Summary of Study ST001527

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 PR001028. The data can be accessed directly via it's Project DOI: 10.21228/M8T99V This work is supported by NIH grant, U2C- DK119886.

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Study IDST001527
Study TitleLung cancer metabolomics analysis
Study SummaryThis study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies. Prospectively collected tissue samples before initial treatment were evaluated with high-resolution 2DLC-MS/MS and 13C-glucose enrichment, and the data were comprehensively analyzed with machine learning techniques. Patients were categorized as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD). Four major types of learning methods (partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), artificial neural networks, and random forests) were applied to differentiate between positive (DC and CR/PR) and poor (PD and SD/PD) responses, and between stage I/II/III and stage IV disease. Models were trained with forward feature selection based on variable importance and tested on validation subsets.
Institute
University of Louisville
DepartmentBioengineering
Last NameFrieboes
First NameHermann
AddressLutz Hall 419
Emailhbfrie01@louisville.edu
Phone502-852-3302
Submit Date2020-09-16
Raw Data AvailableYes
Raw Data File Type(s)raw(Thermo)
Analysis Type DetailLC-MS
Release Date2022-08-01
Release Version1
Hermann Frieboes Hermann Frieboes
https://dx.doi.org/10.21228/M8T99V
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR001028
Project DOI:doi: 10.21228/M8T99V
Project Title:Lung cancer metabolomics analysis
Project Type:MS untargeted analysis
Project Summary:This study explored models predictive of staging and chemotherapy response based on metabolomic analysis of fresh, patient-derived non-small cell lung cancer (NSCLC) core biopsies. Prospectively collected tissue samples before initial treatment were evaluated with high-resolution 2DLC-MS/MS and the data were comprehensively analyzed with machine learning techniques. Patients were categorized as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD). Four major types of learning methods (partial least squares discriminant analysis (PLS-DA), support vector machines (SVM), artificial neural networks, and random forests) were applied to differentiate between positive (DC and CR/PR) and poor (PD and SD/PD) responses, and between stage I/II/III and stage IV disease. Models were trained with forward feature selection based on variable importance and tested on validation subsets.
Institute:University of Louisville
Department:Bioengineering
Last Name:Frieboes
First Name:Hermann
Address:Lutz Hall 419
Email:hbfrie01@louisville.edu
Phone:502-852-3302
Funding Source:NIH/NCI R15CA203605
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