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Potential impact of tissue molecular heterogeneity on ambient mass spectrometry profiles: a note of caution in choosing the right disease model

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Abstract

This review provides a summary of known molecular alterations in commonly used cancer models and strives to stipulate how they may affect ambient mass spectrometry profiles. Immortalized cell lines are known to accumulate mutations, and xenografts derived from cell lines are known to contain tumour microenvironment elements from the host animal. While the use of human specimens for mass spectrometry profiling studies is highly encouraged, patient-derived xenografts with low passage numbers could provide an alternative means of amplifying material for ambient MS research when needed. Similarly, genetic preservation of patient tissue seen in some organoid models, further verified by qualitative proteomic and transcriptomic analyses, may argue in favor of organoid suitability for certain ambient profiling studies. However, to choose the appropriate model, pre-evaluation of the model’s molecular characteristics in the context of the research question(s) being asked will likely provide the most appropriate strategy to move research forward. This can be achieved by performing comparative ambient MS analysis of the disease model of choice against a small amount of patient tissue to verify concordance. Disease models, however, will continue to be useful tools to orthogonally validate metabolic states of patient tissues through controlled genetic alterations that are not possible with patient specimens.

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References

  1. Feider CL, Krieger A, DeHoog RJ, Eberlin LS. Ambient ionization mass spectrometry: recent developments and applications. Anal Chem. 2019;91(7):4266–90. https://doi.org/10.1021/acs.analchem.9b00807.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Takats Z, Strittmatter N, McKenzie JS. Ambient mass spectrometry in cancer research. Adv Cancer Res. 2017;134:231–56. https://doi.org/10.1016/bs.acr.2016.11.011.

    Article  CAS  PubMed  Google Scholar 

  3. Santos CR, Schulze A. Lipid metabolism in cancer. FEBS J. 2012;279(15):2610–23. https://doi.org/10.1111/j.1742-4658.2012.08644.x.

    Article  CAS  PubMed  Google Scholar 

  4. Gredell DA, Schroeder AR, Belk KE, Broeckling CD, Heuberger AL, Kim SY, et al. Comparison of machine learning algorithms for predictive modeling of beef attributes using rapid evaporative ionization mass spectrometry (REIMS) data. Sci Rep. 2019;9(1):5721. https://doi.org/10.1038/s41598-019-40927-6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Huang YC, Chung HH, Dutkiewicz EP, Chen CL, Hsieh HY, Chen BR, et al. Predicting breast cancer by paper spray ion mobility spectrometry mass spectrometry and machine learning. Anal Chem. 2020;92(2):1653–7. https://doi.org/10.1021/acs.analchem.9b03966.

    Article  CAS  PubMed  Google Scholar 

  6. Mirabelli P, Coppola L, Salvatore M. Cancer cell lines are useful model systems for medical research. Cancers (Basel). 2019;11(8). https://doi.org/10.3390/cancers11081098.

  7. Lv D, Hu Z, Lu L, Lu H, Xu X. Three-dimensional cell culture: a powerful tool in tumor research and drug discovery. Oncol Lett. 2017;14(6):6999–7010. https://doi.org/10.3892/ol.2017.7134.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Uthamanthil R, Tinkey P, De Stanchina E. Patient derived tumor xenograft models : promise, potential and practice. Amsterdam: Elsevier/AP, Academic Press is an imprint of Elsevier; 2017.

    Google Scholar 

  9. Ifa DR, Eberlin LS. Ambient ionization mass spectrometry for cancer diagnosis and surgical margin evaluation. Clin Chem. 2016;62(1):111–23. https://doi.org/10.1373/clinchem.2014.237172.

    Article  CAS  PubMed  Google Scholar 

  10. Ben-David U, Beroukhim R, Golub TR. Genomic evolution of cancer models: perils and opportunities. Nat Rev Cancer. 2019;19(2):97–109. https://doi.org/10.1038/s41568-018-0095-3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Mead BE, Karp JM. All models are wrong, but some organoids may be useful. Genome Biol. 2019;20(66):3. https://doi.org/10.1186/s13059-019-1677-4.

    Article  Google Scholar 

  12. Vargo-Gogola T, Rosen JM. Modelling breast cancer: one size does not fit all. Nat Rev Cancer. 2007;7(9):659–72. https://doi.org/10.1038/nrc2193.

    Article  CAS  PubMed  Google Scholar 

  13. Garcia PL, Miller AL, Yoon KJ. Patient-derived xenograft models of pancreatic cancer: overview and comparison with other types of models. Cancers (Basel). 2020;12(5). https://doi.org/10.3390/cancers12051327.

