Experimental study of EGFR-TKI aumolertinib combined with ionizing radiation in EGFR mutated NSCLC brain metastases tumor

Almonertinib and alflutinib show novel inhibition on rare EGFR S768I mutant cells

PURPOSE

EGFR classical mutations respond well to EGFR tyrosine kinase inhibitors. However, it is uncertain whether currently available EGFR-TKIs are effective against rare EGFR mutations and compound mutations. Herein, the effectiveness of almonertinib and alflutinib, the third-generation tyrosine kinase inhibitors developed in China, on rare EGFR S768I mutations and compound mutations is identified.

METHODS

In this study, using CRISPR method, four EGFR S768I mutation cell lines were constructed, and the sensitivity of EGFR to almonertinib and alflutinib was tested, with positive controls being the 1st (gefitinib), 2nd (afatinib), and 3rd (osimertinib) generation drugs.

RESULTS

The present results indicate that almonertinib and alflutinib can effectively inhibit cell viability and proliferation in rare EGFR S768I mutations through the ERK or AKT pathways in a time-dependent manner, by blocking the cell cycle and inhibiting apoptosis.

CONCLUSIONS

These findings suggest that almonertinib and alflutinib may be potential therapeutic options for non-small cell lung cancer patients with the EGFR S768I mutation.

The influence of drug-induced metabolic enzyme activity inhibition and CYP3A4 gene polymorphism on aumolertinib metabolism

The purpose of this study is to clarify the drug interaction profile of aumolertinib, and the influence of CYP3A4 genetic polymorphism on aumolertinib metabolic characteristics. Through microsomal enzyme reactions, we screened 153 drugs and identified 15 that significantly inhibited the metabolism of aumolertinib. Among them, telmisartan and carvedilol exhibited potent inhibitory activities in rat liver microsomes (RLM) and human liver microsomes (HLM). In vivo, the pharmacokinetic parameters of aumolertinib, including AUC and Cmax, were significantly altered when co-administered with carvedilol, with a notable decrease in the clearance rate CLz/F. Interestingly, the pharmacokinetic parameters of the metabolite HAS-719 exhibited a similar trend as aumolertinib when co-administered. Mechanistically, both telmisartan and carvedilol exhibited a mixed-type inhibition on the metabolism of aumolertinib. Additionally, we used a baculovirus-insect cell expression system to prepare 24 recombinant CYP3A4 microsomes and obtained enzymatic kinetic parameters using aumolertinib as a substrate. Enzyme kinetic studies obtained the kinetic parameters of various CYP3A4 variant-mediated metabolism of aumolertinib. Based on the relative clearance rates, CYP3A4.4, 5, 7, 8, 9, 12, 13, 14, 17, 18, 19, 23, 24, 33, and 34 showed significantly lower clearance rates compared to the wild-type. Among the different CYP3A4 variants, the inhibitory potency of telmisartan and carvedilol on the metabolism of aumolertinib also varied. The IC50 values of telmisartan and carvedilol in CYP3A4.1 were 6.68 ± 1.76 μM and 0.60 ± 0.25 μM, respectively, whereas in CYP3A4.12, the IC50 exceeded 100 μM. Finally, we utilized adeno-associated virus to achieve liver-specific high expression of CYP3A4*1 and CYP3A4*12. In the group with high expression of the less active CYP3A4*12, the magnitude of the drug-drug interaction was significantly attenuated. In conclusion, CYP3A4 genetic polymorphism not only influences the pharmacokinetic characteristics of aumolertinib, but also the inhibitory potency of telmisartan and carvedilol on it.

Nicotine suppresses crystalline silica-induced astrocyte activation and neuronal death by inhibiting NF-κB in the mouse hippocampus

AIMS

Exposure to crystalline silica (CS) in occupational settings induces chronic inflammation in the respiratory system and, potentially, the brain. Some workers are frequently concurrently exposed to both CS and nicotine. Here, we explored the impact of nicotine on CS-induced neuroinflammation in the mouse hippocampus.

METHODS

In this study, we established double-exposed models of CS and nicotine in C57BL/6 mice. To assess depression-like behavior, experiments were conducted at 3, 6, and 9 weeks. Serum inflammatory factors were analyzed by ELISA. Hippocampus was collected for RNA sequencing analysis and examining the gene expression patterns linked to inflammation and cell death. Microglia and astrocyte activation and hippocampal neuronal death were assessed using immunohistochemistry and immunofluorescence staining. Western blotting was used to analyze the NF-κB expression level.

RESULTS

Mice exposed to CS for 3 weeks showed signs of depression. This was accompanied by elevated IL-6 in blood, destruction of the blood-brain barrier, and activation of astrocytes caused by an increased NF-κB expression in the CA1 area of the hippocampus. The elevated levels of astrocyte-derived Lcn2 and upregulated genes related to inflammation led to higher neuronal mortality. Moreover, nicotine mitigated the NF-κB expression, astrocyte activation, and neuronal death, thereby ameliorating the associated symptoms.

CONCLUSION

Silica exposure induces neuroinflammation and neuronal death in the mouse hippocampal CA1 region and depressive behavior. However, nicotine inhibits CS-induced neuroinflammation and neuronal apoptosis, alleviating depressive-like behaviors in mice.

