LC-MS based metabolomic profiling for renal cell carcinoma histologic subtypes

Immunometabolic coevolution defines unique microenvironmental niches in ccRCC

Tumor cell phenotypes and anti-tumor immune responses are shaped by local metabolite availability, but intratumoral metabolite heterogeneity (IMH) and its phenotypic consequences remain poorly understood. To study IMH, we profiled tumor/normal regions from clear cell renal cell carcinoma (ccRCC) patients. A common pattern of IMH transcended all patients, characterized by correlated fluctuations in the abundance of metabolites and processes associated with ferroptosis. Analysis of intratumoral metabolite-RNA covariation revealed that the immune composition of the microenvironment, especially the abundance of myeloid cells, drove intratumoral metabolite variation. Motivated by the strength of RNA-metabolite covariation and the clinical significance of RNA biomarkers in ccRCC, we inferred metabolomic profiles from the RNA sequencing data of ccRCC patients enrolled in 7 clinical trials, and we ultimately identifyied metabolite biomarkers associated with response to anti-angiogenic agents. Local metabolic phenotypes, therefore, emerge in tandem with the immune microenvironment, influence ongoing tumor evolution, and are associated with therapeutic sensitivity.

Metabolomic Signatures of Scarff-Bloom-Richardson (SBR) Grade in Non-Metastatic Breast Cancer

PURPOSE

Identification of metabolomic biomarkers of high SBR grade in non-metastatic breast cancer.

METHODS

This retrospective bicentric metabolomic analysis included a training set (n = 51) and a validation set (n = 49) of breast cancer tumors, all classified as high-grade (grade III) or low-grade (grade I-II). Metabolomes of tissue samples were studied by liquid chromatography coupled with mass spectrometry.

RESULTS

A molecular signature of the top 12 metabolites was identified from a database of 602 frequently predicted metabolites. Partial least squares discriminant analyses showed that accuracies were 0.81 and 0.82, the R2 scores were 0.57 and 0.55, and the Q2 scores were 0.44431 and 0.40147 for the training set and validation set, respectively; areas under the curve for the Receiver Operating Characteristic Curve were 0.882 and 0.886. The most relevant metabolite was diacetylspermine. Metabolite set enrichment analyses and metabolic pathway analyses highlighted the tryptophan metabolism pathway, but the concentration of individual metabolites varied between tumor samples.

CONCLUSIONS

This study indicates that high-grade invasive tumors are related to diacetylspermine and tryptophan metabolism, both involved in the inhibition of the immune response. Targeting these pathways could restore anti-tumor immunity and have a synergistic effect with immunotherapy. Recent studies could not demonstrate the effectiveness of this strategy, but the use of theragnostic metabolomic signatures should allow better selection of patients.

A review of sensors for classification and subtype discrimination of cancer: Insights into circulating tumor cells and tumor-derived extracellular vesicles

Liquid biopsy can reflect the state of tumors in vivo non-invasively, thus providing a strong basis for the early diagnosis, individualized treatment monitoring and prognosis of tumors. Circulating tumor cells (CTCs) and tumor-derived extracellular vesicles (tdEVs) contain information-rich components, such as nucleic acids and proteins, and they are essential markers for liquid biopsies. Their capture and analysis are of great importance for the study of disease occurrence and development and, consequently, have been the subject of many reviews. However, both CTCs and tdEVs carry the biological characteristics of their original tissue, and few reviews have focused on their function in the staging and classification of cancer. In this review, we focus on state-of-the-art sensors based on the simultaneous detection of multiple biomarkers within CTCs and tdEVs, with clinical applications centered on cancer classification and subtyping. We also provide a thorough discussion of the current challenges and prospects for novel sensors with the ultimate goal of cancer classification and staging. It is hoped that these most advanced technologies will bring new insights into the clinical practice of cancer screening and diagnosis.

