Lipidomic Profiling of Lung Pleural Effusion Identifies Unique Metabotype for EGFR Mutants in Non-Small Cell Lung Cancer

Lupeol improves bile acid metabolism and metabolic dysfunction-associated steatotic liver disease in mice via FXR signaling pathway and gut-liver axis

Metabolic dysfunction-associated steatotic liver disease (MASLD) has a multifactorial and complex pathogenesis. Notably, the disorder of Bile acid (BA) metabolism and lipid metabolism-induced lipotoxicity are the main risk factors of MASLD. Lupeol, traditional regional medicine from Xinjiang, has a long history of use for its anti-inflammatory, anti-tumor, and immune-modulating properties. Recent research suggests its potential as a therapeutic option for MASLD due to its proposed binding capacity to the nuclear BA receptor, Farnesoid X receptor (FXR), hence could represent a therapeutic option for MASLD. In this study, a natural triterpenoid drug lupeol improved BA metabolism and MASLD in mice through the FXR signaling pathway and the gut-liver axis. Furthermore, lupeol effectively restored gut healthiness and improved intestinal immunity, barrier integrity, and inflammation, as indicated by the reconstructed gut flora. Compared with fenofibrate (Feno), lupeol treatment significantly reduced weight gain, fat deposition, and liver injury, decreased serum total cholesterol (TC) and triglyceride (TG) levels, and alleviated hepatic steatosis and liver inflammation. BA analysis showed that lupeol treatment accelerated BA efflux and decreased uptake of BA by increasing hepatic FXR and bile salt export pump (BSEP) expression. Gut microbiota alterations could be related to enhanced fecal BA excretion in lupeol-treated mice. Therefore, consumption of lupeol may prevent HFD-induced MASLD and BA accumulation, possibly via the FXR signaling pathway and regulating the gut microbiota.

Targeting glucosylceramide synthase induces antiproliferative and proapoptotic effects in osimertinib-resistant NSCLC cell models

The EGFR tyrosine kinase inhibitor osimertinib has been approved for the first-line treatment of EGFR-mutated Non-Small Cell Lung Cancer (NSCLC) patients. Despite its efficacy, patients develop resistance. Mechanisms of resistance are heterogeneous and not fully understood, and their characterization is essential to find new strategies to overcome resistance. Ceramides are well-known regulators of apoptosis and are converted into glucosylceramides (GlcCer) by glucosylceramide synthase (GCS). A higher content of GlcCers was observed in lung pleural effusions from NSCLC patients and their role in osimertinib-resistance has not been documented. The aim of this study was to determine the therapeutic potential of inhibiting GCS in NSCLC EGFR-mutant models resistant to osimertinib in vitro and in vivo. Lipidomic analysis showed a significant increase in the intracellular levels of glycosylceramides, including GlcCers in osimertinib resistant clones compared to sensitive cells. In resistant cells, the GCS inhibitor PDMP caused cell cycle arrest, inhibition of 2D and 3D cell proliferation, colony formation and migration capability, and apoptosis induction. The intratumoral injection of PDMP completely suppressed the growth of OR xenograft models. This study demonstrated that dysregulation of ceramide metabolism is involved in osimertinib-resistance and targeting GCS may be a promising therapeutic strategy for patients progressed to osimertinib.

EGFR signaling pathway as therapeutic target in human cancers

Epidermal Growth Factor Receptor (EGFR) enacts major roles in the maintenance of epithelial tissues. However, when EGFR signaling is altered, it becomes the grand orchestrator of epithelial transformation, and hence one of the most world-wide studied tyrosine kinase receptors involved in neoplasia, in several tissues. In the last decades, EGFR-targeted therapies shaped the new era of precision-oncology. Despite major advances, the dream of converting solid tumors into a chronic disease is still unfulfilled, and long-term remission eludes us. Studies investigating the function of this protein in solid malignancies have revealed numerous ways how tumor cells dysregulate EGFR function. Starting from preclinical models (cell lines, organoids, murine models) and validating in clinical specimens, EGFR-related oncogenic pathways, mechanisms of resistance, and novel avenues to inhibit tumor growth and metastatic spread enriching the therapeutic portfolios, were identified. Focusing on non-small cell lung cancer (NSCLC), where EGFR mutations are major players in the adenocarcinoma subtype, we will go over the most relevant discoveries that led us to understand EGFR and beyond, and highlight how they revolutionized cancer treatment by expanding the therapeutic arsenal at our disposal.

