EGFR mutation-positive NSCLC: factors to consider when deciding first-line therapy

Detection and Significance of Cell-Free DNA Mutation in Pleural Effusion in Patients with Advanced NSCLC

Objective

To detect EGFR/KRAS genes in pleural effusion cell-free DNA in patients with advanced non-small-cell lung cancer (NSCLC) and to explore the clinical significance of EGFR/KRAS mutation status in pleural effusion.

Methods

A retrospective collection was performed on the specimens of pleural effusion and matched tissues from 50 patients with advanced NSCLC admitted to the hospital between January 2019 and January 2021. DNA mutation status of EGFR/KRAS in different specimens was detected and compared by pyrosequencing. The clinicopathological data and follow-up data of survival were collected. The relationship between DNA mutation and clinicopathological characteristics and prognosis was analyzed.

Results

In the 50 pleural effusion specimens, there were 22 cases (44.00%) with EGFR mutations (19/21 exon mutations), including 12 cases with EGFR19 deletion mutation and 10 cases with EGFR21 exon L858R mutation. There were 6 cases (12.00%) with KRAS mutations (single-base substitution mutations), including 4 cases with 12-codon mutation and 2 cases with 13-codon mutation. In the 50 tissue specimens, there were 24 cases (48.00%) with EGFR mutations and 4 cases (8.00%) with KRAS mutations. There was no significant difference between pleural effusion specimens and tissue specimens, with good consistency (kappa = 0.920-0.779, P > 0.05). EGFR mutation in pleural effusion was related to smoking history, types of pathological tissues, and lymph node metastasis (P < 0.05). The incidence of EGFR mutation was higher in nonsmokers, patients with lung adenocarcinoma, and patients with lymph node metastasis. The carcinoembryonic antigen (CEA) in patients with EGFR mutation was higher than that with wild-type EGFR, while the level of cytokeratin 19 fragment (Cy21-1) was lower than that with wild-type EGFR (P < 0.05). The 1-year overall survival rate in the EGFR mutation group was significantly higher than that in the EGFR wild group (68.18% vs. 42.86%) (HR = 0.419, 95% CI = 0.178-0.989, and P < 0.001).

Conclusion

For the detection of EGFR gene mutation, the results of the pleural effusion specimens and the tumor pathological tissue specimens were well consistent and the detection of pleural effusion could be used as an alternative method when tissue specimens cannot be obtained. EGFR gene mutations are present in majority in patients with advanced NSCLC. The incidence of EGFR mutation is higher in nonsmokers, patients with lung adenocarcinoma, those with lymph node metastasis, those with high-expression CEA, and those with low-expression Cy21-1. The prognosis is better in patients with EGFR mutation.

Predicting EGFR mutation, ALK rearrangement, and uncommon EGFR mutation in NSCLC patients by driverless artificial intelligence: a cohort study

Background

Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology and serum tumor markers (STMs).

Methods

In this single-center study, demographic features, pathology, EGFR mutation status, ALK rearrangement, and levels of STMs were collected from Wuhan Union Hospital. One retrospective set (N = 1089) was used to train diagnostic performance using one deep learning model and five machine learning models, as well as the stacked ensemble model for predicting EGFR mutations, uncommon EGFR mutations, and ALK rearrangement status. A consecutive testing cohort (n = 1464) was used to validate the predictive models.

Results

The final AI model using the stacked ensemble yielded optimal diagnostic performance with areas under the curve (AUC) of 0.897 and 0.883 for predicting EGFR mutation status and 0.995 and 0.921 for predicting ALK rearrangement in the training and testing cohorts, respectively. Furthermore, an overall accuracy of 0.93 and 0.83 in the training and testing cohorts, respectively, were achieved in distinguishing common and uncommon EGFR mutations, which were key evidence in guiding TKI selection.

Conclusions

In this study, driverless AI based on robust variables could help clinicians identify EGFR mutations and ALK rearrangement status and provide vital guidance in TKI selection for targeted therapy in NSCLC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02053-2.

Combined Treatment of Cinobufotalin and Gefitinib Exhibits Potent Efficacy against Lung Cancer

This study aimed to evaluate the efficacy of cinobufotalin combined with gefitinib in the treatment of lung cancer. A549 cells were treated with gefitinib, cinobufotalin, or cinobufotalin plus gefitinib. MTT assay, annexin-V/PI staining and flow cytometry, TUNEL staining, DCFH-DA staining, Western blot, and real-time RT-PCR were performed to investigate the synergistic inhibitory effect of cinobufotalin combined with gefitinib on the growth of A549 cells. Results showed that cinobufotalin synergized with gefitinib displayed inhibited cell viability and enhanced apoptosis in the combination group. Cinobufotalin combined with gefitinib induced a significant enhancement in reactive oxygen species (ROS) production accompanied by cell cycle arrest in the S phase arrest, characterized by upregulation of p21 and downregulation of cyclin A, cyclin E, and CDK2. Besides, cinobufotalin plus gefitinib downregulated the levels of HGF and c-Met. In summary, cinobufotalin combined with gefitinib impedes viability and facilitates apoptosis of A549 cells, indicating that the combined therapy might be a new promising treatment for lung cancer patients who are resistant to gefitinib.

Osimertinib for Front-Line Treatment of Locally Advanced or Metastatic EGFR-Mutant NSCLC Patients: Efficacy, Acquired Resistance and Perspectives for Subsequent Treatments

Non-small cell lung cancer (NSCLC) is one of the most efficient models for precision medicine in oncology. The most appropriate therapeutic for the patient is chosen according to the molecular characteristics of the tumor, schematically distributed between immunogenicity and oncogenic addiction. For this last concept, advanced NSCLC with epidermal growth factor receptor (EGFR) mutation is one of the most illustrative models. EGFR-tyrosine kinase inhibitors (TKIs) are the therapeutic backbone for this type of tumor. The recent development of a third-generation TKI, osimertinib, has been a new step forward in the treatment of NSCLC patients. In this article, we first review the clinical development of osimertinib and highlight its efficacy results. We then present the most frequent tumor escape mechanisms when osimertinib is prescribed in first line: off-target (MET amplification, HER2 amplification, BRAF mutation, gene fusions, histologic transformation) and on-target mechanisms (EGFR mutation). Finally, we discuss subsequent biomarker-driven treatment strategies.