Masahiro Sugimoto

MMMDB: Mouse Multiple Tissue Metabolome Database

The Mouse Multiple Tissue Metabolome Database (MMMDB) provides comprehensive and quantitative metabolomic information for multiple tissues from single mice. Manually curated databases that integrate literature-based individual metabolite information have been available so far. However, data sets on the absolute concentration of a single metabolite integrated from multiple resources are often difficult to be used when different metabolomic studies are compared because the relative balance of the multiple metabolite concentrations in the metabolic pathways as a snapshot of a dynamic system is more important than the absolute concentration of a single metabolite. We developed MMMDB by performing non-targeted analyses of cerebra, cerebella, thymus, spleen, lung, liver, kidney, heart, pancreas, testis and plasma using capillary electrophoresis time-of-flight mass spectrometry and detected 428 non-redundant features from which 219 metabolites were successfully identified. Quantified concentrations of the individual metabolites and the corresponding processed raw data; for example, the electropherograms and mass spectra with their annotations, such as isotope and fragment information, are stored in the database. MMMDB is designed to normalize users’ data, which can be submitted online and used to visualize overlaid electropherograms. Thus, MMMDB allows newly measured data to be compared with the other data in the database. MMMDB is available at: http://mmmdb.iab.keio.ac.jp.

Effect of masticatory stimulation on the quantity and quality of saliva and the salivary metabolomic profile

Background This study characterized the changes in quality and quantity of saliva, and changes in the salivary metabolomic profile, to understand the effects of masticatory stimulation. Methods Stimulated and unstimulated saliva samples were collected from 55 subjects and salivary hydrophilic metabolites were comprehensively quantified using capillary electrophoresis-time-of-flight mass spectrometry. Results In total, 137 metabolites were identified and quantified. The concentrations of 44 metabolites in stimulated saliva were significantly higher than those in unstimulated saliva. Pathway analysis identified the upregulation of the urea cycle and synthesis and degradation pathways of glycine, serine, cysteine and threonine in stimulated saliva. A principal component analysis revealed that the effect of masticatory stimulation on salivary metabolomic profiles was less dependent on sample population sex, age, and smoking. The concentrations of only 1 metabolite in unstimulated saliva, and of 3 metabolites stimulated saliva, showed significant correlation with salivary secretion volume, indicating that the salivary metabolomic profile and salivary secretion volume were independent factors. Conclusions Masticatory stimulation affected not only salivary secretion volume, but also metabolite concentration patterns. A low correlation between the secretion volume and these patterns supports the conclusion that the salivary metabolomic profile may be a new indicator to characterize masticatory stimulation.

Urinary Polyamine Biomarker Panels with Machine-Learning Differentiated Colorectal Cancers, Benign Disease, and Healthy Controls

Colorectal cancer (CRC) is one of the most daunting diseases due to its increasing worldwide prevalence, which requires imperative development of minimally or non-invasive screening tests. Urinary polyamines have been reported as potential markers to detect CRC, and an accurate pattern recognition to differentiate CRC with early stage cases from healthy controls are needed. Here, we utilized liquid chromatography triple quadrupole mass spectrometry to profile seven kinds of polyamines, such as spermine and spermidine with their acetylated forms. Urinary samples from 201 CRCs and 31 non-CRCs revealed the N1,N12-diacetylspermine showing the highest area under the receiver operating characteristic curve (AUC), 0.794 (the 95% confidence interval (CI): 0.704–0.885, p < 0.0001), to differentiate CRC from the benign and healthy controls. Overall, 59 samples were analyzed to evaluate the reproducibility of quantified concentrations, acquired by collecting three times on three days each from each healthy control. We confirmed the stability of the observed quantified values. A machine learning method using combinations of polyamines showed a higher AUC value of 0.961 (95% CI: 0.937–0.984, p < 0.0001). Computational validations confirmed the generalization ability of the models. Taken together, polyamines and a machine-learning method showed potential as a screening tool of CRC.

Elevated polyamines in saliva of pancreatic cancer

Detection of pancreatic cancer (PC) at a resectable stage is still difficult because of the lack of accurate detection tests. The development of accurate biomarkers in low or non-invasive biofluids is essential to enable frequent tests, which would help increase the opportunity of PC detection in early stages. Polyamines have been reported as possible biomarkers in urine and saliva samples in various cancers. Here, we analyzed salivary metabolites, including polyamines, using capillary electrophoresis-mass spectrometry. Salivary samples were collected from patients with PC (n = 39), those with chronic pancreatitis (CP, n = 14), and controls (C, n = 26). Polyamines, such as spermine, N1-acetylspermidine, and N1-acetylspermine, showed a significant difference between patients with PC and those with C, and the combination of four metabolites including N1-acetylspermidine showed high accuracy in discriminating PC from the other two groups. These data show the potential of saliva as a source for tests screening for PC.

