The heterogeneity of breast cancer (disease stage and phenotype) makes it challenging to differentiate between each subtype; luminal A, luminal B, HER2, basal-like and claudin-low, on the basis of a single gene or protein. Therefore, a collection of markers is required that can serve as a signature for diagnosing different types of breast cancer. New developments in proteomics have provided the opportunity to look at phenotype-specific breast cancer cell lines and stage-specific liquid biopsies (nipple aspirate fluid [NAF], plasma samples) to identify disease and phenotype specific signature. An 8-plex iTRAQ quantification strategy was employed to compare proteomic profiles of a range of breast cancer and ‘normal-like’ cell lines with primary breast epithelial cells. From this, 2467 proteins were identified on Orbitrap Fusion and Ultraflex II, of which 1430 were common. Matched pairs of NAF samples from four patients with different stages of breast cancer, were analysed by SCX-LC-MS and a total of 1990 unique gene products were identified. More than double the number of proteins previously published data, were detected in NAF, including 300 not detected in plasma. The NAF from the diseased patients have 138 potential phenotype biomarkers that were significantly changed compared to the healthy volunteer (7 for luminal A, 9 for luminal B, 11 for HER2, 14 for basal-like and 52 for claudin-low type). The average coefficient of variation for triplicate analyses by multiple reaction monitoring mass spectrometry (MRM-MS), was 9% in cell lines, 17 % in tissue biopsies, 22% in serum samples and 24% in NAF samples. Overall, the results provide a strong paradigm to develop a clinical assay based on proteomic changes in NAF samples for the early detection of breast cancer supplementary to established mammography programmes.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:732180 |
| Date | January 2017 |
| Creators | Shaheed, Sadr-ul |
| Publisher | University of Bradford |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/10454/14785 |
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