Skip to main content

Advertisement

SpringerLink
Log in

Metabolomic profiling of mouse mammary tumor-derived cell lines reveals targeted therapy options for cancer subtypes

  • Original paper
  • Published:
Cellular Oncology Aims and scope Submit manuscript

Abstract

Purpose

Breast cancer is a heterogeneous disease with several subtypes that currently do not have targeted therapeutic options. Metabolomics has the potential to uncover novel targeted treatment strategies by identifying metabolic pathways required for cancer cells to survive and proliferate.

Methods

The metabolic profiles of two histologically distinct breast cancer subtypes from a MMTV-Myc mouse model, epithelial-mesenchymal-transition (EMT) and papillary, were investigated using mass spectrometry-based metabolomics methods. Based on metabolic profiles, drugs most likely to be effective against each subtype were selected and tested.

Results

We found that the EMT and papillary subtypes display different metabolic preferences. Compared to the papillary subtype, the EMT subtype exhibited increased glutathione and TCA cycle metabolism, while the papillary subtype exhibited increased nucleotide biosynthesis compared to the EMT subtype. Targeting these distinct metabolic pathways effectively inhibited cancer cell proliferation in a subtype-specific manner.

Conclusions

Our results demonstrate the feasibility of metabolic profiling to develop novel personalized therapy strategies for different subtypes of breast cancer.

Graphical abstract

Schematic overview of the experimental design for drug selection based on breast cancer subtype-specific metabolism. The epithelial mesenchymal transition (EMT) and papillary tumors are histologically distinct mouse mammary tumor subtypes from the MMTV-Myc mouse model. Cell lines derived from tumors can be used to determine metabolic pathways that can be used to select drug candidates for each subtype.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. C.M. Perou, T. Sorlie, M.B. Eisen, M. van de Rijn, S.S. Jeffrey, C.A. Rees, J.R. Pollack, D.T. Ross, H. Johnsen, L.A. Akslen, O. Fluge, A. Pergamenschikov, C. Williams, S.X. Zhu, P.E. Lonning, A.L. Borresen-Dale, P.O. Brown, D. Botstein, Molecular portraits of human breast tumours. Nature 406, 747–752 (2000). https://doi.org/10.1038/35021093

    Article  CAS  PubMed  Google Scholar 

  2. T. Sørlie, C.M. Perou, R. Tibshirani, T. Aas, S. Geisler, H. Johnsen, T. Hastie, M.B. Eisen, M. van de Rijn, S.S. Jeffrey, T. Thorsen, H. Quist, J.C. Matese, P.O. Brown, D. Botstein, P.E. Lønning, A.-L. Børresen-Dale, Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl. Acad. Sci. U. S. A. 98, 10869–10874 (2001). https://doi.org/10.1073/pnas.191367098

    Article  PubMed  PubMed Central  Google Scholar 

  3. E. Senkus, S. Kyriakides, F. Penault-Llorca, P. Poortmans, A. Thompson, S. Zackrisson, F. Cardoso, Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up†. Ann. Oncol. 24, vi7–vi23 (2013). https://doi.org/10.1093/annonc/mdt284

    Article  PubMed  Google Scholar 

  4. G. Early Breast Cancer Trialists’ Collaborative, , Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomised trials. Lancet 365, 1687–1717 (2005). https://doi.org/10.1016/s0140-6736(05)66544-0

    Article  Google Scholar 

  5. A.M. Gonzalez-Angulo, F. Morales-Vasquez, G.N. Hortobagyi, in Breast Cancer Chemosensitivity, ed. by D. Yu, M.-C. Hung (Springer New York, New York, NY, 2007), p. 1–22

  6. M.P. Ogrodzinski, J.J. Bernard, S.Y. Lunt, Deciphering metabolic rewiring in breast cancer subtypes. Transl. Res. 189, 105–122 (2017). https://doi.org/10.1016/j.trsl.2017.07.004

