Clinical significance of plasma-free amino acids and tryptophan metabolites in patients with non-small cell lung cancer receiving PD-1 inhibitor: a pilot cohort study for developing a prognostic multivariate model

Variations in Quinolinic Acid Levels in Tuberculosis Patients with Diabetes Comorbidity: A Pilot Prospective Cohort Study

Objective

We aimed to investigate dysregulated metabolic pathways and identify diagnostic and therapeutic targets in patients with tuberculosis-diabetes (TB-DM).

Methods

In our prospective cohort study, plasma samples were collected from healthy individuals, diabetic (DM) patients, untreated TB-only (TB-0)/TB-DM patients (TB-DM-0), and cured TB (TB-6)/TB-DM patients (TB-DM-6) to measure the levels of amino acids, fatty acids, and other metabolites in plasma using high-throughput targeted quantification methods.

Results

Significantly different biological processes and biomarkers were identified in DM, TB-DM-0, and TB-DM-6 patients. Moreover, quinolinic acid (QA) showed excellent predictive accuracy for distinguishing between DM patients and TB-DM-0 patients, with an AUC of 1 (95% CI 1-1). When differentiating between TB-DM-0 patients and TB-DM-6 patients, the AUC was 0.9297 (95% CI 0.8460-1). Compared to those in DM patients, the QA levels were significantly elevated in TB-DM-0 patients and decreased significantly after antituberculosis treatment. We simultaneously compared healthy controls and untreated tuberculosis patients and detected an increase in the level of QA in the plasma of tuberculosis patients, which decreased following treatment.

Conclusion

These findings improve the current understanding of tuberculosis treatment in patients with diabetes. QA may serve as an ideal diagnostic biomarker for TB-DM patients and contribute to the development of more effective treatments.

Neopterin as a potential prognostic and predictive biomarker in metastatic renal cell carcinoma treated with immune checkpoint inhibitors

INTRODUCTION

Immunotherapy represents a significant and essential component of renal carcinoma therapy (RCC), but the selection of an optimal regimen for an individual patient remains unclear. Despite significant improvements in therapeutic options for RCC, predictive biomarkers for immunotherapeutic agents remain elusive. Neopterin is a biomarker of cell-mediated immune response, with concentrations increased in different disorders, including cancer. High neopterin levels herald, in general, a poor prognosis.

AREAS COVERED

This review briefly overviews the contemporary clinical data on biomarkers in metastatic RCC therapy, focusing on neopterin.

EXPERT OPINION

Elevated neopterin levels have been observed in tumors of different primary locations. Research indicates that neopterin may serve as a potential biomarker for assessing the inflammatory status associated with certain cancers. However, it is necessary to interpret neopterin levels in the context of a comprehensive clinical evaluation, as elevated neopterin alone is not specific to cancer and can be influenced by other factors, including comorbid conditions. Neopterin has also been identified as a prognostic biomarker. An increasing neopterin level in serum and urine is associated with advanced cancer, but the role as a potential predictor of response to immunotherapy has yet to be established. A reliable biomarker for optimal therapy selection in metastatic RCC is still putative.

[Research Progress on Tumor Metabolic Biomarkers in Liquid Biopsy of Lung Cancer]

Liquid biopsy is gradually being applied in the clinical diagnosis and treatment of lung cancer. At present, with the development of metabolomics, more and more metabolic biomarkers are considered as potential sensitive markers reflecting the occurrence and development of tumors. This article summarizes the changes in the main metabolic pathways of lung cancer, including glucose metabolism, amino acid metabolism, lipid metabolism, sphingolipid metabolism, glycerophospholipid metabolism, and purine metabolism. Meanwhile, this article reviews the role of metabolic biomarkers in the early diagnosis of lung cancer, predicting disease progression, and evaluating the efficacy of chemotherapy and immunotherapy, aiming to provide effective biomarkers for tumor diagnosis and treatment.
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Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

Serum branched amino acids and the risk of all-cause mortality: a meta-analysis and systematic review

