Molecular mechanism of acquired drug resistance in the EGFR-TKI resistant cell line HCC827-TR

Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Unveiling Gene Regulatory Networks That Characterize Difference of Molecular Interplays Between Gastric Cancer Drug Sensitive and Resistance Cell Lines

Gastric cancer is a leading cause of cancer-related deaths globally and chemotherapy is widely accepted as the standard treatment for gastric cancer. However, drug resistance in cancer cells poses a significant obstacle to the success of chemotherapy, limiting its effectiveness in treating gastric cancer. Although many studies have been conducted to unravel the mechanisms of acquired drug resistance, the existing studies were based on abnormalities of a single gene, that is, differential gene expression (DGE) analysis. Single gene-based analysis alone is insufficient to comprehensively understand the mechanisms of drug resistance in cancer cells, because the underlying processes of the mechanism involve perturbations of the molecular interactions. To uncover the mechanism of acquired gastric cancer drug resistance, we perform for identification of differentially regulated gene networks between drug-sensitive and drug-resistant cell lines. We develop a computational strategy for identifying phenotype-specific gene networks by extending the existing method, CIdrgn, that quantifies the dissimilarity of gene networks based on comprehensive information of network structure, that is, regulatory effect between genes, structure of edge, and expression levels of genes. To enhance the efficiency of identifying differentially regulated gene networks and improve the biological relevance of our findings, we integrate additional information and incorporate knowledge of network biology, such as hubness of genes and weighted adjacency matrices. The outstanding capabilities of the developed strategy are validated through Monte Carlo simulations. By using our strategy, we uncover gene regulatory networks that specifically capture the molecular interplays distinguishing drug-sensitive and drug-resistant profiles in gastric cancer. The reliability and significance of the identified drug-sensitive and resistance-specific gene networks, as well as their related markers, are verified through literature. Our analysis for differentially regulated gene network identification has the capacity to characterize the drug-sensitive and resistance-specific molecular interplays related to mechanisms of acquired drug resistance that cannot be revealed by analysis based solely on abnormalities of a single gene, for example, DGE analysis. Through our analysis and comprehensive examination of relevant literature, we suggest that targeting the suppressors of the identified drug-resistant markers, such as the Melanoma Antigen (MAGE) family, Trefoil Factor (TFF) family, and Ras-Associated Binding 25 (RAB25), while enhancing the expression of inducers of the drug sensitivity markers [e.g., Serum Amyloid A (SAA) family], could potentially reduce drug resistance and enhance the effectiveness of chemotherapy for gastric cancer. We expect that the developed strategy will serve as a useful tool for uncovering cancer-related phenotype-specific gene regulatory networks that provide essential clues for uncovering not only drug resistance mechanisms but also complex biological systems of cancer.

Search for potentially biased epidermal growth factor receptor (EGFR) inhibitors through pharmacophore modelling, molecular docking, and molecular dynamics (MD) simulation approaches

Epidermal growth factor receptor (EGFR), being one of the most crucial receptor in cancer therapy, has been selected as a potential target for the present study. Ligand-based pharmacophore model (n = 30, R²=0.93 with root mean square deviation = 1.14, ΔCost = 144.27 and configuration cost = 21) was developed and validated with Fischer's randomisation (at 95% confidence), test set (n = 225, R² pred = 0.81), external data set (n = 13, R² pred = 0.95) and decoy set (n = 70), further the model has been used to search for novel EGFR inhibitors. The validated model was used for virtual screening of zinc database. A pool of 115,948 candidate molecules was screened through the model. Subsequently, molecules having predicted IC₅₀<0.2 µM were selected for screening through drug-like properties filter. Based on pharmacokinetic profile (ADMET study), Lipinski's rule of five and Veber's rule, 62 molecules were shortlisted for molecular docking. Using consensus docking, five hit molecules were selected, which were further considered for molecular dynamics simulation. Additionally MM-GBSA analysis was carried which showed that affinity of hits towards the receptor of three compound mainly ZINC305, ZINC131796 and ZINC131785 were similar to the standard vanedtinib. The simulation, performed for 100 ns, revealed that two hit molecules, namely ZINC305 and ZINC131785, showing potential interactions at the ligand-binding domain of EGFR protein with good ligand-protein stability. Communicated by Ramaswamy H. Sarma.

LMNA Reduced Acquired Resistance to Erlotinib in NSCLC by Reversing the Epithelial-Mesenchymal Transition via the FGFR/MAPK/c-fos Signaling Pathway

