Discrimination of Patients with Microsatellite Instability Colon Cancer using 1H HR MAS MR Spectroscopy and Chemometric Analysis

Metabolic profiling of colorectal cancer organoids: A comparison between high-resolution magic angle spinning magnetic resonance spectroscopy and solution nuclear magnetic resonance spectroscopy of polar extracts

Patient-derived cancer cells cultured in vitro are a cornerstone of cancer metabolism research. More recently, the introduction of organoids has provided the research community with a more versatile model system. Physiological structure and organization of the cell source tissue are maintained in organoids, representing a closer link to in vivo tumor models. High-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS) is a commonly applied analytical approach for metabolic profiling of intact tissue, but its use has not been reported for organoids. The aim of the current work was to compare the performance of HR MAS MRS and extraction-based nuclear magnetic resonance (NMR) in metabolic profiling of wild-type and tumor progression organoids (TPOs) from human colon cancer, and further to investigate how the sequentially increased genetic alterations of the TPOs affect the metabolic profile. Sixteen metabolites were reliably identified and quantified both in spectra based on NMR of extracts and HR MAS MRS of intact organoids. The metabolite concentrations from the two approaches were highly correlated (r = 0.94), and both approaches were able to capture the systematic changes in metabolic features introduced by the genetic alterations characteristic of colorectal cancer progression (e.g., increased levels of lactate and decreased levels of myo-inositol and phosphocholine with an increasing number of mutations). The current work highlights that HR MAS MRS is a well-suited method for metabolic profiling of intact organoids, with the additional benefit that the nondestructive nature of HR MAS enables subsequent recovery of the organoids for further analyses based on nucleic acids or proteins.

Metabolic Characteristics and M2 Macrophage Infiltrates in Invasive Nonfunctioning Pituitary Adenomas

OBJECTIVE

The aim of this study was to investigate the metabolic differences between invasive and non-invasive nonfunctioning pituitary adenomas (NFPAs), determine the expression of an M2 macrophage marker in NFPAs, and analyze the effects of metabolic changes in invasive NFPAs on M2 macrophage infiltrates.

METHODS

Tissue samples of NFPAs from patients who underwent transsphenoidal or craniotomy surgery from January 2021 to August 2021 were collected. NFPA tissues were analyzed based on a gas chromatography-mass spectrometry non-targeted metabolomics platform, and immunohistochemical staining for M2 macrophage marker CD206 was performed.

RESULTS

We evaluated 15 invasive and 21 non-invasive NFPAs. A total of 22 metabolites were identified through non-targeted metabolomics analysis. Among them, the expression of 1-octadecanol, inosine 5'-monophosphate, adenosine 5'-monophosphate, guanosine 5'-monophosphate, creatinine, desmosterol, taurine, hypotaurine, lactic acid, and succinic acid was upregulated in invasive NFPAs, while that of 1-oleoylglycerol, arachidonic acid, cis-11-eicosenoic acid, docosahexaenoic acid, glyceric acid, hypoxanthine, linoleic acid, lysine, oleic acid, uracil, valine, and xanthine was downregulated. Immunohistochemical analysis suggested that the number of CD206-positive cells was higher in invasive NFPAs than in non-invasive NFPAs.

CONCLUSION

Invasive and non-invasive NFPAs showed distinct metabolite profiles. The levels of succinic acid and lactic acid were higher in invasive NFPAs, and the high expression of the M2 macrophage marker was verified in invasive NFPAs.

Multi-omics characterization and validation of MSI-related molecular features across multiple malignancies

HEADINGS AIMS

To establish a microsatellite instability (MSI) predictive model in pan-cancer and compare the multi-omics characterization of MSI-related molecular features.

MATERIALS AND METHODS

We established a 15-gene signature for predicting MSI status and performed a systematic assessment of MSI-related molecular features including gene and miRNA expression, DNA methylation, and somatic mutation, in approximately 10,000 patients across 30 cancer types from The Cancer Genome Atlas, Gene Expression Omnibus database, and our institution. Then we identified common MSI-associated dysregulated molecular features across six cancers and explored their mutual interfering relationships and the drug sensitivity.

KEY FINDINGS

we demonstrated the model's high prediction performance and found the samples with high-MSI were mainly distributed in six cancers: BRCA, COAD, LUAD, LIHC, STAD, and UCEC. We found RPL22L1 was up-regulated in the high-MSI group of 5/6 cancer types. CYP27A1 and RAI2 were down-regulated in 4/6 cancer types. More than 20 miRNAs and 39 DMGs were found up-regulated in MSI-H at least three cancers. We discovered some drugs, including OSI-027 and AZD8055 had a higher sensitivity in the high MSI-score group. Functional enrichment analysis revealed the correlation between MSI score and APM score, HLA score, or glycolysis score. The complicated regulatory mechanism of tumor MSI status in multiple dimensions was explored by an integrated analysis of the correlations among MSI-related genes, miRNAs, methylation, and drug response data.

SIGNIFICANCE

Our pan-cancer study provides a valuable predictive model and a comprehensive atlas of tumor MSI, which may guide more precise and personalized therapeutic strategies for tumor patients.

