An NMR metabolomics approach for the diagnosis of leptomeningeal carcinomatosis in lung adenocarcinoma cancer patients

The path to leptomeningeal metastasis

The leptomeninges, the cerebrospinal-fluid-filled tissues surrounding the central nervous system, play host to various pathologies including infection, neuroinflammation and malignancy. Spread of systemic cancer into this space, termed leptomeningeal metastasis, occurs in 5-10% of patients with solid tumours and portends a bleak clinical prognosis. Previous, predominantly descriptive, clinical studies have provided few insights. Recent development of preclinical leptomeningeal metastasis models, alongside genomic, transcriptomic and proteomic sequencing efforts, has provided groundwork for mechanistic understanding and identification of long-needed therapeutic targets. Although previously understood as an anatomically isolated compartment, the leptomeninges are increasingly appreciated as a major conduit of communication between the systemic circulation and the central nervous system. Despite the unique nature of the leptomeningeal microenvironment, the general principles of metastasis hold true: cells metastasizing to the leptomeninges must gain access to the new environment, survive within the space and evade the immune system. The study of leptomeningeal metastasis has the potential to uncover novel site-specific metastatic principles and illuminate the physiology of the leptomeningeal space. In this Review, we provide a biology-focused overview of how metastatic cells reach the leptomeninges, thrive in this nutritionally sparse environment and evade the detection of the omnipresent immune system.

Imaging of brain barrier inflammation and brain fluid drainage in human neurological diseases

The intricate relationship between the central nervous system (CNS) and the immune system plays a crucial role in the pathogenesis of various neurological diseases. Understanding the interactions among the immunopathological processes at the brain borders is essential for advancing our knowledge of disease mechanisms and developing novel diagnostic and therapeutic approaches. In this review, we explore the emerging role of neuroimaging in providing valuable insights into brain barrier inflammation and brain fluid drainage in human neurological diseases. Neuroimaging techniques have enabled us not only to visualize and assess brain structures, but also to study the dynamics of the CNS in health and disease in vivo. By analyzing imaging findings, we can gain a deeper understanding of the immunopathology observed at the brain-immune interface barriers, which serve as critical gatekeepers that regulate immune cell trafficking, cytokine release, and clearance of waste products from the brain. This review explores the integration of neuroimaging data with immunopathological findings, providing valuable insights into brain barrier integrity and immune responses in neurological diseases. Such integration may lead to the development of novel diagnostic markers and targeted therapeutic approaches that can benefit patients with neurological disorders.

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.

UBE2C promotes leptomeningeal dissemination and is a therapeutic target in brain metastatic disease

Background

Despite current improvements in systemic cancer treatment, brain metastases (BM) remain incurable, and there is an unmet clinical need for effective targeted therapies.

Methods

Here, we sought common molecular events in brain metastatic disease. RNA sequencing of thirty human BM identified the upregulation of UBE2C, a gene that ensures the correct transition from metaphase to anaphase, across different primary tumor origins.

Results

Tissue microarray analysis of an independent BM patient cohort revealed that high expression of UBE2C was associated with decreased survival. UBE2C-driven orthotopic mouse models developed extensive leptomeningeal dissemination, likely due to increased migration and invasion. Early cancer treatment with dactolisib (dual PI3K/mTOR inhibitor) prevented the development of UBE2C-induced leptomeningeal metastases.

Conclusions

Our findings reveal UBE2C as a key player in the development of metastatic brain disease and highlight PI3K/mTOR inhibition as a promising anticancer therapy to prevent late-stage metastatic brain cancer.

Gas chromatography-mass spectrometry-based untargeted metabolomics reveals metabolic perturbations in medullary thyroid carcinoma

Medullary thyroid cancer (MTC) is a rare tumor that arises from parafollicular cells within the thyroid gland. The molecular mechanism underlying MTC has not yet been fully understood. Here, we aimed to perform plasma metabolomics profiling of MTC patients to explore the perturbation of metabolic pathways contributing to MTC tumorigenesis. Plasma samples from 20 MTC patients and 20 healthy subjects were obtained to carry out an untargeted metabolomics by gas chromatography–mass spectrometry. Multivariate and univariate analyses were employed as diagnostic tools via MetaboAnalyst and SIMCA software. A total of 76 features were structurally annotated; among them, 13 metabolites were selected to be differentially expressed in MTC patients compared to controls (P < 0.05). These metabolites were mainly associated with the biosynthesis of unsaturated fatty acids and amino acid metabolisms, mostly leucine, glutamine, and glutamate, tightly responsible for tumor cells' energy production. Moreover, according to the receiver operating characteristic curve analysis, metabolites with the area under the curve (AUC) value up to 0.90, including linoleic acid (AUC = 0.935), linolenic acid (AUC = 0.92), and leucine (AUC = 0.948) could discriminate MTC from healthy individuals. This preliminary work contributes to existing knowledge of MTC metabolism by providing evidence of a distinctive metabolic profile in MTC patients relying on the metabolomics approach.

