Abstract
Introduction
Translational cancer research has seen an increasing interest in metabolomic profiling to decipher tumor phenotypes. However, the impact of post-surgical freezing delays on mass spectrometric metabolomic measurements of the cancer tissue remains elusive.
Objectives
To evaluate the impact of post-surgical freezing delays on cancer tissue metabolomics and to investigate changes per metabolite and per metabolic pathway.
Methods
We performed untargeted metabolomics on three cortically located and bulk-resected glioblastoma tissues that were sequentially frozen as duplicates at up to six different time delays (0–180 min, 34 samples).
Results
Statistical modelling revealed that 10% of the metabolome (59 of 597 metabolites) changed significantly after a 3 h delay. While carbohydrates and energy metabolites decreased, peptides and lipids increased. After a 2 h delay, these metabolites had changed by as much as 50–100%. We present the first list of metabolites in glioblastoma tissues that are sensitive to post-surgical freezing delays and offer the opportunity to define individualized fold change thresholds for future comparative metabolomic studies.
Conclusion
More researchers should take these pre-analytical factors into consideration when analyzing metabolomic data. We present a strategy for how to work with metabolites that are sensitive to freezing delays.
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Data and software availability
The metabolomic data has been processed and analyzed using the recently released MetaboDiff R package (Mock et al. 2018). MetaboDiff is platform-independent, available at http://github.com/andreasmock/MetaboDiff/ and has been released under the MIT license. The data of this paper, including a step-by-step user guide explaining the preprocessing and statistical analyses performed, can be found at http://github.com/andreasmock/quenching/.
References
Armitage, E. G., Godzien, J., Alonso-Herranz, V., Lpez-Gonz lvez, N., & Barbas, C. (2015). Missing value imputation strategies for metabolomics data. Electrophoresis, 36(24), 3050–3060. https://doi.org/10.1002/elps.201500352.
Chaisaingmongkol, J., Budhu, A., Dang, H., Rabibhadana, S., Pupacdi, B., Kwon, S. M., et al. (2017). Common molecular subtypes among Asian hepatocellular carcinoma and cholangiocarcinoma. Cancer Cell, 32(1), 57–70.e3. https://doi.org/10.1016/j.ccell.2017.05.009.
Chakravarthy, D., Muñoz, A. R., Su, A., Hwang, R. F., Keppler, B. R., Chan, D. E., et al. (2018). Palmatine suppresses glutamine-mediated interaction between pancreatic cancer and stellate cells through simultaneous inhibition of survivin and COL1A1. Cancer Letters, 419, 103–115. https://doi.org/10.1016/j.canlet.2018.01.057.
Chinnaiyan, P., Kensicki, E., Bloom, G., Prabhu, A., Sarcar, B., Kahali, S., et al. (2012). The metabolomic signature of malignant glioma reflects accelerated anabolic metabolism. Cancer Research, 72(22), 5878–5888. https://doi.org/10.1158/0008-5472.CAN-12-1572-T.
Evans, A. M., DeHaven, C. D., Barrett, T., Mitchell, M., & Milgram, E. (2009). Integrated, nontargeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Analytical Chemistry, 81(16), 6656–6667. https://doi.org/10.1021/ac901536h.
Hakimi, A. A., Reznik, E., Lee, C.-H., Creighton, C. J., Brannon, A. R., Luna, A., et al. (2016). An integrated metabolic atlas of clear cell renal cell carcinoma. Cancer Cell, 29(1), 104–116. https://doi.org/10.1016/j.ccell.2015.12.004.
Halama, A., Kulinski, M., Dib, S. S., Zaghlool, S. B., Siveen, K. S., Iskandarani, A., et al. (2018). Accelerated lipid catabolism and autophagy are cancer survival mechanisms under inhibited glutaminolysis. Cancer Letters, 430, 133–147. https://doi.org/10.1016/j.canlet.2018.05.017.
Haukaas, T. H., Moestue, S. A., Vettukattil, R., Sitter, B., Lamichhane, S., Segura, R., et al. (2016). Impact of freezing delay time on tissue samples for metabolomic studies. Frontiers in Oncology, 6(2), 17. https://doi.org/10.3389/fonc.2016.00017.
Huang, J., Mondul, A. M., Weinstein, S. J., Derkach, A., Moore, S. C., Sampson, J. N., et al. (2019). Prospective serum metabolomic profiling of lethal prostate cancer. International Journal of Cancer, 4, 127rv3. https://doi.org/10.1002/ijc.32218.
Huber, W., von Heydebreck, A., Sültmann, H., Poustka, A., & Vingron, M. (2002). Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics, 18(Suppl 1), S96–104.
Johnson, C. H., Ivanisevic, J., & Siuzdak, G. (2016). Metabolomics: Beyond biomarkers and towards mechanisms. Nature reviews. Molecular Cell Biology, 17(7), 451–459. https://doi.org/10.1038/nrm.2016.25.
Lee, J.-H., Mand, M. R., Kao, C.-H., Zhou, Y., Ryu, S. W., Richards, A. L., et al. (2018). ATM directs DNA damage responses and proteostasis via genetically separable pathways. Science Signaling, 11(512), eaan5598. https://doi.org/10.1126/scisignal.aan5598.
