Discovery of Plasma Lipids as Potential Biomarkers Distinguishing Breast Cancer Patients from Healthy Controls
- Desmond Li 1, Kerry Heffernan 1, Forrest C Koch 2, David A Peake 1, Dana Pascovici 3, Mark David 1, Cheka Kehelpannala 1, G Bruce Mann 4, David Speakman 5,6, John Hurrell 1, Simon Preston 1, Fatemeh Vafaee 2,7, Amani Batarseh 1
- Desmond Li 1, Kerry Heffernan 1, Forrest C Koch 2
- 1BCAL Diagnostics Ltd., Sydney, NSW 2000, Australia.
- 2OmniOmics.ai Pty Ltd., Pagewood, NSW 2035, Australia.
- 3InsightStats, Croydon Park, NSW 2133, Australia.
- 4Department of Surgery, The Royal Melbourne Hospital, Parkville, VIC 3050, Australia.
- 5The Peter MacCallum Cancer Centre, Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC 3010, Australia.
- 6BreastScreen Victoria, Carlton, VIC 3053, Australia.
- 7School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW), Sydney, NSW 2052, Australia.
- 0BCAL Diagnostics Ltd., Sydney, NSW 2000, Australia.
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View abstract on PubMed
Summary
This summary is machine-generated.A new blood test using a 20-lipid signature from plasma shows promise for early breast cancer detection. This approach achieved high accuracy, potentially improving cancer screening and patient outcomes.
Area Of Science
- Biochemistry
- Oncology
- Analytical Chemistry
Background
- Early breast cancer detection is vital for improving patient outcomes.
- Mammography has limitations, driving the need for alternative screening methods.
- Circulating factors in blood offer potential biomarkers for cancer detection.
Purpose Of The Study
- To develop a sensitive and specific blood test for early breast cancer detection.
- To identify a plasma-derived lipid biomarker signature for distinguishing breast cancer patients from healthy individuals.
Main Methods
- Utilized liquid chromatography with high-resolution and tandem mass spectrometry (LC-MS/MS) to identify plasma lipids.
- Developed a 20-lipid signature using a random forest feature selection algorithm.
- Employed ensemble machine learning models for performance evaluation.
Main Results
- The plasma 20-lipid signature demonstrated high diagnostic accuracy.
- Achieved an area under the curve (AUC) of 0.95.
- Reported a sensitivity of 0.91 and specificity of 0.79 for breast cancer detection.
Conclusions
- Plasma-derived lipids can serve as effective biomarkers for early-stage breast cancer detection.
- The developed lipid signature shows potential for a novel blood-based breast cancer assay.
- Further development of this assay could enhance breast cancer screening strategies.
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