Metabolic models predict bacterial passengers in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a complex multifactorial disease. Increasing evidence suggests that the microbiome is involved in different stages of CRC initiation and progression. Beyond specific pro-oncogenic mechanisms found in pathogens, metagenomic studies indicate the existence of a microbiome signature, where particular bacterial taxa are enriched in the metagenomes of CRC patients. Here, we investigate to what extent the abundance of bacterial taxa in CRC metagenomes can be explained by the growth advantage resulting from the presence of specific CRC metabolites in the tumor microenvironment.

METHODS

We composed lists of metabolites and bacteria that are enriched on CRC samples by reviewing metabolomics experimental literature and integrating data from metagenomic case-control studies. We computationally evaluated the growth effect of CRC enriched metabolites on over 1500 genome-based metabolic models of human microbiome bacteria. We integrated the metabolomics data and the mechanistic models by using scores that quantify the response of bacterial biomass production to CRC-enriched metabolites and used these scores to rank bacteria as potential CRC passengers.

RESULTS

We found that metabolic networks of bacteria that are significantly enriched in CRC metagenomic samples either depend on metabolites that are more abundant in CRC samples or specifically benefit from these metabolites for biomass production. This suggests that metabolic alterations in the cancer environment are a major component shaping the CRC microbiome.

CONCLUSION

Here, we show with in sillico models that supplementing the intestinal environment with CRC metabolites specifically predicts the outgrowth of CRC-associated bacteria. We thus mechanistically explain why a range of CRC passenger bacteria are associated with CRC, enhancing our understanding of this disease. Our methods are applicable to other microbial communities, since it allows the systematic investigation of how shifts in the microbiome can be explained from changes in the metabolome.

Tissue amino acid profiles are characteristic of tumor type, malignant phenotype, and tumor progression in pancreatic tumors

Tissue amino acid profiles depend on the cell types and extracellular components that constitute the tissue, and their functions and activities. We aimed to characterize the tissue amino acid profiles in several types of pancreatic tumors and lesions. We examined tissue amino acid profiles in 311 patients with pancreatic tumors or lesions. We used newly developed LC-MS/MS methods to obtain the profiles, which were compared with clinicopathological data. Each tumor or lesion presented a characteristic tissue amino acid profile. Certain amino acids were markedly altered during the multistep pancreatic carcinogenesis and pancreatic ductal adenocarcinoma (PDAC) progression. A tissue amino acid index (TAAI) was developed based on the amino acids that were notably changed during both carcinogenesis and cancer progression. Univariate and multivariate survival analyses revealed that PDAC patients with a high TAAI exhibited a significantly shorter survival rate, and these findings were validated using a second cohort. We suggest that tissue amino acid profiles are characteristic for normal tissue type, tumor histological type, and pathological lesion, and are representative of the cancer grade or progression stage in multistep carcinogenesis and of malignant characteristics. The TAAI could serve as an independent prognosticator for patients with PDAC.

Metabolic changes associated with papillary thyroid carcinoma: A nuclear magnetic resonance-based metabolomics study

Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. Nuclear magnetic resonance (NMR)‑based metabolomic technique is the gold standard in metabolite structural elucidation, and can provide different coverage of information compared with other metabolomic techniques. Here, we firstly conducted NMR based metabolomics study regarding detailed metabolic changes especially metabolic pathway changes related to PTC pathogenesis. 1H NMR-based metabolomic technique was adopted in conju-nction with multivariate analysis to analyze matched tumor and normal thyroid tissues obtained from 16 patients. The results were further annotated with Kyoto Encyclopedia of Genes and Genomes (KEGG), and Human Metabolome Database, and then were analyzed using modules of pathway analysis and enrichment analysis of MetaboAnalyst 3.0. Based on the analytical techniques, we established the models of principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least-squares discriminant analysis (OPLS‑DA) which could discriminate PTC from normal thyroid tissue, and found 15 robust differentiated metabolites from two OPLS-DA models. We identified 8 KEGG pathways and 3 pathways of small molecular pathway database which were significantly related to PTC by using pathway analysis and enrichment analysis, respectively, through which we identified metabolisms related to PTC including branched chain amino acid metabolism (leucine and valine), other amino acid metabolism (glycine and taurine), glycolysis (lactate), tricarboxylic acid cycle (citrate), choline metabolism (choline, ethanolamine and glycerolphosphocholine) and lipid metabolism (very-low‑density lipoprotein and low-density lipoprotein). In conclusion, the PTC was characterized with increased glycolysis and inhibited tricarboxylic acid cycle, increased oncogenic amino acids as well as abnormal choline and lipid metabolism. The findings in this study provide new insights into detailed metabolic changes of PTC, and hold great potential in the treatment of PTC.

Applications of high-resolution magic angle spinning MRS in biomedical studies II-Human diseases

High-resolution magic angle spinning (HRMAS) MRS is a powerful method for gaining insight into the physiological and pathological processes of cellular metabolism. Given its ability to obtain high-resolution spectra of non-liquid biological samples, while preserving tissue architecture for subsequent histopathological analysis, the technique has become invaluable for biochemical and biomedical studies. Using HRMAS MRS, alterations in measured metabolites, metabolic ratios, and metabolomic profiles present the possibility to improve identification and prognostication of various diseases and decipher the metabolomic impact of drug therapies. In this review, we evaluate HRMAS MRS results on human tissue specimens from malignancies and non-localized diseases reported in the literature since the inception of the technique in 1996. We present the diverse applications of the technique in understanding pathological processes of different anatomical origins, correlations with in vivo imaging, effectiveness of therapies, and progress in the HRMAS methodology.

