Competitive evolution of NSCLC tumor clones and the drug resistance mechanism of first-generation EGFR-TKIs in Chinese NSCLC patients

STAT3-specific nanocarrier for shRNA/drug dual delivery and tumor synergistic therapy

Non-small cell lung cancer (NSCLC) is a major disease with high incidence, low survival rate and prone to develop drug resistance to chemotherapy. The mechanism of secondary drug resistance in NSCLC chemotherapy is very complex, and studies have shown that the abnormal activation of STAT3 (Signal Transducer and Activator of Transcription 3) plays an important role in it. In this study, the pGPU6/GFP/Neo STAT3-shRNA recombinant plasmid was constructed with STAT3 as the precise target. By modifying hydrophilic and hydrophobic blocks onto chitosan, a multifunctional vitamin E succinate-chitosan-polyethylene glycol monomethyl ether histidine (VES-CTS-mPEG-His) micelles were synthesized. The micelles could encapsulate hydrophobic drug doxorubicin through self-assembly, and load the recombinant pGPU6/GFP/Neo STAT3-shRNA (pDNA) through positive and negative charges to form dual-loaded nanoparticles DOX/VCPH/pDNA. The co-delivery and synergistic effect of DOX and pDNA could up-regulate the expression of PTEN (Phosphatase and Tensin Homolog), down-regulate the expression of CD31, and induce apoptosis of tumor cells. The results of precision targeted therapy showed that DOX/VCPH/pDNA could significantly down-regulate the expression level of STAT3 protein, further enhancing the efficacy of chemotherapy. Through this study, precision personalized treatment of NSCLC could be effectively achieved, reversing its resistance to chemotherapy drugs, and providing new strategies for the treatment of drug-resistant NSCLC.

Can Liquid Biopsy Based on ctDNA/cfDNA Replace Tissue Biopsy for the Precision Treatment of EGFR-Mutated NSCLC?

More and more clinical trials have explored the role of liquid biopsy in the diagnosis and treatment of EGFR-mutated NSCLC. In certain circumstances, liquid biopsy has unique advantages and offers a new way to detect therapeutic targets, analyze drug resistance mechanisms in advanced patients, and monitor MRD in patients with operable NSCLC. Although its potential cannot be ignored, more evidence is needed to support the transition from the research stage to clinical application. We reviewed the latest progress in research on the efficacy and resistance mechanisms of targeted therapy for advanced NSCLC patients with plasma ctDNA EGFR mutation and the evaluation of MRD based on ctDNA detection in perioperative and follow-up monitoring.

Applications of Liquid Biopsies in Non-Small-Cell Lung Cancer

The concept of liquid biopsy as an analysis tool for non-solid tissue carried out for the purpose of providing information about solid tumors was introduced approximately 20 years ago. Additional to the detection of circulating tumor cells (CTCs), the liquid biopsy approach quickly included the analysis of circulating tumor DNA (ctDNA) and other tumor-derived markers such as circulating cell-free RNA or extracellular vesicles. Liquid biopsy is a non-invasive technique for detecting multiple cancer-associated biomarkers that is easy to obtain and can reflect the characteristics of the entire tumor mass. Currently, ctDNA is the key component of the liquid biopsy approach from the point of view of the prognosis assessment, prediction, and monitoring of the treatment of non-small-cell lung cancer (NSCLC) patients. ctDNA in NSCLC patients carries variants or rearrangements that drive carcinogenesis, such as those in EGFR, KRAS, ALK, or ROS1. Due to advances in pharmacology, these variants are the subject of targeted therapy. Therefore, the detection of these variants has gained attention in clinical medicine. Recently, methods based on qPCR (ddPCR, BEAMing) and next-generation sequencing (NGS) are the most effective approaches for ctDNA analysis. This review addresses various aspects of the use of liquid biopsy with an emphasis on ctDNA as a biomarker in NSCLC patients.

Prevalence of targeted therapy-related genetic variations in NSCLC and their relationship with clinicopathological characteristics

Background

Non-small cell lung cancer (NSCLC) is the most common cancer type in China. Targeted therapies have been used to treat NSCLC for two decades, which is only suitable for a subgroup of patients with specific genetic variations. The aim of this study was to investigate the prevalence of genetic variations leading to sensitivity or resistance to targeted therapies in NSCLC, and their relationship with clinicopathological characteristics of the patients.

