Wan Cheung
Cell Signaling Technology
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Featured researches published by Wan Cheung.
Clinical Cancer Research | 2009
Jian Yu; Susan E. Kane; Jiong Wu; Elisa Benedettini; Daiqiang Li; Cynthia Reeves; Gregory Innocenti; Randy Wetzel; Katherine Crosby; Alison Becker; Michelle Ferrante; Wan Cheung Cheung; Xiqiang Hong; Lucian R. Chirieac; Lynette M. Sholl; Herbert Haack; Bradley L. Smith; Roberto Polakiewicz; Yi Tan; Ting-Lei Gu; Massimo Loda; Xinmin Zhou; Michael J. Comb
Purpose: Activating mutations within the tyrosine kinase domain of epidermal growth factor receptor (EGFR) are found in approximately 10% to 20% of non–small-cell lung cancer (NSCLC) patients and are associated with response to EGFR inhibitors. The most common NSCLC-associated EGFR mutations are deletions in exon 19 and L858R mutation in exon 21, together accounting for 90% of EGFR mutations. To develop a simple, sensitive, and reliable clinical assay for the identification of EGFR mutations in NSCLC patients, we generated mutation-specific rabbit monoclonal antibodies against each of these two most common EGFR mutations and aimed to evaluate the detection of EGFR mutations in NSCLC patients by immunohistochemistry. Experimental Design: We tested mutation-specific antibodies by Western blot, immunofluorescence, and immunohistochemistry. In addition, we stained 40 EGFR genotyped NSCLC tumor samples by immunohistochemistry with these antibodies. Finally, with a panel of four antibodies, we screened a large set of NSCLC patient samples with unknown genotype and confirmed the immunohistochemistry results by DNA sequencing. Results: These two antibodies specifically detect the corresponding mutant form of EGFR by Western blotting, immunofluorescence, and immunohistochemistry. Screening a panel of 340 paraffin-embedded NSCLC tumor samples with these antibodies showed that the sensitivity of the immunohistochemistry assay is 92%, with a specificity of 99% as compared with direct and mass spectrometry–based DNA sequencing. Conclusions: This simple assay for detection of EGFR mutations in diagnostic human tissues provides a rapid, sensitive, specific, and cost-effective method to identify lung cancer patients responsive to EGFR-based therapies.
Nature Biotechnology | 2012
Shuji Sato; Sean A. Beausoleil; Lana Popova; Jason G Beaudet; Ravi K Ramenani; Xiaowu Zhang; James S Wieler; Sandra M Schieferl; Wan Cheung Cheung; Roberto D. Polakiewicz
1. Trapnell, C. et al. Nat. Biotechnol. 28, 511–515 (2010). 2. Trapnell, C. et al. Nat. Protoc. 7, 562–578 (2012). 3. Nielsen, C.B., Cantor, M., Dubchak, I., Gordon, D. & Wang, T. Nat. Methods 7, S5–S15 (2010). 4. Rutherford, K. et al. Bioinformatics 16, 944–945 (2000). 5. Kent, W.J. Genome Res. 12, 996–1006 (2002). 6. Robinson, J.T. et al. Nat. Biotechnol. 29, 24–26 (2011). 7. Fiume, M., Williams, V., Brook, A. & Brudno, M. Bioinformatics 26, 1938–1944 (2010). 8. Jankun-Kelly, T.J. & Kwan-Liu, M. IEEE Transactions on Visualization and Computer Graphics 7, 275–287 (2001). 9. Pretorius, A.J., Bray, M.A.P., Carpenter, A.E. & Ruddle, R.A. IEEE Transactions on Visualization and Computer Graphics 17, 2402–2411 (2011). 10. Goecks, J., Nekrutenko, A., Taylor, J. & the Galaxy Team. Genome Biology 11, R86 (2010). 11. Blankenberg, D. et al. Curr. Protoc. Mol. Biol. 89, 19.10.1–19.10.21 (2010). 12. Ron, D. & Walter, P. Nat. Rev. Mol. Cell Biol. 8, 519–529 (2007). 13. Walter, P. & Ron, D. Science 334, 1081–1086 (2011). 14. Mori, K. J. Biochem. 146, 743–750 (2009). 15. Calfon, M. et al. Nature 415, 92–96 (2002). 16. Yanagitani, K., Kimata, Y., Kadokura, H. & Kohno, K. Science 331, 586–589 (2011). 17. Guo, H., Ingolia, N.T., Weissman, J.S. & Bartel, D.P. Nature 466, 835–840 (2010). 