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.

High tumor glycine concentration is an adverse prognostic factor in locally advanced rectal cancer

BACKGROUND AND PURPOSE

Recognizing the link between altered tumor metabolism and disease aggressiveness, this study aimed to identify associations between tumor metabolic profiles and therapeutic outcome in locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

Pretreatment tumor metabolic profiles from 54 LARC patients receiving combined-modality neoadjuvant treatment and surgery were acquired by high-resolution magic angle spinning magnetic resonance spectroscopy (HR MAS MRS). Metabolite concentrations were correlated to TNM and the presence of disseminated tumor cells (DTC) at diagnosis, ypTN and tumor regression grade (TRG) following neoadjuvant treatment, and progression-free survival (PFS).

RESULTS

Pretreatment tumor metabolite concentrations showed no significant associations to TNM, DTC, ypTN or TRG. In univariate regression analysis, high concentrations of glycine, creatine and myo-inositol were significantly associated with poor PFS, with metastasis as main PFS event. In multivariate analysis, high glycine concentration remained most significantly associated with poor PFS (hazard ratio=4.4, 95% confidence interval=1.4-14.3, p=0.008).

CONCLUSIONS

High tumor glycine concentration was identified as adverse prognostic factor for PFS in LARC. In a patient population treated with curative intent but with metastatic disease as main PFS event further investigations of glycine as early predictor of metastatic progression and therapeutic target are warranted.

Distinct Metabolomic Profiles of Papillary Thyroid Carcinoma and Benign Thyroid Adenoma

Papillary thyroid carcinoma (PTC) and benign thyroid adenoma (BTA) are the most common head and neck tumors. However, the metabolic differences between PTC and BTA have not been characterized. The aim of this study was to identify the metabolic profiles of these two types of tumors using a metabolomics approach. Tumors and adjacent nontumor specimens collected from 57 patients with PTC and 48 patients with BTA were profiled using gas chromatography-time-of-flight mass spectrometry and ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. A panel of 46 and 44 differentially expressed metabolites were identified in the PTC and BTA specimens, respetively, and compared with nontumor tissues. Common metabolic signatures, as characterized by increased glycolysis, amino acid metabolism, one carbon metabolism and tryptophan metabolism, were found in both types of tumors. Purine and pyrimidine metabolism was significantly elevated in the PTC specimens, and taurine and hypotaurine levels were also higher in the PTC tissues. Increased fatty acid and bile acid levels were found, especially in the BTA tissues. The metabolic profiles of the PTC and BTA tissues include both similar and remarkably different metabolites, suggesting the presence of common and unique mechanistic pathways in these types of tumors during tumorigenesis.

Characterizing metabolic changes in human colorectal cancer

Colorectal cancer (CRC) remains a leading cause of cancer death worldwide, despite the fact that it is a curable disease when diagnosed early. The development of new screening methods to aid in early diagnosis or identify precursor lesions at risk for progressing to CRC will be vital to improving the survival rate of individuals predisposed to CRC. Metabolomics is an advancing area that has recently seen numerous applications to the field of cancer research. Altered metabolism has been studied for many years as a means to understand and characterize cancer. However, further work is required to establish standard procedures and improve our ability to identify distinct metabolomic profiles that can be used to diagnose CRC or predict disease progression. The present study demonstrates the use of direct infusion traveling wave ion mobility mass spectrometry to distinguish metabolic profiles from CRC samples and matched non-neoplastic epithelium as well as metastatic and primary tumors at different stages of disease (T1-T4). By directly infusing our samples, the analysis time was reduced significantly, thus increasing the speed and efficiency of this method compared to traditional metabolomics platforms. Partial least squares discriminant analysis was used to visualize differences between the metabolic profiles of sample types and to identify the specific m/z features that led to this differentiation. Identification of the distinct m/z features was made using the human metabolome database. We discovered alterations in fatty acid biosynthesis and oxidative, glycolytic, and polyamine pathways that distinguish tumors from non-malignant colonic epithelium as well as various stages of CRC. Although further studies are needed, our results indicate that colonic epithelial cells undergo metabolic reprogramming during their evolution to CRC, and the distinct metabolites could serve as diagnostic tools or potential targets in therapy or primary prevention. Graphical Abstract Colon tissue biopsy samples were collected from patients after which metabolites were extracted via sonication. Two-dimensional data were collected via IMS in tandem with MS (IMMS). Data were then interpreted statistically via PLS-DA. Scores plots provided a visualization of statistical separation and groupings of sample types. Loading plots allowed identification of influential ion features. Lists of these features were exported and analyzed for specific differences. Direct comparisons of the ion features led to the identification and comparative analyses of candidate biomarkers. These differences were then expressed visually in charts and tables.

