#METABOLOMICS WORKBENCH sandra1996_20240218_210811 DATATRACK_ID:4660 STUDY_ID:ST003092 ANALYSIS_ID:AN005060 PROJECT_ID:PR001921 VERSION 1 CREATED_ON February 20, 2024, 8:57 am #PROJECT PR:PROJECT_TITLE Prediction of metabolites associated with somatic mutations in cancers by using PR:PROJECT_TITLE genome-scale metabolic models and mutation data PR:PROJECT_SUMMARY Background Oncometabolites, often generated as a result of a gene mutation, show PR:PROJECT_SUMMARY pro-oncogenic function when abnormally accumulated in cancer cells. PR:PROJECT_SUMMARY Identification of such mutation-associated metabolites will facilitate PR:PROJECT_SUMMARY developing treatment strategies for cancers, but is challenging due to the large PR:PROJECT_SUMMARY number of metabolites in a cell and the presence of multiple genes associated PR:PROJECT_SUMMARY with cancer development. Results Here we report the development of a PR:PROJECT_SUMMARY computational workflow that predicts metabolite-gene-pathway sets. PR:PROJECT_SUMMARY Metabolite-gene-pathway sets present metabolites and metabolic pathways PR:PROJECT_SUMMARY significantly associated with specific somatic mutations in cancers. The PR:PROJECT_SUMMARY computational workflow uses both cancer patient-specific genome-scale metabolic PR:PROJECT_SUMMARY models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM PR:PROJECT_SUMMARY is a computational model that predicts reaction fluxes at a genome scale, and PR:PROJECT_SUMMARY can be constructed in a cell-specific manner by using omics data. The PR:PROJECT_SUMMARY computational workflow is first validated by comparing the resulting PR:PROJECT_SUMMARY metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, PR:PROJECT_SUMMARY and metabolome data) from acute myeloid leukemia and renal cell carcinoma PR:PROJECT_SUMMARY samples collected in this study. The computational workflow is further validated PR:PROJECT_SUMMARY by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by PR:PROJECT_SUMMARY using RNA-seq data publicly available, in comparison with the reported studies. PR:PROJECT_SUMMARY Therapeutic potential of the resulting metabolite-gene-pathway sets is also PR:PROJECT_SUMMARY discussed. Conclusions Validation of the metabolite-gene-pathway set-predicting PR:PROJECT_SUMMARY computational workflow indicates that a decent number of metabolites and PR:PROJECT_SUMMARY metabolic pathways appear to be significantly associated with specific somatic PR:PROJECT_SUMMARY mutations. The computational workflow and the resulting metabolite-gene-pathway PR:PROJECT_SUMMARY sets will help identify novel oncometabolites, and also suggest cancer treatment PR:PROJECT_SUMMARY strategies. PR:INSTITUTE Korea Advanced Institute of Science and Technology (KAIST) PR:LAST_NAME Lee PR:FIRST_NAME Sang Mi PR:ADDRESS 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea PR:EMAIL sandra1996@kaist.ac.kr PR:PHONE +82-42-350-3955 PR:FUNDING_SOURCE This study was supported by National Research Foundation of Korea (NRF) PR:FUNDING_SOURCE (RS-2023-00262527), and by the Project of Promoting Inclusive Growth through PR:FUNDING_SOURCE Artificial Intelligence and Blockchain Technology and Diffusion of Precision PR:FUNDING_SOURCE Medicine (1711125351) funded by the Ministry of Science and ICT through PR:FUNDING_SOURCE KAIST’s Korea Policy Center for the Fourth Industrial Revolution. This study PR:FUNDING_SOURCE was also supported by the KAIST Cross-Generation Collaborative Lab project, the PR:FUNDING_SOURCE KAIST Key Research Institute (Interdisciplinary Research Group) Project, and PR:FUNDING_SOURCE Kwon Oh-Hyun Assistant Professor fund of the KAIST Development Foundation. PR:PUBLICATIONS Will be added soon (In press) PR:CONTRIBUTORS GaRyoung Lee, Sang Mi Lee, Sungyoung Lee, Chang Wook Jeong, Hyojin Song, Sang PR:CONTRIBUTORS Yup Lee, Hongseok Yun, Youngil Koh, Hyun Uk Kim #STUDY ST:STUDY_TITLE Prediction of metabolites associated with somatic mutations in cancers by using ST:STUDY_TITLE genome-scale metabolic models and mutation data ST:STUDY_SUMMARY Background Oncometabolites, often generated as a result of a gene mutation, show ST:STUDY_SUMMARY pro-oncogenic function when abnormally accumulated in cancer cells. ST:STUDY_SUMMARY Identification of such mutation-associated metabolites will facilitate ST:STUDY_SUMMARY developing treatment strategies for cancers, but is challenging due to the large