Abstract
Cancer heterogeneity renders risk stratification and therapy decisions challenging. Thus, genomic and proteomic methodologies have been used in an effort to identify biomarkers that can differentiate tumor subtypes to improve therapeutic outcome. Here, we report a generally applicable strategy to generate tumor type-specific peptide ligand arrays. Peptides that specifically recognize breast tumor-derived cell lines (MDA-MB-231, MCF-7, and T47-D) were identified using cell-displayed peptide libraries carrying an intrinsic fluorescent marker allowing for sorting and characterization with quantitative flow cytometry. Tumor cell specificity was achieved by depleting libraries of ligands binding to normal mammary epithelial cells (HMEC and MCF-10A). Although integrin binding RGD motifs were favored by some cell lines, screening with RGD competitors yielded several novel consensus motifs exhibiting improved tumor specificity. The resultant peptide array contained multiple consensus motifs exhibiting strong similarity to breast tumor-associated proteins. Profiling a panel of breast cancer cell lines with the peptide array revealed receptor expression patterns distinctive for luminal or basal tumor subtypes. In addition, peptide displaying bacteria and peptide functionalized microparticles enabled fluorescent labeling of tumor cells and frozen tumor tissue sections. Our results indicate that cell surface profiling using highly specific breast tumor cell binding ligands may provide an efficient route for tumor subtype classification, biomarker identification, and for the development of targeted diagnostics and therapeutics.
Highlights
Given the immense challenges associated with tumor heterogeneity, the effective classification of cancers and their subtypes using genomic, proteomic, and systems biology methods will substantially effect cancer diagnosis and therapy
Tumor cell–specific peptides were identified by screening for binding to human breast cancer cell lines (MDA-MB231, MCF-7, and T-47D; Table 1), and for nonbinding to normal cell lines (MCF-10A and Human mammary epithelial cells (HMEC))
We chose to profile breast tumor cells lines because genomic expression studies have shown that these cell lines mirror the aberrations found in primary tumors and can be divided into two groups, namely luminal and basal subtypes [13]
Summary
Given the immense challenges associated with tumor heterogeneity, the effective classification of cancers and their subtypes using genomic, proteomic, and systems biology methods will substantially effect cancer diagnosis and therapy. Genomic profiling of breast tumors has resulted in the identification of five distinct molecular subtypes: luminal subtypes A and B, basal-like, ERBB2 positive, and normal breast like [2]. Subtype identification has enabled improved risk stratification because the identified subtypes are associated with significantly different survival times [3] but do not adequately predict patient responses to therapeutic regimens [4]. The development of economical proteomic analysis methodologies would substantially augment diagnostic and therapeutic capabilities enabling the selection of targeted therapies and the elucidation of disease-associated protein networks, posttranslational modifications, and aberrant protein localization [5]
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