HMGA1 drives metabolic reprogramming of intestinal epithelium during hyperproliferation, polyposis, and colorectal carcinogenesis

Although significant progress has been made in the diagnosis and treatment of colorectal cancer (CRC), it remains a leading cause of cancer death worldwide. Early identification and removal of polyps that may progress to overt CRC is the cornerstone of CRC prevention. Expression of the High Mobility Group A1 (HMGA1) gene is significantly elevated in CRCs as compared with adjacent, nonmalignant tissues. We investigated metabolic aberrations induced by HMGA1 overexpression in small intestinal and colonic epithelium using traveling wave ion mobility mass spectrometry (TWIMMS) in a transgenic model in which murine Hmga1 was misexpressed in colonic epithelium. To determine if these Hmga1-induced metabolic alterations in mice were relevant to human colorectal carcinogenesis, we also investigated tumors from patients with CRC and matched, adjacent, nonmalignant tissues. Multivariate statistical methods and manual comparisons were used to identify metabolites specific to Hmga1 and CRC. Statistical modeling of data revealed distinct metabolic patterns in Hmga1 transgenics and human CRC samples as compared with the control tissues. We discovered that 13 metabolites were specific for Hmga1 in murine intestinal epithelium and also found in human CRC. Several of these metabolites function in fatty acid metabolism and membrane composition. Although further validation is needed, our results suggest that high levels of HMGA1 protein drive metabolic alterations that contribute to CRC pathogenesis through fatty acid synthesis. These metabolites could serve as potential biomarkers or therapeutic targets.

Prediction of survival for patients with advanced colorectal cancer using (1) H High-resolution magic angle spinning nuclear MR spectroscopy

PURPOSE

To evaluate whether the metabolic profiles of colorectal cancer specimens can be used for prediction of survival.

MATERIALS AND METHODS

The metabolic profiles of colorectal cancer tissues were determined using the high-resolution magic angle spinning (HR MAS) nuclear magnetic resonance (NMR) technique (16.4 T). HR MAS analysis was performed for 52 tissues taken from patients classified as survivors and nonsurvivors (30). Quantitative analysis was performed for each spectrum. Receiver operating characteristic (ROC) curves were used to evaluate the potential to predict patient survival over 5.5 years.

RESULTS

Analysis of (1) H NMR spectra led to the identification and quantitative analysis of 30 metabolites. A significant increase in the Tau/Gly and Tau/MI ratios were associated with long-term survival (P = 0.004 and P = 0.003, respectively). ROC analysis indicated that the Tau/MI ratio had the best predictive value for survival (sensitivity 64.7% and specificity 100%). Good predictive value of survival was found for Tau/Gly ratio (sensitivity 63.6% and specificity 96.3%). Moreover, the Glu/Gln metabolic ratio with a cutoff level of 1.74 was predictive of survival with a sensitivity of 83.3% and a specificity of 85.7%.

CONCLUSION

Our results indicate that HR MAS spectroscopy is potentially useful for survival prediction in advanced colorectal cancer.

Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC.

From genotype to functional phenotype: unraveling the metabolomic features of colorectal cancer

Much effort in recent years has been expended in defining the genomic and epigenetic alterations that characterize colorectal adenocarcinoma and its subtypes. However, little is known about the functional ramifications related to various subtypes. Metabolomics, the study of small molecule intermediates in disease, provides a snapshot of the functional phenotype of colorectal cancer. Data, thus far, have characterized some of the metabolic perturbations that accompany colorectal cancer. However, further studies will be required to identify biologically meaningful metabolic subsets, including those corresponding to specific genetic aberrations. Moreover, further studies are necessary to distinguish changes due to tumor and the host response to tumor.

Altered tissue metabolites correlate with microbial dysbiosis in colorectal adenomas

Several studies have linked bacterial dysbiosis with elevated risk of colorectal adenomas and cancer. However, the functional implications of gut dysbiosis remain unclear. Gut bacteria contribute to nutrient metabolism and produce small molecules termed the “metabolome”, which may contribute to the development of neoplasia in the large bowel. We assessed the metabolome in normal rectal mucosal biopsies of 15 subjects with colorectal adenomas and 15 nonadenoma controls by liquid chromatography and gas chromatography time-of-flight mass spectrometry. Quantitative real-time PCR was used to measure abundances of specific bacterial taxa. We identified a total of 274 metabolites. Discriminant analysis suggested a separation of metabolomic profiles between adenoma cases and nonadenoma controls. Twenty-three metabolites contributed to the separation, notably an increase in adenoma cases of the inflammatory metabolite prostaglandin E2 and a decrease in antioxidant-related metabolites 5-oxoproline and diketogulonic acid. Pathway analysis suggested that differential metabolites were significantly related to cancer, inflammatory response, carbohydrate metabolism, and GI disease pathways. Abundances of six bacterial taxa assayed were increased in cases. The 23 differential metabolites demonstrated correlations with bacteria that were different between cases and controls. These findings suggest that metabolic products of bacteria may be responsible for the development of colorectal adenomas and CRC.

