24h-gene variation effect of combined bevacizumab/erlotinib in advanced non-squamous non-small cell lung cancer using exon array blood profiling

Florent Baty, Markus Joerger, Martin Früh, Dirk Klingbiel, Francesco Zappa, Martin Brutsche, Florent Baty, Markus Joerger, Martin Früh, Dirk Klingbiel, Francesco Zappa, Martin Brutsche

Abstract

Background: The SAKK 19/05 trial investigated the safety and efficacy of the combined targeted therapy bevacizumab and erlotinib (BE) in unselected patients with advanced non-squamous non-small cell lung cancer (NSCLC). Although activating EGFR mutations were the strongest predictors of the response to BE, some patients not harboring driver mutations could benefit from the combined therapy. The identification of predictive biomarkers before or short after initiation of therapy is therefore paramount for proper patient selection, especially among EGFR wild-types. The first aim of this study was to investigate the early change in blood gene expression in unselected patients with advanced non-squamous NSCLC treated by BE. The second aim was to assess the predictive value of blood gene expression levels at baseline and 24h after BE therapy.

Methods: Blood samples from 43 advanced non-squamous NSCLC patients taken at baseline and 24h after initiation of therapy were profiled using Affymetrix' exon arrays. The 24h gene dysregulation was investigated in the light of gene functional annotations using gene set enrichment analysis. The predictive value of blood gene expression levels was assessed and validated using an independent dataset.

Results: Significant gene dysregulations associated with the 24h-effect of BE were detected from blood-based whole-genome profiling. BE had a direct effect on "Pathways in cancer", by significantly down-regulating genes involved in cytokine-cytokine receptor interaction, MAPK signaling pathway and mTOR signaling pathway. These pathways contribute to phenomena of evasion of apoptosis, proliferation and sustained angiogenesis. Other signaling pathways specifically reflecting the mechanisms of action of erlotinib and the anti-angiogenesis effect of bevacizumab were activated. The magnitude of change of the most dysregulated genes at 24h did not have a predictive value regarding the patients' response to BE. However, predictive markers were identified from the gene expression levels at 24h regarding time to progression under BE.

Conclusions: The 24h-effect of the combined targeted therapy BE could be accurately monitored in advanced non-squamous NSCLC blood samples using whole-genome exon arrays. Putative predictive markers at 24h could reflect patients' response to BE after adjusting for their mutational status. Trial registration ClinicalTrials.gov: NCT00354549.

Keywords: Blood predictive markers; Combined targeted therapies; Exon arrays; Non-small cell lung cancer.

Figures

Fig. 1
Fig. 1
Treatment scheme of the SAKK 19/05 trial. Patients received BE until progression or unacceptable toxicity. Upon disease progression, patients received standard chemotherapy with cisplatin and gemcitabine
Fig. 2
Fig. 2
Design of experiment and scheme of dually constrained correspondence analysis. Two matched tables X and Y are analyzed by DCCA. The 2 tables are rearranged into one stacked table. Additional external information on both rows and columns are used as positive and/or negative constraints
Fig. 3
Fig. 3
Boxplot representation of the gene expression levels (logarithm base 2 normalized intensity) before (B) and 24h after initiation of bevacizumab/erlotinib in the KEGG pathways “Hematopoietic cell lineage” (hsa04640), “ABC transporters” (hsa02010), and “Pathways in cancer” (hsa05200). The genes depicted in this representation belong the list of the 100 most dysregulated genes
Fig. 4
Fig. 4
KEGG pathway hsa05200 “Pathways in cancer”. Genes highlighted in red and green were up-regulated and down-regulated due to the 24h action of bevacizumab/erlotinib, respectively
Fig. 5
Fig. 5
Metagene classifier of time-to-progression under BE. The left panel displays the classification of low- versus high-risk patients based on the 91-gene metagene. The central panel shows the classification obtained by the KMplotter online validation tool using a multigene classifier. The right panel shows the classification obtained by the external CIT validation dataset. Hazard ratios and log rank test p values are reported in the upper right corner of the each panel

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