Leptomeningeal Carcinomatosis: Molecular Landscape, Current Management, and Emerging Therapies

Leptomeningeal carcinomatosis is a devastating consequence of late-stage cancer, and despite multimodal treatment, remains rapidly fatal. Definitive diagnosis requires identification of malignant cells in the cerebrospinal fluid (CSF), or frank disease on MRI. Therapy is generally palliative and consists primarily of radiotherapy and/or chemotherapy, which is administered intrathecally or systemically. Immunotherapies and novel experimental therapies have emerged as promising options for decreasing patient morbidity and mortality. In this review, the authors discuss a refined view of the molecular pathophysiology of leptomeningeal carcinomatosis, current approaches to disease management, and emerging therapies.

Cerebrospinal Fluid Profiles and Their Changes after Intraventricular Chemotherapy as Prognostic or Predictive Markers for Patients with Leptomeningeal Carcinomatosis

Objective

Here, we evaluated whether cerebrospinal fluid (CSF) profiles and their changes after intraventricular chemotherapy for leptomeningeal carcinomatosis (LMC) could predict the treatment response or be prognostic for patient overall survival (OS) along with clinical factors.

Methods

Paired 1) pretreatment lumbar, 2) pretreatment ventricular, and 3) posttreatment ventricular samples and their CSF profiles were collected retrospectively from 148 LMC patients who received Ommaya reservoir installation and intraventricular chemotherapy. CSF profile changes were assessed by calculating the differences between posttreatment and pretreatment samples from the same ventricular compartment. CSF cell counts were further differentiated into total and other based on clinical laboratory reports.

Results

For the treatment response, a decreased CSF ‘total’ cell count tended to be associated with a ‘controlled’ increase in intracranial pressure (ICP) (p=0.059), but other profile changes were not associated with either the control of increased ICP or the cytology response. Among the pretreatment CSF profiles, lumbar protein level and ventricular cell count were significantly correlated with OS in univariable analysis, but they were not significant in multi-variable analysis. Among CSF profile changes, a decrease in ‘other’ cell count showed worse OS than ‘no change’ or increased groups (p=0.001). The cytological response was significant for OS, but the hazard ratio of partial remission was paradoxically higher than that of ‘no response’.

Conclusion

A decrease in other cell count of CSF after intraventricular chemotherapy was associated with poor OS in LMC patients. We suggest that more specific CSF biomarkers of cancer cell origin are needed.

Cancer-associated cells release citrate to support tumour metastatic progression

Citrate is important for lipid synthesis and epigenetic regulation in addition to ATP production. We have previously reported that cancer cells import extracellular citrate via the pmCiC transporter to support their metabolism. Here, we show for the first time that citrate is supplied to cancer by cancer-associated stroma (CAS) and also that citrate synthesis and release is one of the latter's major metabolic tasks. Citrate release from CAS is controlled by cancer cells through cross-cellular communication. The availability of citrate from CAS regulated the cytokine profile, metabolism and features of cellular invasion. Moreover, citrate released by CAS is involved in inducing cancer progression especially enhancing invasiveness and organ colonisation. In line with the in vitro observations, we show that depriving cancer cells of citrate using gluconate, a specific inhibitor of pmCiC, significantly reduced the growth and metastatic spread of human pancreatic cancer cells in vivo and muted stromal activation and angiogenesis. We conclude that citrate is supplied to tumour cells by CAS and citrate uptake plays a significant role in cancer metastatic progression.

Combined Metabolomics with Transcriptomics Reveals Important Serum Biomarkers Correlated with Lung Cancer Proliferation through a Calcium Signaling Pathway

Lung cancer (LC) is one of the most malignant cancers in the world, but currently, it lacks effective noninvasive biomarkers to assist its early diagnosis. Our study aims to discover potential serum diagnostic biomarkers for LC. In our study, untargeted serum metabolomics of a discovery cohort and targeted analysis of a test cohort were performed based on gas chromatography-mass spectrometry. Both univariate and multivariate statistical analyses were employed to screen for differential metabolites between LC and healthy control (HC), followed by the selection of candidate biomarkers through multiple algorithms. The results showed that 15 metabolites were significantly dysregulated between LC and HC, and a panel, comprising cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid, was demonstrated to have excellent differentiating capability for LC based on multiple classification modelings. In addition, the molecular interaction analysis combined with transcriptomics revealed a close correlation between the candidate biomarkers and LC proliferation via a Ca²⁺ signaling pathway. Our study discovered that cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid in combination could be a promising diagnostic biomarker for LC, and most importantly, our results will shed some light on the pathophysiological mechanism underlying LC to understand it deeply. The data that support the findings of this study are openly available in MetaboLights at https://www.ebi.ac.uk/metabolights/, reference number MTBLS1517.