Follicular adenomas exhibit a unique metabolic profile. ¹H NMR studies of thyroid lesions

Thyroid cancer is the most common endocrine malignancy. However, more than 90% of thyroid nodules are benign. It remains unclear whether thyroid carcinoma arises from preexisting benign nodules. Metabolomics can provide valuable and comprehensive information about low molecular weight compounds present in living systems and further our understanding of the biology regulating pathological processes. Herein, we applied ¹H NMR-based metabolic profiling to identify the metabolites present in aqueous tissue extracts of healthy thyroid tissue (H), non-neoplastic nodules (NN), follicular adenomas (FA) and malignant thyroid cancer (TC) as an alternative way of investigating cancer lesions. Multivariate statistical methods provided clear discrimination not only between healthy thyroid tissue and pathological thyroid tissue but also between different types of thyroid lesions. Potential biomarkers common to all thyroid lesions were identified, namely, alanine, methionine, acetone, glutamate, glycine, lactate, tyrosine, phenylalanine and hypoxanthine. Metabolic changes in thyroid cancer were mainly related to osmotic regulators (taurine and scyllo- and myo-inositol), citrate, and amino acids supplying the TCA cycle. Thyroid follicular adenomas were found to display metabolic features of benign non-neoplastic nodules and simultaneously displayed a partial metabolic profile associated with malignancy. This finding allows the discrimination of follicular adenomas from benign non-neoplastic nodules and thyroid cancer with similar accuracy. Moreover, the presented data indicate that follicular adenoma could be an individual stage of thyroid cancer development.

In vivo magnetic resonance imaging and spectroscopy identifies oncolytic adenovirus responders

At present, it is not possible to reliably identify patients who will benefit from oncolytic virus treatments. Conventional modalities such as computed tomography (CT), which measure tumor size, are unreliable owing to inflammation-induced tumor swelling. We hypothesized that magnetic resonance imaging (MRI) and spectroscopy (MRS) might be useful in this regard. However, little previous data exist and neither oncolytic adenovirus nor immunocompetent models have been assessed by MRS. Here, we provide evidence that in T2-weighted MRI a hypointense core area, consistent with coagulative necrosis, develops in immunocompetent Syrian hamster carcinomas that respond to oncolytic adenovirus treatment. The same phenomenon was observed in a neuroblastoma patient while he responded to the treatment. With relapse at a later stage, however, the tumor of this patient became moderately hyperintense. We found that MRS of taurine, choline and unsaturated fatty acids can be useful early indicators of response and provide detailed information about tumor growth and degeneration. In hamsters, calprotectin-positive inflammatory cells (heterophils and macrophages) were found in abundance; particularly surrounding necrotic areas in carcinomas and T cells were significantly increased in sarcomas, when these had been treated with a granulocyte-macrophage colony-stimulating factor-producing virus, suggesting a possible link between oncolysis, necrosis (seen as a hypointense core in MRI) and/or immune response. Our study indicates that both MRI and MRS could be useful in the estimation of oncolytic adenovirus efficacy at early time points after treatment.

Metabolomic profiling in colorectal cancer: opportunities for personalized medicine

Colorectal cancer (CRC) is one of the most common types of cancer in the world, with high prevalence and mortality. Understanding the alterations of cancer metabolism and identifying reliable biomarkers would facilitate the development of novel technologies of CRC screening and early diagnosis, as well as new approaches to providing personalized medicine. Metabolomics, as an emerging molecular phenotyping approach, provides a clinical platform technology with an unprecedented amount of metabolic readout information, which is ideal for theranostic biomarker discovery. Metabolic signatures can link the unique pathophysiological states of patients to personalized health monitoring and intervention strategies. This article presents an overview of the metabolomic studies of CRC with a focus on recent advances in the biomarker discovery in serum, urine, fecal water and tissue samples for cancer diagnosis. The development and application of metabolomics towards personalized medicine, including early diagnosis, cancer staging, treatment and drug discovery are also discussed.

Spatially matched in vivo and ex vivo MR metabolic profiles of prostate cancer -- investigation of a correlation with Gleason score

MR metabolic profiling of the prostate is promising as an additional diagnostic approach to separate indolent from aggressive prostate cancer. The objective of this study was to assess the relationship between the Gleason score and the metabolic biomarker (choline + creatine + spermine)/citrate (CCS/C) measured by ex vivo high-resolution magic angle spinning MRS (HR-MAS MRS) and in vivo MRSI, and to evaluate the correlation between in vivo- and ex vivo-measured metabolite ratios from spatially matched prostate regions. Patients (n = 13) underwent in vivo MRSI prior to radical prostatectomy. A prostate tissue slice was snap-frozen shortly after surgery and the locations of tissue samples (n = 40) collected for ex vivo HR-MAS were matched to in vivo MRSI voxels (n = 40). In vivo MRSI was performed on a 3T clinical MR system and ex vivo HR-MAS on a 14.1T magnet. Relative metabolite concentrations were calculated by LCModel fitting of in vivo spectra and by peak integration of ex vivo spectra. Spearman's rank correlations (ρ) between CCS/C from in vivo and ex vivo MR spectra, and with their corresponding Gleason score, were calculated. There was a strong positive correlation between the Gleason score and CCS/C measured both in vivo and ex vivo (ρ = 0.77 and ρ = 0.69, respectively; p < 0.001), and between in vivo and ex vivo metabolite ratios from spatially matched regions (ρ = 0.67, p < 0.001). Our data indicate that MR metabolic profiling is a potentially useful tool for the assessment of cancer aggressiveness. Moreover, the good correlation between in vivo- and ex vivo-measured CCS/C demonstrates that our method is able to bridge MRSI and HR-MAS molecular analysis.