Blood Plasma Metabolome Profiling at Different Stages of Renal Cell Carcinoma

Early diagnostics significantly improves the survival of patients with renal cell carcinoma (RCC), which is the prevailing type of adult kidney cancer. However, the absence of clinically obvious symptoms and effective screening strategies at the early stages result to disease progression and survival rate reducing. The study was focused on revealing of potential low molecular biomarkers for early-stage RCC. The untargeted direct injection mass spectrometry-based metabolite profiling of blood plasma samples from 51 non-cancer volunteers (control) and 78 patients with different RCC subtypes and stages (early stages of clear cell RCC (ccRCC), papillary RCC (pRCC), chromophobe RCC (chrRCC) and advanced stages of ccRCC) was performed. Comparative analysis of the blood plasma metabolites between the control and cancer groups provided the detection of metabolites associated with different tumor stages. The designed model based on the revealed metabolites demonstrated high diagnostic power and accuracy. Overall, using the metabolomics approach the study revealed the metabolites demonstrating a high value for design of plasma-based test to improve early ccRCC diagnosis.

Molecular signature of renal cell carcinoma by means of a multiplatform metabolomics analysis

Renal cell carcinoma (RCC) is a disease with no specific diagnostic method or treatment. Thus, the evaluation of novel diagnostic tools or treatment possibilities is essential. In this study, a multiplatform untargeted metabolomics analysis of urine was applied to search for a metabolic pattern specific for RCC, which could enable comprehensive assessment of its biochemical background. Thirty patients with diagnosed RCC and 29 healthy volunteers were involved in the first stage of the study. Initially, the utility of the application of the selected approach was checked for RCC with no differentiation for cancer subtypes. In the second stage, this approach was used to study clear cell renal cell carcinoma (ccRCC) in 38 ccRCC patients and 38 healthy volunteers. Three complementary analytical platforms were used: reversed-phase liquid chromatography coupled with time-of-flight mass spectrometry (RP-HPLC-TOF/MS), capillary electrophoresis coupled with time-of-flight mass spectrometry (CE-TOF/MS), and gas chromatography triple quadrupole mass spectrometry (GC-QqQ/MS). As a result of urine sample analyses, two panels of metabolites specific for RCC and ccRCC were selected. Disruptions in amino acid, lipid, purine, and pyrimidine metabolism, the TCA cycle and energetic processes were observed. The most interesting differences were observed for modified nucleosides. This is the first time that the levels of these compounds were found to be changed in RCC and ccRCC patients, providing a framework for further studies. Moreover, the application of the CE-MS technique enabled the determination of statistically significant changes in symmetric dimethylarginine (SDMA) in RCC.

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Multiplatform untargeted metabolomics analysis was applied for selection of tentative diagnostic indicators of RCC.

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LC-MS, GC-MS and CE-MS techniques were utilized for analysis of urine samples collected from RCC and ccRCC patients.

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Alterations in amino acid, purine, and pyrimidine metabolism, as well as TCA cycle and energy processes, were observed.

Targeting strategies in the treatment of fumarate hydratase deficient renal cell carcinoma

Fumarate hydratase (FH) - deficient renal cell carcinoma (FHdRCC) is a rare aggressive subtype of RCC caused by a germline or sporadic loss-of-function mutation in the FH gene. Here, we summarize how FH deficiency results in the accumulation of fumarate, which in turn leads to activation of hypoxia-inducible factor (HIF) through inhibition of prolyl hydroxylases. HIF promotes tumorigenesis by orchestrating a metabolic switch to glycolysis even under normoxia, a phenomenon well-known as the Warburg effect. HIF activates the transcription of many genes, including vascular endothelial growth factor (VEGF). Crosstalk between HIF and epidermal growth factor receptor (EGFR) has also been described as a tumor-promoting mechanism. In this review we discuss therapeutic options for FHdRCC with a focus on anti-angiogenesis and EGFR-blockade. We also address potential targets that arise within the metabolic escape routes taken by FH-deficient cells for cell growth and survival.