    Article  CAS  PubMed  Google Scholar 

  7. D. Hanahan, R.A. Weinberg, Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011). https://doi.org/10.1016/j.cell.2011.02.013

    Article  CAS  PubMed  Google Scholar 

  8. S.Y. Lunt, M.G. Vander Heiden, Aerobic glycolysis: meeting the metabolic requirements of cell proliferation. Annu. Rev. Cell Dev. Biol. 27, 441–464 (2011). https://doi.org/10.1146/annurev-cellbio-092910-154237

    Article  CAS  PubMed  Google Scholar 

  9. H.-N. Kung, J.R. Marks, J.-T. Chi, Glutamine synthetase is a genetic determinant of cell type–specific glutamine independence in breast epithelia. PLoS Genet. 7, e1002229 (2011). https://doi.org/10.1371/journal.pgen.1002229

  10. S. Kim, D.H. Kim, W.-H. Jung, J.S. Koo, Expression of glutamine metabolism-related proteins according to molecular subtype of breast cancer. Endocr. Relat. Cancer 20, 339–348 (2013). https://doi.org/10.1530/erc-12-0398

    Article  CAS  PubMed  Google Scholar 

  11. M.I. Gross, S.D. Demo, J.B. Dennison, L. Chen, T. Chernov-Rogan, B. Goyal, J.R. Janes, G.J. Laidig, E.R. Lewis, J. Li, A.L. MacKinnon, F. Parlati, M.L.M. Rodriguez, P.J. Shwonek, E.B. Sjogren, T.F. Stanton, T. Wang, J. Yang, F. Zhao, M.K. Bennett, Antitumor activity of the glutaminase inhibitor CB-839 in triple-negative breast cancer. Mol. Cancer Ther. 13, 890 (2014). https://doi.org/10.1158/1535-7163.MCT-13-0870

    Article  CAS  PubMed  Google Scholar 

  12. M. Lampa, H. Arlt, T. He, B. Ospina, J. Reeves, B. Zhang, J. Murtie, G. Deng, C. Barberis, D. Hoffmann, H. Cheng, J. Pollard, C. Winter, V. Richon, C. Garcia-Escheverria, F. Adrian, D. Wiederschain, L. Srinivasan, Glutaminase is essential for the growth of triple-negative breast cancer cells with a deregulated glutamine metabolism pathway and its suppression synergizes with mTOR inhibition. PLoS One 12, e0185092 (2017). https://doi.org/10.1371/journal.pone.0185092

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. T.A. Stewart, P.K. Pattengale, P. Leder, Spontaneous mammary adenocarcinomas in transgenic mice that carry and express MTV/myc fusion genes. Cell 38, 627–637 (1984). https://doi.org/10.1016/0092-8674(84)90257-5

    Article  CAS  PubMed  Google Scholar 

  14. E.R. Andrechek, R.D. Cardiff, J.T. Chang, M.L. Gatza, C.R. Acharya, A. Potti, J.R. Nevins, Genetic heterogeneity of Myc-induced mammary tumors reflecting diverse phenotypes including metastatic potential. Proc. Natl. Acad. Sci. U. S. A. 106, 16387–16392 (2009). https://doi.org/10.1073/pnas.0901250106

  15. D.P. Hollern, E.R. Andrechek, A genomic analysis of mouse models of breast cancer reveals molecular features of mouse models and relationships to human breast cancer. Breast Cancer Res. 16, R59–R59 (2014). https://doi.org/10.1186/bcr3672

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. D.P. Hollern, M.R. Swiatnicki, E.R. Andrechek, Histological subtypes of mouse mammary tumors reveal conserved relationships to human cancers. PLoS Genet. 14, (2018)

  17. A. Prat, J.S. Parker, O. Karginova, C. Fan, C. Livasy, J.I. Herschkowitz, X. He, C.M. Perou, Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Res. 12, R68 (2010). https://doi.org/10.1186/bcr2635

