# Differential Serum Peptidomics Reveal Multi-Marker Models That Predict Breast Cancer Progression

## TL;DR

This study uses serum peptidomics to identify biomarkers that can predict breast cancer progression, showing that multi-marker models perform better than single markers.

## Contribution

The study introduces a 'feature-first' approach combining traditional and machine learning methods to identify serum peptide biomarkers for breast cancer staging.

## Key findings

- A peptide fragment of fibrinogen α chain was identified as a biomarker exclusive to early-stage breast cancer.
- Multi-marker models achieved an AUC of 0.84 with 43% sensitivity and 88% specificity in predicting breast cancer progression.
- Machine learning-based analysis correctly categorized all study subjects with high confidence.

## Abstract

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.

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.