  14. Holliday DL, Speirs V. Choosing the right cell line for breast cancer research. Breast Cancer Res. 2011;13(4):215. https://doi.org/10.1186/bcr2889.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Lai Y, Wei X, Lin S, Qin L, Cheng L, Li P. Current status and perspectives of patient-derived xenograft models in cancer research. J Hematol Oncol. 2017;10(1):106. https://doi.org/10.1186/s13045-017-0470-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Abu-Rabie P, Sheelan D, Laures A, Spaull J, Dowell S. Increasing the discrimination power of rapid evaporative ionisation mass spectrometry (REIMS) in analytical control tissue quality screening and cell line sample identification. Rapid Commun Mass Spectrom. 2019. https://doi.org/10.1002/rcm.8525.

  17. Clendinen CS, Monge ME, Fernandez FM. Ambient mass spectrometry in metabolomics. Analyst. 2017;142(17):3101–17. https://doi.org/10.1039/c7an00700k.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Strittmatter N, Lovrics A, Sessler J, McKenzie JS, Bodai Z, Doria ML, et al. Shotgun lipidomic profiling of the NCI60 cell line panel using rapid evaporative ionization mass spectrometry. Anal Chem. 2016;88(15):7507–14. https://doi.org/10.1021/acs.analchem.6b00187.

    Article  CAS  PubMed  Google Scholar 

  19. Landry JJ, Pyl PT, Rausch T, Zichner T, Tekkedil MM, Stutz AM, et al. The genomic and transcriptomic landscape of a HeLa cell line. G3 (Bethesda). 2013;3(8):1213–24. https://doi.org/10.1534/g3.113.005777.

    Article  CAS  Google Scholar 

  20. Frattini A, Fabbri M, Valli R, De Paoli E, Montalbano G, Gribaldo L, et al. High variability of genomic instability and gene expression profiling in different HeLa clones. Sci Rep. 2015;5:15377. https://doi.org/10.1038/srep15377.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Pastor DM, Poritz LS, Olson TL, Kline CL, Harris LR, Koltun WA, et al. Primary cell lines: false representation or model system? A comparison of four human colorectal tumors and their coordinately established cell lines. Int J Clin Exp Med. 2010;3(1):69–83.

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Pamies D, Bal-Price A, Chesne C, Coecke S, Dinnyes A, Eskes C, et al. Advanced Good Cell Culture Practice for human primary, stem cell-derived and organoid models as well as microphysiological systems. ALTEX. 2018;35(3):353–78. https://doi.org/10.14573/altex.1710081.

    Article  PubMed  Google Scholar 

  23. Kaur G, Dufour JM. Cell lines: valuable tools or useless artifacts. Spermatogenesis. 2012;2(1):1–5. https://doi.org/10.4161/spmg.19885.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Greaves M, Maley CC. Clonal evolution in cancer. Nature. 2012;481(7381):306–13. https://doi.org/10.1038/nature10762.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Gupta RG, Somer RA. Intratumor heterogeneity: novel approaches for resolving genomic architecture and clonal evolution. Mol Cancer Res. 2017;15(9):1127–37. https://doi.org/10.1158/1541-7786.MCR-17-0070.

    Article  CAS  PubMed  Google Scholar 

  26. Carmona-Fontaine C, Deforet M, Akkari L, Thompson CB, Joyce JA, Xavier JB. Metabolic origins of spatial organization in the tumor microenvironment. Proc Natl Acad Sci U S A. 2017;114(11):2934–9. https://doi.org/10.1073/pnas.1700600114.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Li H, Ning S, Ghandi M, Kryukov GV, Gopal S, Deik A, et al. The landscape of cancer cell line metabolism. Nat Med. 2019;25(5):850–60. https://doi.org/10.1038/s41591-019-0404-8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Holzlechner M, Eugenin E, Prideaux B. Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer. Cancer Rep (Hoboken). 2019;2(6):e1229. https://doi.org/10.1002/cnr2.1229.

    Article  Google Scholar 

  29. Chik JH, Zhou J, Moh ES, Christopherson R, Clarke SJ, Molloy MP, et al. Comprehensive glycomics comparison between colon cancer cell cultures and tumours: implications for biomarker studies. J Proteome. 2014;108:146–62. https://doi.org/10.1016/j.jprot.2014.05.002.