Hyperpolarized 13C-Pyruvate Metabolism as a Surrogate for Tumor Grade and Poor Outcome in Renal Cell Carcinoma-A Proof of Principle Study

Differentiating aggressive clear cell renal cell carcinoma (ccRCC) from indolent lesions is challenging using conventional imaging. This work prospectively compared the metabolic imaging phenotype of renal tumors using carbon-13 MRI following injection of hyperpolarized [1-13C]pyruvate (HP-13C-MRI) and validated these findings with histopathology. Nine patients with treatment-naïve renal tumors (6 ccRCCs, 1 liposarcoma, 1 pheochromocytoma, 1 oncocytoma) underwent pre-operative HP-13C-MRI and conventional proton (1H) MRI. Multi-regional tissue samples were collected using patient-specific 3D-printed tumor molds for spatial registration between imaging and molecular analysis. The apparent exchange rate constant (kPL) between 13C-pyruvate and 13C-lactate was calculated. Immunohistochemistry for the pyruvate transporter (MCT1) from 44 multi-regional samples, as well as associations between MCT1 expression and outcome in the TCGA-KIRC dataset, were investigated. Increasing kPL in ccRCC was correlated with increasing overall tumor grade (ρ = 0.92, p = 0.009) and MCT1 expression (r = 0.89, p = 0.016), with similar results acquired from the multi-regional analysis. Conventional 1H-MRI parameters did not discriminate tumor grades. The correlation between MCT1 and ccRCC grade was confirmed within a TCGA dataset (p < 0.001), where MCT1 expression was a predictor of overall and disease-free survival. In conclusion, metabolic imaging using HP-13C-MRI differentiates tumor aggressiveness in ccRCC and correlates with the expression of MCT1, a predictor of survival. HP-13C-MRI may non-invasively characterize metabolic phenotypes within renal cancer.

Primal-dual for classification with rejection (PD-CR): a novel method for classification and feature selection-an application in metabolomics studies

Background

Supervised classification methods have been used for many years for feature selection in metabolomics and other omics studies. We developed a novel primal-dual based classification method (PD-CR) that can perform classification with rejection and feature selection on high dimensional datasets. PD-CR projects data onto a low dimension space and performs classification by minimizing an appropriate quadratic cost. It simultaneously optimizes the selected features and the prediction accuracy with a new tailored, constrained primal-dual method. The primal-dual framework is general enough to encompass various robust losses and to allow for convergence analysis. Here, we compare PD-CR to three commonly used methods: partial least squares discriminant analysis (PLS-DA), random forests and support vector machines (SVM). We analyzed two metabolomics datasets: one urinary metabolomics dataset concerning lung cancer patients and healthy controls; and a metabolomics dataset obtained from frozen glial tumor samples with mutated isocitrate dehydrogenase (IDH) or wild-type IDH.

Results

PD-CR was more accurate than PLS-DA, Random Forests and SVM for classification using the 2 metabolomics datasets. It also selected biologically relevant metabolites. PD-CR has the advantage of providing a confidence score for each prediction, which can be used to perform classification with rejection. This substantially reduces the False Discovery Rate.

Conclusion

PD-CR is an accurate method for classification of metabolomics datasets which can outperform PLS-DA, Random Forests and SVM while selecting biologically relevant features. Furthermore the confidence score provided with PD-CR can be used to perform classification with rejection and reduce the false discovery rate.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04478-w.

Gender-Specific Metabolomics Approach to Kidney Cancer

Renal cell carcinoma (RCC) is the most common form of kidney malignancy. RCC is more common among men with a 2/1 male/female incidence ratio worldwide. Given the underlying epidemiological differences in the RCC incidence between males and females, we explored the gender specific ¹H NMR serum metabolic profiles of RCC patients and their matched controls. A number of differential metabolites were shared by male and female RCC patients. These RCC specific changes included lower lactate, threonine, histidine, and choline levels together with increased levels of pyruvate, N-acetylated glycoproteins, beta-hydroxybutyrate, acetoacetate, and lysine. Additionally, serum lactate/pyruvate ratio was a strong predictor of RCC status regardless of gender. Although only moderate changes in metabolic profiles were observed between control males and females there were substantial gender related differences among RCC patients. Gender specific metabolic features associated with RCC status were identified suggesting that different metabolic panels could be leveraged for a more precise diagnostic.

