Overcoming molecular mechanisms of resistance to first-generation epidermal growth factor receptor tyrosine kinase inhibitors

Stratification of non-small cell lung adenocarcinoma patients with EGFR actionable mutations based on drug-resistant stem cell genes

EGFR-TKIs were used in NSCLC patients with actionable EGFR mutations and prolong prognosis. However, most patients treated with EGFR-TKIs developed resistance within around one year. This suggests that residual EGFR-TKIs resistant cells may eventually lead to relapse. Predicting resistance risk in patients will facilitate individualized management. Herein, we built an EGFR-TKIs resistance prediction (R-index) model and validate in cell line, mice, and cohort. We found significantly higher R-index value in resistant cell lines, mice models and relapsed patients. Patients with an elevated R-index had significantly shorter relapse time. We also found that the glycolysis pathway and the KRAS upregulation pathway were related to EGFR-TKIs resistance. MDSC is a significant immunosuppression factor in the resistant microenvironment. Our model provides an executable method for assessing patient resistance status based on transcriptional reprogramming and may contribute to the clinical translation of patient individual management and the study of unclear resistance mechanisms.

Discovery of potent antiproliferative agents from selected oxygen heterocycles as EGFR tyrosine kinase inhibitors from the U.S. National Cancer Institute database by in silico screening and bioactivity evaluation

A similarity search was conducted on the U.S. Enhanced National Cancer Institute Database Browser 2.2 to find structures related to 1,5-dihydroxy-9H-xanthen-9-one, a previously established EGFR-TK inhibitor. Compounds were virtually screened and selected for bioactivity testing revealed 5 candidates, mostly displayed stronger antiproliferative activities than erlotinib with IC₅₀ values between 0.95 and 17.71 μM against overexpressed EGFR-TK cancer cell lines: A431 and HeLa. NSC107228 displayed the strongest antiproliferative effects with IC₅₀ values of 2.84 and 0.95 μM against A431 and HeLa cancer cell lines, respectively. Three compounds, NSC81111, NSC381467 and NSC114126 inhibited EGFR-TK with IC₅₀ values between 0.15 and 30.18 nM. NSC81111 was the best inhibitor with IC₅₀ = 0.15 nM. Molecular docking analysis of the 3 compounds predicted hydrogen bonding and hydrophobic interactions with key residues were important for the bioactivities observed. Furthermore, calculations of the physicochemical properties suggest the compounds are drug-like and are potentially active orally.

Natural tyrosine kinase inhibitors acting on the epidermal growth factor receptor: Their relevance for cancer therapy

Epidermal growth factor receptor (EGFR), also known as ErbB-1/HER-1, plays a key role in the regulation of the cell proliferation, migration, differentiation, and survival. Since the constitutive activation or overexpression of EGFR is nearly found in various cancers, the applications focused on EGFR are the most widely used in the clinical level, including the therapeutic drugs of targeting EGFR, monoclonal antibodies (mAbs) and tyrosine kinase inhibitors (TKIs).Over the past decades, the compounds from natural sources have been a productive source of novel drugs, especially in both discovery and development of anti-tumor drugs by targeting the EGFR pathways as the TKIs. This work presents a review of the compounds from natural sources as potential EGFR-TKIs involved in the regulation of cancer. Moreover, high-throughput drug screening of EGFR-TKIs from the natural compounds has also been summarized.

HOTAIR induces EGFR-TKIs resistance in non-small cell lung cancer through epithelial-mesenchymal transition

OBJECTIVE

Previous research found that HOTAIR, a long non-coding RNA, is aberrantly expressed and associated with tumor invasion, metastasis and chemo-resistance in many cancers. The aim of this study was to investigate the role of HOTAIR in resistance of EGFR-TKIs in NSCLC.

METHODS

HOTAIR expression level was detected by quantitative reverse transcription polymerase chain reaction (qRT-PCR) in NSCLC cell lines or tumor tissues. A total of 62 samples with EGFR-mutant and EGFR-TKI-sensitive NSCLCs, 42 with acquired resistance and 27 with primary resistance to EGFR-TKIs were analyzed. The effect of HOTAIR on cell proliferation and apoptosis was undergone by CCK-8 and flow cytometry assays. The expression of EMT proteins was assessed by western blot.