    Article  CAS  Google Scholar 

  30. Sans M, Gharpure K, Tibshirani R, Zhang J, Liang L, Liu J, et al. Metabolic markers and statistical prediction of serous ovarian cancer aggressiveness by ambient ionization mass spectrometry imaging. Cancer Res. 2017;77(11):2903–13. https://doi.org/10.1158/0008-5472.CAN-16-3044.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Wang M, Zhao J, Zhang L, Wei F, Lian Y, Wu Y, et al. Role of tumor microenvironment in tumorigenesis. J Cancer. 2017;8(5):761–73. https://doi.org/10.7150/jca.17648.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ramamonjisoa N, Ackerstaff E. Characterization of the tumor microenvironment and tumor-stroma interaction by non-invasive preclinical imaging. Front Oncol. 2017;7:3. https://doi.org/10.3389/fonc.2017.00003.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Giatromanolaki A, Koukourakis MI, Koutsopoulos A, Mendrinos S, Sivridis E. The metabolic interactions between tumor cells and tumor-associated stroma (TAS) in prostatic cancer. Cancer Biol Ther. 2012;13(13):1284–9. https://doi.org/10.4161/cbt.21785.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Icard P, Kafara P, Steyaert JM, Schwartz L, Lincet H. The metabolic cooperation between cells in solid cancer tumors. Biochim Biophys Acta. 2014;1846(1):216–25. https://doi.org/10.1016/j.bbcan.2014.06.002.

    Article  CAS  PubMed  Google Scholar 

  35. Nieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR, et al. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat Med. 2011;17(11):1498–503. https://doi.org/10.1038/nm.2492.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Mori N, Wildes F, Takagi T, Glunde K, Bhujwalla ZM. The tumor microenvironment modulates choline and lipid metabolism. Front Oncol. 2016;6:262. https://doi.org/10.3389/fonc.2016.00262.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Woolman M, Ferry I, Kuzan-Fischer CM, Wu M, Zou J, Kiyota T, et al. Rapid determination of medulloblastoma subgroup affiliation with mass spectrometry using a handheld picosecond infrared laser desorption probe. Chem Sci. 2017;8(9):6508–19. https://doi.org/10.1039/c7sc01974b.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Rangarajan A, Weinberg RA. Opinion: comparative biology of mouse versus human cells: modelling human cancer in mice. Nat Rev Cancer. 2003;3(12):952–9. https://doi.org/10.1038/nrc1235.

    Article  CAS  PubMed  Google Scholar 

  39. Orimo A, Gupta PB, Sgroi DC, Arenzana-Seisdedos F, Delaunay T, Naeem R, et al. Stromal fibroblasts present in invasive human breast carcinomas promote tumor growth and angiogenesis through elevated SDF-1/CXCL12 secretion. Cell. 2005;121(3):335–48. https://doi.org/10.1016/j.cell.2005.02.034.

    Article  CAS  PubMed  Google Scholar 

  40. Woolman M, Kuzan-Fischer CM, Ferry I, Kiyota T, Luu B, Wu M, et al. Picosecond infrared laser desorption mass spectrometry identifies medulloblastoma subgroups on intrasurgical timescales. Cancer Res. 2019;79(9):2426–34. https://doi.org/10.1158/0008-5472.CAN-18-3411.

    Article  CAS  PubMed  Google Scholar 

  41. Aichler M, Kunzke T, Buck A, Sun N, Ackermann M, Jonigk D, et al. Molecular similarities and differences from human pulmonary fibrosis and corresponding mouse model: MALDI imaging mass spectrometry in comparative medicine. Lab Investig. 2018;98(1):141–9. https://doi.org/10.1038/labinvest.2017.110.

    Article  CAS  PubMed  Google Scholar 

  42. Gock M, Kuhn F, Mullins CS, Krohn M, Prall F, Klar E, et al. Tumor take rate optimization for colorectal carcinoma patient-derived xenograft models. Biomed Res Int. 2016;2016:1715053. https://doi.org/10.1155/2016/1715053.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Wang D, Li JR, Zhang YH, Chen L, Huang T, Cai YD. Identification of differentially expressed genes between original breast cancer and xenograft using machine learning algorithms. Genes (Basel). 2018;9(3). https://doi.org/10.3390/genes9030155.

  44. Yoshida GJ. Applications of patient-derived tumor xenograft models and tumor organoids. J Hematol Oncol. 2020;13(1):4. https://doi.org/10.1186/s13045-019-0829-z.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Schneeberger VE, Allaj V, Gardner EE, Poirier JT, Rudin CM. Quantitation of murine stroma and selective purification of the human tumor component of patient-derived xenografts for genomic analysis. PLoS One. 2016;11(9):e0160587. https://doi.org/10.1371/journal.pone.0160587.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Blomme A, Van Simaeys G, Doumont G, Costanza B, Bellier J, Otaka Y, et al. Murine stroma adopts a human-like metabolic phenotype in the PDX model of colorectal cancer and liver metastases. Oncogene. 2018;37(9):1237–50. https://doi.org/10.1038/s41388-017-0018-x.

