Journal of Clinical Oncology, 2007 ASCO Annual Meeting Proceedings (Post-Meeting Edition).
Vol 25, No 18S (June 20 Supplement), 2007: 18065
© 2007 American Society of Clinical Oncology

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Abstract

A systems pathology model for predicting overall survival in patients with refractory, advanced non-small cell lung cancer (NSCLC) treated with gefitinib

Aureon Biosciences, Yonkers, NY; Columbia University, New York, NY; AstraZeneca, Macclesfield, United Kingdom

18065

Background: The abundant expression of the epidermal growth factor receptor (EGFR) in a variety of solid tumors including non-small cell lung cancer (NSCLC), head and neck, breast, colon and brain has made it an attractive target for various selective molecular therapeutics, including the tyrosine kinase inhibitor gefitinib. The recent evidence of activating mutations in EGFR combined with clinical - demographic features has suggested that subgroups of patients with NSCLC are most likely to respond to selective therapies. We sought to determine whether the integration of clinical variables, tumor morphometry and quantitative protein profiles using support vector machines could identify a set of features which predicts overall survival in patients with NSCLC treated with gefitinib. Methods: We analyzed tumor samples from 109 patients with advanced refractory NSCLC treated with gefitinib. Formalin fixed, paraffin embedded tissue samples were evaluated with the following assays: Hematoxylin and Eosin image morphometry, EGFR DNA mutation analysis, EGFR protein immunohistochemistry and quantitative immunofluorescence with the following antibodies: CK18, Ki67, Caspase 3 activated, cd34, EGFR, phosphorylated-EGFR, phosphorylated-ERK, phosphorylated-AKT, PTEN, Cyclin D1, phosphorylated-m-TOR, PI3-K, VEGF, KDR (VEGFR2) and phosphorylated KDR. A predictive model was developed using support vector regression for censored data. Results: 4 of 87 patients had tyrosine kinase domain mutations in exons 19, 20 or 21. Utilizing 51 patients with complete data profiles (i.e. clinical, image morphometry and immunofluorescence), a model predicting overall survival was developed with a concordance index of 0.74. Poor performance status, poorly differentiated histology by morphometry and increased levels of activated caspase 3, phosphorylated KDR (VEGFR2) and cyclin D1 were associated with reduced survival. Conclusion: The integration of clinical, imaging and biomarker data identified a set of features which were associated with a more aggressive disease phenotype and resulted in overall poor survival.

Author Disclosure

Employment or Leadership Consultant or Advisory Stock Ownership Honoraria Research Expert Testimony Other Remuneration AstraZeneca Oncology, Aureon Biosciences Aureon Biosciences AstraZeneca Oncology, Aureon Biosciences