Methods

Tumor samples were collected from 404 patients who were diagnosed to have NSCLC and underwent surgery, transthoracic biopsy, bronchoscopy biopsy, or pleural aspiration in Sichuan Provincial People’s Hospital from January 2019 to March 2020. Commercial amplification-refractory mutation system kits were used to detect targeted therapy-related genetic variations in those tumor samples. The prevalence of genetic variations and their relationship with patient clinicopathological characteristics were analyzed using statistical software, followed by subgroup analysis.

Results

In all, 50.7% of the NSCLC patients had sensitive genetic variations to anti-EGFR therapies, and 4.9% of those patients had co-existing resistant genetic variations. Fusions in ALK, ROS1, or RET were found in 7.7% of the patients, including 2 patients with co-existing EGFR exon 19 deletion or L858R. EGFR exon 19 deletion and L858R were more common in female patients and adenocarcinoma. Further subgroup analysis confirmed the observation in female patients in adenocarcinoma subgroup, and in adenocarcinoma in male patients. In addition, smokers were more likely to have squamous cell carcinoma and KRAS mutation and less likely to have EGFR L858R, which were also confirmed after standardization of gender except KRAS mutations.

Conclusion

Nearly half of the NSCLC patients were eligible for anti-EGFR treatments. In NSCLC, female gender and adenocarcinoma may indicate higher chance of EGFR exon 19 deletion or L858R, and smoking history may indicate squamous cell carcinoma and EGFR L858R.

Osimertinib Versus Comparator EGFR TKI as First-Line Treatment for EGFR-Mutated Advanced NSCLC: FLAURA China, A Randomized Study

Background

In the global FLAURA study, first-line osimertinib, a third-generation irreversible tyrosine kinase inhibitor (TKI) of epidermal growth factor receptor (EGFR), significantly improved progression-free survival (PFS) and overall survival (OS) versus comparator EGFR TKIs in patients with EGFR mutation-positive (EGFRm) advanced non-small-cell lung cancer (NSCLC).

Objective

The FLAURA China study assessed first-line osimertinib in Chinese patients with EGFRm advanced NSCLC (NCT02296125).

Methods

FLAURA China was a double-blind, randomized, phase III study. Adults from mainland China with previously untreated EGFRm (Exon 19 deletion or L858R) advanced NSCLC were enrolled in the global study or a China-only study under the same protocol; 136 patients were randomized to osimertinib (80 mg once daily [od]; n = 71) or comparator EGFR TKI (gefitinib or erlotinib; all sites selected gefitinib 250 mg od; n = 65). Patients were randomized and allocated to treatment groups by a central computer system. Treatment continued until disease progression, unacceptable toxicity, or withdrawal of consent. The primary endpoint was investigator-assessed PFS; OS was a secondary endpoint.

Results

All 136 randomized patients were analyzed. Osimertinib extended median PFS by 8.0 months versus comparator EGFR TKI (17.8 vs. 9.8 months; hazard ratio [HR] 0.56; 95% confidence interval [CI] 0.37–0.85). Median OS was 33.1 months in the osimertinib group versus 25.7 months in the comparator group (HR 0.85; 95% CI 0.56–1.29). At 3 years, 20% of patients on osimertinib and 8% on the comparator remained on randomized treatment. Grade 3 or higher adverse events (AEs) were reported in 54 and 28% of patients in the osimertinib and comparator groups, respectively, driven by increased local reporting of laboratory- and disease-related AEs. No new safety signals were identified.

Conclusions

First-line osimertinib treatment resulted in a clinically meaningful PFS and OS benefit versus comparator EGFR TKI in Chinese patients with EGFRm advanced NSCLC. Safety data were consistent with the known safety profile of osimertinib.

Clinical Trial Registration

ClinicalTrials.gov NCT02296125, registered 20 November 2014

Supplementary Information

The online version contains supplementary material available at 10.1007/s11523-021-00794-6.

