Summary of study ST001269

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 PR000854. The data can be accessed directly via it's Project DOI: 10.21228/M8998T This work is supported by NIH grant, U2C- DK119886.

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Study IDST001269
Study TitleExosomal lipids for classifying early and late stage non-small cell lung cancer
Study TypeBiomarker Discovery
Study SummaryLung cancer is the leading cause of cancer deaths in the United States. Patients with early stage lung cancer have the best prognosis with surgical removal of the tumor, but the disease is often asymptomatic until advanced disease develops, and there are no effective blood-based screening methods for early detection of lung cancer in at-risk populations. We have explored the lipid profiles of blood plasma exosomes using ultra high-resolution Fourier transform mass spectrometry (UHR-FTMS) for early detection of the prevalent non-small cell lung cancers (NSCLC). Exosomes are nanovehicles released by various cells and tumor tissues to elicit important biofunctions such as immune modulation and tumor development. Plasma exosomal lipid profiles were acquired from 39 normal and 91 NSCLC subjects (44 early stage and 47 late stage). We have applied two multivariate statistical methods, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO) to classify the data. For the RF method, the Gini importance of the assigned lipids was calculated to select 16 lipids with top importance. Using the LASSO method, 7 features were selected based on a grouped LASSO penalty. The Area Under the Receiver Operating Characteristic curve for early and late stage cancer versus normal subjects using the selected lipid features was 0.85 and 0.88 for RF and 0.79 and 0.77 for LASSO, respectively. These results show the value of RF and LASSO for metabolomics data-based biomarker development, which provide robust an independent classifiers with sparse data sets. Application of LASSO and Random Forests identifies lipid features that successfully distinguish early stage lung cancer patient from healthy individuals.
Institute
University of Kentucky
DepartmentCenter for Environmental and Systems Biochemistry
Last NameThompson
First NamePatrick
Address789 South Limestone, Lexington, Kentucky, 40536, USA
Emailptth222@uky.edu, rick.higashi@uky.edu
Phone8592181027
Submit Date2019-10-17
Total Subjects95
Publicationshttps://doi.org/10.1016/j.aca.2018.02.051
Raw Data AvailableYes
Raw Data File Type(s).raw
Analysis Type DetailLC-MS
Release Date2019-10-11
Release Version1
Patrick Thompson Patrick Thompson
https://dx.doi.org/10.21228/M8998T
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Project:

Project ID:PR000854
Project DOI:doi: 10.21228/M8998T
Project Title:Exosomal lipids for classifying early and late stage non-small cell lung cancer
Project Type:Biomarker Discovery
Project Summary:Lung cancer is the leading cause of cancer deaths in the United States. Patients with early stage lung cancer have the best prognosis with surgical removal of the tumor, but the disease is often asymptomatic until advanced disease develops, and there are no effective blood-based screening methods for early detection of lung cancer in at-risk populations. We have explored the lipid profiles of blood plasma exosomes using ultra high-resolution Fourier transform mass spectrometry (UHR-FTMS) for early detection of the prevalent non-small cell lung cancers (NSCLC). Exosomes are nanovehicles released by various cells and tumor tissues to elicit important biofunctions such as immune modulation and tumor development. Plasma exosomal lipid profiles were acquired from 39 normal and 91 NSCLC subjects (44 early stage and 47 late stage). We have applied two multivariate statistical methods, Random Forest (RF) and Least Absolute Shrinkage and Selection Operator (LASSO) to classify the data. For the RF method, the Gini importance of the assigned lipids was calculated to select 16 lipids with top importance. Using the LASSO method, 7 features were selected based on a grouped LASSO penalty. The Area Under the Receiver Operating Characteristic curve for early and late stage cancer versus normal subjects using the selected lipid features was 0.85 and 0.88 for RF and 0.79 and 0.77 for LASSO, respectively. These results show the value of RF and LASSO for metabolomics data-based biomarker development, which provide robust an independent classifiers with sparse data sets. Application of LASSO and Random Forests identifies lipid features that successfully distinguish early stage lung cancer patient from healthy individuals.
Institute:University of Kentucky
Department:Center for Environmental and Systems Biochemistry
Last Name:Thompson
First Name:Patrick
Address:789 South Limestone, Lexington, Kentucky, 40536, USA
Email:ptth222@uky.edu; rick.higashi@uky.edu
Phone:8592181027
Funding Source:NCI
Publications:https://doi.org/10.1016/j.aca.2018.02.051
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