Summary of Study ST003092
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001921. The data can be accessed directly via it's Project DOI: 10.21228/M8DH8T This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST003092 |
| Study Title | Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data |
| Study Summary | Background Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development. Results Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale, and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed. Conclusions Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites, and also suggest cancer treatment strategies. |
| Institute | Korea Advanced Institute of Science and Technology (KAIST) |
| Last Name | Lee |
| First Name | Sang Mi |
| Address | 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea |
| sandra1996@kaist.ac.kr | |
| Phone | +82-42-350-3955 |
| Submit Date | 2024-02-18 |
| Num Groups | 2 |
| Total Subjects | 38 |
| Analysis Type Detail | LC-MS |
| Release Date | 2024-02-20 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR001921 |
| Project DOI: | doi: 10.21228/M8DH8T |
| Project Title: | Prediction of metabolites associated with somatic mutations in cancers by using genome-scale metabolic models and mutation data |
| Project Summary: | Background Oncometabolites, often generated as a result of a gene mutation, show pro-oncogenic function when abnormally accumulated in cancer cells. Identification of such mutation-associated metabolites will facilitate developing treatment strategies for cancers, but is challenging due to the large number of metabolites in a cell and the presence of multiple genes associated with cancer development. Results Here we report the development of a computational workflow that predicts metabolite-gene-pathway sets. Metabolite-gene-pathway sets present metabolites and metabolic pathways significantly associated with specific somatic mutations in cancers. The computational workflow uses both cancer patient-specific genome-scale metabolic models (GEMs) and mutation data to generate metabolite-gene-pathway sets. A GEM is a computational model that predicts reaction fluxes at a genome scale, and can be constructed in a cell-specific manner by using omics data. The computational workflow is first validated by comparing the resulting metabolite-gene pairs with multi-omics data (i.e., mutation data, RNA-seq data, and metabolome data) from acute myeloid leukemia and renal cell carcinoma samples collected in this study. The computational workflow is further validated by evaluating the metabolite-gene-pathway sets predicted for 18 cancer types, by using RNA-seq data publicly available, in comparison with the reported studies. Therapeutic potential of the resulting metabolite-gene-pathway sets is also discussed. Conclusions Validation of the metabolite-gene-pathway set-predicting computational workflow indicates that a decent number of metabolites and metabolic pathways appear to be significantly associated with specific somatic mutations. The computational workflow and the resulting metabolite-gene-pathway sets will help identify novel oncometabolites, and also suggest cancer treatment strategies. |
| Institute: | Korea Advanced Institute of Science and Technology (KAIST) |
| Last Name: | Lee |
| First Name: | Sang Mi |
| Address: | 291 Daehak-ro, Yuseong-gu, Daejeon, 34141, South Korea |
| Email: | sandra1996@kaist.ac.kr |
| Phone: | +82-42-350-3955 |
| Funding Source: | This study was supported by National Research Foundation of Korea (NRF) (RS-2023-00262527), and by the Project of Promoting Inclusive Growth through Artificial Intelligence and Blockchain Technology and Diffusion of Precision Medicine (1711125351) funded by the Ministry of Science and ICT through KAIST’s Korea Policy Center for the Fourth Industrial Revolution. This study was also supported by the KAIST Cross-Generation Collaborative Lab project, the KAIST Key Research Institute (Interdisciplinary Research Group) Project, and Kwon Oh-Hyun Assistant Professor fund of the KAIST Development Foundation. |
| Publications: | Will be added soon (In press) |
| Contributors: | GaRyoung Lee, Sang Mi Lee, Sungyoung Lee, Chang Wook Jeong, Hyojin Song, Sang Yup Lee, Hongseok Yun, Youngil Koh, Hyun Uk Kim |
Subject:
| Subject ID: | SU003207 |
| Subject Type: | Human |
| Subject Species: | Homo sapiens |
| Taxonomy ID: | 9606 |
| Gender: | Male and female |
Factors:
Subject type: Human; Subject species: Homo sapiens (Factor headings shown in green)
| mb_sample_id | local_sample_id | Sample source | Disease |
|---|---|---|---|
| SA333209 | P14 | Bone marrow | AML |
| SA333210 | P12 | Bone marrow | AML |
| SA333211 | P18 | Bone marrow | AML |
| SA333212 | P11 | Bone marrow | AML |
| SA333213 | P19 | Bone marrow | AML |
| SA333214 | P1 | Bone marrow | AML |
| SA333215 | P20 | Bone marrow | AML |
| SA333216 | P10 | Bone marrow | AML |
| SA333217 | P15 | Bone marrow | AML |
| SA333218 | P3 | Bone marrow | AML |
| SA333219 | P9 | Bone marrow | AML |
| SA333220 | P2 | Bone marrow | AML |
| SA333221 | P5 | Bone marrow | AML |
| SA333222 | P4 | Bone marrow | AML |
| SA333223 | P8 | Bone marrow | AML |
| SA333224 | P6 | Bone marrow | AML |
