Abstract PO5-13-03: Early Detection of Breast Cancer using Targeted Plasma Metabolomic Profiling
Authors: Haince Jean-Francois , Zhang Lun , Bux Rashid Ahmed , Tappia Paramjit S. , Ramjiawan Bram , Wishart David , Maksymiuk Andrew 
Published: May 2024

Abstract
Background:
Breast cancer (BC) is the second leading cause of cancer death among women. Accordingly, early diagnosis is key to the successful treatment, management, and care of BC. Recent studies confirmed that plasma metabolites could be reliable cancer biomarkers, allowing for the development of a minimally invasive routine blood test that can be used for screening, as well as for monitoring disease evolution in patients. The exact metabolic pathways involved in early BC development remain unclear. Metabolomic profiling of women with BC may help to identify new biomarkers to predict breast cancer long before symptoms appear. The purpose of this study was to validate a plasma metabolomic biomarker panel for an improved risk assessment for early detection BC in 241 patients, and to understand the potential role and the relationship between BC subtypes and hormone receptor status.
Methods:
Our study included a total of 185 plasma samples from women with biopsy-confirmed BC and 56 plasma samples from healthy controls. A targeted, quantitative mass spectrometry (MS)-based metabolomics approach was used to analyze 138 metabolites in plasma samples using a combination of direct injection (DI) MS and reverse-phase high performance liquid chromatography (HPLC) tandem mass spectrometry (MS/MS). The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and hormones receptor status were used to determine optimal biomarker sets. The same biomarkers and regression models were used and assessed on the validation models. The area under the receiver operator characteristic curves (AUROC), sensitivities and specificities at selected cut off points were calculated for each subgroup.
Results:
A large proportion of BC patients were at an early stage, with 98 at stage I (53.0 %), 70 at stage II (37.8%), and 17 at stage III (9.2%). The BC patients were of all subtypes: 138 luminal A (76,2%), 23 luminal B (12.7%), 5 hormone receptor-negative/HER2-positive (2.8%), and 15 triples negative (8.3%). A feature selection was performed on the training set using Partial Least-Squares Discriminant Analysis (PLS-DA), and the top performing metabolites were identified as the most important in discriminating BC from healthy subjects. The impact of age was also investigated. Features with &gt;80% missing values were removed. To predict BC, the best signature comprised 9 variables implicated in fatty acid metabolism, amino acid metabolism, polyamine biosynthesis and related signaling pathways We further developed and validated a logistic regression with AUROC &gt; 0.9 using these metabolites and other clinical data for detecting different stages and subtypes of BC.
Conclusion:
This study identified and validated a simple, high-performing, metabolite-based test for the early detection of BC. After confirmation in other independent cohort studies, our findings could provide the foundations for the development of a blood-based routine test for women at the highest risk for BC that is cost-effective, accurate and reliable. In addition, this approach could be used to complement other modalities especially for BC patients with dense breasts.
Citation Format: Jean-Francois Haince, Lun Zhang, Rashid Ahmed Bux, Paramjit S. Tappia, Bram Ramjiawan, David Wishart, Andrew Maksymiuk. Early Detection of Breast Cancer using Targeted Plasma Metabolomic Profiling [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-13-03.