Recently, the serum levels of branched-chain amino acids (BCAAs) have been considered as an indicator to evaluate health status and predict chronic diseases risk. This systematic review and meta-analysis aimed to assess the relationship between Serum BCAAs and the risk of all-cause mortality. We carried out a comprehensive and systematic search in various important databases, including PubMed, Scopus, and Web of Science databases to find the relevant studies published up to October 2022 with no language, design, or time limitation. We extracted the reported hazard ratio (HR) with 95% confidence interval (CI) and odds ratio (OR) with 95%CI in cohorts and case-control studies, respectively, and computed the log HR or OR and its standard error. Then, we used the random-effects model with inverse variance weighting method for the present meta-analysis, to calculate the pooled effect size. Ten observational studies, including nine cohort studies and one case-control study, were included in the present meta-analysis. The number of participants ranges from 53 to 26,711, with an age range of 18-99 years. During 6 months to 24 years of follow-up, 3599 deaths were ascertained. The pooled results indicated that there was no significant association between serum BCAAs (RR: 1.17; 95% CI 0.85-1.60), isoleucine (RR: 1.41; 95%CI 0.92-2.17), leucine (RR: 1.13; 95% CI 0.94-1.36), and valine (RR: 1.02; 95%CI 0.86-1.22) and all-cause mortality. Also, there was significant heterogeneity between studies for serum BCAAs (I2 = 74.1% and P-heterogeneity = 0.021), isoleucine (I2 = 89.4% and P-heterogeneity < 0.001), leucine (I2 = 87.8% and P-heterogeneity < 0.001), and valine (I2 = 86.6% and P-heterogeneity < 0.001). Our results suggested that the serum BCAAs and its components, including isoleucine, leucine, and valine, were not associated with the risk of all-cause mortality.

Fecal microbiota transplantation plus anti-PD-1 immunotherapy in advanced melanoma: a phase I trial

Fecal microbiota transplantation (FMT) represents a potential strategy to overcome resistance to immune checkpoint inhibitors in patients with refractory melanoma; however, the role of FMT in first-line treatment settings has not been evaluated. We conducted a multicenter phase I trial combining healthy donor FMT with the PD-1 inhibitors nivolumab or pembrolizumab in 20 previously untreated patients with advanced melanoma. The primary end point was safety. No grade 3 adverse events were reported from FMT alone. Five patients (25%) experienced grade 3 immune-related adverse events from combination therapy. Key secondary end points were objective response rate, changes in gut microbiome composition and systemic immune and metabolomics analyses. The objective response rate was 65% (13 of 20), including four (20%) complete responses. Longitudinal microbiome profiling revealed that all patients engrafted strains from their respective donors; however, the acquired similarity between donor and patient microbiomes only increased over time in responders. Responders experienced an enrichment of immunogenic and a loss of deleterious bacteria following FMT. Avatar mouse models confirmed the role of healthy donor feces in increasing anti-PD-1 efficacy. Our results show that FMT from healthy donors is safe in the first-line setting and warrants further investigation in combination with immune checkpoint inhibitors. ClinicalTrials.gov identifier NCT03772899 .

Metabolomic analysis of gut metabolites in patients with colorectal cancer: Association with disease development and outcome

Colorectal cancer (CRC) is one of the leading global malignancies with low 5-year survival and high mortality rates. Despite extensive research, the precise role of gut metabolites in CRC development and clinical outcomes remains unclear, while its elucidation may aid the development of improved clinical diagnosis and treatment options. In the present study, targeted metabolomic analysis was conducted on fecal samples from 35 patients with CRC, 37 patients with colorectal adenoma and 30 healthy controls (HC) to identify metabolite biomarkers. Using orthogonal partial least squares discriminant analysis, metabolomic features distinguishing the three groups were identified. Receiver operating characteristic (ROC) curve analysis was used to assess diagnostic utility for distinguishing CRC from HC. The association of gut metabolites with survival in patients with CRC was also analyzed by comparing short-term survivors (STS) and long-term survivors (LTS), and the prognostic ability of metabolites was predicted using Cox regression and Kaplan-Meier analysis. The results of the current study showed that the enriched pathways in CRC included 'caffeine metabolism', 'thiamine metabolism', 'phenylalanine, tyrosine and tryptophan biosynthesis' and 'phenylalanine metabolism'. ROC analysis found that 9,10-dihydroxy-12-octadecenoic acid, cholesterol ester (18:2) and lipoxinA4 distinguished CRC from HC. Joint quantification of these three metabolites resulted in an area under the ROC curve of 0.969 in the diagnosis of CRC. The analysis of the current study also showed that the expression of metabolites involved in 'sphingolipid metabolism' was mainly dysregulated in LTS and STS, while N-acetylmannosamine and 2,5-dihydroxybenzaldehyde were associated with better overall survival. In conclusion, the present study provided preliminary insight into the metabolic changes associated with CRC and may have important implications for the development of future diagnostic and treatment strategies.

