Publication
Gene expression signatures predictive of bevacizumab/erlotinib therapeutic benefit in advanced non-squamous non-small cell lung cancer patients (SAKK 19/05 trial)
Journal Paper/Review - Apr 28, 2015
Franzini Anca, Zappa Francesco, Klingbiel Dirk, Grigoriu Bogdan D, Betticher Daniel, Droege Cornelia, Oliver Dürr, Macovei Ina I, Baty Florent, Brutsche Martin
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PubMed
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Journal
Publication Date
Issn Electronic
Brief description/objective
PURPOSE
We aimed to identify gene expression signatures associated with angiogenesis and hypoxia pathways with predictive value for treatment response to bevacizumab/erlotinib (BE) of non-squamous advanced NSCLC patients.
EXPERIMENTAL DESIGN
Whole genome gene expression profiling was performed on 42 biopsy samples (from SAKK 19/05 trial) using Affymetrix exon arrays, and associations with the following endpoints: time-to-progression (TTP) under therapy, tumor-shrinkage (TS), and overall survival (OS) were investigated. Next, we performed gene set enrichment analyses using genes associated with the angiogenic process and hypoxia response to evaluate their predictive value for patients' outcome.
RESULTS
Our analysis revealed that both the angiogenic and hypoxia response signatures were enriched within the genes predictive of BE response, TS and OS. Higher gene expression levels (GELs) of the 10-gene angiogenesis-associated signature and lower levels of the 10-gene hypoxia response signature predicted improved TTP under BE, 7.1 months vs. 2.1 months for low vs. high-risk patients (P = 0.005), and median TTP 6.9 months vs. 2.9 months (P = 0.016), respectively. The hypoxia response signature associated with higher TS at 12 weeks and improved OS (17.8 months vs. 9.9 months for low vs. high risk patients, P = 0.001).
CONCLUSIONS
We were able to identify gene expression signatures derived from the angiogenesis and hypoxia response pathways with predictive value for clinical outcome in advanced non squamous NSCLC patients. This could lead to the identification of clinically relevant biomarkers, which will allow for selecting the subset of patients who benefit from the treatment and predict drug response.