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Cancer metabolomic markers in urine: evidence, techniques and recommendations

Nature Reviews Urology volume 16, pages 339–362 (2019)Cite this article

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Abstract

Urinary tests have been used as noninvasive, cost-effective tools for screening, diagnosis and monitoring of diseases since ancient times. As we progress through the 21st century, modern analytical platforms have enabled effective measurement of metabolites, with promising results for both a deeper understanding of cancer pathophysiology and, ultimately, clinical translation. The first study to measure metabolomic urinary cancer biomarkers using NMR and mass spectrometry (MS) was published in 2006 and, since then, these techniques have been used to detect cancers of the urological system (kidney, prostate and bladder) and nonurological tumours including those of the breast, ovary, lung, liver, gastrointestinal tract, pancreas, bone and blood. This growing field warrants an assessment of the current status of research developments and recommendations to help systematize future research.

Key points

  • Initial NMR and mass spectrometry (MS) studies of human urine identified biomarkers that can distinguish patients with cancer from healthy controls and outperform many current clinical markers, possibly enabling early detection.

  • Biomarker panels can be used to identify a single type of cancer, stratify grade and stage, differentiate between multiple cancer types and perform longitudinal evaluations.

  • A similar set of urinary metabolites (hippurate, creatine, tyrosine, citrate, isoleucine, phenylalanine, putrescine, succinate, tryptophan and valine) can indicate malignancy of various organs, possibly reflecting the global metabolic effects of cancer.

  • The lack of specificity means that caution must be exercised and that many biomarkers could be too nonspecific for clinical application.

  • Methodological variations impair comparability of existing studies, highlighting the need for guidelines.

  • The expense of NMR and MS instrumentation means that a centralized testing hub might provide the best solution for eventual clinical implementation of cancer urinary biomarkers.

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Fig. 1: Timeline of the development of urine analysis.
Fig. 2: Representative data set acquired by NMR spectroscopy from a urine sample of a patient with prostate cancer87.
Fig. 3: Representative data set acquired by mass spectrometry from a urine sample of a patient with ovarian cancer.
Fig. 4: Significantly altered metabolites in cancer.
Fig. 5: Overlap of urinary metabolites in cancers of different organ systems.

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Acknowledgements

The authors kindly acknowledge J. A. Fordham for editorial assistance. A.H. gratefully acknowledges the German Academic Foundation Cusanuswerk, the German Academic Exchange Service and the Max Weber Programme of the State of Bavaria for financial support. Furthermore, A.H. thanks the Department of Diagnostic and Interventional Neuroradiology, University Hospital of Würzburg. S.S.D. gratefully acknowledges the German Academic Scholarship Foundation for financial support.

Review criteria

Representative papers were selected using a PubMed search with the terms “metabolomics” AND “biomarkers” AND “urine” AND “cancer”, which were combined with search strings for the different techniques. For mass spectrometry, those included “mass spectrometry” OR “gas chromatography-mass spectrometry” OR “gc ms” OR “liquid chromatography”. For NMR spectroscopy, the terms “Magnetic Resonance Spectroscopy” OR “Nuclear Magnetic Resonance Spectroscopy” were used. All terms were input with variations in notation and specifications regarding the type of search term MeSH Terms, TIAB and All Fields.

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Author notes

  1. These authors contributed equally: Sarah S. Dinges, Annika Hohm, Lindsey A. Vandergrift.

Authors and Affiliations

  1. Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Sarah S. Dinges, Annika Hohm, Lindsey A. Vandergrift & Leo L. Cheng

  2. Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Sarah S. Dinges, Annika Hohm, Lindsey A. Vandergrift & Leo L. Cheng

  3. Department of Haematology and Oncology, CCM, Charité–Universitätsmedizin Berlin, Berlin, Germany

    Sarah S. Dinges & Piet Habbel

  4. Department of Diagnostic and Interventional Neuroradiology, University Hospital of Würzburg, Würzburg, Germany

    Annika Hohm

  5. Department of Diagnostic and Interventional Radiology, University Hospital of Würzburg, Würzburg, Germany

    Johannes Nowak

  6. Department of Chemistry, University of Massachusetts–Amherst, Amherst, MA, USA

    Igor A. Kaltashov

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Contributions

S.S.D., A.H. and L.A.V. researched data for the article. L.L.C., S.S.D., A.H., L.A.V. and I.A.K. wrote the manuscript. All authors made substantial contributions to discussions of content and reviewed and edited the manuscript before submission.