Identification of Targetable Liabilities in the Dynamic Metabolic Profile of EGFR-Mutant Lung Adenocarcinoma: Thinking beyond Genomics for Overcoming EGFR TKI Resistance

The use of epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) as first-line treatment in patients with lung adenocarcinoma (LUAD) harboring EGFR-activating mutations has resulted in a dramatic improvement in the management of the disease. However, the long-term clinical benefit is inevitably compromised by multiple resistance mechanisms. Accumulating evidence suggests that metabolic landscape remodeling is one of the mechanisms that EGFR-mutant LUAD cells activate, thus acquiring higher plasticity, tolerating EGFR TKI-mediated cytotoxic stress, and sustaining their oncogenic phenotype. Several metabolic pathways are upregulated in EGFR TKI-resistant models modulating the levels of numerous metabolites such as lipids, carbohydrates, and metabolic enzymes which have been suggested as potential mediators of resistance to EGFR TKIs. Moreover, metabolites have been shown to carry signals and stimulate oncogenic pathways and tumor microenvironment (TME) components such as fibroblasts, facilitating resistance to EGFR TKIs in various ways. Interestingly, metabolic signatures could function as predictive biomarkers of EGFR TKI efficacy, accurately classifying patients with EGFR-mutant LUAD. In this review, we present the identified metabolic rewiring mechanisms and how these act either independently or in concert with epigenetic or TME elements to orchestrate EGFR TKI resistance. Moreover, we discuss potential nutrient dependencies that emerge, highlighting them as candidate druggable metabolic vulnerabilities with already approved drugs which, in combination with EGFR TKIs, might counteract the solid challenge of resistance, hopefully prolonging the clinical benefit.

Reprogramming of Lipid Metabolism in Lung Cancer: An Overview with Focus on EGFR-Mutated Non-Small Cell Lung Cancer

Lung cancer is the leading cause of cancer deaths worldwide. Most of lung cancer cases are classified as non-small cell lung cancers (NSCLC). EGFR has become an important therapeutic target for the treatment of NSCLC patients, and inhibitors targeting the kinase domain of EGFR are currently used in clinical settings. Recently, an increasing interest has emerged toward understanding the mechanisms and biological consequences associated with lipid reprogramming in cancer. Increased uptake, synthesis, oxidation, or storage of lipids has been demonstrated to contribute to the growth of many types of cancer, including lung cancer. In this review, we provide an overview of metabolism in cancer and then explore in more detail the role of lipid metabolic reprogramming in lung cancer development and progression and in resistance to therapies, emphasizing its connection with EGFR signaling. In addition, we summarize the potential therapeutic approaches targeting lipid metabolism for lung cancer treatment.

Liquid chromatography-mass spectrometry based metabolic characterization of pleural effusion in patients with acquired EGFR-TKI resistance

Epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) acquired resistance remains a major barrier in the clinical treatment of lung adenocarcinoma with epidermal growth factor receptor (EGFR) mutations. Despite extensive efforts, mechanism of acquired resistance has not yet been elucidated clearly. The subject of this study was to characterize the metabolic signatures relevant to acquired EGFR-TKI resistance in pleural effusion (PE), and identify potential biomarkers in PE of patients with acquired EGFR-TKI resistance. PE from EGFR-TKI untreated group (n = 30) and EGFR-TKI resistant group (n = 18) was analyzed using liquid chromatography-mass spectrometry (LCMS) based metabolomic. Multivariate statistical analysis revealed distinctive diff ;erences between the groups. A total of 34 significantly differential metabolites in PE were identified, among which, the acquired EGFR-TKI resistant group had higher levels of l-lysine, taurine, ornithine and citrulline, and lower levels of l-tryptophan, kynurenine, l-phenylalanine, l-leucine, N-formyl-l-methionine, 3-hydroxyphenylacetic acid and N-acetyl-d-phenylalanine in PE than that of the EGFR-TKI untreated group. These metabolites are mainly involved in six amino acid metabolic pathways. In addition, 3-hydroxyphenylacetic acid and N-acetyl-d-phenylalanine showed the highest AUC values of 0.934 and 0.929 in receiver operating characteristic analysis. Through LCMS metabolomics, our study identified potential biomarkers in PE, differentiating EGFR-TKI resistant patients from untreated patients, as well as the mechanisms underlying acquired EGFR-TKI resistance; thus, providing novel insights into acquired EGFR-TKI resistance.

