Data from Gene Expression Signatures Predictive of Bevacizumab/Erlotinib Therapeutic Benefit in Advanced Nonsquamous Non–Small Cell Lung Cancer Patients (SAKK 19/05 trial)

Autor: Martin H. Brutsche, Francesco Zappa, Dirk Klingbiel, Bogdan D. Grigoriu, Daniel Betticher, Cornelia Droege, Oliver Dürr, Ina I. Macovei, Florent Baty, Anca Franzini
Rok vydání: 2023
Popis: 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 nonsquamous advanced non–small cell lung cancer (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 (GEL) 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 versus 2.1 months for low versus high-risk patients (P = 0.005), and median TTP 6.9 months versus 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 nonsquamous 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. Clin Cancer Res; 21(23); 5253–63. ©2015 AACR.See related commentary by Cascone and Heymach, p. 5188
Databáze: OpenAIRE