Evidence map›Paper›PMID 26880804›Full record

ArticleCancer research2016

Mass Spectrometry-Based Metabolomics Identifies Longitudinal Urinary Metabolite Profiles Predictive of Radiation-Induced Cancer.

John A Cook, Gadisetti V R Chandramouli, Miriam R Anver, Anastasia L Sowers, Angela Thetford, Kristopher W Krausz, Frank J Gonzalez, James B Mitchell, Andrew D Patterson

Abstract read
In one paragraph

Article in Cancer research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

John A CookRadiation Biology Branch, Center for Cancer Research, NCI, Bethesda, Maryland.
Gadisetti V R ChandramouliRadiation Biology Branch, Center for Cancer Research, NCI, Bethesda, Maryland.
Miriam R AnverPathology/Histotechnology Laboratory, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, Frederick, Maryland.
Anastasia L SowersRadiation Biology Branch, Center for Cancer Research, NCI, Bethesda, Maryland.
Angela ThetfordRadiation Biology Branch, Center for Cancer Research, NCI, Bethesda, Maryland.
Kristopher W KrauszLaboratory of Metabolism, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
Frank J GonzalezLaboratory of Metabolism, Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
James B MitchellRadiation Biology Branch, Center for Cancer Research, NCI, Bethesda, Maryland. james.mitchell3@nih.gov.
Andrew D PattersonDepartment of Veterinary and Biomedical Sciences and the Center for Molecular Toxicology and Carcinogenesis, The Pennsylvania State University, University Park, Pennsylvania.

Funding

Protection against Radiation-Induced CarcinogenesisZIABC010946 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI MITCHELL, JAMES · 2009 to 2025
$12.2M
CCR NIH HHS HHSN261200800001CIntramural NIH HHS ZIA BC010946NCI NIH HHS HHSN261200800001E
6 · The paper itself

Abstract

Nonlethal exposure to ionizing radiation (IR) is a public concern due to its known carcinogenic effects. Although latency periods for IR-induced neoplasms are relatively long, the ability to detect cancer as early as possible is highly advantageous for effective therapeutic intervention. Therefore, we hypothesized that metabolites in the urine from mice exposed to total body radiation (TBI) would predict for the presence of cancer before a palpable mass was detected. In this study, we exposed mice to 0 or 5.4 Gy TBI, collected urine samples periodically over 1 year, and assayed urine metabolites by using mass spectrometry. Longitudinal data analysis within the first year post-TBI revealed that cancers, including hematopoietic, solid, and benign neoplasms, could be distinguished by unique urinary signatures as early as 3 months post-TBI. Furthermore, a distinction among different types of malignancies could be clearly delineated as early as 3 months post-TBI for hematopoietic neoplasms, 6 months for solid neoplasms, and by 1 year for benign neoplasms. Moreover, the feature profile for radiation-exposed mice 6 months post-TBI was found to be similar to nonirradiated control mice at 18 months, suggesting that TBI accelerates aging. These results demonstrate that urine feature profiles following TBI can identify cancers in mice prior to macroscopic detection, with important implications for the early diagnosis and treatment.

Indexed as

AnimalsDose-Response Relationship, RadiationFemaleMass SpectrometryMetabolomicsMiceMice, Inbred C3HNeoplasms, Radiation-InducedRadiation, IonizingWhole-Body Irradiation

Identifiers

PMID26880804
PMCPMC4794383

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.