Effect of storage conditions on salivary polyamines quantified via liquid chromatography-mass spectrometry

Salivary polyamines are potential non-invasive tools for screening various types of cancers. For clinical use, the reproducibility of these metabolites should be evaluated under various storage conditions, including duration and temperature, to establish standard operating protocols. Polyamines and amino acids in unstimulated whole saliva were quantified via liquid chromatography-mass spectrometry. Concentrations of time course samples were analysed after short-term storage for up to 240u2009min and long-term storage for up to 8 days under various storage conditions. As expected, storage at the lowest temperature (−18u2009°C) exerted the least pronounced effects on the quantified values in both tests. At a higher temperature, polyamines were more stable than amino acids, as evident from polyamine profiling. Addition of ethanol significantly stabilized polyamine profiles even at a higher temperature. Comparative processing of saliva revealed a minor effect of the solvent, whereas drying had a more prominent effect on polyamine profiles. Computational analyses evaluated the ability of polyamines to discriminate pancreatic cancer from controls. Repeated noise added tests were designed on the basis of the results of the storage tests; these analyses confirmed that the discriminative abilities were robust. These data contribute to the standardization of salivary storage conditions, thereby highlighting the clinical utility of saliva.

A Metabolomic-Based Evaluation of the Role of Commensal Microbiota throughout the Gastrointestinal Tract in Mice

Commensal microbiota colonize the surface of our bodies. The inside of the gastrointestinal tract is one such surface that provides a habitat for them. The gastrointestinal tract is a long organ system comprising of various parts, and each part possesses various functions. It has been reported that the composition of intestinal luminal metabolites between the small and large intestine are different; however, comprehensive metabolomic and commensal microbiota profiles specific to each part of the gastrointestinal lumen remain obscure. In this study, by using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS)-based metabolome and 16S rRNA gene-based microbiome analyses of specific pathogen-free (SPF) and germ-free (GF) murine gastrointestinal luminal profiles, we observed the different roles of commensal microbiota in each part of the gastrointestinal tract involved in carbohydrate metabolism and nutrient production. We found that the concentrations of most amino acids in the SPF small intestine were higher than those in the GF small intestine. Furthermore, sugar alcohols such as mannitol and sorbitol accumulated only in the GF large intestine, but not in the SPF large intestine. On the other hand, pentoses, such as arabinose and xylose, gradually accumulated from the cecum to the colon only in SPF mice, but were undetected in GF mice. Correlation network analysis between the gastrointestinal microbes and metabolites showed that niacin metabolism might be correlated to Methylobacteriaceae. Collectively, commensal microbiota partially affects the gastrointestinal luminal metabolite composition based on their metabolic dynamics, in cooperation with host digestion and absorption.

Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm

PurposeThis study aimed to develop mathematical tools to predict the likelihood of recurrence after neoadjuvant chemotherapy (NAC) plus trastuzumab in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer.MethodsData of 776 patients from a multicenter retrospective cohort study were collected. All patients had HER2-positive breast cancer and received NAC plus trastuzumab between 2001 and 2010. Two mathematical tools using a machine learning method were developed to predict the likelihood of disease-free survival (DFS) (DFS model) and brain metastasis (BM) (BM model) within 5xa0years after surgery. For validation, bootstrap analyses were conducted. The area under the receiver operating characteristics curve (AUC) was calculated to examine the discrimination.ResultsThe AUC values were 0.785 (95% CI 0.740–0.831, Pu2009<u20090.001) for the DFS model and 0.871 (95% CI 0.830–0.912, Pu2009<u20090.001) for the BM model. Patients with low-risk DFS or BM events, as predicted by the models, showed better 5-year DFS and BM rates than those with high-risk DFS or BM events (89% vs. 61% for the DFS model, Pu2009<u20090.001; 99% vs. 87% for the BM model, Pu2009<u20090.001). These models maintained discrimination abilities in both luminal and non-luminal subtypes, providing prognostic information independent of pathological response. Bootstrap validation confirmed the high generalization abilities of the models.ConclusionsThe DFS and BM models have a high accuracy to predict prognosis among HER2-positive patients treated with NAC plus trastuzumab. Our models can help optimize adjuvant therapy and postoperative surveillance.