ST:STUDY_SUMMARY number of metabolites in a cell and the presence of multiple genes associated ST:STUDY_SUMMARY with cancer development. Results Here we report the development of a ST:STUDY_SUMMARY computational workflow that predicts metabolite-gene-pathway sets. ST:STUDY_SUMMARY Metabolite-gene-pathway sets present metabolites and metabolic pathways ST:STUDY_SUMMARY significantly associated with specific somatic mutations in cancers. The ST:STUDY_SUMMARY computational workflow uses both cancer patient-specific genome-scale metabolic ST:STUDY_SUMMARY models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM ST:STUDY_SUMMARY is a computational model that predicts reaction fluxes at a genome scale, and ST:STUDY_SUMMARY can be constructed in a cell-specific manner by using omics data. The ST:STUDY_SUMMARY computational workflow is first validated by comparing the resulting ST:STUDY_SUMMARY metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, ST:STUDY_SUMMARY and metabolome data) from acute myeloid leukemia and renal cell carcinoma ST:STUDY_SUMMARY samples collected in this study. The computational workflow is further validated ST:STUDY_SUMMARY by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by ST:STUDY_SUMMARY using RNA-seq data publicly available, in comparison with the reported studies. ST:STUDY_SUMMARY Therapeutic potential of the resulting metabolite-gene-pathway sets is also ST:STUDY_SUMMARY discussed. Conclusions Validation of the metabolite-gene-pathway set-predicting ST:STUDY_SUMMARY computational workflow indicates that a decent number of metabolites and ST:STUDY_SUMMARY metabolic pathways appear to be significantly associated with specific somatic ST:STUDY_SUMMARY mutations. The computational workflow and the resulting metabolite-gene-pathway ST:STUDY_SUMMARY sets will help identify novel oncometabolites, and also suggest cancer treatment ST:STUDY_SUMMARY strategies. ST:INSTITUTE Korea Advanced Institute of Science and Technology (KAIST) ST:LAST_NAME Lee ST:FIRST_NAME Sang Mi ST:ADDRESS 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea ST:EMAIL sandra1996@kaist.ac.kr ST:NUM_GROUPS 2 ST:TOTAL_SUBJECTS 38 ST:PHONE +82-42-350-3955 #SUBJECT SU:SUBJECT_TYPE Human SU:SUBJECT_SPECIES Homo sapiens SU:TAXONOMY_ID 9606 SU:GENDER Male and female #FACTORS #SUBJECT_SAMPLE_FACTORS: SUBJECT(optional)[tab]SAMPLE[tab]FACTORS(NAME:VALUE pairs separated by |)[tab]Raw file names and additional sample data SUBJECT_SAMPLE_FACTORS - P1 Sample source:Bone marrow | Disease:AML Age at diagnosis=70; Sex=M SUBJECT_SAMPLE_FACTORS - P2 Sample source:Bone marrow | Disease:AML Age at diagnosis=55; Sex=F SUBJECT_SAMPLE_FACTORS - P3 Sample source:Bone marrow | Disease:AML Age at diagnosis=67; Sex=M SUBJECT_SAMPLE_FACTORS - P4 Sample source:Bone marrow | Disease:AML Age at diagnosis=64; Sex=F SUBJECT_SAMPLE_FACTORS - P5 Sample source:Bone marrow | Disease:AML Age at diagnosis=49; Sex=F SUBJECT_SAMPLE_FACTORS - P6 Sample source:Bone marrow | Disease:AML Age at diagnosis=78; Sex=F SUBJECT_SAMPLE_FACTORS - P7 Sample source:Bone marrow | Disease:MDS/AML Age at diagnosis=59; Sex=M SUBJECT_SAMPLE_FACTORS - P8 Sample source:Bone marrow | Disease:AML Age at diagnosis=52; Sex=M SUBJECT_SAMPLE_FACTORS - P9 Sample source:Bone marrow | Disease:AML Age at diagnosis=66; Sex=M SUBJECT_SAMPLE_FACTORS - P10 Sample source:Bone marrow | Disease:AML Age at diagnosis=51; Sex=F SUBJECT_SAMPLE_FACTORS - P11 Sample source:Bone marrow | Disease:AML Age at diagnosis=68; Sex=F SUBJECT_SAMPLE_FACTORS - P12 Sample source:Bone marrow | Disease:AML Age at diagnosis=84; Sex=F SUBJECT_SAMPLE_FACTORS - P14 Sample source:Bone marrow | Disease:AML Age at diagnosis=72; Sex=M SUBJECT_SAMPLE_FACTORS - P15 Sample source:Bone marrow | Disease:AML Age at diagnosis=55; Sex=F SUBJECT_SAMPLE_FACTORS - P18 Sample source:Bone marrow | Disease:AML Age at diagnosis=65; Sex=M SUBJECT_SAMPLE_FACTORS - P19 Sample source:Bone marrow | Disease:AML Age at diagnosis=68; Sex=F SUBJECT_SAMPLE_FACTORS - P20 Sample source:Bone marrow | Disease:AML Age at diagnosis=69; Sex=F SUBJECT_SAMPLE_FACTORS - P21 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=83; Sex=F SUBJECT_SAMPLE_FACTORS - P22 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=68; Sex=M SUBJECT_SAMPLE_FACTORS - P23 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=47; Sex=M SUBJECT_SAMPLE_FACTORS - P24 