Advertisement

Magnetic Resonance Spectroscopy Studies of Mouse Models of Cancer

  • Menglin Cheng
  • Kristine GlundeEmail author
Protocol
First Online:
Part of the Methods in Molecular Biology book series (MIMB, volume 1718)

Abstract

Magnetic resonance spectroscopy (MRS) or spectroscopic imaging (MRSI) enables the detection of metabolites, amino acids, and lipids, among other biomolecules, in tumors of live mouse models of cancer. Tumor-bearing mice are anesthetized by breathing isoflurane in a magnetic resonance (MR) scanner dedicated to small animal MR. Here we describe the overall setup and steps for measuring 1H and 31P MRS and 1H MRSI of orthotopic breast tumor models in mice with surface coils. This protocol can be adapted to the use of volume coils to measure 1H and 31P MRS(I) of tumor models that grow inside the body. We address issues of animal handling, setting up the measurement, measurement options, and data analysis.

Key words

Cancer Magnetic resonance spectroscopic imaging Animal setup Shimming Chemical shift imaging Metabolite Amino acid Lipid 
This is a preview of subscription content, log in to check access.

Notes

Acknowledgements

This work was supported by NIH R01 CA134695, R01 CA154725, and P50 CA103175.

References

  1. 1.
    Gatenby RA, Gillies RJ (2004) Why do cancers have high aerobic glycolysis? Nat Rev Cancer 4(11):891–899. https://doi.org/10.1038/nrc1478 CrossRefPubMedGoogle Scholar
  2. 2.
    Warburg O (1956) On the origin of cancer cells. Science 123(3191):309–314CrossRefPubMedGoogle Scholar
  3. 3.
    Kaelin WG Jr, Thompson CB (2010) Q&A: Cancer: clues from cell metabolism. Nature 465(7298):562–564. https://doi.org/10.1038/465562a CrossRefPubMedGoogle Scholar
  4. 4.
    Negendank W (1992) Studies of human tumors by MRS: a review. NMR Biomed 5(5):303–324CrossRefPubMedGoogle Scholar
  5. 5.
    Glunde K, Bhujwalla ZM, Ronen SM (2011) Choline metabolism in malignant transformation. Nat Rev Cancer 11(12):835–848. https://doi.org/10.1038/nrc3162 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674. https://doi.org/10.1016/j.cell.2011.02.013 CrossRefPubMedGoogle Scholar
  7. 7.
    DeBerardinis RJ, Lum JJ, Hatzivassiliou G, Thompson CB (2008) The biology of cancer: metabolic reprogramming fuels cell growth and proliferation. Cell Metab 7(1):11–20. https://doi.org/10.1016/j.cmet.2007.10.002 CrossRefPubMedGoogle Scholar
  8. 8.
    Wise DR, Thompson CB (2010) Glutamine addiction: a new therapeutic target in cancer. Trends Biochem Sci 35(8):427–433. https://doi.org/10.1016/j.tibs.2010.05.003 CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Moestue SA, Giskeodegard GF, Cao MD, Bathen TF, Gribbestad IS (2012) Glycerophosphocholine (GPC) is a poorly understood biomarker in breast cancer. Proc Natl Acad Sci U S A 109(38):E2506; author reply E2507. https://doi.org/10.1073/pnas.1208226109 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Danishad KK, Sharma U, Sah RG, Seenu V, Parshad R, Jagannathan NR (2010) Assessment of therapeutic response of locally advanced breast cancer (LABC) patients undergoing neoadjuvant chemotherapy (NACT) monitored using sequential magnetic resonance spectroscopic imaging (MRSI). NMR Biomed 23(3):233–241. https://doi.org/10.1002/nbm.1436 PubMedGoogle Scholar
  11. 11.
    Haddadin IS, McIntosh A, Meisamy S, Corum C, Styczynski Snyder AL, Powell NJ, Nelson MT, Yee D, Garwood M, Bolan PJ (2009) Metabolite quantification and high-field MRS in breast cancer. NMR Biomed 22(1):65–76. https://doi.org/10.1002/nbm.1217 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Howe FA, Barton SJ, Cudlip SA, Stubbs M, Saunders DE, Murphy M, Wilkins P, Opstad KS, Doyle VL, McLean MA, Bell BA, Griffiths JR (2003) Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy. Magn Reson Med 49(2):223–232. https://doi.org/10.1002/mrm.10367 CrossRefPubMedGoogle Scholar
