Herein, we report a dual dye competitive screening method for the identification of five boronic acid functionalized synthetic lectins (SLs) that are selective for prostate-associated targets with the goal of detecting and staging prostate cancer. This method uses differently labeled normal (RWEP-1) and diseased (PC3) cell membrane extracts in a competitive binding assay to identify SLs that bind either the cancerous or normal extracts but not both. Subsequent studies examined the efficacy of these new SL hits in an array format to discriminate six prostate cell lines. The SL array was able to (a) classify the prostate cell lines with 83% accuracy, (b) discriminate the same cell lines based on their metastatic potential (noncancerous/healthy, cancerous/lowly metastatic, and cancerous/metastatic) with 96% classification accuracy, and (c) exhibit enhanced selectivity for prostate-derived versus colon-derived cell lines. Further analysis delineated the contribution from each SL in these studies, providing a focused SL array having potential utility as a cancer diagnostic.
Glycans represent promising biomarkers for cancer diagnostics. Aberrant glycosylation is known to occur in cancer cells and changes continue to occur as the disease progresses. This study sought to determine if glycan ‘fingerprints’ can distinguish between differing cell types associated with cancer; specifically for the early detection and subtyping of breast cancers. Synthetic Lectins (SLs) are small peptides functionalized with boronic acids that selectively bind to glycans. Different SLs, with differing peptide backbones, acting as an array can be used to study glycans and generate glycan ‘fingerprints’ of analytes. An array of nine SLs, present on resin beads, were incubated with fluorescently labeled membrane proteins extracted from 1.) Breast cancer cell lines 2.) Matched normal and cancerous breast tissue from patients. Binding intensity information was obtained for each sample and analyzed statistically. Linear Discriminant Analysis was used to build two models, of breast cancer cell lines, based on a) metastatic potential and b) breast cancer subtype. Leave one out analysis determined that the models could distinguish between healthy/cancerous non-metastatic/cancerous metastatic cells with 98% accuracy and between breast cancer subtypes with 99% accuracy. Classification accuracy based on metastatic potential was validated using clinical specimens. N = 3 matched normal and cancerous tissues were analyzed and classification accuracies of 97% were obtained. We have developed an array, which distinguishes between healthy and cancerous cells as well as breast cancer subtypes with high accuracy. Work to expand validation to a larger group of patient samples as well as testing the array using patient serum is ongoing. Citation Format: Kathleen M. O'Connell, Erin E. Gatrone, Anna A. Veldkamp, John J. Lavigne. SYNTHETIC LECTINS FOR THE DETERMINATION OF BREAST CANCER SUBTYPE. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2013. doi:10.1158/1538-7445.AM2015-2013
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