Modelling the Tumour Microenvironment, but What Exactly Do We Mean by "Model"?

The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.

Analysis of the Circulating Metabolome of Patients with Cutaneous, Mucosal and Uveal Melanoma Reveals Distinct Metabolic Profiles with Implications for Response to Immunotherapy

Cutaneous melanoma (CM) patients respond better to immune checkpoint inhibitors (ICI) than mucosal and uveal melanoma patients (MM/UM). Aiming to explore these differences and understand the distinct response to ICI, we evaluated the serum metabolome of advanced CM, MM, and UM patients. Levels of 115 metabolites were analyzed in samples collected before ICI, using a targeted metabolomics platform. In our analysis, molecules involved in the tryptophan-kynurenine axis distinguished UM/MM from CM. UM/MM patients had higher levels of 3-hydroxykynurenine (3-HKyn), whilst patients with CM were found to have higher levels of kynurenic acid (KA). The KA/3-HKyn ratio was significantly higher in CM versus the other subtypes. UM, the most ICI-resistant subtype, was also associated with higher levels of sphingomyelin-d18:1/22:1 and the polyamine spermine (SPM). Overall survival was prolonged in a cohort of CM patients with lower SPM levels, suggesting there are also conserved metabolic factors promoting ICI resistance across melanoma subtypes. Our study revealed a distinct metabolomic profile between the most resistant melanoma subtypes, UM and MM, compared to CM. Alterations within the kynurenine pathway, polyamine metabolism, and sphingolipid metabolic pathway may contribute to the poor response to ICI. Understanding the different metabolomic profiles introduces opportunities for novel therapies with potential synergic activity to ICI, to improve responses of UM/MM.

Dietary tryptophan metabolite released by intratumoral Lactobacillus reuteri facilitates immune checkpoint inhibitor treatment

The use of probiotics by cancer patients is increasing, including among those undergoing immune checkpoint inhibitor (ICI) treatment. Here, we elucidate a critical microbial-host crosstalk between probiotic-released aryl hydrocarbon receptor (AhR) agonist indole-3-aldehyde (I3A) and CD8 T cells within the tumor microenvironment that potently enhances antitumor immunity and facilitates ICI in preclinical melanoma. Our study reveals that probiotic Lactobacillus reuteri (Lr) translocates to, colonizes, and persists within melanoma, where via its released dietary tryptophan catabolite I3A, it locally promotes interferon-γ-producing CD8 T cells, thereby bolstering ICI. Moreover, Lr-secreted I3A was both necessary and sufficient to drive antitumor immunity, and loss of AhR signaling within CD8 T cells abrogated Lr's antitumor effects. Further, a tryptophan-enriched diet potentiated both Lr- and ICI-induced antitumor immunity, dependent on CD8 T cell AhR signaling. Finally, we provide evidence for a potential role of I3A in promoting ICI efficacy and survival in advanced melanoma patients.

Uveal Melanoma Patients Have a Distinct Metabolic Phenotype in Peripheral Blood

Uveal melanomas (UM) are detected earlier. Consequently, tumors are smaller, allowing for novel eye-preserving treatments. This reduces tumor tissue available for genomic profiling. Additionally, these small tumors can be hard to differentiate from nevi, creating the need for minimally invasive detection and prognostication. Metabolites show promise as minimally invasive detection by resembling the biological phenotype. In this pilot study, we determined metabolite patterns in the peripheral blood of UM patients (n = 113) and controls (n = 46) using untargeted metabolomics. Using a random forest classifier (RFC) and leave-one-out cross-validation, we confirmed discriminatory metabolite patterns in UM patients compared to controls with an area under the curve of the receiver operating characteristic of 0.99 in both positive and negative ion modes. The RFC and leave-one-out cross-validation did not reveal discriminatory metabolite patterns in high-risk versus low-risk of metastasizing in UM patients. Ten-time repeated analyses of the RFC and LOOCV using 50% randomly distributed samples showed similar results for UM patients versus controls and prognostic groups. Pathway analysis using annotated metabolites indicated dysregulation of several processes associated with malignancies. Consequently, minimally invasive metabolomics could potentially allow for screening as it distinguishes metabolite patterns that are putatively associated with oncogenic processes in the peripheral blood plasma of UM patients from controls at the time of diagnosis.

