To quantify the relative variability of the features or peaks, we generated Coefficients of Variation (CV) using 139 common peaks for all samples, and for samples from each of the 3 replicated days (Figure 1range. and a control population. Result A proteomic profile based on 11 distinct features was developed. This predictive algorithm was associated with outcome using the univariate Cox proportional hazard model in the training set (= 0.0006 for overall survival; = 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 (= 82, 0.0001 and = 0.0018, respectively) but not when applied to a cohort of patients treated with chemotherapy alone (= 61, = 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. mutations, increased gene copy number, mutations, and overexpression of the EGFR protein have been explored as predictive markers for the response to treatment response with EGFR-TKIs. To date, mutations, copy number, and EGFR expression levels have been predictive of the response or the survival in some studies. 5 EGFR gene copy number was also predictive for the EGFR-TKI response in the second and third line settings.6 These biomarkers require tumor tissue analysis and are not sufficiently conclusive for routinely selected patients who would derive benefits from therapy with EGFR-TKI. In addition, although there are candidate markers to predict response to erlotinib treatment, no markers are available to predict benefit from bevacizumab. Despite considerable evidence for the association of intratumoral and/or plasma VEGF levels with tumor progression and/or poor prognosis, pretreatment Anisotropine Methylbromide (CB-154) VEGF levels are not predictive of response to bevacizumab therapy.7 Thus, better prediction tools are needed to maximize treatment benefits while minimizing toxicity. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) can be used to generate protein signatures from biologic specimens such as tissue, urine, and serum. The technique also offers the advantages of rapidity and sensitivity. Unfortunately, previous studies with serum MS proteomics as biomarkers have suffered from the lack of reproducibility and validation. These problems have led to general skepticism about this technology and its use in the development of cancer biomarkers.8 Recently, utilizing serum MALDI-TOF MS, Taguchi et al.9 reported a proteomic signature that independently classified Anisotropine Methylbromide (CB-154) patients according to their clinical outcome after treatment with EGFR-TKI therapy, but not with chemotherapy. This finding suggests that MALDI-TOF MS may still be useful for biomarker development and eventual clinical utility. In the present study, we developed another independent proteomic signature obtained from patients Anisotropine Methylbromide (CB-154) treated with erlotinib and bevacizumab that can not only accurately classify this group of patients based on clinical outcome in a leave-one-out analysis, but also can be used to independently classify outcome in patients treated with erlotinib alone. Furthermore, despite Rabbit Polyclonal to PLCB3 (phospho-Ser1105) the small training set, the variability of signals between obtained spectra was small, suggesting that data generated from MS are reliable and reproducible. This study thus lends further support to the use of serum MALDI-TOF in biomarker discovery. METHODS Patients and Samples MS was performed on pretreatment serum samples from patients who were treated with erlotinib and bevacizumab in an open-label, phase I/II study. Forty patients were enrolled in this study. All were diagnosed with histologically proven stage IIIB (with pleural effusion) or stage IV, recurrent, nonsquamous NSCLC. Pretreatment patient samples were available for 37 of 40 patients in the clinical trial. Further details regarding the patient population and the clinical trial were described previously.4 The validation cohort (= 82) comprised of patients enrolled in Eastern Cooperative Oncology Group (ECOG) 350. The Vanderbilt University control group patients were comprised of unselected patients treated under various institutional review board approved chemotherapy protocols at Vanderbilt University Medical Center.9 These patients were treated in both the first and second line settings. None were treated with EGFR-TKI at time of relapse. Sample Preparation and Mass Spectrometry The sera were thawed on ice.