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Biospecimen Science Technologies
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We propose to develop an efficient, label-free method of isolating and screening CTCs from peripheral blood of breast-cancer patients. For this proposed R21 project, we will focus on breast cancer, but our technique could be generally applied to other cancers such as colon, prostate, lung, and melanoma. Our method will involve isolating candidate CTCs based on size and then screening in a label-free manner the isolated cells for multiple surface markers simultaneously, e.g. EpCAM, Axl, CD44, CD24, ALDH1, and CD45 (to identify contaminating leukocytes), thereby identifying CTCs which are epithelial, EMT, or stem-cell like. Cells would be sorted based on phenotype and readily available for further downstream analysis/molecular characterization. We will parameterize and optimize our platform using healthy human donor blood spiked with cells from breast-cancer cell lines, MCF-7, SKBR3 (a HER2+ cell line), and MDA-MB- 231 (a 'triple-negative' mesenchymal breast cancer cell line), and with MCF-10A cells induced into EMT via the ectopic expression of the EMT master regulator LBX1. We will screen label-free isolated cells for the epithelial marker, EpCAM, mesenchymal markers CD44 and CD24, stem-cell marker ALDH1, EMT marker Axl, and leukocyte marker CD45 (to identify and remove contaminating leukocytes). We will benchmark our platform using breast-cancer patient blood and compare our results (number of CTCs/patient blood draw) with CellSearch. The key innovative aspects of our proposed technology include: 1) the label-free enrichment and screening of CTCs from whole blood; and 2) multi-marking screening and subsequent sorting of CTCs into sub-populations. Performing label-free multi-marker screening and sorting CTCs into sub-populations on a single platform are unique to our method, alone, and address unmet needs of both biomedical research and medical communities. Researchers could perform secondary analysis or molecular diagnostics of CTCs without worry that the cells have not been affected by labeling. Clinicians could potentially correlate sub- populations with prognostic outcome and disease progression. PI Lydia L. Sohn, Assoc. Prof. of Mechanical Eng. at the University of California, Berkeley will lead this NIH R21 project. Drs. Mark LaBarge, an expert cancer biologist at Lawrence Berkeley National Laboratory, who will provide guidance on the biological aspects of the project, and Tianhong Li, physician scientist and co-Director of the Phase I Program at UC Davis Comprehensive Cancer Center, who will provide guidance on sample choice, experimental design, data analysis, and clinical relevance, will advise her