The cost-effectiveness of dacomitinib in first-line treatment of advanced/metastatic epidermal growth factor receptor mutation-positive non-small-cell lung cancer (EGFRm NSCLC) in Sweden

AIMS

Although the benefit of first-line epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) over chemotherapy in EGFR mutation-positive (EGFRm) non-small-cell lung cancer (NSCLC) has been demonstrated in clinical trials, the optimal treatment sequence remains unclear. The objective of our study was to evaluate the cost-effectiveness of dacomitinib in Sweden vs afatinib and osimertinib in first-line treatment of EGFRm NSCLC.

MATERIALS AND METHODS

A partitioned survival model was developed with three health states: progression-free, post-progression, and death. Progression-free and overall survival curves were used to inform movements between states. Clinical data were taken from randomized trials, compared via a network meta-analysis (NMA). Utility data were taken from published studies and costs from national Swedish sources. The model used a 15-year time horizon and a Swedish healthcare payer perspective. Sensitivity and scenario analyses were performed.

RESULTS

The base-case analysis showed that dacomitinib accrued a total of 2.10 quality-adjusted life-years (QALYs) at a total cost of Swedish krona (SEK) 874,615. The incremental cost-effectiveness ratio (ICER) for dacomitinib vs afatinib was SEK 461,556 per QALY gained. The ICER of osimertinib vs dacomitinib, where the small QALY gains of the former came at a high additional cost, was SEK 11,444,709. Deterministic and probabilistic sensitivity analyses confirmed the robustness of these results; changes to drug and medical resource use costs and overall survival had the greatest impact on ICER estimates.

LIMITATIONS

This model is subject to uncertainty associated with extrapolating long-term treatment effects from shorter trial follow-up periods, although this would also be a limitation when using direct comparison or time-dependent hazard ratios. The NMA was limited by the use of indirect comparison, although sensitivity analyses supported the robustness of our findings.

CONCLUSIONS

Our model demonstrated that dacomitinib is cost-effective for first-line EGFRm NSCLC treatment in Sweden vs afatinib and osimertinib.

Discovery of a novel third-generation EGFR inhibitor and identification of a potential combination strategy to overcome resistance

BACKGROUND

Non-small cell lung cancer (NSCLC) patients with activating EGFR mutations initially respond to first-generation EGFR inhibitors; however, the efficacy of these drugs is limited by acquired resistance driven by the EGFR T790M mutation. The discovery of third-generation EGFR inhibitors overcoming EGFR T790M and their new resistance mechanisms have attracted much attention.

METHODS

We examined the antitumor activities and potential resistance mechanism of a novel EGFR third-generation inhibitor in vitro and in vivo using ELISA, SRB assay, immunoblotting, flow cytometric analysis, kinase array, qRT-PCR and tumor xenograft models. The clinical effect on a patient was evaluated by computed tomography scan.

RESULTS

We identified compound ASK120067 as a novel inhibitor of EGFR T790M, with selectivity over EGFR WT. ASK120067 exhibited potent anti-proliferation activity in tumor cells harboring EGFR T790M (NCI-H1975) and sensitizing mutations (PC-9 and HCC827) while showed moderate or weak inhibition in cells expressing EGFR WT. Oral administration of ASK120067 induced tumor regression in NSCLC xenograft models and in a PDX model harboring EGFR T790M. The treatment of one patient with advanced EGFR T790M-positive NSCLC was described as proof of principle. Moreover, we found that hyperphosphorylation of Ack1 and the subsequent activation of antiapoptotic signaling via the AKT pathway contributed to ASK120067 resistance. Concomitant targeting of EGFR and Ack1 effectively overrode the acquired resistance of ASK120067 both in vitro and in vivo.

CONCLUSIONS

Our results idenfity ASK120067 as a promising third-generation EGFR inhibitor and reveal for the first time that Ack1 activation as a novel resistance mechanism to EGFR inhibitors that guide to potential combination strategy.

