Breast
cancer (BC) is a common cause of morbidity and mortality,
particularly in women. Moreover, the discovery of diagnostic biomarkers
for early BC remains a challenging task. Previously, we [
Jasbi
Jasbi
J. Chromatogr. B.201911052637] demonstrated
a targeted metabolic profiling approach capable of identifying metabolite
marker candidates that could enable highly sensitive and specific
detection of BC. However, the coverage of this targeted method was
limited and exhibited suboptimal classification of early BC (EBC).
To expand the metabolome coverage and articulate a better panel of
metabolites or mass spectral features for classification of EBC, we
evaluated untargeted liquid chromatography quadrupole time-of-flight
mass spectrometry (LC-QTOF-MS) data, both individually as well as
in conjunction with previously published targeted LC-triple quadruple
(QQQ)-MS data. Variable importance in projection scores were used
to refine the biomarker panel, whereas orthogonal partial least squares-discriminant
analysis was used to operationalize the enhanced biomarker panel for
early diagnosis. In this approach, 33 altered metabolites/features
were detected by LC-QTOF-MS from 124 BC patients and 86 healthy controls.
For EBC diagnosis, significance testing and analysis of the area under
receiver operating characteristic (AUROC) curve identified six metabolites/features
[ethyl (R)-3-hydroxyhexanoate; caprylic acid; hypoxanthine;
and m/z 358.0018, 354.0053, and 356.0037] with p < 0.05 and AUROC > 0.7. These metabolites informed
the construction of EBC diagnostic models; evaluation of model performance
for the prediction of EBC showed an AUROC = 0.938 (95% CI: 0.895–0.975),
with sensitivity = 0.90 when specificity = 0.90. Using the combined
untargeted and targeted data set, eight metabolic pathways of potential
biological relevance were indicated to be significantly altered as
a result of EBC. Metabolic pathway analysis showed fatty acid and
aminoacyl-tRNA biosynthesis as well as inositol phosphate metabolism
to be most impacted in response to the disease. The combination of
untargeted and targeted metabolomics platforms has provided a highly
predictive and accurate method for BC and EBC diagnosis from plasma
samples. Furthermore, such a complementary approach yielded critical
information regarding potential pathogenic mechanisms underlying EBC
that, although critical to improved prognosis and enhanced survival,
are understudied in the current literature. All mass spectrometry
data and deidentified subject metadata analyzed in this study have
been deposited to Mendeley Data and are publicly available (DOI: ).