Personalized Prediction of Acquired Resistance to EGFR-Targeted Inhibitors Using a Pathway-Based Machine Learning Approach

Causal relationship between key genes and metabolic dysfunction-associated fatty liver disease risk mediated by immune cells: A Mendelian randomization and mediation analysis

AIM

Non-invasive diagnostics for metabolic dysfunction-associated fatty liver disease (MAFLD) remain challenging. We aimed to identify novel key genes as non-invasive biomarkers for MAFLD, elucidate causal relationships between biomarkers and MAFLD and determine the role of immune cells as potential mediators.

MATERIALS AND METHODS

Utilizing published transcriptome data of patients with biopsy-proven MAFLD, we applied linear models for microarray data, least absolute shrinkage and selector operation (LASSO) regressions and receiver operating characteristic (ROC) curve analyses to identify and validate biomarkers for MAFLD. Using the expression quantitative trait loci database and a cohort of 778 614 Europeans, we used Mendelian randomization to analyse the causal relationships between key biomarkers and MAFLD. Additionally, mediation analysis was performed to examine the involvement of 731 immunophenotypes in these relationships.

RESULTS

We identified 31 differentially expressed genes, and LASSO regression showed three hub genes, IGFBP2, PEG10, and P4HA1, with area under the receiver operating characteristic (AUROC) curve of 0.807, 0.772 and 0.791, respectively, for identifying MAFLD. The model of these three genes had an AUROC of 0.959 and 0.800 in the development and validation data sets, respectively. This model was also validated using serum-based enzyme-linked immunosorbent assay data from MAFLD patients and control subjects (AUROC: 0.819, 95% confidence interval: 0.736-0.902). PEG10 was associated with an increased MAFLD risk (odds ratio = 1.106, p = 0.032) via inverse variance-weighted analysis, and about 30% of this risk was mediated by the percentage of CD11c + CD62L- monocytes.

CONCLUSIONS

The MAFLD panels have good diagnostic accuracy, and the causal link between PEG10 and MAFLD was mediated by the percentage of CD11c + CD62L- monocytes.

Effects of dacomitinib on the pharmacokinetics of poziotinib in vivo and in vitro

CONTEXT

Dacomitinib and poziotinib, irreversible ErbB family blockers, are often used for treatment of non-small cell lung cancer (NSCLC) in the clinic.

OBJECTIVE

This study investigates the effect of dacomitinib on the pharmacokinetics of poziotinib in rats.

MATERIALS AND METHODS

Twelve Sprague-Dawley rats were randomly divided into two groups: the test group (20 mg/kg dacomitinib for 14 consecutive days) and the control group (equal amounts of vehicle). Each group was given an oral dose of 10 mg/kg poziotinib 30 min after administration of dacomitinib or vehicle at the end of the 14 day administration. The concentration of poziotinib in plasma was quantified by UPLC-MS/MS. Both in vitro effects of dacomitinib on poziotinib and the mechanism of the observed inhibition were studied in rat liver microsomes and human liver microsomes.

RESULTS

When orally administered, dacomitinib increased the AUC, T_max and decreased CL of poziotinib (p < 0.05). The IC₅₀ values of M1 in RLM, HLM and CYP3A4 were 11.36, 30.49 and 19.57 µM, respectively. The IC₅₀ values of M2 in RLM, HLM and CYP2D6 were 43.69, 0.34 and 0.11 µM, respectively, and dacomitinib inhibited poziotinib by a mixed way in CYP3A4 and CYP2D6. The results of the in vivo experiments were consistent with those of the in vitro experiments.

CONCLUSIONS

This research demonstrates that a drug-drug interaction between poziotinib and dacomitinib possibly exists when readministered with poziotinib; thus, clinicians should pay attention to the resulting changes in pharmacokinetic parameters and accordingly, adjust the dose of poziotinib in clinical settings.

FGFR-TKI resistance in cancer: current status and perspectives

Fibroblast growth factor receptors (FGFRs) play key roles in promoting the proliferation, differentiation, and migration of cancer cell. Inactivation of FGFRs by tyrosine kinase inhibitors (TKI) has achieved great success in tumor-targeted therapy. However, resistance to FGFR-TKI has become a concern. Here, we review the mechanisms of FGFR-TKI resistance in cancer, including gatekeeper mutations, alternative signaling pathway activation, lysosome-mediated TKI sequestration, and gene fusion. In addition, we summarize strategies to overcome resistance, including developing covalent inhibitors, developing dual-target inhibitors, adopting combination therapy, and targeting lysosomes, which will facilitate the transition to precision medicine and individualized treatment.