Lehmann, R. (2015). Preanalytics: What can metabolomics learn from clinical chemistry? Bioanalysis, 7(8), 927–930. https://doi.org/10.4155/bio.15.23.
Mayerle, J., Kalthoff, H., Reszka, R., Kamlage, B., Peter, E., Schniewind, B., et al. (2018). Metabolic biomarker signature to differentiate pancreatic ductal adenocarcinoma from chronic pancreatitis. Gut, 67(1), 128–137. https://doi.org/10.1136/gutjnl-2016-312432.
Mock, A., Warta, R., Dettling, S., Brors, B., Jäger, D., & Herold-Mende, C. (2018). MetaboDiff: An R package for differential metabolomic analysis. Bioinformatics. https://doi.org/10.1093/bioinformatics/bty344.
Moore, S. C., Playdon, M. C., Sampson, J. N., Hoover, R. N., Trabert, B., Matthews, C. E., et al. (2018). A metabolomics analysis of Body Mass Index and postmenopausal breast cancer risk. Journal of the National Cancer Institute, 110(6), 588–597. https://doi.org/10.1093/jnci/djx244.
More, T. H., RoyChoudhury, S., Christie, J., Taunk, K., Mane, A., Santra, M. K., et al. (2018). Metabolomic alterations in invasive ductal carcinoma of breast: A comprehensive metabolomic study using tissue and serum samples. Oncotarget, 9(2), 2678–2696. https://doi.org/10.18632/oncotarget.23626.
Moreno, P., Jiménez-Jiménez, C., Garrido-Rodríguez, M., Calderón-Santiago, M., Molina, S., Lara-Chica, M., et al. (2018). Metabolomic profiling of human lung tumor tissues—nucleotide metabolism as a candidate for therapeutic interventions and biomarkers. Molecular Oncology, 12(10), 1778–1796. https://doi.org/10.1002/1878-0261.12369.
Pera, B., Krumsiek, J., Assouline, S. E., Marullo, R., Patel, J., Phillip, J. M., et al. (2018). Metabolomic profiling reveals cellular reprogramming of B-cell lymphoma by a lysine deacetylase inhibitor through the choline pathway. EBioMedicine, 28, 80–89. https://doi.org/10.1016/j.ebiom.2018.01.014.
Pinheiro, J. (2009). nlme: Linear and nonlinear mixed effects models. https://cran.r-project.org/, https://doi.org/10.20710/dojo.82.3_191.
Ramos, M., Schiffer, L., Re, A., Azhar, R., Basunia, A., Rodriguez, C., et al. (2017). Software for the integration of multiomics experiments in bioconductor. Cancer Research, 77(21), e39–e42. https://doi.org/10.1158/0008-5472.CAN-17-0344.
Schulte, M. L., Fu, A., Zhao, P., Li, J., Geng, L., Smith, S. T., et al. (2018). Pharmacological blockade of ASCT2-dependent glutamine transport leads to antitumor efficacy in preclinical models. Nature Medicine, 24(2), 194–202. https://doi.org/10.1038/nm.4464.
Tzoneva, G., Dieck, C. L., Oshima, K., Ambesi-Impiombato, A., Sánchez-Martín, M., Madubata, C. J., et al. (2018). Clonal evolution mechanisms in NT5C2 mutant-relapsed acute lymphoblastic leukaemia. Nature, 553(7689), 511–514. https://doi.org/10.1038/nature25186.
Waitkus, M. S., Pirozzi, C. J., Moure, C. J., Diplas, B. H., Hansen, L. J., Carpenter, A. B., et al. (2018). Adaptive evolution of the GDH2 allosteric domain promotes gliomagenesis by resolving IDH1R132H-induced metabolic liabilities. Cancer Research, 78(1), 36–50. https://doi.org/10.1158/0008-5472.CAN-17-1352.
Yang, K., Xia, B., Wang, W., Cheng, J., Yin, M., Xie, H., et al. (2017). A comprehensive analysis of metabolomics and transcriptomics in cervical cancer. Scientific Reports, 7(1), 43353. https://doi.org/10.1038/srep43353.
Acknowledgements
We thank Evelin Eiswirth for her help in visualizing the study design. Furthermore, we thank Farzaneh Kashfi, Ilka Hearn, Melanie Greibich, and Mandy Barthel for their excellent technical assistance.
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AM and CHM conceived and designed the study, undertook analysis, and drafted the manuscript. OS, CJ, and AU prospectively assessed the suitability of brain tumor operations for the planned study and intraoperatively provided the glioblastoma tissue. AvD performed the histopathological evaluation, quality control of tumor tissue, and confirmation of IDH1 status. AM, CR, AA, and RW analyzed data. DJ critically revised the manuscript. BB contributed new statistical methods. All authors read and approved the final manuscript.
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The authors declare no conflict of interest.
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The study was approved by the ethics committee of the Medical Faculty, University of Heidelberg (reference number: 005/2003) and written informed consent was obtained from all patients in accordance with the Declaration of Helsinki and its later amendments.
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Mock, A., Rapp, C., Warta, R. et al. Impact of post-surgical freezing delay on brain tumor metabolomics. Metabolomics 15, 78 (2019). https://doi.org/10.1007/s11306-019-1541-2
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DOI: https://doi.org/10.1007/s11306-019-1541-2