18. Reid, D.W. & Nicchitta, C.V. J. Biol. Chem. 287, 5518– 5527 (2012). share or publish the new visualization on the web. Trackster’s use of data subsets to reduce analysis computation time is applicable to a wide set of genomic tools. For instance, genomic interval operations (such as intersect and subtract), transcript assembly and quantification, and human variation analysis (such as SNP calling) are compatible with Trackster’s analysis approach. However, tools (such as some peak callers) that use data from many or all genomic regions to build a global model require additional support to work with Trackster. These tools must be run once in full to generate the model, and then the model can be stored in Galaxy and reused in Trackster. Transcript quantification in Cufflinks benefits from a global model, and Trackster makes use of it when it available. Alternatively, a tool (for example, a read mapper) may require all input data because it is not possible to identify, before runtime, a subset of input data needed to produce correct output in a particular genomic region. For such tools, dynamic filtering can be used to simulate running a tool using different parameters. In this approach, a tool’s parameters are relaxed so that many potential outputs are produced and attribute values are attached to output data. Filtering can then be used to observe the data that would be produced for particular parameter values. Visualization and data analysis tools are used in nearly all high-throughput sequencing experiments, yet too often they are not well integrated. Coupling visualization and analysis tools into a visual analysis environment where analysis output can be generated and visually assessed in real time is a powerful approach for computational science. Trackster provides an environment for interactive visual analysis that is widely applicable to many different high-throughput sequencing experiments. General visual analysis techniques that can be performed in Trackster include tool parameter-space visualization and exploration, systematic sweeps of parameter values and dynamic filtering. Trackster makes visual analysis possible for a wide variety of tools by leveraging the Galaxy framework, thereby tapping into the large collection of tools already integrated into Galaxy and providing a simple path for integrating additional tools into Trackster. This approach to tool integration enables popular, production-level tools, such as Cufflinks in our example, to be integrated into Trackster without modification to the tools themselves. In our experiment, Trackster’s visual analysis features made it possible to use interactive visualization to improve Cufflinks’ transcript assemblies via parameter-space exploration and to remove assembly artifacts using dynamic filtering. Trackster also supports collaborative visual analysis via web-based, fully functional shared visualizations that can be modified, extended, re-shared and published.
Scientific Reports | 2016
Jason J. Lohmueller; Shuji Sato; Lana Popova; Isabel Chu; Meghan Tucker; Roberto Barberena; Gregory Innocenti; Mare Cudic; James D. Ham; Wan Cheung Cheung; Roberto Polakiewicz; Olivera J. Finn
MUC1 is a shared tumor antigen expressed on >80% of human cancers. We completed the first prophylactic cancer vaccine clinical trial based on a non-viral antigen, MUC1, in healthy individuals at-risk for colon cancer. This trial provided a unique source of potentially effective and safe immunotherapeutic drugs, fully-human antibodies affinity-matured in a healthy host to a tumor antigen. We purified, cloned, and characterized 13 IgGs specific for several tumor-associated MUC1 epitopes with a wide range of binding affinities. These antibodies bind hypoglycosylated MUC1 on human cancer cell lines and tumor tissues but show no reactivity against fully-glycosylated MUC1 on normal cells and tissues. We found that several antibodies activate complement-mediated cytotoxicity and that T cells carrying chimeric antigen receptors with the antibody variable regions kill MUC1+ target cells, express activation markers, and produce interferon gamma. Fully-human and tumor-specific, these antibodies are candidates for further testing and development as immunotherapeutic drugs.
Journal of Clinical Investigation | 2004
Wan Cheung Cheung; Joong Su Kim; Michael A. Linden; Liangping Peng; Brian Van Ness; Roberto D. Polakiewicz; Siegfried Janz
Archive | 2010
Wan Cheung Cheung; Shuji Sato; Roberto D. Polakiewicz
Archive | 2012
Roberto D. Polakiewicz; Wan Cheung Cheung; John Edward Rush; Sean A. Beausoleil
Archive | 2014
Robert E. Schoen; Olivera J. Finn; Shuji Sato; Wan Cheung Cheung; Roberto D. Polakiewicz
Archive | 2013
Shuji Sato; Sean A. Beausoleil; Wan Cheung Cheung; Roberto D. Polakiewicz
Archive | 2013
Shuji Sato; Sean A. Beausoleil; Wan Cheung Cheung; Roberto Polakiewicz
Cancer Research | 2015
Jason J. Lohmueller; Shuji Sato; Wan Cheung Cheung; Isabel Chu; Lana Popova; Christopher A. Manning; Katherine Crosby; Christopher Grange; James D. Ham; Roberto Polakiewicz; Olivera J. Finn