ABSTRACT
Combinatorial libraries of artificial zinc-finger transcription factors (ZF-TFs) provide a robust tool for inducing and understanding various functional components of the cancer phenotype. Herein, we utilized combinatorial ZF-TF library technology to better understand how breast cancer cells acquire resistance to fulvestrant, a clinically important anti-endocrine therapeutic agent. From a diverse collection of nearly 400,000 different ZF-TFs, we isolated six ZF-TF library members capable of inducing stable, long-term anti-endocrine drug-resistance in two independent estrogen receptor-positive breast cancer cell lines. Comparative gene expression profile analysis of the six different ZF-TF-transduced breast cancer cell lines revealed five distinct clusters of differentially expressed genes. One cluster was shared among all 6 ZF-TF-transduced cell lines and therefore constituted a common fulvestrant-resistant gene expression signature. Pathway enrichment-analysis of this common fulvestrant resistant signature also revealed significant overlap with gene sets associated with an estrogen receptor-negative-like state and with gene sets associated with drug resistance to different classes of breast cancer anti-endocrine therapeutic agents. Enrichment-analysis of the four remaining unique gene clusters revealed overlap with myb-regulated genes. Finally, we also demonstrated that the common fulvestrant-resistant signature is associated with poor prognosis by interrogating five independent, publicly available human breast cancer gene expression datasets. Our results demonstrate that artificial ZF-TF libraries can be used successfully to induce stable drug-resistance in human cancer cell lines and to identify a gene expression signature that is associated with a clinically relevant drug-resistance phenotype.
Subject(s)
Breast Neoplasms/pathology , Combinatorial Chemistry Techniques/methods , Drug Resistance, Neoplasm , Peptide Library , Transcription Factors/metabolism , Zinc Fingers , Breast Neoplasms/genetics , Cell Line, Tumor , Cluster Analysis , Drug Resistance, Neoplasm/drug effects , Estradiol/analogs & derivatives , Estradiol/pharmacology , Female , Fulvestrant , Gene Expression Profiling , Gene Expression Regulation, Neoplastic/drug effects , Humans , Treatment OutcomeABSTRACT
Engineered zinc finger nucleases can stimulate gene targeting at specific genomic loci in insect, plant and human cells. Although several platforms for constructing artificial zinc finger arrays using "modular assembly" have been described, standardized reagents and protocols that permit rapid, cross-platform "mixing-and-matching" of the various zinc finger modules are not available. Here we describe a comprehensive, publicly available archive of plasmids encoding more than 140 well-characterized zinc finger modules together with complementary web-based software (termed ZiFiT) for identifying potential zinc finger target sites in a gene of interest. Our reagents have been standardized on a single platform, enabling facile mixing-and-matching of modules and transfer of assembled arrays to expression vectors without the need for specialized knowledge of zinc finger sequences or complicated oligonucleotide design. We also describe a bacterial cell-based reporter assay for rapidly screening the DNA-binding activities of assembled multi-finger arrays. This protocol can be completed in approximately 24-26 d.
Subject(s)
Computational Biology/methods , Deoxyribonucleases/chemical synthesis , Genetic Engineering/methods , Software , Zinc Fingers/genetics , Bacteria/genetics , Deoxyribonucleases/chemistry , Plasmids/genetics , Two-Hybrid System TechniquesABSTRACT
Engineered Cys2His2 zinc finger proteins (ZFPs) can mediate regulation of endogenous gene expression in mammalian cells. Ideally, all zinc fingers in an engineered multifinger protein should be optimized concurrently because cooperative and context-dependent contacts can affect DNA recognition. However, the simultaneous selection of key contacts in even three fingers from fully randomized libraries would require the consideration of >10(24) possible combinations. To address this challenge, we have developed a novel strategy that utilizes directed domain shuffling and rapid cell-based selections. Unlike previously described methods, our strategy is amenable to scale-up and does not sacrifice combinatorial diversity. Using this approach, we have successfully isolated multifinger proteins with improved in vitro and in vivo function. Our results demonstrate that both DNA binding affinity and specificity are important for cellular function and also provide a general approach for optimizing multidomain proteins.