RESULTS

HOTAIR was significantly down-regulated in lung cancer cells (PC9/R, H1975, H1299 and A549) and patients with primary and acquired resistance to EGFR-TKIs. In clinical setting, high levels of HOTAIR expression was significantly correlated with longer progression-free survival (PFS) [P < 0.01] compared with low HOTAIR expression subgroup in tumors which respond to EGFR-TKIs. In vitro, over-expression HOTAIR could restore gefitinib sensitivity in gefitinib-resistant cells (PC9/R, H1299 and A549), but this change in sensitivity was not observed in H1975. Up-regulated HOTAIR induced cell apoptosis in PC9/R, H1299 and A549, and activated epithelial-mesenchymal transition (EMT).

CONCLUSIONS

HOTAIR expression was associated with primary and acquired resistance to EGFR-TKIs and could regulate cell proliferation through activating cell apoptosis and EMT, which suggest that HOTAIR might be able to act as a biomarker to predict the EGFR-TKIs resistance.

An Eigen-Binding Site Based Method for the Analysis of Anti-EGFR Drug Resistance in Lung Cancer Treatment

We explore the drug resistance mechanism in non-small cell lung cancer treatment by characterizing the drug-binding site of a protein mutant based on local surface and energy features. These features are transformed to an eigen-binding site space and used for drug resistance level prediction and analysis.

Glucose metabolism-targeted therapy and withaferin A are effective for epidermal growth factor receptor tyrosine kinase inhibitor-induced drug-tolerant persisters

In pathway‐targeted cancer drug therapies, the relatively rapid emergence of drug‐tolerant persisters (DTPs) substantially limits the overall therapeutic benefit. However, little is known about the roles of DTPs in drug resistance. In this study, we investigated the features of epidermal growth factor receptor–tyrosine kinase inhibitor‐induced DTPs and explored a new treatment strategy to overcome the emergence of these DTPs. We used two EGFR‐mutated lung adenocarcinoma cell lines, PC9 and II‐18. They were treated with 2 μM gefitinib for 6, 12, or 24 days or 6 months. We analyzed the mRNA expression of the stem cell‐related markers by quantitative RT‐PCR and the expression of the cellular senescence‐associated proteins. Then we sorted DTPs according to the expression pattern of CD133 and analyzed the features of sorted cells. Finally, we tried to ablate DTPs by glucose metabolism targeting therapies and a stem‐like cell targeting drug, withaferin A. Drug‐tolerant persisters were composed of at least two types of cells, one with the properties of cancer stem‐like cells (CSCs) and the other with the properties of therapy‐induced senescent (TIS) cells. The CD133^high cell population had CSC properties and the CD133^low cell population had TIS properties. The CD133^low cell population containing TIS cells showed a senescence‐associated secretory phenotype that supported the emergence of the CD133^high cell population containing CSCs. Glucose metabolism inhibitors effectively eliminated the CD133^low cell population. Withaferin A effectively eliminated the CD133^high cell population. The combination of phloretin and withaferin A effectively suppressed gefitinib‐resistant tumor growth.

LncRNA UCA1 in anti-cancer drug resistance

The pivotal role of the long non-coding RNA (lncRNA) urothelial carcinoma associated 1 (UCA1) in anti-cancer drug resistance has been confirmed in many cancers. Overexpression of lncRNA UCA1 correlates with resistance to chemotherapeutics such as cisplatin, gemcitabine, 5-FU, tamoxifen, imatinib and EGFR-TKIs, whereas lncRNA UCA1 knockdown restores drug sensitivity. These studies highlight the potential of lncRNA UCA1 as a diagnostic and prognostic biomarker, and a therapeutic target in malignant tumors. In this review, we address the role of lncRNA UCA1 in anti-cancer drug resistance and discuss its potential in future clinical applications.