Gene expression profile of peripheral blood mononuclear cells may contribute to the identification and immunological classification of breast cancer patients

BackgroundIt has been reported that the gene expression profile of peripheral blood mononuclear cells (PBMCs) exhibits a unique gene expression signature in several types of cancer. In this study, we aimed to explore the breast cancer patient-specific gene expression profile of PBMCs and discuss immunological insight on host antitumor immune responses.MethodsWe comprehensively analyzed the gene expression of PBMCs by RNA sequencing in the breast cancer patients as compared to that of healthy volunteers (HVs). Pathway enrichment analysis was performed on MetaCoretm to search the molecular pathways associated with the gene expression profile of PBMCs in cancer patients compared with HVs.ResultsWe found a significant unique gene expression signature, such as the Toll-like receptor (TLR) 3- and TLR4-induced Toll/interleukin-1 receptor domain-containing adapter molecule 1 (TICAM1)-specific signaling pathway in the breast cancer patients as compared to that of healthy volunteers. Distinctive immunological gene expression profiles also showed the possibility of classifying breast cancer patients into subgroups such as T-cell inhibitory and monocyte-activating groups independent of known phenotypes of breast cancer.ConclusionsThese preliminary findings suggest that evaluation of gene expression patterns of PBMCs might be both a less invasive diagnostic procedure and a useful way to reveal immunological insight of breast cancer, including biomarkers for cancer immunotherapy, such as immune checkpoint inhibitor therapy.

Robust volcano plot: identification of differential metabolites in the presence of outliers

BackgroundThe identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers.ResultsWe propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower misclassification error rate and a greater area under the receiver operating characteristic curve compared with existing methods. An experimentally measured breast cancer dataset to which outliers were artificially added reveals that our proposed method produces only two non-overlapping differential metabolites whereas the other nine methods produced between seven and 57 non-overlapping differential metabolites.ConclusionOur data analyses show that the performance of the proposed differential metabolite identification technique is better than that of existing methods. Thus, the proposed method can contribute to analysis of metabolomics data with outliers. The R package and user manual of the proposed method are available at https://github.com/nishithkumarpaul/Rvolcano.

Future Paradigm of Breast Cancer Resistance and Treatment

Despite advances in early detection and the understanding of the molecular bases of breast cancer biology, the real challenges in therapeutics lie in detecting the disease progression and relapse. Resistance to therapy is not only common but expected. Multidisciplinary joint efforts are required in making necessary progress with breast cancer treatment. With the recent advances in multiplex genotyping and high-throughput genomic sequencing technologies, breast cancer is now considered as a group of diseases characterised by varied clonal evolution with different molecular and cellular mechanisms which drive tumour initiation, proliferation and progression with underlying resistance. Using the liquid biopsy, attempt to discover clinical molecular biomarkers are progressing rapidly as we begin to understand the complex mechanisms that transform a normal cell to a cancer cell and leading to a resistant cell. One of the examples of these molecularly targeted biomarker therapies in HER2/neu-positive breast cancer is HER2/neu blockage. Following endocrine therapy, the occurrence of secondary resistance, such as ESR1 mutations, poses a significant challenge. Drugs like lapatinib may be effective to overcome EGFR therapy resistance but it needs to be established yet. FGFR target therapy may also be interesting, but still little is known about its clinical significance. Analysis of liquid biopsy has the potential to change the clinical practice by exploiting the blood rather than the tissue as a source of underlying mechanisms. Multiple clinical studies on liquid biopsies have already been used to monitor disease response and track the emergence of drug resistance. With the computing power, the sheer amounts of data generated through sequencing and other technologies are exponentially increasing with each day. This also creates a gap between the possibilities and which can be practiced clinically. Artificial intelligence algorithms could help to determine what type of resistance a patient attains with the disease relapse and whether a tumor is a new cancer or a recurrence of previous disease, all of which has implications for treatment. Targeted therapeutics including EGFR and FGFR amplifications have been detected and associated with endocrine resistance in hormone receptor-positive breast cancers have been discussed in the previous chapters of this book. In this chapter, we present the potential of circulating tumour DNA in improving and understanding the possible resistance mechanisms through cancer genomics and integrating with artificial intelligence.