Liquid Chromatography-Mass Spectrometry-Based Tissue Metabolic Profiling Reveals Major Metabolic Pathway Alterations and Potential Biomarkers of Lung Cancer

Unclarified molecular mechanism and lack of practical diagnosis biomarkers hinder the effective treatment of non-small-cell lung cancer. Herein, we performed liquid chromatography-mass spectrometry-based nontargeted metabolomics analysis in 131 patients with their lung tissue pairs to study the metabolic characteristics and disordered metabolic pathways in lung tumor. A total of 339 metabolites were identified in metabolic profiling. Also, 241 differential metabolites were found between lung carcinoma tissues (LCTs) and paired distal noncancerous tissues; amino acids, purine metabolites, fatty acids, phospholipids, and most of lysophospholipids significantly increased in LCTs, while 3-phosphoglyceric acid, phosphoenolpyruvate, 6-phosphogluconate, and citrate decreased. Additionally, pathway enrichment analysis revealed that energy, purine, amino acid, lipid, and glutathione metabolism are markedly disturbed in lung cancer (LCa). Using binary logistic regression, we further defined candidate biomarkers for different subtypes of lung tumor. Xanthine and PC 35:2 were selected as combinational biomarkers for distinguishing benign from malignant lung tumors with a 0.886 area under curve (AUC) value, and creatine, myoinositol and LPE 16:0 were defined as combinational biomarkers for discriminating adenocarcinoma from squamous cell lung carcinoma with a 0.934 AUC value. Overall, metabolic characterization and pathway disturbance demonstrated apparent metabolic reprogramming in LCa. The defined candidate metabolite marker panels are useful for subtyping of lung tumors to assist clinical diagnosis.

Toward understanding polymer micelle stability: Density ultracentrifugation offers insight into polymer micelle stability in human fluids

Micelles, as a class of drug delivery systems, are underrepresented among United States Food and Drug Administration approved drugs. A lack of clinical translation of these systems may be due to, in part, to a lack of understanding of micelle interactions with biologic fluids following injection. Despite the limited clinical translation, micelles remain an active area of research focus and pre-clinical development. The goal of the present study was to examine the stability of amphiphilic block copolymer micelles in biologic fluids to identify the properties and components of biologic fluids that influence micelle stability. Micelle stability, measured via Förster resonance energy transfer-based fluorescent spectrometry, was complemented with density ultracentrifugation to reveal the colocalized, or dissociated, state of the dye cargo after exposure to human biologic fluids. Polymeric micelles composed of poly(ethylene glycol-block-caprolactone) (mPEG-CL) and poly(ethylene glycol-block-lactide) (mPEG-LA) were unstable in fetal bovine serum, human serum and synovial fluid, with varying levels of instability observed in ascites and pleural fluid. All polymeric micelles exhibited stability in cerebrospinal fluid, highlighting the potential for local cerebro-spinal administration of micelles. Interestingly, mPEG2.2k-CL3.1k and mPEG2k-LA2.7k micelles favored dissolution whereas mPEG5.4k-LA28.5k micelles favored stability. Taken together, our data offers both quantitative and qualitative evidence for micelle stability within human biologic fluids and offers evidence of polymer micelle instability in biologic fluids that is not explained by either total protein content or total unsaturated lipid content. The results help to identify potential sites for local delivery where stability is maintained.

Molecular testing on serous effusions

Serous effusions constitute a significant part of the material processed and diagnosed by cytopathology laboratories. Effusions may occur in a variety of clinical settings and the differential diagnosis between these conditions often requires ancillary tests. Immunohistochemistry is still the most frequently used method in this context. However, a wide array of other methods measuring the expression of DNA, mRNA, noncoding RNA, proteins, and other compounds may be applied to the diagnosis of serous effusions, particularly in the setting of cancer, as well as to studies focusing on tumor biology and understanding of tumor progression. In addition, as serous effusions provide ideal material for molecular testing, they have in recent years assumed central role as specimens informative of prediction in the context of targeted therapy, as well as prognostication. This review discusses recent studies in this field.

Metabolic and lipidomic characterization of malignant pleural effusion in human lung cancer

Malignant pleural effusion (MPE) is an important hallmark for late-stage lung cancer with metastasis. Current clinical diagnosis methods require tedious work to distinguish MPE from benign pleural effusion (BPE). The objective of this study was to characterize the metabolic signatures in MPE of lung cancer, and identify potential metabolite biomarkers for diagnosis of MPE. MPE from lung cancer (n = 46) and BPE from tuberculosis patients (n = 32) were investigated by liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based global metabolomic and lipidomic profiling. Multivariate partial least-square discriminative analysis models exhibited distinct metabolic profiles between MPE and BPE. A total of 25 ether lipids, including phosphatidylcholines (PC), lysophosphatidylcholines (LPC) and phosphatidylethanolamines (PE), were observed to be significantly downregulated in MPE with excellent diagnostic potential. Plasmalogen PC(40:3p) showed highest AUC value of 0.953 in receiver operating characteristic (ROC) model. Oxidized polyunsaturated fatty acids (PUFA) were upregulated in MPE. The obtained results implied a high oxidative stress and peroxisome disorder in lung cancer patients. Combined metabolomic and lipidomic profiling have discovered potential biomarkers in MPE with excellent clinical diagnostic capability. Dysregulated ether lipids and oxidized PUFAs have implied an aberrant redox metabolism, which provides novel insights into the pathology of MPE in lung cancer.