Targeted Quantification of Carbon Metabolites Identifies Metabolic Progression Markers and an Undiagnosed Case of SDH-Deficient Clear Cell Renal Cell Carcinoma in a German Cohort

Renal cell carcinoma (RCC) is among the 10 most common cancer entities and can be categorised into distinct subtypes by differential expression of Krebs cycle genes. We investigated the predictive value of several targeted metabolites with regards to tumour stages and patient survival in an unselected cohort of 420 RCCs. Unsupervised hierarchical clustering of metabolite ratios identified two main clusters separated by α-ketoglutarate (α-KG) levels and sub-clusters with differential levels of the oncometabolite 2-hydroxyglutarate (2HG). Sub-clusters characterised by high 2HG were enriched in higher tumour stages, suggesting metabolite profiles might be suitable predictors of tumour stage or survival. Bootstrap forest models based on single metabolite signatures showed that lactate, 2HG, citrate, aspartate, asparagine, and glutamine better predicted the cancer-specific survival (CSS) of clear cell RCC patients, whereas succinate and α-ketoglutarate were better CSS predictors for papillary RCC patients. Additionally, this assay identifies rare cases of tumours with SDHx mutations, which are caused predominantly by germline mutations and which predispose to development of different neoplasms. Hence, analysis of selected metabolites should be further evaluated for potential utility in liquid biopsies, which can be obtained using less invasive methods and potentially facilitate disease monitoring for both patients and caregivers.

Mass Spectrometry-Based Metabolomic and Lipidomic Analysis of the Effect of High Fat/High Sugar Diet and GreenshellTM Mussel Feeding on Plasma of Ovariectomized Rats

This study aimed to examine the changes in lipid and metabolite profiles of ovariectomized (OVX) rats with diet-induced metabolic syndrome-associated osteoarthritis (MetOA) after supplementation with greenshell mussel (GSM) using an untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics approach. Ninety-six rats were fed with one of four diets: control, control supplemented with GSM + GSM, high fat/high sugar (HFHS), or high fat/high sugar enriched with GSM (HFHS + GSM). After 8 weeks on experimental diets, half of the rats in each group underwent OVX and the other half were sham operated. After being fed for an additional 28 weeks, blood samples were collected for the metabolomics analysis. Lipid and polar metabolites were extracted from plasma and analysed by LC-MS. We identified 29 lipid species from four lipid subclasses (phosphatidylcholine, lysophosphatidylcholine, diacylglycerol, and triacylglycerol) and a set of eight metabolites involved in amino acid metabolism (serine, threonine, lysine, valine, histidine, pipecolic acid, 3-methylcytidine, and cholic acid) as potential biomarkers for the effect of HFHS diet and GSM supplementation. GSM incorporation more specifically in the control diet generated significant alterations in the levels of several lipids and metabolites. Further studies are required to validate these findings that identify potential biomarkers to follow OA progression and to monitor the impact of GSM supplementation.

Involvement of Tricarboxylic Acid Cycle Metabolites in Kidney Diseases

Mitochondria are complex organelles that orchestrate several functions in the cell. The primary function recognized is energy production; however, other functions involve the communication with the rest of the cell through reactive oxygen species (ROS), calcium influx, mitochondrial DNA (mtDNA), adenosine triphosphate (ATP) levels, cytochrome c release, and also through tricarboxylic acid (TCA) metabolites. Kidney function highly depends on mitochondria; hence mitochondrial dysfunction is associated with kidney diseases. In addition to oxidative phosphorylation impairment, other mitochondrial abnormalities have been described in kidney diseases, such as induction of mitophagy, intrinsic pathway of apoptosis, and releasing molecules to communicate to the rest of the cell. The TCA cycle is a metabolic pathway whose primary function is to generate electrons to feed the electron transport system (ETS) to drives energy production. However, TCA cycle metabolites can also release from mitochondria or produced in the cytosol to exert different functions and modify cell behavior. Here we review the involvement of some of the functions of TCA metabolites in kidney diseases.

Metabolomic and elemental profiling of human tissue in kidney cancer

INTRODUCTION

Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite advances in treatment, no specific biomarker is currently in use to guide therapeutic interventions.

OBJECTIVES

Major aim of this work was to perform metabolomic and elemental profiling of human kidney cancer and normal tissue and to evaluate cancer biomarkers.

METHODS

Metabolic and elemental profiling of tumor and adjacent normal human kidney tissue from 50 patients with kidney cancer was undertaken using three different analytical methods.

