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

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)1569-1577
Number of pages9
JournalCancer Research
Volume76
Issue number6
DOIs
StatePublished - Mar 15 2016

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Radiation-Induced Neoplasms
Metabolomics
Mass Spectrometry
Radiation
Neoplasms
Urine
Hematologic Neoplasms
Ionizing Radiation
Early Diagnosis

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

Cite this

Cook, John A. ; Chandramouli, Gadisetti V.R. ; Anver, Miriam R. ; Sowers, Anastasia L. ; Thetford, Angela ; Krausz, Kristopher W. ; Gonzalez, Frank J. ; Mitchell, James B. ; Patterson, Andrew D. / Mass spectrometry-based metabolomics identifies longitudinal urinary metabolite profiles predictive of radiation-induced cancer. In: Cancer Research. 2016 ; Vol. 76, No. 6. pp. 1569-1577.
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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.",
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Cook, JA, Chandramouli, GVR, Anver, MR, Sowers, AL, Thetford, A, Krausz, KW, Gonzalez, FJ, Mitchell, JB & Patterson, AD 2016, 'Mass spectrometry-based metabolomics identifies longitudinal urinary metabolite profiles predictive of radiation-induced cancer', Cancer Research, vol. 76, no. 6, pp. 1569-1577. https://doi.org/10.1158/0008-5472.CAN-15-2416

Mass spectrometry-based metabolomics identifies longitudinal urinary metabolite profiles predictive of radiation-induced cancer. / Cook, John A.; Chandramouli, Gadisetti V.R.; Anver, Miriam R.; Sowers, Anastasia L.; Thetford, Angela; Krausz, Kristopher W.; Gonzalez, Frank J.; Mitchell, James B.; Patterson, Andrew D.

In: Cancer Research, Vol. 76, No. 6, 15.03.2016, p. 1569-1577.

Research output: Contribution to journalArticle

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AU - Patterson, Andrew D.

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