Identifying EGFR mutation-induced drug resistance based on alpha shape model analysis of the dynamics

Background

Epidermal growth factor receptor (EGFR) mutation-induced drug resistance is a difficult problem in lung cancer treatment. Studying the molecular mechanisms of drug resistance can help to develop corresponding treatment strategies and benefit new drug design.

Methods

In this study, Rosetta was employed to model the EGFR mutant structures. Then Amber was carried out to conduct molecular dynamics (MD) simulation. Afterwards, we used Computational Geometry Algorithms Library (CGAL) to compute the alpha shape model of the mutants.

Results

We analyzed the EGFR mutation-induced drug resistance based on the motion trajectories obtained from MD simulation. We computed alpha shape model of all the trajectory frames for each mutation type. Solid angle was used to characterize the curvature of the atoms at the drug binding site. We measured the knob level of the drug binding pocket of each mutant from two ways and analyzed its relationship with the drug response level. Results show that 90 % of the mutants can be grouped correctly by setting a certain knob level threshold.

Conclusions

There is a strong correlation between the geometric properties of the drug binding pocket of the EGFR mutants and the corresponding drug responses, which can be used to predict the response of a new EGFR mutant to a drug molecule.

Long non-coding RNA UCA1 induces non-T790M acquired resistance to EGFR-TKIs by activating the AKT/mTOR pathway in EGFR-mutant non-small cell lung cancer

The aim of this study was to explore the role of long non-coding RNA UCA1 (urothelial cancer-associated 1) in acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) in EGFR-mutant non-small cell lung cancer (NSCLC). In our study, UCA1 expression was significantly increased in lung cancer cells and patients with acquired resistance to EGFR-TKIs. Over-expression of UCA1 was significantly associated with a shorter progression-free survival (PFS) [13.0 vs. 8.5 months, P < 0.01] in tumors with respond to EGFR-TKIs. The significant relationship was not observed in patients with T790M mutation (10.5 vs. 12.0 months, P = 0.778), but in patients with non-T790M (19.0 vs. 9.0 months, P = 0.023). UCA1 knockdown restored gefitinib sensitivity in acquired resistant cells with non-T790M and inhibited the activation of the AKT/mTOR pathway and epithelial-mesenchymal transition (EMT). The mTOR inhibitor was effective in UCA1-expressing cell PC9/R. Inhibiting mTOR could change the expression of UCA1, although there was no significant difference. In conclusion, the influence of over-expression of UCA1 on PFS for patients with acquired resistance to EGFR-TKIs was from the subgroup with non-T790M mutation. UCA1 may induce non-T790M acquired resistance to EGFR-TKIs by activating the AKT/mTOR pathway and EMT.

Development of a Drug-Response Modeling Framework to Identify Cell Line Derived Translational Biomarkers That Can Predict Treatment Outcome to Erlotinib or Sorafenib

Development of drug responsive biomarkers from pre-clinical data is a critical step in drug discovery, as it enables patient stratification in clinical trial design. Such translational biomarkers can be validated in early clinical trial phases and utilized as a patient inclusion parameter in later stage trials. Here we present a study on building accurate and selective drug sensitivity models for Erlotinib or Sorafenib from pre-clinical in vitro data, followed by validation of individual models on corresponding treatment arms from patient data generated in the BATTLE clinical trial. A Partial Least Squares Regression (PLSR) based modeling framework was designed and implemented, using a special splitting strategy and canonical pathways to capture robust information for model building. Erlotinib and Sorafenib predictive models could be used to identify a sub-group of patients that respond better to the corresponding treatment, and these models are specific to the corresponding drugs. The model derived signature genes reflect each drug’s known mechanism of action. Also, the models predict each drug’s potential cancer indications consistent with clinical trial results from a selection of globally normalized GEO expression datasets.