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=60; Sex=F SUBJECT_SAMPLE_FACTORS - P25 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=66; Sex=M SUBJECT_SAMPLE_FACTORS - P26 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=45; Sex=M SUBJECT_SAMPLE_FACTORS - P27 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=68; Sex=M SUBJECT_SAMPLE_FACTORS - P28 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=55; Sex=M SUBJECT_SAMPLE_FACTORS - P29 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=80; Sex=M SUBJECT_SAMPLE_FACTORS - P30 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=75; Sex=M SUBJECT_SAMPLE_FACTORS - P31 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=70; Sex=M SUBJECT_SAMPLE_FACTORS - P32 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=61; Sex=F SUBJECT_SAMPLE_FACTORS - P33 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=71; Sex=M SUBJECT_SAMPLE_FACTORS - P34 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=67; Sex=F SUBJECT_SAMPLE_FACTORS - P35 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=50; Sex=M SUBJECT_SAMPLE_FACTORS - P36 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=60; Sex=M SUBJECT_SAMPLE_FACTORS - P37 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=76; Sex=M SUBJECT_SAMPLE_FACTORS - P38 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=60; Sex=F SUBJECT_SAMPLE_FACTORS - P39 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=55; Sex=M SUBJECT_SAMPLE_FACTORS - P40 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=70; Sex=M SUBJECT_SAMPLE_FACTORS - P41 Sample source:Kidney | Disease:Renal cell carcinoma Age at diagnosis=61; Sex=F #COLLECTION CO:COLLECTION_SUMMARY Both bone marrow samples and RCC samples (primary kidney cancer samples) were CO:COLLECTION_SUMMARY collected at Seoul National University Hospital. Bone marrow samples were CO:COLLECTION_SUMMARY obtained from 17 patients diagnosed with acute myeloid leukemia (AML) from 2016 CO:COLLECTION_SUMMARY to 2019. RCC samples were obtained from 21 patients diagnosed with RCC from 2016 CO:COLLECTION_SUMMARY to 2021. CO:SAMPLE_TYPE Bone marrow (Blood); Kidney (Tissue) #TREATMENT TR:TREATMENT_SUMMARY Not applicable. #SAMPLEPREP SP:SAMPLEPREP_SUMMARY The AML and RCC samples for metabolome analysis were prepared in accordance with SP:SAMPLEPREP_SUMMARY instructions from Human Metabolome Technologies (HMT). #CHROMATOGRAPHY CH:CHROMATOGRAPHY_SUMMARY The compounds were measured in the Cation and Anion modes of CE-TOFMS based CH:CHROMATOGRAPHY_SUMMARY metabolome analysis. CH:CHROMATOGRAPHY_TYPE CE CH:INSTRUMENT_NAME Agilent CE-TOFMS system CH:COLUMN_NAME Capillary: Fused silica capillary i.d. 50 μm × 80 cm CH:SOLVENT_A 50% acetonitrile/50% water; internal standards (20 μM) CH:SOLVENT_B - CH:FLOW_GRADIENT 10 μL/min CH:FLOW_RATE 10 μL/min CH:COLUMN_TEMPERATURE 20 ℃ CH:SAMPLE_INJECTION Pressure injection 50 mbar, 10 sec CH:CAPILLARY_VOLTAGE 4,000 V (Cation Mode); 3,500 V (Anion Mode) CH:RUNNING_BUFFER Cation Buffer Solution (p/n : H3301-1001); Anion Buffer Solution (p/n : CH:RUNNING_BUFFER I3302-1023) CH:RUNNING_VOLTAGE 30 kV CH:SHEATH_LIQUID HMT Sheath Liquid (p/n : H3301-1020) CH:WASHING_BUFFER Cation Buffer Solution (p/n : H3301-1001); Anion Buffer Solution (p/n : CH:WASHING_BUFFER I3302-1023) #ANALYSIS AN:ANALYSIS_TYPE MS #MS MS:INSTRUMENT_NAME Agilent CE-TOFMS system MS:INSTRUMENT_TYPE Other MS:MS_TYPE ESI MS:ION_MODE UNSPECIFIED MS:MS_COMMENTS For the acute myeloid leukemia (AML) samples, 354 peaks, covering 243 peaks from MS:MS_COMMENTS Cation mode and 111 peaks from Anion mode, were detected, and among them, 185 MS:MS_COMMENTS peaks were annotated on the basis of HMT’s standard library and MS:MS_COMMENTS ‘Known-Unknown’ peak library:Peaks detected in CE-TOFMS analysis were MS:MS_COMMENTS extracted using automatic integration software (MasterHands ver. 2.17.1.11 MS:MS_COMMENTS developed at Keio University) in order to obtain peak information including m/z, MS:MS_COMMENTS migration time (MT), and peak area. The peak area was then converted to relative MS:MS_COMMENTS peak area by the following equation. The peak detection limit was determined MS:MS_COMMENTS based on signal-noise ratio; S/N = 3. Relative Peak Area = Metabolite Peak Area MS:MS_COMMENTS / (Internal Standard Peak Area × Sample Amount) MS:MS_RESULTS_FILE ST003092_AN005060_Results.txt UNITS:Relative peak area Has m/z:Yes Has RT:Yes RT units:Minutes #END