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. S.L. Deming, S.J. Nass, R.B. Dickson, B.J. Trock, C-myc amplification in breast cancer: a meta-analysis of its occurrence and prognostic relevance. Br. J. Cancer 83, 1688–1695 (2000). https://doi.org/10.1054/bjoc.2000.1522

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. C.V. Dang, c-Myc target genes involved in cell growth, apoptosis, and metabolism. Mol. Cell. Biol. 19, 1–11 (1999). https://doi.org/10.1128/mcb.19.1.1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. C.V. Dang, K.A. O’Donnell, K.I. Zeller, T. Nguyen, R.C. Osthus, F. Li, The c-Myc target gene network. Semin. Cancer Biol. 16, 253–264 (2006). https://doi.org/10.1016/j.semcancer.2006.07.014

    Article  CAS  PubMed  Google Scholar 

  21. T. Wahlstrom, M.A. Henriksson, Impact of MYC in regulation of tumor cell metabolism. Biochim. Biophys. Acta 1849, 563–569 (2015). https://doi.org/10.1016/j.bbagrm.2014.07.004

    Article  CAS  PubMed  Google Scholar 

  22. Z.E. Stine, Z.E. Walton, B.J. Altman, A.L. Hsieh, C.V. Dang, MYC, metabolism, and cancer. Cancer Discov. 5, 1024–1039 (2015). https://doi.org/10.1158/2159-8290.CD-15-0507

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. A.N. Lane, T.W.-M. Fan, Regulation of mammalian nucleotide metabolism and biosynthesis. Nucleic Acids Res. 43, 2466–2485 (2015). https://doi.org/10.1093/nar/gkv047

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. M.P. Ogrodzinski, S.T. Teoh, L. Yu, D. Broadwater, E. Ensink, S.Y. Lunt, in Methods Mol. Biol., ed. by S.M. Fendt, S. Lunt (Humana Press, New York, NY, Metabolic Signaling. Methods Mol Biol, 2019), p. 37–52

  25. A. Mitra, L. Mishra, S. Li, Technologies for deriving primary tumor cells for use in personalized cancer therapy. Trends Biotechnol. 31, 347–354 (2013). https://doi.org/10.1016/j.tibtech.2013.03.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. E. Melamud, L. Vastag, J.D. Rabinowitz, Metabolomic analysis and visualization engine for LC – MS Data. Anal. Chem. 82, 9818–9826 (2010). https://doi.org/10.1021/ac1021166

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. M.J. De Hoon, S. Imoto, J. Nolan, S. Miyano, Open source clustering software. Bioinformatics 20, 1453–1454 (2004). https://doi.org/10.1093/bioinformatics/bth078

    Article  CAS  PubMed  Google Scholar 

  28. A.J. Saldanha, Java Treeview—extensible visualization of microarray data. Bioinformatics 20, 3246–3248 (2004). https://doi.org/10.1093/bioinformatics/bth349

    Article  CAS  PubMed  Google Scholar 

  29. P. Millard, F. Letisse, S. Sokol, J.C. Portais, IsoCor: correcting MS data in isotope labeling experiments. Bioinformatics 28, 1294–1296 (2012). https://doi.org/10.1093/bioinformatics/bts127

    Article  CAS  PubMed  Google Scholar 

  30. J.M. Buescher, M.R. Antoniewicz, L.G. Boros, S.C. Burgess, H. Brunengraber, C.B. Clish, R.J. DeBerardinis, O. Feron, C. Frezza, B. Ghesquiere, E. Gottlieb, K. Hiller, R.G. Jones, J.J. Kamphorst, R.G. Kibbey, A.C. Kimmelman, J.W. Locasale, S.Y. Lunt, O.D. Maddocks, C. Malloy, C.M. Metallo, E.J. Meuillet, J. Munger, K. Noh, J.D. Rabinowitz, M. Ralser, U. Sauer, G. Stephanopoulos, J. St-Pierre, D.A. Tennant, C. Wittmann, M.G. Vander Heiden, A. Vazquez, K. Vousden, J.D. Young, N. Zamboni, S.M. Fendt, A roadmap for interpreting (13)C metabolite labeling patterns from cells. Curr. Opin. Biotechnol. 34, 189–201 (2015). https://doi.org/10.1016/j.copbio.2015.02.003

