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Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression
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Department of Human and Clinical Anatomy, Sultan Qaboos University, 35, Muscat 123, Oman
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Department of Physiology and Developmental Biology, Brigham Young University, Provo, UT 84602, USA
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Department of Biology, Brigham Young University, Provo, UT 84602, USA
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Department of Biomedical Informatics, University of Utah, Salt Lake City, UT 84112, USA
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Department of Chemistry and Biochemistry (Emeritus), Brigham Young University, Provo, UT 84606, USA
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Magellan Bioanalytics, Inc., Pleasant Grove, UT 84062, USA
*
Author to whom correspondence should be addressed.
Cancers 2024, 16(13), 2365; https://doi.org/10.3390/cancers16132365
Submission received: 7 May 2024
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Revised: 17 June 2024
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Accepted: 21 June 2024
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Published: 27 June 2024
(This article belongs to the Special Issue Breast Cancer Metastasis: Novel Insights into Molecular Mechanisms and Treatments)
Simple Summary
This manuscript details an investigation into the potential of mass spectrometry-based serum biomarker discovery for differentiating between early and late-stage breast cancer patients. The study employs a ‘feature-first’ approach, focusing on MS1 scan spectra differences and applies both traditional and computational methods to analyze these differences. The traditional method involves manual data alignment and validation, while the computational approach utilizes machine learning to assess biomarker relevance and validate predictive models. A key finding includes the identification of a peptide fragment of fibrinogen α chain as a biomarker exclusive to early-stage breast cancer.
Abstract
Here, we assess how the differential expression of low molecular weight serum peptides might predict breast cancer progression with high confidence. We apply an LC/MS-MS-based, unbiased ‘omics’ analysis of serum samples from breast cancer patients to identify molecules that are differentially expressed in stage I and III breast cancer. Results were generated using standard and machine learning-based analytical workflows. With standard workflow, a discovery study yielded 65 circulating biomarker candidates with statistically significant differential expression. A second study confirmed the differential expression of a subset of these markers. Models based on combinations of multiple biomarkers were generated using an exploratory algorithm designed to generate greater diagnostic power and accuracy than any individual markers. Individual biomarkers and the more complex multi-marker models were then tested in a blinded validation study. The multi-marker models retained their predictive power in the validation study, the best of which attained an AUC of 0.84, with a sensitivity of 43% and a specificity of 88%. One of the markers with m/z 761.38, which was downregulated, was identified as a fibrinogen alpha chain. Machine learning-based analysis yielded a classifier that correctly categorizes every subject in the study and demonstrates parameter constraints required for high confidence in classifier output. These results suggest that serum peptide biomarker models could be optimized to assess breast cancer stage in a clinical setting.
Keywords:
breast cancer; metastasis; cancer progression; biomarker; serum; peptidomics; proteomics; machine learning
