Elsevier

Lung Cancer

Volume 69, Issue 3, September 2010, Pages 337-340
Lung Cancer

VeriStrat® classifier for survival and time to progression in non-small cell lung cancer (NSCLC) patients treated with erlotinib and bevacizumab

https://doi.org/10.1016/j.lungcan.2009.11.019Get rights and content

Abstract

We applied an established and commercially available serum proteomic classifier for survival after treatment with erlotinib (VeriStrat®) in a blinded manner to pretreatment sera obtained from recurrent advanced NSCLC patients before treatment with the combination of erlotinib plus bevacizumab. We found that VeriStrat® could classify these patients into two groups with significantly better or worse outcomes and may enable rational selection of patients more likely to benefit from this costly and potentially toxic regimen.

Introduction

Inhibition of the epidermal growth factor (EGFR) pathway with erlotinib improves survival compared to placebo for patients with advanced lung cancer [1], and antibodies against VEGF improve survival when combined with chemotherapy [2], [3]. Response rates and progression-free survival (PFS) in unselected patients treated with both erlotinib and bevacizumab are much higher than those in unselected patients treated with erlotinib alone, suggesting significant activity for this combination [4]. However, many patients are exposed to toxicities without evident clinical benefit and these patients may be better served by earlier access to alternative therapies. This has encouraged an intense search for biomarkers of clinical significance for this and other targeted therapies. In the Iressa Non-small cell lung cancer Trial Evaluating REsponse and Survival against Taxotere (INTEREST) trial, in which the patients were randomized between gefitinib and docetaxel, no statistically significant prediction of survival benefit was seen for any of the biomarkers tested, including EGFR expression and mutation, EGFR gene amplification, or ras mutation [5]. These biomarkers are also present only in a small minority of patients with NSCLC, and require the availability of a significant amount of fresh tumor tissue for analysis; thus none are adequate to practically stratify patients who can derive survival benefit from erlotinib-based therapy in a western population. Furthermore, there are no validated biomarkers for benefit from bevacizumab therapy [6], [7]. Better predictive tools are thus needed to guide and optimize treatment decisions to maximize treatment benefits while minimizing cost and toxicity.

Recently, we reported a matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) proteomic signature (VeriStrat®), comprised of eight protein features, that was able to classify patients for improved PFS and overall survival (OS) after treatment with EGFR-tyrosine kinase inhibitor (TKI) therapy but not with chemotherapy [8]. This signature was validated in two independent cohorts treated with gefitinib or erlotinib. In this study, we tested whether VeriStrat® could also predict outcome in an independent multi-institutional cohort of patients treated with erlotinib in combination with bevacizumab.

Section snippets

Materials and methods

Mass spectrometry was performed on 35 available pretreatment serum samples from an open-labeled, phase I/II study (n = 40) in which the patients were treated with erlotinib in combination with bevacizumab. All patients included in this study were diagnosed with NSCLC, were previously treated with chemotherapy, had good performance status (0–1), stage IIIB (with pleural effusion) or stage IV, and nonsquamous histology (Table 1). Additional details regarding patient demographics were described

Results

From the available 35 samples with associated with clinical data, we generated 276 spectra, with 5–9 replicates per sample. A concordant classification among the replicates was obtained, except in one patient. In this sample, of the 7 spectra obtained, 3 were classified as “good” and 4 were classified as “poor”, likely due to tiny variations in the replicates in this borderline case. This patient was classified as “undefined” and excluded from the survival analyses.

Representative baseline

Discussion

Identification of biomarkers is important to select the patients most likely to benefit from specific targeted therapies. It is also crucial for these biomarkers to be validated and be reproducible between independent studies and patient cohorts. We have shown in our current study that VeriStrat®, which was developed on spectra from patients treated with single agent gefitinib, can accurately classify this new cohort of patients, who received both erlotinib and bevacizumab, into good and poor

Conclusions

We have demonstrated that an established proteomic classifier based on MALDI MS of pretreatment serum samples can accurately classify patients into two groups differing in their survival benefit from treatment with the clinically active combination of erlotinib and bevacizumab. The classifier has demonstrated no such ability on pretreatment samples in multiple cohorts treated with chemotherapy or surgery, suggesting that this classifier may be predictive and not prognostic. The biology

Conflict of interest statement

David Carbone is an unpaid advisor to BioDesix, but holds no patents or ownership interest with them.

Heinrich Roder, Joanna Roder, and Maxim Tsypin are Biodesix employees who performed the proteomic classification.

Acknowledgements

This study was funded by the Vanderbilt Lung Cancer SPORE (P50 CA-90949) and SPECS (UO1 CA114771) grants.

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