Feasibility of MR metabolomics for immediate analysis of resection margins during breast cancer surgery

In this study, the feasibility of high resolution magic angle spinning (HR MAS) magnetic resonance spectroscopy (MRS) of small tissue biopsies to distinguish between tumor and non-involved adjacent tissue was investigated. With the current methods, delineation of the tumor borders during breast cancer surgery is a challenging task for the surgeon, and a significant number of re-surgeries occur. We analyzed 328 tissue samples from 228 breast cancer patients using HR MAS MRS. Partial least squares discriminant analysis (PLS-DA) was applied to discriminate between tumor and non-involved adjacent tissue. Using proper double cross validation, high sensitivity and specificity of 91% and 93%, respectively was achieved. Analysis of the loading profiles from both principal component analysis (PCA) and PLS-DA showed the choline-containing metabolites as main biomarkers for tumor content, with phosphocholine being especially high in tumor tissue. Other indicative metabolites include glycine, taurine and glucose. We conclude that metabolic profiling by HR MAS MRS may be a potential method for on-line analysis of resection margins during breast cancer surgery to reduce the number of re-surgeries and risk of local recurrence.

Gastric cancers with microsatellite instability exhibit high fluorodeoxyglucose uptake on positron emission tomography

BACKGROUND

Gastric cancers exhibit various degrees of (18)F-fluorodeoxyglucose (FDG) uptakes on positron emission tomography/computed tomography (PET/CT) imaging. The aim of this study was to evaluate whether FDG uptake in gastric cancer varies according to the microsatellite instability (MSI) status.

METHODS

Consecutive gastric cancer patients who underwent PET/CT imaging and MSI analysis were included in the study. The maximum standardized uptake value (SUVmax) of gastric cancer was assessed using PET/CT imaging.

RESULTS

Of 131 gastric cancers, 16 exhibited a high incidence of MSI (MSI-H) and 3 exhibited a low incidence of MSI (MSI-L). In 29 subjects who showed no uptake on PET/CT imaging the gastric cancers were all microsatellite stable (MSS). Gastric cancers with MSI were related to age older than 60 years (p = 0.002), cancer volume larger than 10 cm(3) (p = 0.015), and the presence of FDG uptake on PET/CT imaging (p = 0.001). A higher SUVmax of gastric cancer was linked to the presence of MSI (p < 0.001).

CONCLUSION

The presence of MSI is related to FDG uptake in gastric cancer. Care should be taken with MSS gastric cancers, because they show lower SUVmax on PET/CT imaging than MSI gastric cancers.

Metabolomics of colorectal cancer: past and current analytical platforms

Metabolomics is coming of age as an important area of investigation which may help reveal answers to questions left unanswered or only partially understood from proteomic or genomic approaches. Increased knowledge of the relationship of genes and proteins to smaller biomolecules (metabolites) will advance our ability to diagnose, treat, and perhaps prevent cancer and other diseases that have eluded scientists for generations. Colorectal tumors are the second leading cause of cancer mortality in the USA, and the incidence is rising. Many patients present late, after the onset of symptoms, when the tumor has spread from the primary site. Once metastases have occurred, the prognosis is significantly worse. Understanding alterations in metabolic profiles that occur with tumor onset and progression could lead to better diagnostic tests as well as uncover new approaches to treat or even prevent colorectal cancer (CRC). In this review, we explore the various analytical technologies that have been applied in CRC metabolomics research and summarize all metabolites measured in CRC and integrate them into metabolic pathways. Early studies with nuclear magnetic resonance and gas-chromatographic mass spectrometry suggest that tumor cells are characterized by aerobic glycolysis, increased purine metabolism for DNA synthesis, and protein synthesis. Liquid chromatography, capillary electrophoresis, and ion mobility, each coupled with mass spectrometry, promise to advance the field and provide new insight into metabolic pathways used by cancer cells. Studies with improved technology are needed to identify better biomarkers and targets for treatment or prevention of CRC.

Metabolomics approach to thyroid nodules: a high-resolution magic-angle spinning nuclear magnetic resonance-based study

BACKGROUND

Proton magnetic resonance spectroscopy of operative specimens has been reported to successfully differentiate normal tissue from malignant thyroid tissue. We used a new high-resolution magnetic resonance spectroscopy technique for the differentiation of benign and malignant thyroid neoplasms.

METHODS

Histological specimens from 72 patients undergoing a total thyroidectomy were processed into a 4-mm ZrO(2) high-resolution magic angle spinning (HRMAS) rotor with 5 μL of D(2)O. A Bruker Avance spectrometer operating at 400 MHz for the (1)H frequency and equipped with a (1)H/(13)C/(31)P HRMAS probe was used.

RESULTS

Normal and neoplastic thyroid tissues could be discriminated from each other by different relative concentrations of several amino acids and lipids, as well as benign and malignant neoplasms, that differed in terms of a greater lactate and taurine and a lesser lipid choline, phosphocholine, myo-inositol, and scyllo-inositol levels in malignant samples. A statistical analysis with a receiver operating characteristic curve revealed that 77% of the samples were accurately predicted. Similar results were obtained with specimens obtained from ex vivo aspirates.

CONCLUSION

A further development of this project will be to use the metabolomics approach on specimens obtained from aspirates in vivo after the resolution of technical problems attributable to possible contamination.