  13. 13.
    Serkova NJ, Brown MS (2012) Quantitative analysis in magnetic resonance spectroscopy: from metabolic profiling to in vivo biomarkers. Bioanalysis 4(3):321–341. https://doi.org/10.4155/bio.11.320 CrossRefPubMedGoogle Scholar
  14. 14.
    Nelson MT, Everson LI, Garwood M, Emory T, Bolan PJ (2008) MR spectroscopy in the diagnosis and treatment of breast cancer. Semin Breast Dis 11(2):100–105. https://doi.org/10.1053/j.sembd.2008.03.004 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Wijnen JP, Jiang L, Greenwood TR, Cheng M, Dopkens M, Cao MD, Bhujwalla ZM, Krishnamachary B, Klomp DW, Glunde K (2014) Silencing of the glycerophosphocholine phosphodiesterase GDPD5 alters the phospholipid metabolite profile in a breast cancer model in vivo as monitored by (31) P MRS. NMR Biomed 27(6):692–699. https://doi.org/10.1002/nbm.3106 CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Wijnen JP, Jiang L, Greenwood TR, van der Kemp WJ, Klomp DW, Glunde K (2014) 1H/31P polarization transfer at 9.4 Tesla for improved specificity of detecting phosphomonoesters and phosphodiesters in breast tumor models. PLoS One 9(7):e102256. https://doi.org/10.1371/journal.pone.0102256 CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Meisamy S, Bolan PJ, Baker EH, Pollema MG, Le CT, Kelcz F, Lechner MC, Luikens BA, Carlson RA, Brandt KR, Amrami KK, Nelson MT, Everson LI, Emory TH, Tuttle TM, Yee D, Garwood M (2005) Adding in vivo quantitative 1H MR spectroscopy to improve diagnostic accuracy of breast MR imaging: preliminary results of observer performance study at 4.0 T. Radiology 236(2):465–475. https://doi.org/10.1148/radiol.2362040836. 236/2/465 [pii]CrossRefPubMedGoogle Scholar
  18. 18.
    Klomp DW, van de Bank BL, Raaijmakers A, Korteweg MA, Possanzini C, Boer VO, van de Berg CA, van de Bosch MA, Luijten PR (2011) 31P MRSI and 1H MRS at 7 T: initial results in human breast cancer. NMR Biomed 24(10):1337–1342. https://doi.org/10.1002/nbm.1696 CrossRefPubMedGoogle Scholar
  19. 19.
    Naressi A, Couturier C, Devos JM, Janssen M, Mangeat C, de Beer R, Graveron-Demilly D (2001) Java-based graphical user interface for the MRUI quantitation package. Magma (New York, NY) 12(2–3):141–152Google Scholar
  20. 20.
    Bolan PJ, Meisamy S, Baker EH, Lin J, Emory T, Nelson M, Everson LI, Yee D, Garwood M (2003) In vivo quantification of choline compounds in the breast with 1H MR spectroscopy. Magn Reson Med 50(6):1134–1143. https://doi.org/10.1002/mrm.10654 CrossRefPubMedGoogle Scholar
  21. 21.
    Jiang L, Greenwood TR, Artemov D, Raman V, Winnard PT Jr, Heeren RM, Bhujwalla ZM, Glunde K (2012) Localized hypoxia results in spatially heterogeneous metabolic signatures in breast tumor models. Neoplasia 14(8):732–741CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Jiang L, Greenwood TR, van Hove ER, Chughtai K, Raman V, Winnard PT Jr, Heeren RM, Artemov D, Glunde K (2013) Combined MR, fluorescence and histology imaging strategy in a human breast tumor xenograft model. NMR Biomed 26(3):285–298. https://doi.org/10.1002/nbm.2846 CrossRefPubMedGoogle Scholar
  23. 23.
    Choi IY, Tkac I, Gruetter R (2000) Single-shot, three-dimensional “non-echo” localization method for in vivo NMR spectroscopy. Magn Reson Med 44(3):387–394. https://doi.org/10.1002/1522-2594(200009)44:3<387::AID-MRM8>3.0.CO;2-3. [pii]CrossRefPubMedGoogle Scholar
  24. 24.