[Auxiliary Diagnostic Value of Tumor Markers in the Cerebrospinal Fluid and Blood for Leptomeningeal Metastasis from Non-small Cell Lung Cancer]

背景与目的

软脑膜转移（leptomeningeal metastasis, LM）是指恶性肿瘤细胞浸润软脑膜，并在脑脊液（cerebrospinal fluid, CSF）中播散，预后极差，是晚期非小细胞肺癌（non-small cell lung cancer, NSCLC）患者致死的重要原因之一，因此早期的诊断和及时的治疗具有重要意义，CSF细胞学是LM诊断的金标准，但常常伴随着检测敏感性低、无法评估疗效等问题。本文旨在探讨血清及CSF中肿瘤标志物（tumor markers, TM）在NSCLC伴LM患者诊治的临床价值。

方法

选取NSCLC伴LM患者19例，另选同期27例神经系统良性肿瘤（nonmalignant neurological diseases, NMNDs）患者作为对照组。观察比较两组患者血清和CSF中癌胚抗原（carbohydrate antigen, CEA）、糖类抗原125（carbohydrate antigen-125, CA125）、细胞角蛋白19片断抗原（cytokeratin 19 fragments, CYFRA21-1）和神经元烯醇化酶（neurone specific enolase, NSE）检测水平和检出阳性率，比较不同组TM的敏感性和特异性，并分析血清与CSF中TM检出情况相关性，最后动态监测2例LM患者血清和CSF中TM水平，分别评估颅外和颅内治疗疗效。

结果

LM组CSF和血清中TM水平和检出阳性率均高于对照组（P < 0.05），同时LM组CSF中CEA、CYFRA21-1、NSE水平高于血清，差异有统计学意义（P < 0.05）。CSF中TM检出阳性率与血清差异不具有统计学意义（P > 0.05）。CSF中CYFRA21-1敏感性最高（88.2%），CEA特异性最好（92.3%），联合指标中CEA或NSE任一项超过临界值则敏感性和阴性预测值为100%，特异性为74.1%。CYFRA21-1和NSE同时超过临界值时特异性和阳性预测值为100%，敏感性为78.9%。亚组分析显示，CSF细胞学阳性人群TM检出阳性率超过有磁共振成像（magnetic resonance imaging, MRI）异常的人群，但不具有统计学差异（P > 0.05）。LM组血清与CSF中TM检出阳性率不一致。另外，脑室中CSF与腰穿中CSF具有相同的生化性质，动态监测血清和CSF中TM浓度，可分别评估颅外和颅内病灶的疗效。

结论

血清和CSF中TM为NSCLC伴LM患者增加了一个早期辅助诊断指标，动态监测可评估治疗疗效，值得临床推广应用。

Magnetic resonance spectroscopy for the study of cns malignancies

Despite intensive research, brain tumors are amongst the malignancies with the worst prognosis; therefore, a prompt diagnosis and thoughtful assessment of the disease is required. The resistance of brain tumors to most forms of conventional therapy has led researchers to explore the underlying biology in search of new vulnerabilities and biomarkers. The unique metabolism of brain tumors represents one potential vulnerability and the basis for a system of classification. Profiling this aberrant metabolism requires a method to accurately measure and report differences in metabolite concentrations. Magnetic resonance-based techniques provide a framework for examining tumor tissue and the evolution of disease. Nuclear Magnetic Resonance (NMR) analysis of biofluids collected from patients suffering from brain cancer can provide biological information about disease status. In particular, urine and plasma can serve to monitor the evolution of disease through the changes observed in the metabolic profiles. Moreover, cerebrospinal fluid can be utilized as a direct reporter of cerebral activity since it carries the chemicals exchanged with the brain tissue and the tumor mass. Metabolic reprogramming has recently been included as one of the hallmarks of cancer. Accordingly, the metabolic rewiring experienced by these tumors to sustain rapid growth and proliferation can also serve as a potential therapeutic target. The combination of 13C tracing approaches with the utilization of different NMR spectral modalities has allowed investigations of the upregulation of glycolysis in the aggressive forms of brain tumors, including glioblastomas, and the discovery of the utilization of acetate as an alternative cellular fuel in brain metastasis and gliomas. One of the major contributions of magnetic resonance to the assessment of brain tumors has been the non-invasive determination of 2-hydroxyglutarate (2HG) in tumors harboring a mutation in isocitrate dehydrogenase 1 (IDH1). The mutational status of this enzyme already serves as a key feature in the clinical classification of brain neoplasia in routine clinical practice and pilot studies have established the use of in vivo magnetic resonance spectroscopy (MRS) for monitoring disease progression and treatment response in IDH mutant gliomas. However, the development of bespoke methods for 2HG detection by MRS has been required, and this has prevented the wider implementation of MRS methodology into the clinic. One of the main challenges for improving the management of the disease is to obtain an accurate insight into the response to treatment, so that the patient can be promptly diverted into a new therapy if resistant or maintained on the original therapy if responsive. The implementation of 13C hyperpolarized magnetic resonance spectroscopic imaging (MRSI) has allowed detection of changes in tumor metabolism associated with a treatment, and as such has been revealed as a remarkable tool for monitoring response to therapeutic strategies. In summary, the application of magnetic resonance-based methodologies to the diagnosis and management of brain tumor patients, in addition to its utilization in the investigation of its tumor-associated metabolic rewiring, is helping to unravel the biological basis of malignancies of the central nervous system.