| SA333225 | P7 | Bone marrow | MDS/AML |
| SA333226 | P36 | Kidney | Renal cell carcinoma |
| SA333227 | P35 | Kidney | Renal cell carcinoma |
| SA333228 | P34 | Kidney | Renal cell carcinoma |
| SA333229 | P37 | Kidney | Renal cell carcinoma |
| SA333230 | P33 | Kidney | Renal cell carcinoma |
| SA333231 | P41 | Kidney | Renal cell carcinoma |
| SA333232 | P32 | Kidney | Renal cell carcinoma |
| SA333233 | P40 | Kidney | Renal cell carcinoma |
| SA333234 | P39 | Kidney | Renal cell carcinoma |
| SA333235 | P38 | Kidney | Renal cell carcinoma |
| SA333236 | P23 | Kidney | Renal cell carcinoma |
| SA333237 | P25 | Kidney | Renal cell carcinoma |
| SA333238 | P24 | Kidney | Renal cell carcinoma |
| SA333239 | P22 | Kidney | Renal cell carcinoma |
| SA333240 | P21 | Kidney | Renal cell carcinoma |
| SA333241 | P26 | Kidney | Renal cell carcinoma |
| SA333242 | P27 | Kidney | Renal cell carcinoma |
| SA333243 | P30 | Kidney | Renal cell carcinoma |
| SA333244 | P29 | Kidney | Renal cell carcinoma |
| SA333245 | P28 | Kidney | Renal cell carcinoma |
| SA333246 | P31 | Kidney | Renal cell carcinoma |
| Showing results 1 to 38 of 38 |
Collection:
| Collection ID: | CO003200 |
| Collection Summary: | Both bone marrow samples and RCC samples (primary kidney cancer samples) were collected at Seoul National University Hospital. Bone marrow samples were obtained from 17 patients diagnosed with acute myeloid leukemia (AML) from 2016 to 2019. RCC samples were obtained from 21 patients diagnosed with RCC from 2016 to 2021. |
| Sample Type: | Bone marrow (Blood); Kidney (Tissue) |
Treatment:
| Treatment ID: | TR003216 |
| Treatment Summary: | Not applicable. |
Sample Preparation:
| Sampleprep ID: | SP003213 |
| Sampleprep Summary: | The AML and RCC samples for metabolome analysis were prepared in accordance with instructions from Human Metabolome Technologies (HMT). |
Combined analysis:
| Analysis ID | AN005060 | AN005061 |
|---|---|---|
| Analysis type | MS | MS |
| Chromatography type | CE | CE |
| Chromatography system | Agilent CE-TOFMS system | Agilent CE-TOFMS system |
| Column | Capillary: Fused silica capillary i.d. 50 μm × 80 cm | Capillary: Fused silica capillary i.d. 50 μm × 80 cm |
| MS Type | ESI | ESI |
| MS instrument type | Other | Other |
| MS instrument name | Agilent CE-TOFMS system | Agilent CE-TOFMS system |
| Ion Mode | UNSPECIFIED | UNSPECIFIED |
| Units | Relative peak area | Relative peak area |
Chromatography:
| Chromatography ID: | CH003821 |
| Chromatography Summary: | The compounds were measured in the Cation and Anion modes of CE-TOFMS based metabolome analysis. |
| Instrument Name: | Agilent CE-TOFMS system |
| Column Name: | Capillary: Fused silica capillary i.d. 50 μm × 80 cm |
| Column Temperature: | 20 ℃ |
| Flow Gradient: | 10 μL/min |
| Flow Rate: | 10 μL/min |
| Sample Injection: | Pressure injection 50 mbar, 10 sec |
| Solvent A: | 50% acetonitrile/50% water; internal standards (20 μM) |
| Solvent B: | - |
| Capillary Voltage: | 4,000 V (Cation Mode); 3,500 V (Anion Mode) |
| Running Buffer: | Cation Buffer Solution (p/n : H3301-1001); Anion Buffer Solution (p/n : I3302-1023) |
| Running Voltage: | 30 kV |
| Sheath Liquid: | HMT Sheath Liquid (p/n : H3301-1020) |
| Washing Buffer: | Cation Buffer Solution (p/n : H3301-1001); Anion Buffer Solution (p/n : I3302-1023) |
| Chromatography Type: | CE |
MS:
| MS ID: | MS004798 |
| Analysis ID: | AN005060 |
| Instrument Name: | Agilent CE-TOFMS system |
| Instrument Type: | Other |
| MS Type: | ESI |
| MS Comments: | For the acute myeloid leukemia (AML) samples, 354 peaks, covering 243 peaks from Cation mode and 111 peaks from Anion mode, were detected, and among them, 185 peaks were annotated on the basis of HMT’s standard library and ‘Known-Unknown’ peak library:Peaks detected in CE-TOFMS analysis were extracted using automatic integration software (MasterHands ver. 2.17.1.11 developed at Keio University) in order to obtain peak information including m/z, migration time (MT), and peak area. The peak area was then converted to relative peak area by the following equation. The peak detection limit was determined based on signal-noise ratio; S/N = 3. Relative Peak Area = Metabolite Peak Area / (Internal Standard Peak Area × Sample Amount) |
| Ion Mode: | UNSPECIFIED |
| MS ID: | MS004799 |
| Analysis ID: | AN005061 |
| Instrument Name: | Agilent CE-TOFMS system |
| Instrument Type: | Other |
| MS Type: | ESI |
| MS Comments: | For the renal cell carcinoma (RCC) samples, 363 peaks, covering 243 peaks from Cation mode and 120 peaks from Anion mode, were detected; the 363 peaks were annotated using the same libraries as the AML samples. Peaks detected in the CE-TOFMS analysis were extracted using automatic integration software (MasterHands ver. 2.19.0.2 developed at Keio University) in order to obtain peak information, which includes m/z, migration time (MT), and peak area. The peak area was then converted to relative peak area by the following equation. The peak detection limit was determined based on signal-noise ratio; S/N = 3. Relative Peak Area = Metabolite Peak Area / (Internal Standard Peak Area × Sample Amount) |
| Ion Mode: | UNSPECIFIED |
