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1.
Oncogenesis ; 6(4): e307, 2017 Apr 03.
Artículo en Inglés | MEDLINE | ID: mdl-28368392

RESUMEN

Although many epidermal growth factor receptor (EGFR)-mutated lung cancer patients initially benefit from the EGFR-inhibitor erlotinib, all acquire resistance. So far, several mechanisms implicated in resistance have been identified, but the existence of multiple resistance mechanisms in parallel have only been sparsely investigated. In this study, we investigated parallel resistance mechanisms acquired by HCC827, an EGFR-mutated adenocarcinoma cell line dependent on EGFR activity and sensitive to erlotinib. The cell line was treated with erlotinib by stepwise escalation of the drug-concentration and erlotinib-resistant (HCC827ER) cells created. HCC827ER cells depicted a mixed epithelial and mesenchymal phenotype. To clarify potential parallel resistance mechanisms, 14 resistant subclones were established by limited dilution. Interestingly, all HCC827ER subclones harbored either a MET-amplification (6/14) or underwent EMT (8/14), mechanisms both found in previous studies, but not in co-occurrence. Both subclone-types were resistant to erlotinib, but only MET-subclones responded to the MET-inhibitors crizotinib and capmatinib. EMT-subclones on the other hand had markedly increased FGFR1 expression and responded to the FGFR-inhibitor AZD4547, whereas MET-subclones did not. Monitoring gene expression through the development of HCC827ER revealed upregulation of FGFR1 expression as an early response to erlotinib. In addition, FGFR1 expression increased upon short-term erlotinib treatment (48 h) identifying a physiological role immediately after erlotinib exposure. The high FGFR1 expression seen in EMT-subclones was stable even after five passages without erlotinib. Here we show, that parallel resistance mechanisms appear during erlotinib-resistance development in EGFR-mutated NSCLC cells and highlight a role for FGFR1 expression changes as an early response to erlotinib as well as a bypass-signaling mechanism.

2.
Mol Oncol ; 10(10): 1595-1602, 2016 12.
Artículo en Inglés | MEDLINE | ID: mdl-27856179

RESUMEN

BACKGROUND: Use of exosomes as biomarkers in non-small cell lung cancer (NSCLC) is an intriguing approach in the liquid-biopsy era. Exosomes are nano-sized vesicles with membrane-bound proteins that reflect their originating cell. Prognostic biomarkers are needed to improve patient selection for optimal treatment. We here evaluate exosomes by protein phenotyping as a prognostic biomarker in NSCLC. METHODS: Exosomes from plasma of 276 NSCLC patients were phenotyped using the Extracellular Vesicle Array; 49 antibodies captured the proteins on the exosomes, and a cocktail of biotin-conjugated antibodies binding the general exosome markers CD9, CD81 and CD63 was used to visualise the captured exosomes. For each individual membrane-bound protein, results were analysed based on presence, in a concentration-dependent manner, and correlated to overall survival (OS). RESULTS: The 49 proteins attached to the exosomal membrane were evaluated. NY-ESO-1, EGFR, PLAP, EpCam and Alix had a significant concentration-dependent impact on inferior OS. Due to multiple testing, NY-ESO-1 was the only marker that maintained a significant impact on inferior survival (hazard rate (HR) 1.78 95% (1.78-2.44); p = 0.0001) after Bonferroni correction. Results were adjusted for clinico-pathological characteristics, stage, histology, age, sex and performance status. CONCLUSION: We illustrate the promising aspects associated with the use of exosomal membrane-bound proteins as a biomarker and demonstrate that they are a strong prognostic biomarker in NSCLC.


Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas/diagnóstico , Exosomas/patología , Neoplasias Pulmonares/diagnóstico , Pulmón/patología , Proteínas de la Membrana/análisis , Adulto , Anciano , Anciano de 80 o más Años , Biomarcadores de Tumor/análisis , Carcinoma de Pulmón de Células no Pequeñas/patología , Femenino , Humanos , Neoplasias Pulmonares/patología , Masculino , Persona de Mediana Edad , Pronóstico , Análisis de Supervivencia
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