  31. O.W. Griffith, A. Meister, Potent and specific inhibition of glutathione synthesis by buthionine sulfoximine (Sn-butyl homocysteine sulfoximine). J. Biol. Chem. 254, 7558–7560 (1979)

    CAS  PubMed  Google Scholar 

  32. Z. Zachar, J. Marecek, C. Maturo, S. Gupta, S.D. Stuart, K. Howell, A. Schauble, J. Lem, A. Piramzadian, S. Karnik, Non-redox-active lipoate derivates disrupt cancer cell mitochondrial metabolism and are potent anticancer agents in vivo. J. Mol. Med. 89, 1137 (2011). https://doi.org/10.1007/s00109-011-0785-8

    Article  CAS  PubMed  Google Scholar 

  33. S.D. Stuart, A. Schauble, S. Gupta, A.D. Kennedy, B.R. Keppler, P.M. Bingham, Z. Zachar, A strategically designed small molecule attacks alpha-ketoglutarate dehydrogenase in tumor cells through a redox process. Cancer Metab. 2, 4–4 (2014). https://doi.org/10.1186/2049-3002-2-4

    Article  PubMed  PubMed Central  Google Scholar 

  34. S.S. Cohen, J.G. Flaks, H.D. Barner, M.R. Loeb, J. Lichtenstein, The mode of action of 5-fluorouracil and its derivatives. Proc. Natl. Acad. Sci. U. S. A. 44, 1004 (1958). https://doi.org/10.1073/pnas.44.10.1004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. D.B. Longley, D.P. Harkin, P.G. Johnston, 5-Fluorouracil: mechanisms of action and clinical strategies. Nat. Rev. Cancer 3, 330–338 (2003). https://doi.org/10.1038/nrc1074

    Article  CAS  PubMed  Google Scholar 

  36. L.A. McConnachie, I. Mohar, F.N. Hudson, C.B. Ware, W.C. Ladiges, C. Fernandez, S. Chatterton-Kirchmeier, C.C. White, R.H. Pierce, T.J. Kavanagh, Glutamate cysteine ligase modifier subunit deficiency and gender as determinants of acetaminophen-induced hepatotoxicity in mice. Toxicol. Sci. 99, 628–636 (2007)

    Article  CAS  PubMed  Google Scholar 

  37. D.S. Backos, K.S. Fritz, D.G. McArthur, J.K. Kepa, A.M. Donson, D.R. Petersen, N.K. Foreman, C.C. Franklin, P. Reigan, Glycation of glutamate cysteine ligase by 2-deoxy-d-ribose and its potential impact on chemoresistance in glioblastoma. Neurochem. Res. 38, 1838–1849 (2013)

    Article  CAS  PubMed  Google Scholar 

  38. A. Luengo, D.Y. Gui, M.G. Vander, Heiden, Targeting metabolism for cancer therapy. Cell Chem. Biol. 24, 1161–1180 (2017). https://doi.org/10.1016/j.chembiol.2017.08.028

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. U.E. Martinez-Outschoorn, M. Peiris-Pages, R.G. Pestell, F. Sotgia, M.P. Lisanti, Cancer metabolism: a therapeutic perspective. Nat. Rev. Clin. Oncol. 14, 11–31 (2017). https://doi.org/10.1038/nrclinonc.2016.60

