| Literature DB >> 29531020 |
Jinho Lee1, Gary K Geiss2, Gokhan Demirkan3, Christopher P Vellano4, Brian Filanoski3, Yiling Lu4, Zhenlin Ju5, Shuangxing Yu4, Huifang Guo4, Lisa Y Bogatzki3, Warren Carter3, Rhonda K Meredith3, Savitri Krishnamurthy5, Zhiyong Ding4, Joseph M Beechem3, Gordon B Mills1.
Abstract
Molecular analysis of tumors forms the basis for personalized cancer medicine and increasingly guides patient selection for targeted therapy. Future opportunities for personalized medicine are highlighted by the measurement of protein expression levels via immunohistochemistry, protein arrays, and other approaches; however, sample type, sample quantity, batch effects, and "time to result" are limiting factors for clinical application. Here, we present a development pipeline for a novel multiplexed DNA-labeled antibody platform which digitally quantifies protein expression from lysate samples. We implemented a rigorous validation process for each antibody and show that the platform is amenable to multiple protocols covering nitrocellulose and plate-based methods. Results are highly reproducible across technical and biological replicates, and there are no observed "batch effects" which are common for most multiplex molecular assays. Tests from basal and perturbed cancer cell lines indicate that this platform is comparable to orthogonal proteomic assays such as Reverse-Phase Protein Array, and applicable to measuring the pharmacodynamic effects of clinically-relevant cancer therapeutics. Furthermore, we demonstrate the potential clinical utility of the platform with protein profiling from breast cancer patient samples to identify molecular subtypes. Together, these findings highlight the potential of this platform for enhancing our understanding of cancer biology in a clinical translation setting.Entities:
Keywords: Antibodies*; Cancer Biology*; DNA-barcoded antibody; Multiplexed; Omics; Protein array; Protein lysate protocol; Proteomics Platform; Systems biology*; nCounter
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Year: 2018 PMID: 29531020 PMCID: PMC5986246 DOI: 10.1074/mcp.RA117.000291
Source DB: PubMed Journal: Mol Cell Proteomics ISSN: 1535-9476 Impact factor: 5.911