The Potential of Metabolomics in the Diagnosis of Thyroid Cancer

Thyroid cancer is the most common endocrine system malignancy. However, there is still a lack of reliable and specific markers for the detection and staging of this disease. Fine needle aspiration biopsy is the current gold standard for diagnosis of thyroid cancer, but drawbacks to this technique include indeterminate results or an inability to discriminate different carcinomas, thereby requiring additional surgical procedures to obtain a final diagnosis. It is, therefore, necessary to seek more reliable markers to complement and improve current methods. "Omics" approaches have gained much attention in the last decade in the field of biomarker discovery for diagnostic and prognostic characterisation of various pathophysiological conditions. Metabolomics, in particular, has the potential to identify molecular markers of thyroid cancer and identify novel metabolic profiles of the disease, which can, in turn, help in the classification of pathological conditions and lead to a more personalised therapy, assisting in the diagnosis and in the prediction of cancer behaviour. This review considers the current results in thyroid cancer biomarker research with a focus on metabolomics.

A High-Performing Plasma Metabolite Panel for Early-Stage Lung Cancer Detection

The objective of this research is to use metabolomic techniques to discover and validate plasma metabolite biomarkers for the diagnosis of early-stage non-small cell lung cancer (NSCLC). The study included plasma samples from 156 patients with biopsy-confirmed NSCLC along with age and gender-matched plasma samples from 60 healthy controls. A fully quantitative targeted mass spectrometry (MS) analysis (targeting 138 metabolites) was performed on all samples. The sample set was split into a discovery set and validation set. Metabolite concentration data, clinical data, and smoking history were used to determine optimal sets of biomarkers and optimal regression models for identifying different stages of NSCLC using the discovery sets. The same biomarkers and regression models were used and assessed on the validation models. Univariate and multivariate statistical analysis identified β-hydroxybutyric acid, LysoPC 20:3, PC ae C40:6, citric acid, and fumaric acid as being significantly different between healthy controls and stage I/II NSCLC. Robust predictive models with areas under the curve (AUC) > 0.9 were developed and validated using these metabolites and other, easily measured clinical data for detecting different stages of NSCLC. This study successfully identified and validated a simple, high-performing, metabolite-based test for detecting early stage (I/II) NSCLC patients in plasma. While promising, further validation on larger and more diverse cohorts is still required.

Exploring Cancer Metabolism: Applications of Metabolomics and Metabolic Phenotyping in Cancer Research and Diagnostics

Altered metabolism is one of the key hallmarks of cancer. The development of sensitive, reproducible and robust bioanalytical tools such as Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry techniques offers numerous opportunities for cancer metabolism research, and provides additional and exciting avenues in cancer diagnosis, prognosis and for the development of more effective and personalized treatments. In this chapter, we introduce the current state of the art of metabolomics and metabolic phenotyping approaches in cancer research and clinical diagnostics.

Nuclear magnetic resonance (NMR)-based metabolomics for cancer research

Nuclear magnetic resonance (NMR) has emerged as an effective tool in various spheres of biomedical research, amongst which metabolomics is an important method for the study of various types of disease. Metabolomics has proved its stronghold in cancer research by the development of different NMR methods over time for the study of metabolites, thus identifying key players in the aetiology of cancer. A plethora of one-dimensional and two-dimensional NMR experiments (in solids, semi-solids and solution phases) are utilized to obtain metabolic profiles of biofluids, cell extracts and tissue biopsy samples, which can further be subjected to statistical analysis. Any alteration in the assigned metabolite peaks gives an indication of changes in metabolic pathways. These defined changes demonstrate the utility of NMR in the early diagnosis of cancer and provide further measures to combat malignancy and its progression. This review provides a snapshot of the trending NMR techniques and the statistical analysis involved in the metabolomics of diseases, with emphasis on advances in NMR methodology developed for cancer research.

Diagnostic Reliability of Leptomeningeal Disease Using Magnetic Resonance Imaging

Brain metastases are seen in 20%-50% of patients with metastatic solid tumors. On the other hand, leptomeningeal disease (LMD) occurs more rarely. The gold standard for the diagnosis of LMD is serial cerebrospinal fluid (CSF) analyses, although in daily practice, the diagnosis of LMD is often made by neuroimaging. Leptomeningeal metastases (LM) have been a relative contra-indication to radiosurgery. It can be noted that focal LMD can be difficult to distinguish from a superficially located/cortical-based brain metastasis which is not a contra-indication for radiosurgery. Hence, justifying the need of a reliable diagnosis method.