Metabolic profiles of brain metastases

Metastasis to the brain is a feared complication of systemic cancer, associated with significant morbidity and poor prognosis. A better understanding of the tumor metabolism might help us meet the challenges in controlling brain metastases. The study aims to characterize the metabolic profile of brain metastases of different origin using high resolution magic angle spinning (HR-MAS) magnetic resonance spectroscopy (MRS) to correlate the metabolic profiles to clinical and pathological information. Biopsy samples of human brain metastases (n = 49) were investigated. A significant correlation between lipid signals and necrosis in brain metastases was observed (p < 0.01), irrespective of their primary origin. The principal component analysis (PCA) showed that brain metastases from malignant melanomas cluster together, while lung carcinomas were metabolically heterogeneous and overlap with other subtypes. Metastatic melanomas have higher amounts of glycerophosphocholine than other brain metastases. A significant correlation between microscopically visible lipid droplets estimated by Nile Red staining and MR visible lipid signals was observed in metastatic lung carcinomas (p = 0.01), indicating that the proton MR visible lipid signals arise from cytoplasmic lipid droplets. MRS-based metabolomic profiling is a useful tool for exploring the metabolic profiles of metastatic brain tumors.

Analysis of cancer tissues by means of spectroscopic methods

The personalized approach in cancer treatment stimulates the search for new analytical techniques, including spectroscopic methods such as Raman spectroscopy, mass spectrometry MALDI (matrix-assisted laser desorption/ionization) imaging and high-resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR). The purpose of these studies is determination of metabolic profiles of cancer tissues, and their application in diagnostics and therapy of cancers. The review is mainly focused on application of HR MAS NMR technique. Qualitative and quantitative analysis of metabolites by means of this method is described for breast cancer tissues. In the near future HR MAS NMR in vitro studies of metabolic profiles combined with in vivo studies using MRI scanners may be applied as a new diagnostic tool.

Serum amino acid profiles and their alterations in colorectal cancer

Mass spectrometry-based serum metabolic profiling is a promising tool to analyse complex cancer associated metabolic alterations, which may broaden our pathophysiological understanding of the disease and may function as a source of new cancer-associated biomarkers. Highly standardized serum samples of patients suffering from colon cancer (n = 59) and controls (n = 58) were collected at the University Hospital Leipzig. We based our investigations on amino acid screening profiles using electrospray tandem-mass spectrometry. Metabolic profiles were evaluated using the Analyst 1.4.2 software. General, comparative and equivalence statistics were performed by R 2.12.2. 11 out of 26 serum amino acid concentrations were significantly different between colorectal cancer patients and healthy controls. We found a model including CEA, glycine, and tyrosine as best discriminating and superior to CEA alone with an AUROC of 0.878 (95% CI 0.815-0.941). Our serum metabolic profiling in colon cancer revealed multiple significant disease-associated alterations in the amino acid profile with promising diagnostic power. Further large-scale studies are necessary to elucidate the potential of our model also to discriminate between cancer and potential differential diagnoses. In conclusion, serum glycine and tyrosine in combination with CEA are superior to CEA for the discrimination between colorectal cancer patients and controls.

Toward the reliable diagnosis of indeterminate thyroid lesions: a HRMAS NMR-based metabolomics case of study

Cytological analysis of thyroid nodules detected using ultrasound-guided fine-needle aspiration technique is an efficient method for the diagnosis of well-differenciated tumors such as papillary thyroid carcinoma. However, for between 10 to 30% of all the nodules, the cytological analysis based on fine-needle aspiration biopsies leads to an "indeterminated" identification. Consequently, a surgical excision is then necessary for a definite histological diagnosis of the lesions, resulting in 85% of the patient with indeterminated nodules undergoing unnecessary surgery since their tumor is finally diagnosed as benign. In this work, we discuss how HRMAS (1)H NMR-based metabolomics could be a complementary tool for the diagnosis of these elusive cases. We first showed that our approach was able to discriminate clearly any types of thyroid lesions from healthy tissues. Then we proceeded to demonstrate that the information produced by (1)H HRMAS NMR spectra differentiate tumors according to their malignancy grade, even when they belong to the "indeterminate" category. Analysis of the discriminating spectral area in this last case points out toward a possible increase of phenylalanine, taurine, and lactate and a decrease of choline and choline derivatives, myo- and scyllo-inositol in the malignant tumors compared to the benign ones.

Detectability of colorectal neoplasia with fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography and computed tomography (FDG-PET/CT)

BACKGROUND

The purpose of this study was to analyze the detectability of colorectal neoplasia with fluorine-18-2-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (FDG-PET/CT).

METHODS

Data for a total of 492 patients who had undergone both PET/CT and colonoscopy were analyzed. After the findings of PET/CT and colonoscopy were determined independently, the results were compared in each of the six colonic sites examined in all patients. The efficacy of PET/CT was determined using colonoscopic examination as the gold standard.

RESULTS

In all, 270 colorectal lesions 5 mm or more in size, including 70 pathologically confirmed malignant lesions, were found in 172 patients by colonoscopy. The sensitivity and specificity of PET/CT for detecting any of the colorectal lesions were 36 and 98%, respectively. For detecting lesions 11 mm or larger, the sensitivity was increased to 85%, with the specificity remaining consistent (97%). Moreover, the sensitivity for tumors 21 mm or larger was 96% (48/50). Tumors with malignant or high-grade pathology were likely to be positive with PET/CT. A size of 10 mm or smaller [odds ratio (OR) 44.14, 95% confidence interval (95% CI) 11.44-221.67] and flat morphology (OR 7.78, 95% CI 1.79-36.25) were significant factors that were associated with false-negative cases on PET/CT.

