Convergent Akt activation drives acquired EGFR inhibitor resistance in lung cancer

EGFR-mutant non-small cell lung cancer are often resistant to EGFR tyrosine kinase inhibitor treatment. In this study, the authors show that resistant tumors display high Akt activation and that a combined treatment with AKT inhibitors causes synergistic tumour growth inhibition in vitro and in vivo

Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer

Background: The analysis of liquid biopsies brings new opportunities in the precision oncology field. Under this context, extracellular vesicle circular RNAs (EV-circRNAs) have gained interest as biomarkers for lung cancer (LC) detection. However, standardized and robust protocols need to be developed to boost their potential in the clinical setting. Although nCounter has been used for the analysis of other liquid biopsy substrates and biomarkers, it has never been employed for EV-circRNA analysis of LC patients. Methods: EVs were isolated from early-stage LC patients (n = 36) and controls (n = 30). Different volumes of plasma, together with different number of pre-amplification cycles, were tested to reach the best nCounter outcome. Differential expression analysis of circRNAs was performed, along with the testing of different machine learning (ML) methods for the development of a prognostic signature for LC. Results: A combination of 500 μL of plasma input with 10 cycles of pre-amplification was selected for the rest of the study. Eight circRNAs were found upregulated in LC. Further ML analysis selected a 10-circRNA signature able to discriminate LC from controls with AUC ROC of 0.86. Conclusions: This study validates the use of the nCounter platform for multiplexed EV-circRNA expression studies in LC patient samples, allowing the development of prognostic signatures

First-line therapy and methylation status of CHFR in serum influence outcome to chemotherapy versus EGFR tyrosine kinase inhibitors as second-line therapy in stage IV non-small-cell lung cancer patients

The potential differential effect of first-line treatment and molecular mechanisms on survival to second-line chemotherapy or EGFR tyrosine kinase inhibitors (TKIs) in non-small-cell lung cancer (NSCLC) has not been fully investigated. In particular, CHFR is frequently methylated in NSCLC and may influence outcome. We analyzed the outcome of second-line chemotherapy or EGFR TKIs in 179 of 366 patients who had been treated in an ERCC1 mRNA-based customized cisplatin trial and correlated the results with CHFR methylation status. CHFR methylation in circulating DNA was examined by methylation-specific assay. A panel of seven human EGFR wild-type NSCLC cell lines was characterized for their sensitivity to sequential treatment with cisplatin and erlotinib, and the results were correlated with CHFR. Patients who had received first-line docetaxel/cisplatin attained an overall survival of 19.2 months when treated with second-line EGFR TKIs, in comparison with 10.7 months when treated with second-line chemotherapy (P = 0.0002). However, for patients who had received first-line docetaxel/gemcitabine, overall survival was 14.8 months with EGFR TKIs and 10.8 months with chemotherapy (P = 0.29). For patients with unmethylated CHFR overall survival to EGFR TKIs was 21.4 months, and 11.2 months for those with treated with chemotherapy (P = 0.0001). In the only lung tumor cell line not expressing CHFR, pretreatment with cisplatin was antagonistic to erlotinib, while it was synergistic in the other six lines. Second-line EGFR TKIs improved survival in patients receiving first-line cisplatin-based treatment. Unmethylated CHFR predicts increased survival to EGFR TKIs

Multiplex Analysis of CircRNAs from Plasma Extracellular Vesicle-Enriched Samples for the Detection of Early-Stage Non-Small Cell Lung Cancer

Background: The analysis of liquid biopsies brings new opportunities in the precision oncology field. Under this context, extracellular vesicle circular RNAs (EV-circRNAs) have gained interest as biomarkers for lung cancer (LC) detection. However, standardized and robust protocols need to be developed to boost their potential in the clinical setting. Although nCounter has been used for the analysis of other liquid biopsy substrates and biomarkers, it has never been employed for EV-circRNA analysis of LC patients. Methods: EVs were isolated from early-stage LC patients (n = 36) and controls (n = 30). Different volumes of plasma, together with different number of pre-amplification cycles, were tested to reach the best nCounter outcome. Differential expression analysis of circRNAs was performed, along with the testing of different machine learning (ML) methods for the development of a prognostic signature for LC. Results: A combination of 500 μL of plasma input with 10 cycles of pre-amplification was selected for the rest of the study. Eight circRNAs were found upregulated in LC. Further ML analysis selected a 10-circRNA signature able to discriminate LC from controls with AUC ROC of 0.86. Conclusions: This study validates the use of the nCounter platform for multiplexed EV-circRNA expression studies in LC patient samples, allowing the development of prognostic signatures