    Vanhamme L, van den Boogaart A, van Huffel S (1997) Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. J Magn Reson 129(1):35–43CrossRefPubMedGoogle Scholar
  25. 25.
    Penet MF, Pathak AP, Raman V, Ballesteros P, Artemov D, Bhujwalla ZM (2009) Noninvasive multiparametric imaging of metastasis-permissive microenvironments in a human prostate cancer xenograft. Cancer Res 69(22):8822–8829. https://doi.org/10.1158/0008-5472.CAN-09-1782 CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Rashid OM, Takabe K (2015) Animal models for exploring the pharmacokinetics of breast cancer therapies. Expert Opin Drug Metab Toxicol 11(2):221–230. https://doi.org/10.1517/17425255.2015.983073 CrossRefPubMedGoogle Scholar
  27. 27.
    Kocaturk B, Versteeg HH (2015) Orthotopic injection of breast cancer cells into the mammary fat pad of mice to study tumor growth. J Vis Exp 96. https://doi.org/10.3791/51967
  28. 28.
    Rizwan A, Bulte C, Kalaichelvan A, Cheng M, Krishnamachary B, Bhujwalla ZM, Jiang L, Glunde K (2015) Metastatic breast cancer cells in lymph nodes increase nodal collagen density. Sci Rep 5:10002. https://doi.org/10.1038/srep10002 CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Rizwan A, Cheng M, Bhujwalla ZM, Krishnamachary B, Jiang L, Glunde K (2015) Breast cancer cell adhesome and degradome interact to drive metastasis. NPJ Breast Cancer 1:15017CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Krishnamachary B, Glunde K, Wildes F, Mori N, Takagi T, Raman V, Bhujwalla ZM (2009) Noninvasive detection of lentiviral-mediated choline kinase targeting in a human breast cancer xenograft. Cancer Res 69(8):3464–3471. https://doi.org/10.1158/0008-5472.CAN-08-4120 CrossRefPubMedGoogle Scholar
  31. 31.
    Tentler JJ, Tan AC, Weekes CD, Jimeno A, Leong S, Pitts TM, Arcaroli JJ, Messersmith WA, Eckhardt SG (2012) Patient-derived tumour xenografts as models for oncology drug development. Nat Rev Clin Oncol 9(6):338–350. https://doi.org/10.1038/nrclinonc.2012.61 CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Cho SY, Kang W, Han JY, Min S, Kang J, Lee A, Kwon JY, Lee C, Park H (2016) An integrative approach to precision cancer medicine using patient-derived xenografts. Mol Cells 39(2):77–86. 10.14348/molcells.2016.2350 CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    Borowsky AD (2011) Choosing a mouse model: experimental biology in context--the utility and limitations of mouse models of breast cancer. Cold Spring Harb Perspect Biol 3(9):a009670. https://doi.org/10.1101/cshperspect.a009670 CrossRefPubMedPubMedCentralGoogle Scholar
  34. 34.
    Usary J, Zhao W, Darr D, Roberts PJ, Liu M, Balletta L, Karginova O, Jordan J, Combest A, Bridges A, Prat A, Cheang MCU, Herschkowitz JI, Rosen JM, Zamboni W, Sharpless NE, Perou CM (2013) Predicting drug responsiveness in human cancers using genetically engineered mice. Clin Cancer Res 19(17):4889–4899. https://doi.org/10.1158/1078-0432.Ccr-13-0522 CrossRefPubMedPubMedCentralGoogle Scholar
  35. 35.
    Osbakken MD, Kreider JW, Taczanowsky P (1986) Nuclear magnetic resonance imaging characterization of a rat mammary tumor. Magn Reson Med 3(1):1–9CrossRefPubMedGoogle Scholar
  36. 36.
    Dodd NJ, Moore JV, Poppitt DG, Wood B (1989) In vivo magnetic resonance imaging of the effects of photodynamic therapy. Br J Cancer 60(2):164–167CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  1. 1.Division of Cancer Imaging Research, Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreUSA
  2. 2.Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins University School of MedicineBaltimoreUSA

Personalised recommendations

Cite protocol