CONCLUSION

The sensitivity of PET/CT for detecting colorectal lesions is acceptable, showing size- and pathology-dependence, suggesting, for the most part, that clinically relevant lesions are detectable with PET/CT. However, when considering PET/CT for screening purposes caution must be exercised because there are cases of false-negative results.

A modified artificial immune system based pattern recognition approach--an application to clinical diagnostics

OBJECTIVE

This paper introduces a modified artificial immune system (AIS)-based pattern recognition method to enhance the recognition ability of the existing conventional AIS-based classification approach and demonstrates the superiority of the proposed new AIS-based method via two case studies of breast cancer diagnosis.

METHODS AND MATERIALS

Conventionally, the AIS approach is often coupled with the k nearest neighbor (k-NN) algorithm to form a classification method called AIS-kNN. In this paper we discuss the basic principle and possible problems of this conventional approach, and propose a new approach where AIS is integrated with the radial basis function--partial least square regression (AIS-RBFPLS). Additionally, both the two AIS-based approaches are compared with two classical and powerful machine learning methods, back-propagation neural network (BPNN) and orthogonal radial basis function network (Ortho-RBF network).

RESULTS

The diagnosis results show that: (1) both the AIS-kNN and the AIS-RBFPLS proved to be a good machine leaning method for clinical diagnosis, but the proposed AIS-RBFPLS generated an even lower misclassification ratio, especially in the cases where the conventional AIS-kNN approach generated poor classification results because of possible improper AIS parameters. For example, based upon the AIS memory cells of "replacement threshold=0.3", the average misclassification ratios of two approaches for study 1 are 3.36% (AIS-RBFPLS) and 9.07% (AIS-kNN), and the misclassification ratios for study 2 are 19.18% (AIS-RBFPLS) and 28.36% (AIS-kNN); (2) the proposed AIS-RBFPLS presented its robustness in terms of the AIS-created memory cells, showing a smaller standard deviation of the results from the multiple trials than AIS-kNN. For example, using the result from the first set of AIS memory cells as an example, the standard deviations of the misclassification ratios for study 1 are 0.45% (AIS-RBFPLS) and 8.71% (AIS-kNN) and those for study 2 are 0.49% (AIS-RBFPLS) and 6.61% (AIS-kNN); and (3) the proposed AIS-RBFPLS classification approaches also yielded better diagnosis results than two classical neural network approaches of BPNN and Ortho-RBF network.

CONCLUSION

In summary, this paper proposed a new machine learning method for complex systems by integrating the AIS system with RBFPLS. This new method demonstrates its satisfactory effect on classification accuracy for clinical diagnosis, and also indicates its wide potential applications to other diagnosis and detection problems.

H HRMAS NMR Derived Bio-markers Related to Tumor Grade, Tumor Cell Fraction, and Cell Proliferation in Prostate Tissue Samples

A high-resolution magic angle spinning NMR spectroscopic approach is presented for evaluating the occurrence, amount and aggressiveness of cancer in human prostate tissue samples. Using this technique, key metabolites in malignant and non-malignant samples (n = 149) were identified, and patterns of their relative abundance were analyzed by multivariate statistical methods. Ratios of various metabolites – including (glycerophophorylcholine + phosphorylcholine)/creatine, myo-inositol/scyllo-inositol, scyllo-inositol/creatine, choline/creatine, and citrate/creatine – correlated with: i) for non-malignant tissue samples, the distance to the nearest tumor and its Gleason score and; ii) the fraction of tumor cells present in the sample; and iii) tumor cell proliferation (Ki67 labelling index). This NMR-based approach allows the extraction of information that could be useful for developing novel diagnostic methods for prostate cancer.

Alignment of high resolution magic angle spinning magnetic resonance spectra using warping methods

The peaks of magnetic resonance (MR) spectra can be shifted due to variations in physiological and experimental conditions, and correcting for misaligned peaks is an important part of data processing prior to multivariate analysis. In this paper, five warping algorithms (icoshift, COW, fastpa, VPdtw and PTW) are compared for their feasibility in aligning spectral peaks in three sets of high resolution magic angle spinning (HR-MAS) MR spectra with different degrees of misalignments, and their merits are discussed. In addition, extraction of information that might be present in the shifts is examined, both for simulated data and the real MR spectra. The generic evaluation methodology employs a number of frequently used quality criteria for evaluation of the alignments, together with PLS-DA to assess the influence of alignment on the classification outcome. Peak alignment greatly improved the internal similarity of the data sets. Especially icoshift and COW seem suitable for aligning HR-MAS MR spectra, possibly because they perform alignment segment-wise. The choice of reference spectrum can influence the alignment result, and it is advisable to test several references. Information from the peak shifts was extracted, and in one case cancer samples were successfully discriminated from normal tissue based on shift information only. Based on these findings, general recommendations for alignment of HR-MAS MRS data are presented. Where possible, observations are generalized to other data types (e.g. chromatographic data).