The goal of this study was to determine the inter-observer reliability of contrast-enhanced magnetic resonance imaging (gdMRI) in the differentiation of focal cortical-based metastases from leptomeningeal spread.

This is a retrospective review of a prospectively collected database of patients with brain metastases. A total of 42 cases with superficial lesions were selected for review. Additionally, eight control cases demonstrating deep and/or white-matter based lesions were included in the study.

Three neuroradiologists and three radiation oncologists were asked to review each study and score the presence of LM. Inter-observer agreement was calculated using group-derived agreement coefficients (Gwet’s AC1 and Gwet's AC2). Pair-wise inter-observer agreement coefficients never reached substantial values for trichotomized outcomes (LMD, non-LMD or indeterminate) but did reach a substantial value in a minority of cases for dichotomised outcomes (LMD or non-LMD). The control subgroup analysis revealed substantial agreement between most pairs for both trichotomized and dichotomised outcomes.

We observed low inter-observer agreement amongst specialists for the diagnosis of focal LMD by gdMRI. Neuroimaging should not be relied upon to make treatment decisions, notably to deny patients radiosurgery.

Serum Metabolomics Study of Nonsmoking Female Patients with Non-Small Cell Lung Cancer Using Gas Chromatography-Mass Spectrometry

The incidence of nonsmoking female patients with non-small cell lung cancer (NSCLC) has increased in recent decades; however, the pathogenesis of patients is unclear, and early diagnosis biomarkers are in urgent need. In this study, 136 nonsmoking female subjects (65 patients with NSCLC, 6 patients with benign lung tumors, and 65 healthy controls) were enrolled, and their metabolic profiling was investigated by using pseudotargeted gas chromatography-mass spectrometry. A total of 56 annotated metabolites were found and verified to be significantly different in nonsmoking females with NSCLC compared with the control. The metabolic profiling was featured by disturbed energy metabolism, amino acid metabolism, oxidative stress, lipid metabolism, and so on. Cysteine, serine, and 1-monooleoylglycerol were defined as the biomarker panel for the diagnosis of NSCLC patients. 98.5 and 91.4% of subjects were correctly distinguished in the discovery and validation sets, respectively. The biomarker panel was also useful for the diagnosis of in situ malignancy patients, with an accuracy of 97.7 and 97.8% in the discovery and validation sets, respectively. The study provides a biomarker panel for the auxiliary diagnosis of nonsmoking females with NSCLC.

Metabolite Profiling of Clinical Cancer Biofluid Samples by NMR Spectroscopy

Metabolomics is a comprehensive characterization of the small polar molecules (metabolites) in different biological systems. One of the analytical platforms commonly used to study metabolic alterations in biofluid samples is proton nuclear magnetic resonance (¹H NMR) spectroscopy. NMR spectroscopy is very specific, quantitative, and highly reproducible. Moreover, sample preparation for NMR experiments is very simple and straightforward, and this gives NMR spectroscopy a distinct advantage over other metabolic profiling methods. It has already been shown that ¹H NMR-based profiling of biological fluids can be effective in differentiating benign from malignant lesions and in investigating the efficacy of specific cancer treatments. Therefore, ¹H NMR spectroscopy may become a promising tool for early noninvasive diagnosis and rapid assessment of treatment effects in cancer patients. Here, we describe a detailed protocol for ¹H NMR metabolite profiling in serum, plasma, and urine samples, including sample collection procedures, sample preparation for ¹H NMR experiments, spectral acquisition and processing, and quantitative profiling of ¹H NMR spectra. We also discuss several aspects of appropriate study design and some multivariate statistical methods that are commonly used to analyze metabolomics datasets.

Application of 3D Fast Spin-Echo T1 Black-Blood Imaging in the Diagnosis and Prognostic Prediction of Patients with Leptomeningeal Carcinomatosis

BACKGROUND AND PURPOSE

Contrast-enhanced 3D fast spin-echo T1 black-blood imaging selectively suppresses the signal of blood flow and could provide a higher contrast-to-noise ratio compared with contrast-enhanced 3D ultrafast gradient recalled echo (contrast-enhanced gradient recalled echo) and 2D spin-echo T1WI (contrast-enhanced spin-echo). The purpose of our study was to evaluate whether black-blood imaging can improve the diagnostic accuracy for leptomeningeal carcinomatosis compared with contrast-enhanced gradient recalled-echo and contrast-enhanced spin-echo and, furthermore, to determine whether the grade of leptomeningeal carcinomatosis evaluated on black-blood imaging is a significant predictor of progression-free survival.

