Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling Journal Article


Authors: Chmielecki, J.; Foo, J.; Oxnard, G. R.; Hutchinson, K.; Ohashi, K.; Somwar, R.; Wang, L.; Amato, K. R.; Arcila, M.; Sos, M. L.; Socci, N. D.; Viale, A.; de Stanchina, E.; Ginsberg, M. S.; Thomas, R. K.; Kris, M. G.; Inoue, A.; Ladanyi, M.; Miller, V. A.; Michor, F.; Pao, W.
Article Title: Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling
Abstract: Non-small cell lung cancers (NSCLCs) that harbor mutations within the epidermal growth factor receptor (EGFR) gene are sensitive to the tyrosine kinase inhibitors (TKIs) gefitinib and erlotinib. Unfortunately, all patients treated with these drugs will acquire resistance, most commonly as a result of a secondary mutation within EGFR (T790M). Because both drugs were developed to target wild-type EGFR, we hypothesized that current dosing schedules were not optimized for mutant EGFR or to prevent resistance. To investigate this further, we developed isogenic TKI-sensitive and TKI-resistant pairs of cell lines that mimic the behavior of human tumors. We determined that the drug-sensitive and drug-resistant EGFR-mutant cells exhibited differential growth kinetics, with the drug-resistant cells showing slower growth. We incorporated these data into evolutionary mathematical cancer models with constraints derived from clinical data sets. This modeling predicted alternative therapeutic strategies that could prolong the clinical benefit of TKIs against EGFR-mutant NSCLCs by delaying the development of resistance.
Journal Title: Science Translational Medicine
Volume: 3
Issue: 90
ISSN: 1946-6234
Publisher: American Association for the Advancement of Science  
Date Published: 2011-07-06
Start Page: 90ra59
Language: English
DOI: 10.1126/scitranslmed.3002356
PROVIDER: scopus
PUBMED: 21734175
PMCID: PMC3500629
DOI/URL:
Notes: --- - "Export Date: 17 August 2011" - "Art. No.: 90ra59" - "Source: Scopus"
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MSK Authors
  1. Michelle S Ginsberg
    235 Ginsberg
  2. Vincent Miller
    270 Miller
  3. Marc Ladanyi
    1328 Ladanyi
  4. Geoffrey R Oxnard
    24 Oxnard
  5. Romel Somwar
    111 Somwar
  6. Lu Wang
    147 Wang
  7. Agnes Viale
    245 Viale
  8. Nicholas D Socci
    266 Socci
  9. Maria Eugenia Arcila
    660 Arcila
  10. Mark Kris
    869 Kris