Kynureninase Upregulation Is a Prominent Feature of NFR2-Activated Cancers and Is Associated with Tumor Immunosuppression and Poor Prognosis

The nuclear factor erythroid 2-related factor 2 (NRF2) pathway is frequently activated in various cancer types. Aberrant activation of NRF2 in cancer is attributed to gain-of-function mutations in the NRF2-encoding gene NFE2L2 or a loss of function of its suppressor, Kelch-like ECH-associated protein 1 (KEAP1). NRF2 activation exerts pro-tumoral effects in part by altering cancer cell metabolism. Previously, we reported a novel mechanism of NRF2 tumoral immune suppression through the selective upregulation of the tryptophan-metabolizing enzyme kynureninase (KYNU) in lung adenocarcinoma. In the current study, we explored the relevance of NRF2-mediated KYNU upregulation across multiple cancer types. Specifically, using a gene expression dataset for 9801 tumors representing 32 cancer types from The Cancer Genome Atlas (TCGA), we demonstrated that elevated KYNU parallels increased gene-based signatures of NRF2-activation and that elevated tumoral KYNU mRNA expression is strongly associated with an immunosuppressive tumor microenvironment, marked by high expression of gene-based signatures of Tregs as well as the immune checkpoint blockade-related genes CD274 (PDL-1), PDCD1 (PD-1), and CTLA4, regardless of the cancer type. Cox proportional hazard models further revealed that increased tumoral KYNU gene expression was prognostic for poor overall survival in several cancer types, including thymoma, acute myeloid leukemia, low-grade glioma, kidney renal papillary cell carcinoma, stomach adenocarcinoma, and pancreatic ductal adenocarcinoma (PDAC). Using PDAC as a model system, we confirmed that siRNA-mediated knockdown of NRF2 reduced KYNU mRNA expression, whereas activation of NFE2L2 (the coding gene for NRF2) through either small-molecule agonists or siRNA-mediated knockdown of KEAP1 upregulated KYNU in PDAC cells. Metabolomic analyses of the conditioned medium from PDAC cell lines revealed elevated levels of KYNU-derived anthranilate, confirming that KYNU was enzymatically functional. Collectively, our study highlights the activation of the NRF2-KYNU axis as a multi-cancer phenomenon and supports the relevance of tumoral KYNU as a marker of tumor immunosuppression and as a prognostic marker for poor overall survival.

Decreased IGF-1 level is associated with restrained amino acid metabolism in NSCLC with diabetes mellitus

The discovery of a large number of small pulmonary nodules and early diagnosis of lung cancer in the diabetic patients prompt us to re-examine the relationship between diabetes and the occurrence and development of lung cancer. The aim of this study was to explore the underlying metabolites changes in diabetes with NSCLC or benign nodule patients, and further to investigate the association of serum IGF-1 level and differentially expressed metabolites (DEMs). An untargeted metabolomics method was used to detect the changes of metabolism in diabetic patients with NSCLC on the platform of HR-MS. Serum level of IGF-1 was measured by ELISA. The patients were divided to three groups, DM, DLB (nodule), and DLC (cancer). we have identified numerous DEMs, which include amino acid, choline, and fatty acid derivatives. Further analysis of the involved metabolic pathways suggested that linoleate metabolism, tryptophan metabolism, histidine metabolism, putative anti-Inflammatory metabolites formation from EPA, and arachidonic acid metabolism were considered to be the most significant metabolic pathways between groups. Networks analysis suggested that a series of metabolites were associated with serum IGF-1among the three groups, which can be divided into 6 categories. Nine metabolites have been identified as the main DEMs among the DLC, DLB, and DM groups. In conclusion, metabolomics is a powerful and promising tool for the cancer risk evaluation in diabetic patients. Our results suggest that decreased IGF-1 level is associated with restrained amino acid metabolism in NSCLC with diabetes mellitus.