. Interestingly, 13 metabolites were significantly differently expressed [p-value <0.01 (unpaired t-test) and 2-fold change] in 19% of the patients who had relapses in their treatment. Down-regulated metabolites in relapsed patients were 1-tetrahexanoyl-2-(8-[3]-ladderane-octanyl)-sn-GPEtn, GPEtn (18:1(9Z)/0:0), GPCho(O-6:0/O-6:0), GPCho(O-2:0/O-1:0), methyl 8-[2-(2-formyl-vinyl)-3-hydroxy-5-oxo-cyclopentyl]-octanoate and plasma free amino acids (PFAA). Characterizing the subjects according to their ITPA 94C>A genotypes reveal differential expression of metabolites. CONCLUSIONS: Our research contributes to identification of metabolites that could be used to monitor disease progress of patients and allow targeted therapy for ALL at different stages, especially in preventing complication of relapse.
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