Pathological Mechanistic Studies of Osimertinib Resistance in Non-Small-Cell Lung Cancer Cells Using an Integrative Metabolomics-Proteomics Analysis

The combined inhibition of SLC1A3 and glutaminase in osimertinib-resistant EGFR mutant cells

BACKGROUND

We investigated the unknown mechanisms of osimertinib-resistant EGFR-mutant lung cancer.

METHODS

An osimertinib-resistant cell line (PC-9/OsmR2) was established through continuous exposure to osimertinib using an EGFR exon 19 deletion (19Del) lung adenocarcinoma cell line (PC-9). EGFR 19Del (M1), L858R/T790M/C797S (M6), and L858R/C797S (M8) expression vectors were introduced into Ba/F3 cells. A second osimertinib-resistant line (M1/OsmR) was established through continuous exposure to osimertinib using M1 cells.

RESULTS

SLC1A3 had the highest mRNA expression level in PC-9/OsmR2 compared to PC-9 cells by microarray analysis and SLC1A3 was increased by flow cytometry. In PC-9/OsmR2 cells, osimertinib sensitivity was significantly increased in combination with siSLC1A3. Because SLC1A3 functions in glutamic acid transport, osimertinib with a glutaminase inhibitor (CB-839) or an SLC1A3 inhibitor (TFB-TBOA) increased the sensitivity. Also, CB-839 plus TFB-TBOA without osimertinib resulted in greater susceptibility than did CB-839 or TFB-TBOA plus osimertinib. Comprehensive metabolome analysis showed that the M1/OsmR cells had significantly more glutamine and glutamic acid than M1 cells. CB-839 plus osimertinib exerted a synergistic effect on M6 cells and an additive effect on M8 cells.

CONCLUSION

Targeting glutaminase and glutamic acid may overcome the osimertinib-resistant EGFR-mutant lung cancer.

Cross-omics strategies and personalised options for lung cancer immunotherapy

Lung cancer is one of the most common malignant tumours worldwide and its high mortality rate makes it a leading cause of cancer-related deaths. To address this daunting challenge, we need a comprehensive understanding of the pathogenesis and progression of lung cancer in order to adopt more effective therapeutic strategies. In this regard, integrating multi-omics data of the lung provides a highly promising avenue. Multi-omics approaches such as genomics, transcriptomics, proteomics, and metabolomics have become key tools in the study of lung cancer. The application of these methods not only helps to resolve the immunotherapeutic mechanisms of lung cancer, but also provides a theoretical basis for the development of personalised treatment plans. By integrating multi-omics, we have gained a more comprehensive understanding of the process of lung cancer development and progression, and discovered potential immunotherapy targets. This review summarises the studies on multi-omics and immunology in lung cancer, and explores the application of these studies in early diagnosis, treatment selection and prognostic assessment of lung cancer, with the aim of providing more personalised and effective treatment options for lung cancer patients.

Metabolomics at the tumor microenvironment interface: Decoding cellular conversations

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

A Systematic Role of Metabolomics, Metabolic Pathways, and Chemical Metabolism in Lung Cancer

Lung cancer (LC) is considered as one of the leading causes of cancer-associated mortalities. Cancer cells' reprogrammed metabolism results in changes in metabolite concentrations, which can be utilized to identify a distinct metabolic pattern or fingerprint for cancer detection or diagnosis. By detecting different metabolic variations in the expression levels of LC patients, this will help and enhance early diagnosis methods as well as new treatment strategies. The majority of patients are identified at advanced stages after undergoing a number of surgical procedures or diagnostic testing, including the invasive procedures. This could be overcome by understanding the mechanism and function of differently regulated metabolites. Significant variations in the metabolites present in the different samples can be analyzed and used as early biomarkers. They could also be used to analyze the specific progression and type as well as stages of cancer type making it easier for the treatment process. The main aim of this review article is to focus on rewired metabolic pathways and the associated metabolite alterations that can be used as diagnostic and therapeutic targets in lung cancer diagnosis as well as treatment strategies.