For patients exhibiting non-small-cell lung cancer (NSCLC) with activating epidermal growth factor receptor (EGFR) mutations, epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) are a first-line treatment. However, most patients who initially responded to EGFR-TKIs eventually developed acquired resistance, limiting the effectiveness of therapy. It has long been known that epithelial-mesenchymal transition (EMT) leads to acquired resistance to EGFR-TKIs in NSCLC. However, the mechanisms underlying the resistance dependent on EMT are unknown. This research aimed to reveal the effects of LMNA in the regulation of acquired resistance to erlotinib by EMT in NSCLC. The acquired erlotinib-resistant cells (HCC827/ER) were induced by gradual increase of concentrations of erlotinib in erlotinib-sensitive HCC827 cells. RNA sequencing and bioinformatics analysis were performed to uncover the involvement of LMNA in the EMT process that induced acquired resistance to erlotinib. The effect of LMNA on cell proliferation and migration was measured by clone-formation, wound-healing, and transwell assays, respectively. The EMT-related protein, nuclear shape and volume, and cytoskeleton changes were examined by immunofluorescence. Western blot was used to identify the underlying molecular mechanism of LMNA regulation of EMT. HCC827/ER cells with acquired resistance to erlotinib underwent EMT and exhibited lower LMNA expression compared to parental sensitive cells. LMNA negatively regulated the expression of EMT markers; HCC827/ER cells showed a significant up-regulation of mesenchymal markers, such as CDH2, SNAI2, VIM, ZEB1, and TWIST1. The overexpression of LMNA in HCC827/ER cells significantly inhibited EMT and cell proliferation, and this inhibitory effect of LMNA was enhanced in the presence of 2.5 μM erlotinib. Furthermore, a decrease in LMNA expression resulted in a higher nuclear deformability and cytoskeletal changes. In HCC827/ER cells, AKT, FGFR, ERK1/2, and c-fos phosphorylation levels were higher than those in HCC827 cells; Furthermore, overexpression of LMNA in HCC827/ER cells reduced the phosphorylation of AKT, ERK1/2, c-fos, and FGFR. In conclusion, our findings first demonstrated that downregulation of LMNA promotes acquired EGFR-TKI resistance in NSCLC with EGFR mutations by EMT. LMNA inhibits cell proliferation and migration of erlotinib-resistant cells via inhibition of the FGFR/MAPK/c-fos signaling pathway. These findings indicated LMNA as a driver of acquired resistance to erlotinib and provided important information about the development of resistance to erlotinib treatment in NSCLC patients with EGFR mutations.

Functions of non-coding RNAs in regulating cancer drug targets

With the development of precision medicine, the efficiency of tumor treatment has been significantly improved. More attention has been paid to targeted therapy and immunotherapy as the key to precision treatment of cancer. Targeting epidermal growth factor receptor (EGFR) has become one of the most important targeted treatments for various cancers. Comparing with traditional chemotherapy drugs, targeting EGFR is highly selective in killing tumor cells with better safety, tolerability and less side effect. In addition, tumor immunotherapy has become the fourth largest tumor therapy after surgery, radiotherapy and chemotherapy, especially immune checkpoint inhibitors. However, these treatments still produce a certain degree of drug resistance. Non-coding RNAs (ncRNAs) were found to play a key role in carcinogenesis, treatment and regulation of the efficacy of anticancer drugs in the past few years. Therefore, in this review, we aim to summarize the targeted treatment of cancers and the functions of ncRNAs in cancer treatment.

Cetuximab Combined With Sonodynamic Therapy Achieves Dual-Modal Image Monitoring for the Treatment of EGFR-Sensitive Non-Small-Cell Lung Cancer

Background

Blocking signaling by epidermal growth factor receptor (EGFR), can effectively inhibit the proliferation and differentiation of non-small-cell lung cancer (NSCLC). Additionally, an increasing number of NSCLC patients have treatment limitations caused by EGFR overexpression or mutations. Therefore, we constructed a nanotherapy platform consisting of cetuximab (CTX) to target EGFR-sensitive NSCLC with an iron tetroxide core loading the sound-sensitive agent IR780 for dual-mode imaging diagnosis by combining targeting and sonodynamic therapy (SDT) to reshape the tumor microenvironment (TME), enhance the SDT antitumor effects and improve the therapeutic effects of EGFR sensitivity.

Methods

IR780@INPs were prepared by reverse rotary evaporation, CTX was adsorbed/coupled to obtain IR780@INPs-CTX, and the morphology and structure were characterized. Intracellular ROS levels and cell apoptosis first verified its killing effects against tumor cells. Then, a nude mouse lung cancer subcutaneous xenograft model was established with HCC827 cells. A real-time fluorescence IVIS imaging system determined the targeting and live distribution of IR780@INPs-CTX in the transplanted tumors and the imaging effects of the T2 sequence of the INPs by magnetic resonance imaging (MRI) 0 h, 2 h, 4 h and 6 h after administration to confirm drug efficacy.

Results

In vitro, US+IR780@INPs-CTX produced a large amount of ROS after SDT to induce cell apoptosis, and significant cell death after live/dead staining was observed. In vivo fluorescence imaging showed the IR780@INPs-CTX was mainly concentrated in the tumor with a small amount in the liver. MRI displayed rapid enrichment of the IR780@INPs into tumor tissue 0h after injection and the T2 signal intensity gradually decreases with time without obvious drug enrichment in the surrounding tissues. In vivo, at the end of treatment, the US+IR780@INPs-CTX group showed disappearance or a continued decrease in tumor volume, indicating strong SDT killing effects.

Conclusion

The combination of CTX and SDT is expected to become a novel treatment for EGFR-sensitive NSCLC.