Proteomic Classifier Research Articles

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

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. 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. 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). The independently derived classifier supports the hypothesis that MS can reliably predict the outcome of patients treated with epidermal growth factor receptor kinase inhibitors.

Genetic and proteomic features associated with survival after treatment with erlotinib in first-line therapy of non-small cell lung cancer

8089 Background: An improved understanding of molecular features of cancers and cancer patients associated with benefit from targeted therapies could allow the rational personalization of therapies to increase the probability of efficacy and decrease toxicity and cost. Multiple biomarkers have been proposed for predicting benefit after therapy with EGF receptor targeted therapies in first line colon and second line lung cancer therapy. Methods: In this study, we analyzed available tumor and serum samples from ECOG 3503, a single arm phase II study of erlotinib in first line lung cancer, for mutations in Kras and EGFR, as well as the previously described serum MALDI proteomic classifier (Veristrat). Out of 137 enrolled patients, there were 93 serum samples and 43 tumor samples available. Results: Molecular analysis of a subset of tumors from patients enrolled in ECOG 3503 shows that 10/43 (23%) contained Kras mutations and 3/43 (7%) harbored EGFR mutations. Classification of the 93 available sera for the pattern of proteins previously published as associated with survival after treatment with gefitinib identified 68/93 (73%) as predicted to be “good” and 25/93 (27%) predicted to have poor survival. Of the 6 responders with available serum, 5 were classified as MALDI good. Correlation with survival demonstrated a highly statistically significant correlation with MALDI status (p &lt; 0.001), and a marginally significant association of EGFR mutation with survival (p = 0.05), but no correlation with ras mutation status. Median survival was 10.8 months in MALDI good patients and 3.9 in MALDI poor patients. MALDI status was independent of both ras and EGFR mutation status. Conclusions: Thus, in distinct contrast to colon cancer, ras gene mutations do not appear to be associated with survival after first line EGFR-targeted therapy in lung cancer. The previously defined MALDI predictor is potent and highly clinically significantly associated with survival after first line treatment with erlotinib, and is independent of mutations in ras and EGFR in this dataset. [Table: see text]

Validation of proteomic classifier for clinical benefit from erlotinib as first line treatment for advanced non-small cell lung cancer (ECOG 3503)

7508 Background: In this study we tested the ability of our serum mass spectrometry classifier of clinical benefit from gefitinib to classify pre-treatment sera and plasma samples from non-small cell lung cancer (NSCLC) patients treated first line with erlotinib in ECOG E3503. Methods: Pretreatment serum and plasma samples were available from 73 of the 96 previously untreated advanced NSCLC patients treated with single agent erlotinib on ECOG protocol 3503, 13 subjects had only serum samples, and 10 had only plasma samples. All of these samples analyzed in replicate by MALDI mass spectrometry (MS). A prediction algorithm we established based on a training cohort of 139 patients treated second or third line with gefitinib was used to classify these patients for survival and time to progression, and results obtained from serum and plasma were compared when both were available Results: We found that the signals for the 8 distinct mass-to-charge (m/z) features used in our classifier were highly concordant between serum and plasma samples from the same patient, and that there was no difference in the classification of the patients between serum and plasma when both were available. Therefore we classified all 96 patients using serum if available, and plasma if not. The classification algorithm very successfully classified patients into groups with good and poor survival (median survival of 306 days vs 107 days, p = 0.0007). With the available follow-up, the time to progression was also statistically significant in this group (p = 0.007, data not shown). In a Cox multivariate analysis including the most significant univariate parameters PS (0 vs. 1 vs 2), number of involved sites (=3 vs &gt;3) and prior weight loss (&lt;5% vs = 5%), the MALDI MS classification algorithm retained independent significance at p = 0.03, with a hazard ratio of 0.53. Conclusions: This multi-institutional ECOG study demonstrates that MALDI MS can assist in the pre-treatment selection of a subgroup of NSCLC patients who are likely to show improved survival after treatment with first line erlotinib. No significant financial relationships to disclose.