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Supplementary information

Glossary

Uroscopy

Medical examination of the urine in order to facilitate diagnosis of a disease or disorder. It is a non-laboratory examination that usually relies on examination of colour, cloudiness and precipitates.

Pseudotargeted approaches

Pseudotargeted approaches combine both untargeted and targeted mass spectrometry methods, thereby aiming to reduce disadvantages of either technique alone. A pseudotargeted metabolomics method contains nontargeted profiling, ion pair picking and multiple reaction monitoring measurement. Consequently, global metabolome information can be retrieved, even if some metabolites are not identified. At the same time, pseudotargeted approaches offer improved reproducibility, simplified data processing and a more expansive linear range than the traditional untargeted metabolomics methods.

Multiple reaction monitoring

(MRM). MRM is an extension of selected reaction monitoring (SRM), a popular tandem mass spectrometry (MS/MS) technique in which the instrument is set up to select the precursor ions at a single mass-to-charge ratio (m/z) value and transmit the product ions at a single m/z value. SRM enables a great degree of selectivity to be achieved and is typically used to detect a single metabolite in a complex mixture (assuming the fragmentation pattern of its molecular ion is known and a suitable fragment ion can be selected). MRM takes advantage of the ability of many modern mass spectrometers to perform multiple MS/MS experiments on a chromatographic timescale, thereby enabling multiple (chromatographically unresolved) metabolites to be identified on the basis of the unique precursor and/or fragment pair for each of them.

Dynamic nuclear polarization

(DNP). A method to hyperpolarize NMR-active nuclei such as 13C or 15N, thereby amplifying the signal. In DNP, the sample is polarized in the presence of microwave-irradiated free radicals. An alternative method, SABRE-RELAY (signal amplification by reversible exchange–relayed coherence transfer), can also be used. In SABRE, a latent singlet spin order of parahydrogen is switched on so that it can polarize a substrate. RELAY is an NMR pulse sequence.

Principal component analysis

(PCA). A popular unsupervised approach for deriving a low-dimensional set of features from a large set of variables. It is often used to identify clusters or outliers.

Linear discriminant analysis

(L-DA). A supervised approach for predicting a categorical response. L-DA is a popular approach when there are more than two response classes.

Leave-one-out cross-validation

A validation approach suitable for small data sets. The data set is repeatedly split into a training set containing all but one observation, and a validation set that contains only that observation and the test error is measured each time.

Partial least squares discriminant analysis

(PLS-DA). A supervised analysis to identify features that contribute most to variation or separation of groups, applicable for separation to several groups. OPLS-DA incorporates an orthogonal signal correction filter and is, therefore, useful for the separation of two groups.

AUROC

(Area under the receiver operating characteristic curve). A graphic for simultaneously displaying the true-positive rate and the false-positive rate of a test. An ideal curve is close to the top left corner; thus, the larger the area under the curve, the better the classifier.

Least absolute shrinkage and selection operator (LASSO) regularization

Also known as regression with L1 regularization. LASSO regulation relies upon a linear model but uses an alternative procedure to estimate the coefficients, making it less flexible and easier to interpret because the response variable will only be related to a small subset of the predictors. The remaining predictors are set to zero.

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Dinges, S.S., Hohm, A., Vandergrift, L.A. et al. Cancer metabolomic markers in urine: evidence, techniques and recommendations. Nat Rev Urol 16, 339–362 (2019). https://doi.org/10.1038/s41585-019-0185-3

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