MATERIALS AND METHODS

Leptomeningeal carcinomatosis (n = 78) and healthy (n = 31) groups were enrolled. Contrast-enhanced gradient recalled-echo, contrast-enhanced spin-echo, and black-blood imaging were separately reviewed, and a diagnostic rating (positive, indeterminate, or negative) and grading of leptomeningeal carcinomatosis were assigned. The diagnostic accuracies of the 3 imaging sequences were compared in terms of leptomeningeal carcinomatosis detection. The Kaplan-Meier and the Cox proportional hazards model analyses were performed to determine the relationship between the leptomeningeal carcinomatosis grade evaluated on black-blood imaging and progression-free survival.

RESULTS

Black-blood imaging showed a significantly higher sensitivity (97.43%) than contrast-enhanced gradient recalled-echo (64.1%) and contrast-enhanced spin-echo (66.67%) (P < .05). In terms of specificities, we did not find any significant differences among contrast-enhanced gradient recalled-echo (90.32%), contrast-enhanced spin-echo (90.32%), and black-blood imaging (96.77%) (P > .05). A Cox proportional hazards model identified the time to metastasis, Karnofsky Performance Scale status, and a combination of the leptomeningeal carcinomatosis grade with a linear pattern as independent predictors of progression-free survival (P < .05).

CONCLUSIONS

Black-blood imaging can improve the diagnostic accuracy and predict progression-free survival in patients with leptomeningeal carcinomatosis.

A characteristic biosignature for discrimination of gastric cancer from healthy population by high throughput GC-MS analysis

Early diagnosis of gastric cancer is crucial to improve patient′ outcome. A good biomarker will function in early diagnosis for gastric cancer. In order to find practical and cost-effective biomarkers, we used gas chromatography combined mass spectrometer (GC-MS) to profile urinary metabolites on 293 urine samples. Ninety-four samples are taken as training set, others for validating study. Orthogonal partial least squares discriminant analysis (OPLS-DA), significance analysis of microarray (SAM) and Mann-Whitney U test are used for data analysis. The diagnostic value of urinary metabolites was evaluated by ROC curve. As results, Seventeen metabolites are significantly different between patients and healthy controls in training set. Among them, 14 metabolites show diagnostic value better than classic blood biomarkers by quantitative assay on validation set. Ten of them are amino acids and four are organic metabolites. Importantly, proline, p-cresol and 4-hydroxybenzoic acid disclose outcome-prediction value by means of survival analysis. Therefore, the examination of urinary metabolites is a promising noninvasive strategy for gastric cancer screening.

Next-generation metabolomics in lung cancer diagnosis, treatment and precision medicine: mini review

Lung cancer is the leading cause of cancer-related death. Next-generation metabolomics is becoming a powerful emerging technology for studying the systems biology and chemistry of health and disease. This mini review summarized the main platforms of next-generation metabolomics and its main applications in lung cancer including early diagnosis, pathogenesis, classifications and precision medicine. The period covers between 2009 and August, 2017. The major issues and future directions of metabolomics in lung cancer research and clinical applications were also discussed.

Complement Component 3 Adapts the Cerebrospinal Fluid for Leptomeningeal Metastasis

We molecularly dissected leptomeningeal metastasis, or spread of cancer to the cerebrospinal fluid (CSF), which is a frequent and fatal condition mediated by unknown mechanisms. We selected lung and breast cancer cell lines for the ability to infiltrate and grow in CSF, a remarkably acellular, mitogen-poor metastasis microenvironment. Complement component 3 (C3) was upregulated in four leptomeningeal metastatic models and proved necessary for cancer growth within the leptomeningeal space. In human disease, cancer cells within the CSF produced C3 in correlation with clinical course. C3 expression in primary tumors was predictive of leptomeningeal relapse. Mechanistically, we found that cancer-cell-derived C3 activates the C3a receptor in the choroid plexus epithelium to disrupt the blood-CSF barrier. This effect allows plasma components, including amphiregulin, and other mitogens to enter the CSF and promote cancer cell growth. Pharmacologic interference with C3 signaling proved therapeutically beneficial in suppressing leptomeningeal metastasis in these preclinical models.

Retrospective Analysis of Cerebrospinal Fluid Profiles in 228 Patients with Leptomeningeal Carcinomatosis : Differences According to the Sampling Site, Symptoms, and Systemic Factors

Objective

Elevated cell counts and protein levels in cerebrospinal fluid (CSF) result from disease activity in patients with leptomeningeal carcinomatosis (LMC). Previous studies evaluated the use of CSF profiles to monitor a treatment response or predict prognosis. CSF profiles vary, however, according to the sampling site and the patient's systemic condition. We compared lumbar and ventricular CSF profiles collected before intraventricular chemotherapy for LMC and evaluated the association of these profiles with patients' systemic factors and LMC disease activity.