Clinical lipidomics in understanding of lung cancer: Opportunity and challenge

Disordered lipid metabolisms have been evidenced in lung cancer as well as its subtypes. Lipidomics with in-depth mining is considered as a critical member of the multiple omics family and a lipid-specific tool to understand disease-associated lipid metabolism and disease-specific dysfunctions of lipid species, discover biomarkers and targets for monitoring therapeutic strategies, and provide insights into lipid profiling and pathophysiological mechanisms in lung cancer. The present review describes the characters and patterns of lipidomic profiles in patients with different lung cancer subtypes, important values of comprehensive lipidomic profiles in understanding of lung cancer heterogeneity, urgent needs of standardized methodologies, potential mechanisms by lipid-associated enzymes and proteins, and the importance of integration between clinical phenomes and lipidomic profiles. The characteristics of lipidomic profiles in different lung cancer subtypes are extremely varied among study designs, objects, methods, and analyses. Preliminary data from recent studies demonstrate the specificity of lipidomic profiles specific for lung cancer stage, severity, subtype, and response to drugs. The heterogeneity of lipidomic profiles and lipid metabolism may be part of systems heterogeneity in lung cancer and be responsible for the development of drug resistance, although there are needs for direct evidence to show the existence of intra- or inter-lung cancer heterogeneity of lipidomic profiles. With an increasing understanding of expression profiles of genes and proteins, lipidomic profiles should be associated with activities of enzymes and proteins involved in the processes of lipid metabolism, which can be profiled with genomics and proteomics, and to provide the opportunity for the integration of lipidomic profiles with gene and protein expression profiles. The concept of clinical trans-omics should be emphasized to integrate data of lipidomics with clinical phenomics to identify disease-specific and phenome-specific biomarkers and targets, although there are still a large number of challenges to be overcome in the integration between clinical phenomes and lipidomic profiles.

Accurate Classification of Non-small Cell Lung Cancer (NSCLC) Pathology and Mapping of EGFR Mutation Spatial Distribution by Ambient Mass Spectrometry Imaging

Objectives: Tumor pathology examination especially epidermal growth factor receptor (EGFR) mutations molecular testing has been integral part of lung cancer clinical practices. However, the EGFR mutations spatial distribution characteristics remains poorly investigated, which is critical to tumor heterogeneity analysis and precision diagnosis. Here, we conducted an exploratory study for label-free lung cancer pathology diagnosis and mapping of EGFR mutation spatial distribution using ambient mass spectrometry imaging (MSI).

Materials and Methods: MSI analysis were performed in 55 post-operative non-small cell lung cancer (NSCLC) tumor and paired normal tissues to distinguish tumor from normal and classify pathology. We then compared diagnostic sensitivity of MSI and ADx-amplification refractory mutation system (ARMS) for the detection of EGFR mutation in pathological confirmed lung adenocarcinoma (AC) and explored EGFR mutations associated biomarkers to depict EGFR spatial distribution base on ambient MSI.

Results: Of 55 pathological confirmed NSCLC, MSI achieved a diagnostic sensitivity of 85.2% (23/27) and 82.1% (23/28) for AC and squamous cell carcinoma (SCC), respectively. Among 27 AC, there were 17 EGFR-wild-type and 10 EGFR-mutated-positive samples detected by ARMS, and MSI achieved a diagnostic sensitivity of 82.3% (14/17) and 80% (8/10) for these two groups. Several phospholipids were specially enriched in AC compared with SCC tissues, with the higher ions intensity of phospholipids in EGFR-mutated-positive compared with EGFR-wild-type AC tissues. We also found EGFR mutations distribution was heterogeneous in different regions of same tumor by multi-regions ARMS detection, and only the regions with higher ions intensity of phospholipids were EGFR-mutated-positive.

Conclusion: MSI method could accurately distinguish tumor pathology and subtypes, and phospholipids were reliable EGFR mutations associated biomarkers, phospholipids imaging could intuitively visualize EGFR mutations spatial distribution, may facilitate our understanding of tumor heterogeneity.

