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Patient-derived models of acquired resistance can identify effective drug combinations for cancer

Academic Article
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Overview

authors

  • Crystal, A. S.
  • Shaw, A. T.
  • Sequist, L. V.
  • Friboulet, L.
  • Niederst, M. J.
  • Lockerman, E. L.
  • Frias, R. L.
  • Gainor, J. F.
  • Amzallag, A.
  • Greninger, P.
  • Lee, D.
  • Kalsy, A.
  • Gomez-Caraballo, M.
  • Elamine, L.
  • Howe, E.
  • Hur, Wooyoung
  • Lifshits, E.
  • Robinson, H. E.
  • Katayama, R.
  • Faber, A. C.
  • Awad, M. M.
  • Ramaswamy, S.
  • Mino-Kenudson, M.
  • Iafrate, A. J.
  • Benes, C. H.
  • Engelman, J. A.

publication date

  • December 2014

journal

  • Science  Journal

subject areas

  • Antineoplastic Combined Chemotherapy Protocols
  • Carcinoma, Non-Small-Cell Lung
  • DNA Mutational Analysis
  • Drug Resistance, Neoplasm
  • Drug Screening Assays, Antitumor
  • Enzyme Activation
  • Humans
  • Lung Neoplasms
  • MAP Kinase Kinase 1
  • Molecular Targeted Therapy
  • Mutation
  • Patient-Specific Modeling
  • Protein Kinase Inhibitors
  • Proto-Oncogene Proteins pp60(c-src)
  • Pyrimidines
  • Receptor Protein-Tyrosine Kinases
  • Receptor, Epidermal Growth Factor
  • Receptor, Fibroblast Growth Factor, Type 3
  • Sulfones
  • Tumor Cells, Cultured
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Identity

International Standard Serial Number (ISSN)

  • 0036-8075

Digital Object Identifier (DOI)

  • 10.1126/science.1254721

PubMed ID

  • 25394791
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Additional Document Info

start page

  • 1480

end page

  • 1486

volume

  • 346

issue

  • 6216

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