EGFR Mutant Structural Database: computationally predicted 3D structures and the corresponding binding free energies with gefitinib and erlotinib

Background

Epidermal growth factor receptor (EGFR) mutation-induced drug resistance has caused great difficulties in the treatment of non-small-cell lung cancer (NSCLC). However, structural information is available for just a few EGFR mutants. In this study, we created an EGFR Mutant Structural Database (freely available at http://bcc.ee.cityu.edu.hk/data/EGFR.html), including the 3D EGFR mutant structures and their corresponding binding free energies with two commonly used inhibitors (gefitinib and erlotinib).

Results

We collected the information of 942 NSCLC patients belonging to 112 mutation types. These mutation types are divided into five groups (insertion, deletion, duplication, modification and substitution), and substitution accounts for 61.61% of the mutation types and 54.14% of all the patients. Among all the 942 patients, 388 cases experienced a mutation at residue site 858 with leucine replaced by arginine (L858R), making it the most common mutation type. Moreover, 36 (32.14%) mutation types occur at exon 19, and 419 (44.48%) patients carried a mutation at exon 21. In this study, we predicted the EGFR mutant structures using Rosetta with the collected mutation types. In addition, Amber was employed to refine the structures followed by calculating the binding free energies of mutant-drug complexes.

Conclusions

The EGFR Mutant Structural Database provides resources of 3D structures and the binding affinity with inhibitors, which can be used by other researchers to study NSCLC further and by medical doctors as reference for NSCLC treatment.

Microarray expression profile of long non-coding RNAs in EGFR-TKIs resistance of human non-small cell lung cancer

The application of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) is limited by drug resistance in non-small cell lung cancer (NSCLC). Long non-coding RNAs (lncRNAs) are known to be involved in tumor development and metastasis, as well as chemotherapy resistance. To gain insight into the molecular mechanisms of EGFR-TKIs resistance, EGFR-TKIs‑sensitive and ‑resistant human lung cancer cells were analyzed by lncRNA microarray. In the present study, we found a total of 22,587 lncRNAs expressed in lung cancer cells. Of these, the expression level of 1,731 lncRNAs was upregulated >2-fold compared with gefitinib-sensitive cells while that of 2,936 was downregulated. Bioinformatics analysis (GO and pathway analyses) revealed that some classical pathways participating in cell proliferation and apoptosis were aberrantly expressed in these cells (P-value cut-off was 0.05). Enhancer-like lncRNAs and their nearby coding genes were analyzed. Six lncRNAs were identified as potential enhancers. Several lncRNAs were validated in lung cancer cell lines using RT-qPCR. To the best of our knowledge, the results showed for the first time that differentially expressed lncRNAs responded to EGFR-TKIs resistance in NSCLC cells. LncRNAs may therefore be novel candidate biomarkers and potential targets for EGFR-TKIs therapy in the future.

Pilot phase I/II personalized therapy trial for metastatic colorectal cancer: evaluating the feasibility of protein pathway activation mapping for stratifying patients to therapy with imatinib and panitumumab

This nonrandomized phase I/II trial assessed the efficacy/tolerability of imatinib plus panitumumab in patients affected by metastatic colorectal cancer (mCRC) after stratification to treatment by selection of activated imatinib drug targets using reverse-phase protein array (RPPA). mCRC patients presenting with a biopsiable liver metastasis were enrolled. Allocation to the experimental and control arms was established using functional pathway activation mapping of c-Kit, PDGFR, and c-Abl phosphorylation by RPPA. The experimental arm received run-in escalation therapy with imatinib followed by panitumumab. The control arm received panitumumab alone. Seven patients were enrolled in the study. For three of the seven patients, sequential pre- and post-treatment biopsies were used to evaluate the effect of the therapeutic compounds on the drug targets and substrates. A decrease in the activation level of the drug targets and downstream substrates was observed in two of three patients. Combination therapy increased the activation of the AKT-mTOR pathway and several receptor tyrosine kinases. This study proposes a novel methodology for stratifying patients to personalized treatment based on the activation level of the drug targets. This workflow provides the ability to monitor changes in the signaling pathways after the administration of targeted therapies and to identify compensatory mechanisms.