TY - JOUR
T1 - A systems pathology model for predicting overall survival in patients with refractory, advanced non-small-cell lung cancer treated with gefitinib
AU - Donovan, Michael J.
AU - Kotsianti, Angeliki
AU - Bayer-Zubek, Valentina
AU - Verbel, David
AU - Teverovskiy, Mikhail
AU - Cordon-Cardo, Carlos
AU - Costa, Jose
AU - Greco, F. Anthony
AU - Hainsworth, John D.
AU - Parums, Dinah V.
N1 - Funding Information:
We thank Drs. Judy Ochs and Alan Barge for clinical input, and Drs. Brian Holloway, Claire Watkins, Rose McCormack and Georgina Speake from the Iressa Science team at AstraZeneca for scientific and statistical input and helpful discussions. We also thank all support personnel at Aureon, including Faysal Elkhettabi, Olivier Saidi, Faisal Khan, Marina Sapir, Peter Angione, Mark Clayton, Stefan Hamann, Henry Pang, Yevgen Vengrenyuk, and Nicole Roberts. Special thanks to Janet Novak for insightful commentary on the manuscript. This work was supported in part by AstraZeneca and Aureon Laboratories.
PY - 2009/5
Y1 - 2009/5
N2 - Purpose: To identify clinical and biometric features associated with overall survival of patients with advanced refractory non-small-cell lung cancer (NSCLC) treated with gefitinib. Experimental design: One hundred and nine diagnostic NSCLC samples were analysed for EGFR mutation status, EGFR immunohistochemistry, histologic morphometry and quantitative immunofluorescence of 15 markers. Support vector regression modelling using the concordance index was employed to predict overall survival. Results: Tumours from 4 of 87 patients (5%) contained EGFR tyrosine kinase domain mutations. A multivariate model identified ECOG performance status, and tumour morphometry, along with cyclin D1, caspase-3 activated, and phosphorylated KDR to be associated with overall survival, concordance index of 0.74 (hazard ratio (HR) 5.26, p-value 0.0002). Conclusions: System-based models can be used to identify a set of baseline features that are associated with reduced overall survival in patients with NSCLC treated with gefitinib. This is a preliminary study, and further analyses are required to validate the model in a randomised, controlled treatment setting.
AB - Purpose: To identify clinical and biometric features associated with overall survival of patients with advanced refractory non-small-cell lung cancer (NSCLC) treated with gefitinib. Experimental design: One hundred and nine diagnostic NSCLC samples were analysed for EGFR mutation status, EGFR immunohistochemistry, histologic morphometry and quantitative immunofluorescence of 15 markers. Support vector regression modelling using the concordance index was employed to predict overall survival. Results: Tumours from 4 of 87 patients (5%) contained EGFR tyrosine kinase domain mutations. A multivariate model identified ECOG performance status, and tumour morphometry, along with cyclin D1, caspase-3 activated, and phosphorylated KDR to be associated with overall survival, concordance index of 0.74 (hazard ratio (HR) 5.26, p-value 0.0002). Conclusions: System-based models can be used to identify a set of baseline features that are associated with reduced overall survival in patients with NSCLC treated with gefitinib. This is a preliminary study, and further analyses are required to validate the model in a randomised, controlled treatment setting.
KW - Biological tumour markers
KW - Clinical pathology
KW - Epidermal growth factor receptor
KW - Gefitinib
KW - Non-small-cell lung carcinoma
KW - Statistical models
KW - Survival analysis
UR - https://www.scopus.com/pages/publications/65249147752
U2 - 10.1016/j.ejca.2009.02.004
DO - 10.1016/j.ejca.2009.02.004
M3 - Article
C2 - 19272767
AN - SCOPUS:65249147752
SN - 0959-8049
VL - 45
SP - 1518
EP - 1526
JO - European Journal of Cancer
JF - European Journal of Cancer
IS - 8
ER -