Classification by Mass Spectrometry Can Accurately and Reliably Predict Outcome in Patients with Non-small Cell Lung Cancer Treated with Erlotinib-Containing Regimen

Purpose: Although many lung cancers express the epidermal growth factor receptor and the vascular endothelial growth factor, only a small fraction of patients will respond to inhibitors of these pathways. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MS) has shown promise in biomarker discovery, potentially allowing the selection of patients who may benefit from such therapies. Here, we use a matrix-assisted laser desorption/ionization MS proteomic algorithm developed from a small dataset of erlotinib-bevacizumab treated patients to predict the clinical outcome of patients treated with erlotinib alone. Methods: Pretreatment serum collected from patients in a phase I/II study of erlotinib in combination with bevacizumab for recurrent or refractory non-small cell lung cancer was used to develop a proteomic classifier. This classifier was validated using an independent treatment cohort and a control population. Result: A proteomic profile based on 11 distinct m/z features was developed. This predictive algorithm was associated with outcome using the univariate Cox proportional hazard model in the training set (p = 0.0006 for overall survival; p = 0.0012 for progression-free survival). The signature also predicted overall survival and progression-free survival outcome when applied to a blinded test set of patients treated with erlotinib alone on Eastern Cooperative Oncology Group 3503 (n = 82, p < 0.0001 and p = 0.0018, respectively) but not when applied to a cohort of patients treated with chemotherapy alone (n = 61, p = 0.128). Conclusion: The independently derived classifier supports the hypothesis that MS can reliably predict the outcome of patients treated with epidermal growth factor receptor kinase inhibitors.

[1]  Lesley Seymour,et al.  Role of KRAS and EGFR as biomarkers of response to erlotinib in National Cancer Institute of Canada Clinical Trials Group Study BR.21. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[2]  William L. Bigbee,et al.  Diagnostic Accuracy of MALDI Mass Spectrometric Analysis of Unfractionated Serum in Lung Cancer , 2007, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

[3]  R. Gray,et al.  Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study. , 2007, Journal of the National Cancer Institute.

[4]  M. Socinski,et al.  Bevacizumab in the treatment of non-small-cell lung cancer , 2007, Oncogene.

[5]  D. Haber,et al.  Molecular predictors of response to epidermal growth factor receptor antagonists in non-small-cell lung cancer. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[6]  Robert Gray,et al.  Paclitaxel-carboplatin alone or with bevacizumab for non-small-cell lung cancer. , 2006, The New England journal of medicine.

[7]  Renato Martins,et al.  Erlotinib in previously treated non-small-cell lung cancer. , 2005, The New England journal of medicine.

[8]  R. Herbst,et al.  Angiogenesis and lung cancer: prognostic and therapeutic implications. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[9]  Edward S. Kim,et al.  Phase I/II trial evaluating the anti-vascular endothelial growth factor monoclonal antibody bevacizumab in combination with the HER-1/epidermal growth factor receptor tyrosine kinase inhibitor erlotinib for patients with recurrent non-small-cell lung cancer. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[10]  Jeffrey S. Morris,et al.  Signal in noise: evaluating reported reproducibility of serum proteomic tests for ovarian cancer. , 2005, Journal of the National Cancer Institute.

[11]  E. Diamandis Analysis of serum proteomic patterns for early cancer diagnosis: drawing attention to potential problems. , 2004, Journal of the National Cancer Institute.

[12]  E. Petricoin,et al.  Use of proteomic patterns in serum to identify ovarian cancer , 2002, The Lancet.