Dysregulated Metabolism in EGFR-TKI Drug Resistant Non-Small-Cell Lung Cancer: A Systematic Review

Drug resistance is a common barrier to continued effective treatment in cancer. In non-small-cell lung cancer (NSCLC), tyrosine kinase inhibitors that target the epidermal growth factor receptor (EGFR-TKIs) exhibit good efficacy in cancer treatment until acquired resistance occurs. It has been observed that drug resistance is accompanied by numerous molecular-level changes, including significant shifts in cellular metabolism. The purpose of this study was to critically and systematically review the published literature with respect to how metabolism differs in drug-resistant compared to drug-sensitive NSCLC. Understanding the differences between resistant and sensitive cells is vital and has the potential to allow interventions that enable the re-sensitisation of resistant cells to treatment, and consequently reinitiate the therapeutic effect of EGFR-TKIs. The main literature search was performed using relevant keywords in PubMed and Ovid (Medline) and reviewed using the Covidence platform. Of the 1331 potentially relevant literature records retrieved, 27 studies were subsequently selected for comprehensive analysis. Collectively, the literature revealed that NSCLC cell lines resistant to EGFR-TKI treatment possess characteristic metabolic and lipidomic phenotypic signatures that differentiate them from sensitive lines. Further exploration of these reported differences suggests that drug-resistant cell lines are differentially reliant on cellular energy sources and that modulation of relative energy production pathways may lead to the reversal of drug resistance.

Integrated microbiome, metabolome, and proteome analysis identifies a novel interplay among commensal bacteria, metabolites and candidate targets in non-small cell lung cancer

BACKGROUND

Accumulation of evidence suggests that the gut microbiome, its specific metabolites, and differentially expressed proteins (DEPs) are related to non-small cell lung cancer (NSCLC) pathogenesis. We now report the influences of the gut microbiota, metabolites, and DEPs on the mediation of NSCLC's chronic inflammation and immune dysregulation.

METHODS

We conducted 16S ribosomal RNA sequencing for the gut microbiome in healthy volunteers and NSCLC patients. Liquid chromatography-mass spectrometry (LC-MS) analysis was employed to explore differences between metabolites and DEPs in serum samples. Additionally, LC-MS-based metabolomic analysis was conducted in 40 NSCLC tissues and 40 adjacent tissues. The omics data were separately analysed and integrated by using Spearman's correlation coefficient. Then, faecal microbiota transplantation (FMT) assay was used to assess the effects of the gut microbiome and specific metabolites in mice.

RESULTS

Faecal microbiome analysis revealed gut microflora dysbiosis in NSCLC patients with Prevotella, Gemmiger, and Roseburia significantly upregulated at the genus level. Then, we identified that nervonic acid/all-trans-retinoic acid level was negatively related to Prevotella. Additionally, a total of core 8 DEPs were selected in the proteome analysis, which mainly participated in the production of IL-8 and NF-κB pathways. CRP, LBP, and CD14 were identified as potential biomarkers for NSCLC. Transplantation of faecal microbiota from patients with NSCLC or Prevotella copri-colonized recipient in mice resulted in inflammation and immune dysregulation. In turn, nervonic acid/all-trans-retinoic acid treatment improved the phenotype of C57BL/6 mice bearing P. copri-treated Lewis lung cancer (LLC).

CONCLUSIONS

Overall, these results pointed out that P. copri-nervonic acid/all-trans-retinoic acid axis may contribute to the pathogenesis of NSCLC.

A novel EGFR inhibitor suppresses survivin expression and tumor growth in human gefitinib-resistant EGFR-wild type and -T790M non-small cell lung cancer

Tyrosine kinase inhibitors of epidermal growth factor receptor (EGFR-TKIs) are currently used therapy for non-small cell lung cancer (NSCLC) patients; however, drug resistance during cancer treatment is a critical problem. Survivin is an anti-apoptosis protein, which promotes cell proliferation and tumor growth that highly expressed in various human cancers. Here, we show a novel synthetic compound derived from gefitinib, do-decyl-4-(4-(3-(4-(3-chloro-4-fluorophenylamino)-7-methoxyquinazolin-6-yloxy)propyl) piper-azin-1-yl)-4-oxobutanoate, which is named as SP101 that inhibits survivin expression and tumor growth in both the EGFR-wild type and -T790M of NSCLC. SP101 blocked EGFR kinase activity and induced apoptosis in the A549 (EGFR-wild type) and H1975 (EGFR-T790M) lung cancer cells. SP101 reduced survivin proteins and increased active caspase 3 for inducing apoptosis. Ectopic expression of survivin by a survivin-expressed vector attenuated the SP101-induced cell death in lung cancer cells. Moreover, SP101 inhibited the gefitinib-resistant tumor growth in the xenograft human H1975 lung tumors of nude mice. SP101 substantially reduced survivin proteins but conversely elicited active caspase 3 proteins in tumor tissues. Besides, SP101 exerted anticancer abilities in the gefitinib resistant cancer cells separated from pleural effusion of a clinical lung cancer patient. Consistently, SP101 decreased the survivin proteins and the patient-derived xenografted lung tumor growth in nude mice. Anti-tumor ability of SP101 was also confirmed in the murine lung cancer model harboring EGFR T790M-L858R. Together, SP101 is a new EGFR inhibitor with inhibiting survivin that can be developed for treating EGFR wild-type and EGFR-mutational gefitinib-resistance in human lung cancers.