The Validity and Predictive Value of Blood-Based Biomarkers in Prediction of Response in the Treatment of Metastatic Non-Small Cell Lung Cancer: A Systematic Review

With the introduction of targeted therapies and immunotherapy, molecular diagnostics gained a more profound role in the management of non-small cell lung cancer (NSCLC). This study aimed to systematically search for studies reporting on the use of liquid biopsies (LB), the correlation between LBs and tissue biopsies, and finally the predictive value in the management of NSCLC. A systematic literature search was performed, including results published after 1 January 2014. Articles studying the predictive value or validity of a LB were included. The search (up to 1 September 2019) retrieved 1704 articles, 1323 articles were excluded after title and abstract screening. Remaining articles were assessed for eligibility by full-text review. After full-text review, 64 articles investigating the predictive value and 78 articles describing the validity were included. The majority of studies investigated the predictive value of LBs in relation to therapies targeting the epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) receptor (n = 38). Of studies describing the validity of a biomarker, 55 articles report on one or more EGFR mutations. Although a variety of blood-based biomarkers are currently under investigation, most studies evaluated the validity of LBs to determine EGFR mutation status and the subsequent targeting of EGFR tyrosine kinase inhibitors based on the mutation status found in LBs of NSCLC patients.

A novel virtual barcode strategy for accurate panel-wide variant calling in circulating tumor DNA

Background

Hybrid capture-based next-generation sequencing of DNA has been widely applied in the detection of circulating tumor DNA (ctDNA). Various methods have been proposed for ctDNA detection, but low-allelic-fraction (AF) variants are still a great challenge. In addition, no panel-wide calling algorithm is available, which hiders the full usage of ctDNA based ‘liquid biopsy’. Thus, we developed the VBCALAVD (Virtual Barcode-based Calling Algorithm for Low Allelic Variant Detection) in silico to overcome these limitations.

Results

Based on the understanding of the nature of ctDNA fragmentation, a novel platform-independent virtual barcode strategy was established to eliminate random sequencing errors by clustering sequencing reads into virtual families. Stereotypical mutant-family-level background artifacts were polished by constructing AF distributions. Three additional robust fine-tuning filters were obtained to eliminate stochastic mutant-family-level noises. The performance of our algorithm was validated using cell-free DNA reference standard samples (cfDNA RSDs) and normal healthy cfDNA samples (cfDNA controls). For the RSDs with AFs of 0.1, 0.2, 0.5, 1 and 5%, the mean F1 scores were 0.43 (0.25~0.56), 0.77, 0.92, 0.926 (0.86~1.0) and 0.89 (0.75~1.0), respectively, which indicates that the proposed approach significantly outperforms the published algorithms. Among controls, no false positives were detected. Meanwhile, characteristics of mutant-family-level noise and quantitative determinants of divergence between mutant-family-level noises from controls and RSDs were clearly depicted.

Conclusions

Due to its good performance in the detection of low-AF variants, our algorithm will greatly facilitate the noninvasive panel-wide detection of ctDNA in research and clinical settings. The whole pipeline is available at https://github.com/zhaodalv/VBCALAVD.

Icotinib: efficacy in different solid tumors and gene mutations

Icotinib is a first-generation inhibitor of epidermal growth factor receptor, which has been approved by the Chinese National Medical Products Administration, for the treatment of non-small cell lung cancer with epidermal growth factor receptor sensitive mutations. In addition, icotinib also shows moderate activity in other solid tumors driven by epidermal growth factor receptor, including non-small cell lung cancer with epidermal growth factor receptor rare non-resistant mutations, and esophageal cancer with epidermal growth factor receptor amplification or overexpression. This article reviews the efficacy of icotinib in different solid tumors with different epidermal growth factor receptor alterations.

Integration of Random Forest Classifiers and Deep Convolutional Neural Networks for Classification and Biomolecular Modeling of Cancer Driver Mutations

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to preprocess the DNA information. These classifiers were benchmarked against their tree-based alternatives in order to evaluate the performance on a relative scale. We then integrated DNA-based scores generated by CNN with various categories of conservational, evolutionary and functional features into a generalized random forest classifier. The results of this study have demonstrated that CNN can learn high level features from genomic information that are complementary to the ensemble-based predictors often employed for classification of cancer mutations. By combining deep learning-generated score with only two main ensemble-based functional features, we can achieve a superior performance of various machine learning classifiers. Our findings have also suggested that synergy of nucleotide-based deep learning scores and integrated metrics derived from protein sequence conservation scores can allow for robust classification of cancer driver mutations with a limited number of highly informative features. Machine learning predictions are leveraged in molecular simulations, protein stability, and network-based analysis of cancer mutations in the protein kinase genes to obtain insights about molecular signatures of driver mutations and enhance the interpretability of cancer-specific classification models.