RESULTS

Five potential tissue biomarkers of kidney cancer were identified and quantified using with high-resolution nuclear magnetic resonance spectroscopy. The contents of selected chemical elements in tissues was analyzed using inductively coupled plasma optical emission spectrometry. Eleven mass spectral features differentiating between kidney cancer and normal tissues were detected using silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry.

CONCLUSIONS

Our results, derived from the combination of ICP-OES, LDI MS and 1H NMR methods, suggest that tissue biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer.

Tumor microenvironment affects exogenous sodium/iodide symporter expression

For decades, sodium/iodide symporter NIS-mediated iodide uptake has played a crucial role in the radioactive ablation of thyroid cancer cells. NIS-based gene therapy has also become a promising tool for the treatment of tumors of extrathyroidal origin. But its applicability has been hampered by reduced expression of NIS, resulting in a moderated capacity to accumulate 131I and in inefficient ablation. Despite numerous preclinical enhancement strategies, the understanding of NIS expression within tumors remains limited. This study aims at a better understanding of the functional behavior of exogenous NIS expression in the context of malignant solid tumors that are characterized by rapid growth with an insufficient vasculature, leading to hypoxia and quiescence. Using subcutaneous HT29NIS and K7M2NIS tumors, we show that NIS-mediated uptake and NIS expression at the plasma membrane of cancer cells are impaired in the intratumoral regions. For a better understanding of the underlying molecular mechanisms induced by hypoxia and quiescence (separately and in combination), we performed experiments on HT29NIS cancer cells. Hypoxia and quiescence were both found to impair NIS-mediated uptake through mechanisms including NIS mis-localization. Modifications in the expression of proteins and metabolites involved in plasma membrane localization and in energy metabolism were found using untargeted proteomics and metabolomics approaches. In conclusion, our results provide evidence that hypoxia and quiescence impair NIS expression at the plasma membrane, and iodide uptake. Our study also shows that the tumor microenvironment is an important parameter for successful NIS-based cancer treatment.

Metabolomics in renal cell carcinoma: From biomarker identification to pathomechanism insights

Renal cell carcinoma (RCC) is a frequently diagnosed cancer with high prevalence, which is inversely associated with survival benefit. Although myriad studies have shed light on disease causality, unfortunately, thus far, RCC diagnosis is faced with numerous obstacles partly due to the insufficient knowledge of effective biomarkers, hinting deeper mechanistic understanding are urgently needed. Metabolites are recognized as final proxies for gene-environment interactions and physiological homeostasis as they reflect dynamic processes that are ongoing or have been taken place, and metabolomics may therefore offer a far more productive and cost-effective route to disease discovery, particularly within the arena for new biomarker identification. In this review, we primarily expatiate recent advances in metabolomics that may be amenable to novel biomarkers or therapeutic targets for RCC, which may expand our armaments to win more bettles against RCC.

Volatilomics Reveals Potential Biomarkers for Identification of Renal Cell Carcinoma: An In Vitro Approach

The identification of noninvasive biomarkers able to detect renal cell carcinoma (RCC) at an early stage remains an unmet clinical need. The recognition that altered metabolism is a core hallmark of cancer boosted metabolomic studies focused in the search for cancer biomarkers. The present work aims to evaluate the performance of the volatile metabolites present in the extracellular medium to discriminate RCC cell lines with distinct histological subtypes (clear cell and papillary) and metastatic potential from non-tumorigenic renal cells. Hence, volatile organic compounds (VOCs) and volatile carbonyl compounds (VCCs) were extracted by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography-mass spectrometry (GC-MS). Multivariate and univariate analysis unveiled a panel of metabolites responsible for the separation between groups, mostly belonging to ketones, alcohols, alkanes and aldehydes classes. Some metabolites were found similarly altered for all RCC cell lines compared to non-tumorigenic cells, namely 2-ethylhexanol, tetradecane, formaldehyde, acetone (increased) and cyclohexanone and acetaldehyde (decreased). Furthermore, significantly altered levels of cyclohexanol, decanal, decane, dodecane and 4-methylbenzaldehyde were observed in all metastatic RCC cell lines when compared with the non-metastatic ones. Moreover, some alterations in the volatile composition were also observed between RCC histological subtypes. Overall, our results demonstrate the potential of volatile profiling for identification of noninvasive candidate biomarkers for early RCC diagnosis.