Proteomics analysis of human breast milk to assess breast cancer risk.
Roshanak AslebaghDevika ChannaveerappaKathleen F ArcaroCostel C DariePublished in: Electrophoresis (2018)
Detection of breast cancer (BC) in young women is challenging because mammography, the most common tool for detecting BC, is not effective on the dense breast tissue characteristic of young women. In addition to the limited means for detecting their BC, young women face a transient increased risk of pregnancy-associated BC. As a consequence, reproductively active women could benefit significantly from a tool that provides them with accurate risk assessment and early detection of BC. One potential method for detection of BC is biochemical monitoring of proteins and other molecules in bodily fluids such as serum, nipple aspirate, ductal lavage, tear, urine, saliva and breast milk. Of all these fluids, only breast milk provides access to a large volume of breast tissue, in the form of exfoliated epithelial cells, and to the local breast environment, in the form of molecules in the milk. Thus, analysis of breast milk is a non-invasive method with significant potential for assessing BC risk. Here we analyzed human breast milk by mass spectrometry (MS)-based proteomics to build a biomarker signature for early detection of BC. Ten milk samples from eight women provided five paired-groups (cancer versus control) for analysis of dysregulatedproteins: two within woman comparisons (milk from a diseased breast versus a healthy breast of the same woman) and three across women comparisons (milk from a woman with cancer versus a woman without cancer). Despite a wide range in the time between milk donation and cancer diagnosis (cancer diagnosis occurred from 1 month before to 24 months after milk donation), the levels of some proteins differed significantly between cancer and control in several of the five comparison groups. These pilot data are supportive of the idea that molecular analysis of breast milk will identify proteins informative for early detection and accurate assessment of BC risk, and warrant further research. Data are available via ProteomeXchange with identifier PXD007066.
Keyphrases
- papillary thyroid
- mass spectrometry
- squamous cell
- risk assessment
- breast cancer risk
- endothelial cells
- multiple sclerosis
- lymph node metastasis
- clinical trial
- magnetic resonance
- high resolution
- type diabetes
- case report
- ms ms
- heavy metals
- liquid chromatography
- metabolic syndrome
- polycystic ovary syndrome
- machine learning
- computed tomography
- insulin resistance
- study protocol
- skeletal muscle
- climate change
- gas chromatography
- deep learning
- simultaneous determination
- image quality
- cervical cancer screening