Methods

CSF profiles were retrospectively collected from 228 patients who underwent Ommaya reservoir insertion for intraventricular chemotherapy after a diagnosis of LMC. Lumbar samples taken via lumbar puncture were used for the diagnosis, and ventricular samples were obtained later at the time of Ommaya reservoir insertion. LMC disease activity was defined as the presence of LMC-related symptoms such as increased intracranial pressure, hydrocephalus, cranial neuropathy, and cauda equina syndrome.

Results

Cell counts (median : 8 vs. 1 cells/mL) and protein levels (median : 68 vs. 17 mg/dL) significantly higher in lumbar CSF than in ventricular CSF (p<0.001). Among the evaluated systemic factors, concomitant brain metastasis and previous radiation were significantly correlated with higher protein levels in the lumbar CSF (p=0.01 and <0.001, respectively). Among the LMC disease activity, patients presenting with hydrocephalus or cauda equina syndrome showed higher lumbar CSF protein level compared with that in patients without those symptoms (p=0.049 and p<0.001, respectively). The lumbar CSF cell count was significantly lower in patients with cranial neuropathy (p=0.046). The ventricular CSF cell counts and protein levels showed no correlation with LMC symptoms. Carcinoembryonic antigen (CEA), which was measured from ventricular CSF after the diagnosis in 109 patients, showed a significant association with the presence of hydrocephalus (p=0.01).

Conclusion

The protein level in lumbar CSF indicated the localized disease activity of hydrocephalus and cauda equina syndrome. In the ventricular CSF, only the CEA level reflected the presence of hydrocephalus. We suggest using more specific biomarkers for the evaluation of ventricular CSF to monitor disease activity and treatment response.

Monitoring cancer prognosis, diagnosis and treatment efficacy using metabolomics and lipidomics

INTRODUCTION

Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.

OBJECTIVES

Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case-control, prognostic and longitudinal study designs.

METHODS

A literature search of the current relevant primary research was performed.

RESULTS

Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case-control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.

CONCLUSION

Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.

Metabolomic analysis of percutaneous fine-needle aspiration specimens of thyroid nodules: Potential application for the preoperative diagnosis of thyroid cancer

Thyroid nodules are a very common problem. Since malignant thyroid nodules should be treated surgically, preoperative diagnosis of thyroid cancer is very crucial. Cytopathologic analysis of percutaneous fine-needle aspiration (FNA) specimens is the current gold standard for diagnosing thyroid nodules. However, this method has led to high rates of inconclusive results. Metabolomics has emerged as a useful tool in medical fields and shown great potential in diagnosing various cancers. Here, we evaluated the potential of nuclear magnetic resonance (NMR) analysis of percutaneous FNA specimens for preoperative diagnosis of thyroid cancer. We analyzed metabolome of FNA samples of papillary thyroid carcinoma (n = 35) and benign follicular nodule (n = 69) using a proton NMR spectrometer. The metabolomic profiles showed a considerable discrimination between benign and malignant nodules. Receiver operating characteristic (ROC) curve analysis indicated that seven metabolites could serve as discriminators (area under ROC curve value, 0.64–0.85). These findings demonstrated that NMR analysis of percutaneous FNA specimens of thyroid nodules can be potentially useful in the accurate and rapid preoperative diagnosis of thyroid cancer.

Molecular Targeted Therapies for the Treatment of Leptomeningeal Carcinomatosis: Current Evidence and Future Directions

Leptomeningeal carcinomatosis (LMC) is the multifocal seeding of cerebrospinal fluid and leptomeninges by malignant cells. The incidence of LMC is approximately 5% in patients with malignant tumors overall and the rate is increasing due to increasing survival time of cancer patients. Eradication of the disease is not yet possible, so the treatment goals of LMC are to improve neurologic symptoms and to prolong survival. A standard treatment for LMC has not been established due to low incidences of LMC, the rapidly progressing nature of the disease, heterogeneous populations with LMC, and a lack of randomized clinical trial results. Treatment options for LMC include intrathecal chemotherapy, systemic chemotherapy, and radiation therapy, but the prognoses remain poor with a median survival of <3 months. Recently, molecular targeted agents have been applied in the clinic and have shown groundbreaking results in specific patient groups epidermal growth factor receptor (EGFR)-targeted therapy or an anaplastic lymphoma kinase (ALK) inhibitor in lung cancer, human epidermal growth factor receptor 2 (HER2)-directed therapy in breast cancer, and CD20-targeted therapy in B cell lymphoma). Moreover, there are results indicating that the use of these agents under proper dose and administration routes can be effective for managing LMC. In this article, we review molecular targeted agents for managing LMC.

NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in patients with colorectal cancer

Colorectal cancer (CRC) is a growing cause of mortality in developing countries, warranting investigation into its earlier detection for optimal disease management. A metabolomics based approach provides potential for noninvasive identification of biomarkers of colorectal carcinogenesis, as well as dissection of molecular pathways of pathophysiological conditions. Here, proton nuclear magnetic resonance spectroscopy (1HNMR) -based metabolomic approach was used to profile fecal metabolites of 68 CRC patients (stage I/II=20; stage III=25 and stage IV=23) and 32 healthy controls (HC). Pattern recognition through principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) was applied on 1H-NMR processed data for dimension reduction. OPLS-DA revealed that each stage of CRC could be clearly distinguished from HC based on their metabolomic profiles. Successive analyses identified distinct disturbances to fecal metabolites of CRC patients at various stages, compared with those in cancer free controls, including reduced levels of acetate, butyrate, propionate, glucose, glutamine, and elevated quantities of succinate, proline, alanine, dimethylglycine, valine, glutamate, leucine, isoleucine and lactate. These altered fecal metabolites potentially involved in the disruption of normal bacterial ecology, malabsorption of nutrients, increased glycolysis and glutaminolysis. Our findings revealed that the fecal metabolic profiles of healthy controls can be distinguished from CRC patients, even in the early stage (stage I/II), highlighting the potential utility of NMR-based fecal metabolomics fingerprinting as predictors of earlier diagnosis in CRC patients.

SENSI: signal enhancement by spectral integration for the analysis of metabolic mixtures

The method provided here can overcome the low S/N problem in ¹³C NMR by the integration of plural spectra to increase the resolution based on non-bucketing analysis without measurements.

Inhibition of pulmonary adenoma in diethylnitrosamine-induced rats by Rhizoma paridis saponins

Nowadays, people pay more and more attention to the natural products based on their multiple targets in the antitumor treatment. In our previous research, Rhizoma paridis saponins (RPS) were regarded as potent anticancer agent that elicits programmed cell death and inhibits metastases in murine lung adenocarcinoma in vivo. In the present study, we set up a rat model of diethylnitrosamine (DEN) induced pulmonary adenoma to evaluate the antitumor effects of RPS again. After 20 weeks treatment, rats were sacrificed in order to perform histopathological examinations, blood biochemistry, immunohistochemistry, western blot, PCR and metabonomics. As a result, DEN induced pulmonary adenoma generation in the lungs and damaged hepatocytes and hepatoma formation in the livers. RPS effectively attenuated hepatotoxic and inhibited pulmonary adenoma through down-regulating expression of MMP-9 and up-regulating level of TIMP-2 in DEN-induced rats. Meanwhile, RPS remarkably decreased energy metabolism, and glycine, serine and threonine metabolism to block the tumor growth. In conclusion, RPS would be a potent anticancer agent used in the prospective application.

Leptomeningeal disease following stereotactic radiosurgery for brain metastases from breast cancer

Leptomeningeal disease (LMD) is a highly aggressive and usually rapidly fatal condition. The purpose of this study is to identify clinical factors that can serve to predict for LMD at the time of stereotactic radiosurgery (SRS) for brain metastases from breast carcinoma. We conducted a retrospective review of patients with brain metastases from breast cancer treated with SRS from 1995 to 2014 at our institution. Clinical, radiographic, and dosimetric data were collected. LMD was diagnosed by cerebrospinal fluid (CSF) cytology or MRI demonstrating CSF seeding. Comparative statistical analyses were conducted using Cox proportional hazards regression, binary logistic regression, and/or log-rank test. 126 patients met inclusion criteria. Eighteen patients (14 %) developed LMD following SRS. From the time of SRS, the actuarial rate of LMD at 12 months from diagnosis of brain metastasis was 9 % (11 patients). Active disease in the chest at the time of SRS was associated with development of LMD (p = 0.038). Factors including receptor status, tumor size, number of intra-axial tumors, cystic tumor morphology, prior WBRT, active bone metastases, and active liver metastases were not significantly associated with the development of LMD. In patients with brain metastasis from breast cancer that undergo SRS, there is a relatively low rate of LMD. We found that while tumor hormonal status, bone metastases, and hepatic metastases were not associated with the development of LMD, active lung metastases at SRS was associated with LMD. Further research may help to delineate a causative relationship between metastatic lung disease and LMD.

Prediction of advanced ovarian cancer recurrence by plasma metabolic profiling

Epithelial ovarian cancer (EOC) is the most lethal of gynecologic malignancies due to the high rate of recurrence and poor prognosis. Predicting the prognosis in patients with EOC is clinically challenging, partly because appropriate biomarkers of recurrence have yet to be explored. In this prospective study, pre-treatment plasma samples were collected from 38 patients with stage III or IV EOC who were subsequently followed up. Ultra-performance liquid chromatography mass spectrometry was used to perform metabolic profiling, which yielded five metabolites that were potential biomarkers for EOC recurrence: l-tryptophan, kynurenine, bilirubin, LysoPC (14 : 0) and LysoPE (18 : 2). A combination of these five potential biomarkers strongly predicted recurrence, the area under the curve being 0.91. In summary, the candidate biomarkers identified in this study may both facilitate clinical prediction of EOC recurrence and prognosis and serve as potential therapeutic targets in patients with EOC.