    Article  CAS  PubMed  Google Scholar 

  40. Y. Zhao, E.B. Butler, M. Tan, Targeting cellular metabolism to improve cancer therapeutics. Cell Death Dis. 4, e532 (2013). https://doi.org/10.1038/cddis.2013.60

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. J.S. Lewis-Wambi, H.R. Kim, C. Wambi, R. Patel, J.R. Pyle, A.J. Klein-Szanto, V.C. Jordan, Buthionine sulfoximine sensitizes antihormone-resistant human breast cancer cells to estrogen-induced apoptosis. Breast Cancer Res. 10, R104 (2008). https://doi.org/10.1186/bcr2208

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. A. Tagde, H. Singh, M. Kang, C. Reynolds, The glutathione synthesis inhibitor buthionine sulfoximine synergistically enhanced melphalan activity against preclinical models of multiple myeloma. Blood Cancer J. 4, e229 (2014). https://doi.org/10.1038/bcj.2014.45

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. C.P. Anderson, K.K. Matthay, J.P. Perentesis, J.P. Neglia, H.H. Bailey, J.G. Villablanca, S. Groshen, B. Hasenauer, J.M. Maris, R.C. Seeger, Pilot study of intravenous melphalan combined with continuous infusion L-S, R‐buthionine sulfoximine for children with recurrent neuroblastoma. Pediatr. Blood Cancer 62, 1739–1746 (2015). https://doi.org/10.1002/pbc.25594

    Article  CAS  PubMed  Google Scholar 

  44. J.G. Villablanca, S.L. Volchenboum, H. Cho, M.H. Kang, S.L. Cohn, C.P. Anderson, A. Marachelian, S. Groshen, D. Tsao-Wei, K.K. Matthay, A phase I new approaches to neuroblastoma therapy study of buthionine sulfoximine and melphalan with autologous stem cells for recurrent/refractory high‐risk neuroblastoma. Pediatr. Blood Cancer 63, 1349–1356 (2016). https://doi.org/10.1002/pbc.25994

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. C.M.S.P.R. Lima, A.T. Alistar, R.J. Desnoyers, S. Sorscher, G.H. Yacoub, E.V.M. Dressler, T.S. Pardee, S.C. Grant, S. Luther, D. Butler, O. Ogburn, K. Strickland, B. Pasche, A phase I clinical trial of fluorouracil (5-FU) + devimistat (CPI-613) combination in previously treated patients (pts) with metastatic colorectal cancer (MCR). J. Clin. Oncol. 37, e15054–e15054 (2019). https://doi.org/10.1200/JCO.2019.37.15_suppl.e15054

    Article  Google Scholar 

  46. T.W. Lycan, T.S. Pardee, W.J. Petty, M. Bonomi, A. Alistar, Z.S. Lamar, S. Isom, M.D. Chan, A.A. Miller, J. Ruiz, A phase II clinical trial of CPI-613 in patients with relapsed or refractory small cell lung carcinoma. PLoS One 11, e0164244 (2016). https://doi.org/10.1371/journal.pone.0164244

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. A. Alistar, B.B. Morris, R. Desnoyer, H.D. Klepin, K. Hosseinzadeh, C. Clark, A. Cameron, J. Leyendecker, R. D’Agostino Jr., U. Topaloglu, Safety and tolerability of the first-in-class agent CPI-613 in combination with modified FOLFIRINOX in patients with metastatic pancreatic cancer: a single-centre, open-label, dose-escalation, phase 1 trial. Lancet Oncol. 18, 770–778 (2017). https://doi.org/10.1016/S1470-2045(17)30314-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. P. Johnston, S. Kaye, Capecitabine: a novel agent for the treatment of solid tumors. Anticancer Drugs 12, 639–646 (2001). https://doi.org/10.1097/00001813-200109000-00001

