Breast cancer (BC) is the most common cancer type in women. According to the American Cancer Society, the incidence of breast cancer has increased by 0.5% per year. However, deaths rates have decreased by 1% per year, primarily due to earlier detection through screening and increased awareness. The 5-year survival rate for all-stage BC is 90%. If detected in the early stages, the survival rate is nearly 99%. For symptomatic patients (i.e., breast lump), imaging techniques are highly sensitive for diagnosis. Yet, an effective diagnostic tool that enables accurate and early diagnosis and screening of early-stage BC is not available. Such a diagnostic could facilitate early treatment and better BC survival.
Since reprogrammed metabolism is a hallmark of cancer, many research groups have dedicated their efforts to identifying cancer-related metabolic changes using various techniques, predominantly mass spectrometry (MS), in multiple platforms. The identification of metabolic alterations has uncovered novel targets for treatment and key biomarkers to improve diagnostic tools. Researchers from the Arizona Metabolomics Laboratory at Arizona State University, in collaboration with Dr. Peggy Porter –a UW/Fred Hutch Cancer Consortium member from the Human Biology Division– have developed a metabolomics approach using a combination of targeted and untargeted metabolomics for early BC biomarker discovery. The study is now published in the American Chemistry Society Journal of Proteome Research.
Metabolomic analytic platforms come in two flavors: targeted and untargeted. Targeted metabolomics identify and quantify a pre-determined subset of metabolites, thus achieving high specificity and sensitivity for a limited set of metabolites. In contrast, untargeted metabolomics aims to capture and analyze all metabolites present in a sample, achieving greater coverage but sacrificing specificity and selectivity. Previous studies have utilized targeted metabolomics for BC biomarker identification but did not focus on early-stage BC. In this study, Wei and colleagues have successfully integrated the use of complementary targeted and untargeted MS approaches to enhance coverage while maintaining high sensitivity and specificity for early BC diagnosis.
First, the researchers analyzed plasma samples from breast cancer patients (124 cases) and healthy controls (86 cases) using an untargeted liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). They identified 33 altered metabolites/features in cancer samples relative to the healthy controls. For early-stage BC diagnostic, six metabolites were predictive features between early BC samples and controls. The investigators then used this data to build early-stage breast cancer diagnostic models that accurately distinguished early-stage BC samples from controls. Finally, combining the untargeted data set with a previously published targeted data set and evaluating the data for pathway enrichment analyses, the investigators identified eight interconnected metabolic pathways altered in early-stage BC. In summary, the novel application of untargeted and targeted metabolomics platforms provides a comprehensive view that may provide valuable information regarding BC's early diagnosis and etiology.
Wei, Y., Jasbi, P., Shi, X., Turner, C., Hrovat, J., Liu, L., Rabena, Y., Porter, P., & Gu, H. (2021). Early Breast Cancer Detection Using Untargeted and Targeted Metabolomics. Journal of proteome research, 20(6), 3124–3133. https://doi.org/10.1021/acs.jproteome.1c00019
UW/FH cancer consortium member Peggy Porter contributed to this study.
This work was supported by the College of Health Solutions at Arizona State University, the Rising Star Program at the Institute of Translational Health Sciences (ITHS), the Fred Hutchinson Cancer Research Center Breast Specimen Repository, and Brander Beacons Cancer Research.