Liquid chromatography coupled to high-resolution mass spectrometry platforms are increasingly employed to comprehensively measure metabolome changes in systems biology and complex diseases. Over the past decade, several powerful computational pipelines have been developed for spectral processing, annotation, and analysis. However, significant obstacles remain with regard to parameter settings, computational efficiencies, batch effects, and functional interpretations. Here, we introduce MetaboAnalystR 3.0, a significantly improved pipeline with three key new features: (1) efficient parameter optimization for peak picking; (2) automated batch effect correction; and 3) more accurate pathway activity prediction. Our benchmark studies showed that this workflow was 20~100X faster compared to other well-established workflows and produced more biologically meaningful results. In summary, MetaboAnalystR 3.0 offers an efficient pipeline to support high-throughput global metabolomics in the open-source R environment.

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MetaboAnalystR 3.0: Toward an Optimized Workflow for Global Metabolomics.

L'existence d'un cancer dans le colon influence directement la production de methane. Dans le groupe des maladies a haut risque de cancer, la prevalence des sujets producteurs de methane est significativement plus elevee que dans la population normale et chez les malades avec des maladies benignes du colon

Methane production and colon cancer

To determine the location patterns of distant metastases at initial staging and outcomes of ovarian, uterine, and cervical cancer patients. Data were obtained from the SEER database from 2010 to 2015. Analyses were performed using Kaplan–Meier and multivariate Cox proportional hazard methods. Of 3035 patients (median age: 63, range: 17–95) with stage IV gynecologic cancer, ovarian, uterine, and cervical cancers were present in 42%, 40%, and 18% of the cohort. The proportion of lung, liver, bone and brain metastases were identified in 38%, 57%, 4%, and 1% of ovarian cancer patients, 62%, 22%, 13%, and 3% of uterine cancer patients, and 59%, 16%, 23%, and 2% of cervical cancer patients, respectively. The 5-year disease-specific survival for all patients was 19%. Those with liver metastases had survival rates of 26% compared to 15% for lung, 13% for bone, and 6% for brain (p < 0.0001). Patients with ovarian, uterine, and cervical cancers had survival rates of 28%, 12%, and 12%, respectively (p < 0.0001). On multivariate analysis, brain metastasis (HR = 1.64, 95% CI 1.21–2.22, p < 0.01), uterine (HR = 1.77, 95 CI 1.56–2.02, p < 0.0001) and cervical (HR = 1.35, 95% CI 1.11–1.63, p < 0.01) cancers, and lack of insurance (HR = 1.41, 95% CI 1.16–1.73, p < 0.001) were independent predictors for poorer survival. Age, year, region, and race did not affect prognosis. Stage IV ovarian cancer most frequently metastasizes to the liver, whereas uterine and cervical cancers spread more to the lung. Overall, these patients have poor prognosis, particularly those with uterine or cervical primary disease or brain metastases.

Ovarian, uterine, and cervical cancer patients with distant metastases at diagnosis: most common locations and outcomes.

Ovarian cancer (OC) is a heterogeneous disease usually diagnosed at a late stage. Cancer stem cells (CSCs) that exist within the bulk tumor survive first-line chemotherapy and contribute to resistant disease with metastasis. Understanding the key features of CSC biology provides valuable opportunities to develop OCSC-directed therapeutics, which will eventually improve the clinical outcomes of patients. Although significant developments have occurred since OCSCs were first described, the involvement of CSCs in ovarian tumor metastasis is not fully understood. Here, we discuss putative CSC markers and the fundamental role of CSCs in facilitating tumor dissemination in OC. Additionally, we focus on promising CSC-targeting strategies in preclinical and clinical studies of OC and discuss potential challenges in CSC research.

Ovarian Cancer Stem Cells: Role in Metastasis and Opportunity for Therapeutic Targeting.

Large-scale metabolomics study based on large population cohorts is increasingly applied to identify important metabolites or critical metabolic alterations related to the metabolite perturbation in disease states or under interventions and to investigate metabolic difference and stimuli response in genetically different individuals. Mass spectrometry (MS) coupled with different chromatographic methods, is suitable for large-scale metabolomics study due to its high sensitivity and selectivity, wide dynamic range, and rich information. However, there are still a series of challenges for really realizing large-scale metabolomics applications. Hence, in this review, we mainly focused on new advances in sample pretreatment methods, nontargeted, targeted and pseudotargeted metabolic data collection techniques, and data correction methods used for MS-based large-scale metabolomics study. Typical applications of MS-based large-scale metabolomics methods in molecular epidemiology, precision medicine, and genome-wide association studies with metabolomics (mGWAS) are also given.

New advances in analytical methods for mass spectrometry-based large-scale metabolomics study

Sites of distant metastases and overall survival in ovarian cancer: A study of 1481 patients

WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis.

Plasma metabolomic profiling distinguishes right-sided from left-sided colon cancer

Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT). An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes. In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n = 30) and response (n = 27) patients to NACT were studied using UHPLC–quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods. The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199). These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.

Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer.

Objective We sought to identify novel molecular subtypes of high-grade serous ovarian cancer (HGSC) by the integration of gene expression and proteomics data and to find the underlying biological characteristics of ovarian cancer to improve the clinical outcome. Methods The iCluster method was utilized to analysis 131 common HGSC samples between TCGA and Clinical Proteomic Tumor Analysis Consortium databases. Kaplan-Meier survival curves were used to estimate the overall survival of patients, and the differences in survival curves were assessed using the log-rank test. Results Two novel ovarian cancer subtypes with different overall survival (P = .00114) and different platinum status (P = .0061) were identified. Eighteen messenger RNAs and 38 proteins were selected as differential molecules between subtypes. Pathway analysis demonstrated arrhythmogenic right ventricular cardiomyopathy pathway played a critical role in the discrimination of these two subtypes and desmosomal cadherin DSG2, DSP, JUP, and PKP2 in this pathway were overexpression in subtype I compared with subtype II. Conclusion Our study extended the underlying prognosis-related biological characteristics of high-grade serous ovarian cancer. Enrichment of desmosomal cadherin increased the risk for HGSC prognosis among platinum-sensitive patients, the results guided the revision of the treatment options for platinum-sensitive ovarian cancer patients to improve outcomes.

Wei Song

Papers

Sites of distant metastases and overall survival in ovarian cancer: A study of 1481 patients

WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet analysis.

Plasma metabolomic profiling distinguishes right-sided from left-sided colon cancer

Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer.

Integrative prognostic subtype discovery in high‐grade serous ovarian cancer