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Miller ML, Reznik E, Gauthier NP, et al. Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. Cell Syst. 2015;1(3):197-209doi: 10.1016/j.cels.2015.08.014.
Miller, M. L., Reznik, E., Gauthier, N. P., Aksoy, B. A., Korkut, A., Gao, J., Ciriello, G., Schultz, N., & Sander, C. (2015). Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. Cell systems, 1(3), 197-209. https://doi.org/10.1016/j.cels.2015.08.014
Miller, Martin L, et al. "Pan-Cancer Analysis of Mutation Hotspots in Protein Domains." Cell systems vol. 1,3 (2015): 197-209. doi: https://doi.org/10.1016/j.cels.2015.08.014
Miller ML, Reznik E, Gauthier NP, Aksoy BA, Korkut A, Gao J, Ciriello G, Schultz N, Sander C. Pan-Cancer Analysis of Mutation Hotspots in Protein Domains. Cell Syst. 2015 Sep 23;1(3):197-209. doi: 10.1016/j.cels.2015.08.014. Epub 2015 Sep 23. PMID: 27135912; PMCID: PMC4982675.
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Cell Syst. 2015 Sep 23;1(3):197-209. doi: 10.1016/j.cels.2015.08.014. Epub 2015 Sep 23.

Pan-Cancer Analysis of Mutation Hotspots in Protein Domains.

Cell systems

Martin L Miller, Ed Reznik, Nicholas P Gauthier, Bülent Arman Aksoy, Anil Korkut, Jianjiong Gao, Giovanni Ciriello, Nikolaus Schultz, Chris Sander

Affiliations

  1. Computational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK. Electronic address: [email protected].
  2. Computational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
  3. Computational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. Electronic address: [email protected].

PMID: 27135912 PMCID: PMC4982675 DOI: 10.1016/j.cels.2015.08.014

Abstract

In cancer genomics, recurrence of mutations in independent tumor samples is a strong indicator of functional impact. However, rare functional mutations can escape detection by recurrence analysis owing to lack of statistical power. We enhance statistical power by extending the notion of recurrence of mutations from single genes to gene families that share homologous protein domains. Domain mutation analysis also sharpens the functional interpretation of the impact of mutations, as domains more succinctly embody function than entire genes. By mapping mutations in 22 different tumor types to equivalent positions in multiple sequence alignments of domains, we confirm well-known functional mutation hotspots, identify uncharacterized rare variants in one gene that are equivalent to well-characterized mutations in another gene, detect previously unknown mutation hotspots, and provide hypotheses about molecular mechanisms and downstream effects of domain mutations. With the rapid expansion of cancer genomics projects, protein domain hotspot analysis will likely provide many more leads linking mutations in proteins to the cancer phenotype.

Copyright © 2015 Elsevier Inc. All rights reserved.

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