Metabolic signatures of four major histological types of lung cancer cells

INTRODUCTION

Histologically lung cancer is classified into four major types: adenocarcinoma (Ad), squamous cell carcinoma (SqCC), large cell carcinoma (LCC), and small cell lung cancer (SCLC). Presently, our understanding of cellular metabolism among them is still not clear.

OBJECTIVES

The goal of this study was to assess the cellular metabolic profiles across these four types of lung cancer using an untargeted metabolomics approach.

METHODS

Six lung cancer cell lines, viz., Ad (A549 and HCC827), SqCC (NCl-H226 and NCl-H520), LCC (NCl-H460), and SCLC (NCl-H526), were analyzed using liquid chromatography quadrupole time-of-flight mass spectrometry, with normal human small airway epithelial cells (SAEC) as the control group. The principal component analysis (PCA) was performed to identify the metabolic signatures that had characteristic alterations in each histological type. Further, a metabolite set enrichment analysis was performed for pathway analysis.

RESULTS

Compared to the SAEC, 31, 27, 34, 34, 32, and 39 differential metabolites mainly in relation to nucleotides, amino acid, and fatty acid metabolism were identified in A549, HCC827, NCl-H226, NCl-H520, NCl-H460, and NCl-H526 cells, respectively. The metabolic signatures allowed the six cancerous cell lines to be clearly separated in a PCA score plot.

CONCLUSION

The metabolic signatures are unique to each histological type, and appeared to be related to their cell-of-origin and mutation status. The changes are useful for assessing the metabolic characteristics of lung cancer, and offer potential for the establishment of novel diagnostic tools for different origin and oncogenic mutation of lung cancer.

Machine learning to detect signatures of disease in liquid biopsies - a user's guide

New technologies that measure sparse molecular biomarkers from easily accessible bodily fluids (e.g. blood, urine, and saliva) are revolutionizing disease diagnostics and precision medicine. Microchip devices can measure more disease biomarkers with better sensitivity and specificity each year, but clinical interpretation of these biomarkers remains a challenge. Single biomarkers in 'liquid biopsy' often cannot accurately predict the state of a disease due to heterogeneity in phenotype and disease expression across individuals. To address this challenge, investigators are combining multiplexed measurements of different biomarkers that together define robust signatures for specific disease states. Machine learning is a useful tool to automatically discover and detect these signatures, especially as new technologies output increasing quantities of molecular data. In this paper, we review the state of the field of machine learning applied to molecular diagnostics and provide practical guidance to use this tool effectively and to avoid common pitfalls.

Prognostic factors of refractory NSCLC patients receiving anlotinib hydrochloride as the third- or further-line treatment

Objective:

Anlotinib hydrochloride is a multitarget tyrosine kinase inhibitor that targets vascular endothelial growth factor receptor, fibroblast growth factor receptor, platelet-derived growth factor receptor, c-Kit, and c-MET; therefore, it exhibits both antitumor and anti-angiogenetic activities. A phase III trial has shown that anlotinib improved progression-free survival (PFS) and overall survival (OS) in patients with advanced non-small cell lung cancer (NSCLC), who presented with progressive disease or intolerance after standard chemotherapy. This study aimed to analyze the characteristics of patients receiving anlotinib treatment to determine the dominant populations who are fit for the treatment.

Methods:

Data were collected from March 2015 to January 2017 from a randomized, double-blind, placebo-controlled, multicenter, phase III trial of anlotinib (ALTER0303). A total of 437 patients were enrolled and randomly allocated (2:1) to the anlotinib and placebo groups. Kaplan–Meier analysis and log-rank test were performed to compare PFS and OS. Cox proportional hazards model was adopted for multivariate prognostic analysis.

Results:

Multivariate analysis indicated that high post-therapeutic peripheral blood granulocyte/lymphocyte ratio and elevated alkaline phosphatase levels were independent risk factors for PFS. Meanwhile, elevated thyroid-stimulating hormone, blood glucose, and triglyceride levels; hypertension; and hand–foot syndrome were independent protective factors of PFS. High post-therapeutic peripheral blood granulocyte/lymphocyte ratio, an Eastern Cooperative Oncology Group (ECOG) score ≥ 2, and the sum of the maximal target lesion length at baseline were independent risk factors of OS, and hypertriglyceridemia was an independent protective factor of OS.

Conclusions:

This study preliminarily explored the possible factors that affected PFS and OS after anlotinib treatment in patients with advanced refractory NSCLC, and the baseline characteristics of the therapeutically dominant populations were then identified.