Optimal sequence of LT for symptomatic BM in EGFR-mutant NSCLC: a comparative study of first-line EGFR-TKIs with/without upfront LT

BACKGROUND

The third-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) can penetrate blood-brain barrier and are effective for brain metastases (BMs). There is no consensus on the optimal sequence of local therapy (LT) and EGFR-TKIs for symptomatic BM patients because patients suffering neurological symptoms were not enrolled in most clinical trials.

METHODS

Non-small cell lung cancer (NSCLC) patients with EGFR mutation (EGFRm) and symptomatic BM receiving first-line osimertinib and aumolertinib from two medical centers were collected. All participants were allocated into the third-generation EGFR-TKIs (TKIs) group and the upfront LT (uLT) plus third-generation EGFR-TKIs (TKIs + uLT) group. Demographic data, survival outcomes, treatment failure patterns, and adverse events were evaluated between the two groups. We also conducted subgroup analyses to explore the impact of BM number on survival outcomes.

RESULTS

86 patients were enrolled, 44 in the TKIs group and 42 in the TKIs + uLT group. There were no significant differences in the short-term response between the groups. TKIs + uLT was associated with significantly longer overall survival (OS) (43 vs. 28 months; hazard ratio [HR], 0.36, 95% confidence interval [CI], 0.17-0.77; p = .011). No differences in progression-free survival (PFS), intracranial PFS (iPFS), failure patterns, or safety were observed. In subgroup analyses of oligo-BM patients, TKIs + uLT could prolong OS (43 vs. 31 months; HR 0.22; 95% CI 0.05-0.92; p = .015).

CONCLUSIONS

EGFRm NSCLC patients with symptomatic BM might benefit from uLT, particularly oligo-BM patients. However, larger prospective cohort studies should be carried out to confirm the responses of the TKIs + uLT scheme.

Animal models of brain and spinal cord metastases of NSCLC established using a brain stereotactic instrument

Objective

Animal models of brain and spinal cord metastases of non-small cell lung cancer were established through the intracranial injection of PC-9 Luc cells with a brain stereotaxic device. This method provides a reliable modeling method for studying brain and spinal cord metastases of non-small cell lung cancer.

Methods

PC-9 Luc cells at logarithmic growth stage were injected into the skulls of 5-week-old BALB/c nude mice at different cell volumes (30 × 104, 80 × 104) and different locations (using anterior fontanel as a location point, 1 mm from the coronal suture, and 1.5 mm from the sagittal suture on the right upper and right lower side of the skull). After 1 week of cell inoculation, fluorescence signals of tumor cells in the brain and spinal were detected using the IVIS Xenogen Imaging system. After 4 weeks, brain and spinal tissues from the nude mice were harvested. Following paraffin-embedded sectioning, HE staining was performed on the tissues.

Results

The fluorescence signals revealed that both brain and spinal cord metastasis occurred in the mice where the cells were injected at the lower right side of the skull. There was only brain metastasis in the nude mice injected with 30 × 104 cells at the upper right side of the skull. Both brain and spinal cord metastasis occurred in the nude mice injected with 80 × 104 cells. The HE staining revealed that both brain and spinal cord metastasis occurred in the mice injected with different amounts of PC-9 Luc cells, consistent with the results detected using the IVIS Xenogen Imaging system, thereby demonstrating the reliability of detecting fluorescent signals in vivo to determine tumor growth.

Conclusion

It is a reliable method to establish the animal model of brain and spinal cord metastases of non-small cell lung cancer by injecting different quantities of cells from different positions with a brain stereotaxic device. The IVIS Xenogen Imaging system has high reliability in detecting the fluorescence signals of brain and spinal cord metastatic tumors.

A multi-task deep learning model for EGFR genotyping prediction and GTV segmentation of brain metastasis

BACKGROUND

The precise prediction of epidermal growth factor receptor (EGFR) mutation status and gross tumor volume (GTV) segmentation are crucial goals in computer-aided lung adenocarcinoma brain metastasis diagnosis. However, these two tasks present continuous difficulties due to the nonuniform intensity distributions, ambiguous boundaries, and variable shapes of brain metastasis (BM) in MR images.The existing approaches for tackling these challenges mainly rely on single-task algorithms, which overlook the interdependence between these two tasks.

METHODS

To comprehensively address these challenges, we propose a multi-task deep learning model that simultaneously enables GTV segmentation and EGFR subtype classification. Specifically, a multi-scale self-attention encoder that consists of a convolutional self-attention module is designed to extract the shared spatial and global information for a GTV segmentation decoder and an EGFR genotype classifier. Then, a hybrid CNN-Transformer classifier consisting of a convolutional block and a Transformer block is designed to combine the global and local information. Furthermore, the task correlation and heterogeneity issues are solved with a multi-task loss function, aiming to balance the above two tasks by incorporating segmentation and classification loss functions with learnable weights.

RESULTS

The experimental results demonstrate that our proposed model achieves excellent performance, surpassing that of single-task learning approaches. Our proposed model achieves a mean Dice score of 0.89 for GTV segmentation and an EGFR genotyping accuracy of 0.88 on an internal testing set, and attains an accuracy of 0.81 in the EGFR genotype prediction task and an average Dice score of 0.85 in the GTV segmentation task on the external testing set. This shows that our proposed method has outstanding performance and generalization.

CONCLUSION

With the introduction of an efficient feature extraction module, a hybrid CNN-Transformer classifier, and a multi-task loss function, the proposed multi-task deep learning network significantly enhances the performance achieved in both GTV segmentation and EGFR genotyping tasks. Thus, the model can serve as a noninvasive tool for facilitating clinical treatment.