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• W2945367294 abstract "Cancer cell detection in liquid biopsies has been a widely studied application in many microfluidic devices. The use of a common antibody, such as the epithelial cell adhesion molecule (Anti-EpCAM) or other specific antibodies, has facilitated the detection and study of many cancers. However, the use of such antibodies requires a priori knowledge of the cancer source, and many cancer subtypes are missed in screening applications. There remains a need to study a wider range of cancers that maintain the streamlined antibody approach in cell affinity separations. The Human transferrin receptor (CD71) has recently been demonstrated as a cancer cell affinity target in blood samples. CD71 expression in blood cells is low, whereas proliferating cancer cells have a higher expression of the surface protein. CD71 expression is variable with cell cycle, which can impact cell separations. In this work, we investigated the effects of cell cycle and CD71 expression on cell capture metrics. Six cancer cell lines were isolated from blood via CD71 affinity capture, with a capture efficiency and purity that varied with CD71 expression. Despite variation in CD71 expression, the affinity was sufficient to isolate cancer cells spiked into blood; under optimal conditions, CD71-based capture resulted in capture purity >80%. We conclude that CD71 affinity separations show potential as a biomarker for cancer studies without sacrificing sensitivity and selectivity, and that cancer cells can be isolated from liquid biopsies over a range of expression of the target protein." @default.
• W2945367294 created "2019-05-29" @default.
• W2945367294 creator A5007588293 @default.
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• W2945367294 date "2019-10-01" @default.
• W2945367294 modified "2023-09-24" @default.
• W2945367294 title "The effect of protein expression on cancer cell capture using the Human Transferrin Receptor (CD71) as an affinity ligand" @default.
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• W2945367294 doi "https://doi.org/10.1016/j.aca.2019.05.040" @default.
• W2945367294 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31203960" @default.
• W2945367294 hasPublicationYear "2019" @default.
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