Abstract
Simple SummaryReprogramming of cellular metabolism is a major hallmark of cancer cells, and play an important role in tumor initiation and progression. The aim of our study is to discover circulating early metabolic markers of brain tumors, as discovery and development of reliable predictive molecular markers are needed for precision oncology applications. We use a study design tailored to minimize confounding factors and a novel machine learning and visualization approach (SMART) to identify a panel of 15 interlinked metabolites related to glioma development. The presented SMART strategy facilitates early molecular marker discovery and can be used for many types of molecular data.Here, we present a strategy for early molecular marker pattern detection—Subset analysis of Matched Repeated Time points (SMART)—used in a mass-spectrometry-based metabolomics study of repeated blood samples from future glioma patients and their matched controls. The outcome from SMART is a predictive time span when disease-related changes are detectable, defined by time to diagnosis and time between longitudinal sampling, and visualization of molecular marker patterns related to future disease. For glioma, we detect significant changes in metabolite levels as early as eight years before diagnosis, with longitudinal follow up within seven years. Elevated blood plasma levels of myo-inositol, cysteine, N-acetylglucosamine, creatinine, glycine, proline, erythronic-, 4-hydroxyphenylacetic-, uric-, and aceturic acid were particularly evident in glioma cases. We use data simulation to ensure non-random events and a separate data set for biomarker validation. The latent biomarker, consisting of 15 interlinked and significantly altered metabolites, shows a strong correlation to oxidative metabolism, glutathione biosynthesis and monosaccharide metabolism, linked to known early events in tumor development. This study highlights the benefits of progression pattern analysis and provide a tool for the discovery of early markers of disease.
Highlights
IntroductionBiomarker development is a multistep and iterative process, where screening blood samples from human population biobanks have received increasing interest
Circulating biomarkers are increasingly utilized in the advance towards molecular medicine.Blood-based biomarkers are needed for patient stratification, early detection of disease for personalized screening in risk groups, and for the development of new therapies for patients with poor prognosis [1,2].Biomarker development is a multistep and iterative process, where screening blood samples from human population biobanks have received increasing interest
By use of Subset analysis of Matched Repeated Time points (SMART), we identify a latent biomarker for glioma, consisting of 15 significantly altered metabolites
Summary
Biomarker development is a multistep and iterative process, where screening blood samples from human population biobanks have received increasing interest. Cancers 2020, 12, 3349 applied, which implies looking for disease-related systematic differences or trends in high-dimensional data. Identifying these differences can be challenging in the presence of other systematic variation sources and random noise, complications that dilute the variation of interest. This dilution is addressed by increasing the power of cross-sectional studies to achieve statistical significance
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