    Article  CAS  PubMed  Google Scholar 

  49. D. Horiuchi, L. Kusdra, N.E. Huskey, S. Chandriani, M.E. Lenburg, A.M. Gonzalez-Angulo, K.J. Creasman, A.V. Bazarov, J.W. Smyth, S.E. Davis, P. Yaswen, G.B. Mills, L.J. Esserman, A. Goga, MYC pathway activation in triple-negative breast cancer is synthetic lethal with CDK inhibition. J. Exp. Med. 209, 679–696 (2012). https://doi.org/10.1084/jem.20111512

  50. C. Liedtke, C. Mazouni, K.R. Hess, F. André, A. Tordai, J.A. Mejia, W.F. Symmans, A.M. Gonzalez-Angulo, B. Hennessy, M. Green, Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J. Clin. Oncol. 26, 1275–1281 (2008). https://doi.org/10.1200/JCO.2007.14.4147

    Article  PubMed  Google Scholar 

  51. B. Anderton, R. Camarda, S. Balakrishnan, A. Balakrishnan, R.A. Kohnz, L. Lim, K.J. Evason, O. Momcilovic, K. Kruttwig, Q. Huang, MYC-driven inhibition of the glutamate‐cysteine ligase promotes glutathione depletion in liver cancer. EMBO Rep. 18, 569–585 (2017). https://doi.org/10.15252/embr.201643068

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. P. Gao, I. Tchernyshyov, T.C. Chang, Y.S. Lee, K. Kita, T. Ochi, K.I. Zeller, A.M. De Marzo, J.E. Van Eyk, J.T. Mendell, Dang, c-Myc suppression of miR-23a/b enhances mitochondrial glutaminase expression and glutamine metabolism. Nature 458, 762–765 (2009). https://doi.org/10.1038/nature07823

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. R. Camarda, A.Y. Zhou, R.A. Kohnz, S. Balakrishnan, C. Mahieu, B. Anderton, H. Eyob, S. Kajimura, A. Tward, G. Krings, D.K. Nomura, A. Goga, Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer. Nat. Med. 22, 427–432 (2016). https://doi.org/10.1038/nm.4055

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank Deanna Broadwater, Elliot Ensink, Hyllana Medeiros, and Lei Yu for helpful discussions and critical reading of this manuscript. The authors thank Eran Andrechek for providing primary MMTV-Myc EMT and MMTV-Myc papillary tumors. The authors also thank the MSU Mass Spectrometry and Metabolomics Core and the MSU Investigative HistoPathology Laboratory.

Funding

This work was supported by the Office of the Assistant Secretary of Defense for Health Affairs, through the Breast Cancer Research Program, under Award No. W81XWH-15-1-0453 to SYL. This work was also supported by the Spectrum Health MD/PhD Fellowship and the Aitch Foundation Graduate Fellowship to MPO.

Author information

Authors and Affiliations

  1. Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, USA

    Martin P. Ogrodzinski, Shao Thing Teoh & Sophia Y. Lunt

  2. Department of Physiology, Michigan State University, East Lansing, MI, USA

    Martin P. Ogrodzinski

  3. Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, MI, USA

    Sophia Y. Lunt

Authors
  1. Martin P. Ogrodzinski
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shao Thing Teoh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sophia Y. Lunt
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MPO performed metabolic profiling, isotope labeling studies, cell culture, drug response studies, experimental design and data analysis. STT performed gene expression studies and assisted with interpretation of results. SYL conceived, designed, and supervised the study. All authors contributed to writing the manuscript and have critically read, edited, and approved the manuscript.

Corresponding author

Correspondence to Sophia Y. Lunt.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

ESM 1

(DOCX 884 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ogrodzinski, M.P., Teoh, S.T. & Lunt, S.Y. Metabolomic profiling of mouse mammary tumor-derived cell lines reveals targeted therapy options for cancer subtypes. Cell Oncol. 43, 1117–1127 (2020). https://doi.org/10.1007/s13402-020-00545-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13402-020-00545-1

Keywords

Search

Navigation