Discovery of Plasma Lipids as Potential Biomarkers Distinguishing Breast Cancer Patients from Healthy Controls

The development of a sensitive and specific blood test for the early detection of breast cancer is crucial to improve screening and patient outcomes. Existing methods, such as mammography, have limitations, necessitating the exploration of alternative approaches, including circulating factors. Using 598 prospectively collected blood samples, a multivariate plasma-derived lipid biomarker signature was developed that can distinguish healthy control individuals from those with breast cancer. Liquid chromatography with high-resolution and tandem mass spectrometry (LC-MS/MS) was employed to identify lipids for both extracellular vesicle-derived and plasma-derived signatures. For each dataset, we identified a signature of 20 lipids using a robust, statistically rigorous feature selection algorithm based on random forest feature importance applied to cross-validated training samples. Using an ensemble of machine learning models, the plasma 20-lipid signature generated an area under the curve (AUC) of 0.95, sensitivity of 0.91, and specificity of 0.79. The results from this study indicate that lipids extracted from plasma can be used as target analytes in the development of assays to detect the presence of early-stage breast cancer.

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Publication Details

Journal
International Journal of Molecular Sciences
Published
2024-10-28
DOI
https://doi.org/10.3390/ijms252111559
Citations
4
Primary Topic
Metabolomics and Mass Spectrometry Studies
Type
article
Field-Weighted Citation Impact
0.94

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article

Discovery of Plasma Lipids as Potential Biomarkers Distinguishing Breast Cancer Patients from Healthy Controls

Mark David, David A. Peake, John G.R. Hurrell, Desmond Li et al.
4 citations
International Journal of Molecular Sciences
Metabolomics and Mass Spectrometry Studies
0.94
article

Discovery of Plasma Lipids as Potential Biomarkers Distinguishing Breast Cancer Patients from Healthy Controls

Mark David, David A. Peake, John G.R. Hurrell, Desmond Li, Kerry Heffernan, Cheka Kehelpannala, Simon Preston, Forrest Koch, Dana Pascovici, G. Bruce Mann, Amani Batarseh, David Speakman, Fatemeh Vafaee
article en
4 citations

Abstract

The development of a sensitive and specific blood test for the early detection of breast cancer is crucial to improve screening and patient outcomes. Existing methods, such as mammography, have limitations, necessitating the exploration of alternative approaches, including circulating factors. Using 598 prospectively collected blood samples, a multivariate plasma-derived lipid biomarker signature was developed that can distinguish healthy control individuals from those with breast cancer. Liquid chromatography with high-resolution and tandem mass spectrometry (LC-MS/MS) was employed to identify lipids for both extracellular vesicle-derived and plasma-derived signatures. For each dataset, we identified a signature of 20 lipids using a robust, statistically rigorous feature selection algorithm based on random forest feature importance applied to cross-validated training samples. Using an ensemble of machine learning models, the plasma 20-lipid signature generated an area under the curve (AUC) of 0.95, sensitivity of 0.91, and specificity of 0.79. The results from this study indicate that lipids extracted from plasma can be used as target analytes in the development of assays to detect the presence of early-stage breast cancer.

International Journal of Molecular SciencesVol. 25(21)
The Royal Melbourne Hospital (AU), The University of Melbourne (AU), Peter MacCallum Cancer Centre (AU), UNSW Sydney (AU), MoTeC (Australia) (AU), Breastscreen Victoria (AU)
Ochsner Health
Good health and well-being
Openalex Percentile: Top 25%
Metabolomics and Mass Spectrometry Studies
0.94
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