A Serum Metabolomic Study Reveals Changes in Metabolites During the Treatment of Lung Cancer-Bearing Mice with Anlotinib

Background

Anlotinib is a vascular endothelial growth factor receptor tyrosine kinase inhibitor recommended for the treatment of advanced lung cancer patients after at least two previous systemic chemotherapies. Currently, many patients with lung cancer do not respond well to anlotinib treatment. Therefore, the aim of this metabolomic study was to determine the internal mechanism of anlotinib action at the molecular level and to identify the potential biomarkers and pathways associated with the therapeutic effects of anlotinib.

Methods

A total of 20 male nude mice were randomly divided into 2 groups and treated with anlotinib or physiological saline. Ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry was performed to analyze the serum samples and determine the differential metabolites and pathways between anlotinib and control groups.

Results

We observed significant differences between the anlotinib and control groups, and 13 endogenous differential metabolites and 5 potential metabolic pathways were identified. Glyoxylate and dicarboxylate metabolism, tryptophan metabolism, glycine, serine and threonine metabolism, phenylalanine metabolism and valine, leucine and isoleucine biosynthesis were the most important pathways regulated by anlotinib in vivo. Notably, these 5 differential pathways were highly associated with the TCA cycle, which is important in the proliferation and apoptosis of cancer cells.

Conclusion

This serum metabolomic study revealed distinct metabolic profiles in lung cancer-bearing mice treated with anlotinib and identified differential metabolites and pathways between the anlotinib and control groups, which may provide new ideas for the clinical application of anlotinib.

Whole-exome sequencing reveals potential mechanisms of drug resistance to FGFR3-TACC3 targeted therapy and subsequent drug selection: towards a personalized medicine

Background

Drug resistance is a major obstacle to effective cancer therapy. In order to detect the change in tumor genomic states under drug selection pressure, we use next-generation sequencing technology to investigate the underlying potential mechanisms of drug resistance.

Methods

In our study, we presented a bladder cancer patient who had been a bona fide responder to first-line gemcitabine plus cisplatin regimen and second-line pazopanib (tyrosine kinase inhibitor (TKI) for FGFR3-TACC3 fusion) but finally had disease progression as an ideal case for showing genomic alteration during drug resistance. We applied whole-exome sequencing and ultra-deep target sequencing to the patient pre- and post- pazopanib resistance. Protein-protein interaction (PPI) network and Gene Ontology (GO) analyses were used to analysis protein interactions and genomic alterations. Patient-derived xenograft (PDX) model was built to test drug sensitivity.

Results

Twelve mutations scattered in 12 genes were identified by WES pre- pazopanib resistance, while 63 mutations in 50 genes arose post- pazopanib resistance. PPI network showed proteins from multiple epigenetic regulator families were involved post- pazopanib resistance, including subunits of chromatin remodeler SWI/SNF complex ARID1A/1B and SMARCA4, histone acetylation writers CREBBP, histone methylation writer NSD1 and erasers KDM6A/5A. GO enrichment analysis showed pazopanib resistance genes were prominently tagged for chromatin modification, transcription, as well as gland development, leaving genes with the best adaptive FGFR TKI-coping mechanisms. In addition, significantly elevated tumor mutational burden suggested possible utility of immunotherapy. Intriguingly, PDX model suggested that, sensitivity to original chemotherapy regimen (cisplatin) was restored in patient tumor post-pazopanib.

Conclusions

Epigenetic regulation may play a role in acquired TKI resistance. Our study traced the complete tumor genomic variation course from chemo-resistant but TKI-sensitive to TKI-resistant but chemo-(re) sensitive, revealing the potential complex dynamic drug-driven mechanisms of resistance.