    Article  CAS  PubMed  Google Scholar 

  47. Stewart E, Federico SM, Chen X, Shelat AA, Bradley C, Gordon B, et al. Orthotopic patient-derived xenografts of paediatric solid tumours. Nature. 2017;549(7670):96–100. https://doi.org/10.1038/nature23647.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ricci F, Guffanti F, Affatato R, Brunelli L, Roberta P, Fruscio R, et al. Establishment of patient-derived tumor xenograft models of mucinous ovarian cancer. Am J Cancer Res. 2020;10(2):572–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Jun E, Hong SM, Yoo HJ, Kim MB, Won JS, An S, et al. Genetic and metabolic comparison of orthotopic and heterotopic patient-derived pancreatic-cancer xenografts to the original patient tumors. Oncotarget. 2018;9(8):7867–81. https://doi.org/10.18632/oncotarget.23567.

    Article  PubMed  Google Scholar 

  50. Sharick JT, Walsh CM, Sprackling CM, Pasch CA, Pham DL, Esbona K, et al. Metabolic heterogeneity in patient tumor-derived organoids by primary site and drug treatment. Front Oncol. 2020;10:553. https://doi.org/10.3389/fonc.2020.00553.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Vlachogiannis G, Hedayat S, Vatsiou A, Jamin Y, Fernandez-Mateos J, Khan K, et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science. 2018;359(6378):920–6. https://doi.org/10.1126/science.aao2774.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Cristobal A, van den Toorn HWP, van de Wetering M, Clevers H, Heck AJR, Mohammed S. Personalized proteome profiles of healthy and tumor human colon organoids reveal both individual diversity and basic features of colorectal cancer. Cell Rep. 2017;18(1):263–74. https://doi.org/10.1016/j.celrep.2016.12.016.

    Article  CAS  PubMed  Google Scholar 

  53. Weeber F, van de Wetering M, Hoogstraat M, Dijkstra KK, Krijgsman O, Kuilman T, et al. Preserved genetic diversity in organoids cultured from biopsies of human colorectal cancer metastases. Proc Natl Acad Sci U S A. 2015;112(43):13308–11. https://doi.org/10.1073/pnas.1516689112.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Boj SF, Hwang CI, Baker LA, Chio II, Engle DD, Corbo V, et al. Organoid models of human and mouse ductal pancreatic cancer. Cell. 2015;160(1–2):324–38. https://doi.org/10.1016/j.cell.2014.12.021.

    Article  CAS  PubMed  Google Scholar 

  55. Gonneaud A, Jones C, Turgeon N, Levesque D, Asselin C, Boudreau F, et al. A SILAC-based method for quantitative proteomic analysis of intestinal organoids. Sci Rep. 2016;6:38195. https://doi.org/10.1038/srep38195.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Gonneaud A, Asselin C, Boudreau F, Boisvert FM. Phenotypic analysis of organoids by proteomics. Proteomics. 2017;17(20). https://doi.org/10.1002/pmic.201700023.

  57. Ehmsen S, Pedersen MH, Wang G, Terp MG, Arslanagic A, Hood BL, et al. Increased cholesterol biosynthesis is a key characteristic of breast cancer stem cells influencing patient outcome. Cell Rep. 2019;27(13):3927–38 e6. https://doi.org/10.1016/j.celrep.2019.05.104.

    Article  CAS  PubMed  Google Scholar 

  58. Liu X, Flinders C, Mumenthaler SM, Hummon AB. MALDI mass spectrometry imaging for evaluation of therapeutics in colorectal tumor organoids. J Am Soc Mass Spectrom. 2018;29(3):516–26. https://doi.org/10.1007/s13361-017-1851-4.

    Article  CAS  PubMed  Google Scholar 

  59. Henderson F, Jones E, Denbigh J, Christie L, Chapman R, Hoyes E, et al. 3D DESI-MS lipid imaging in a xenograft model of glioblastoma: a proof of principle. Sci Rep. 2020;10(1):16512. https://doi.org/10.1038/s41598-020-73518-x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Williams KE, Lemieux GA, Hassis ME, Olshen AB, Fisher SJ, Werb Z. Quantitative proteomic analyses of mammary organoids reveals distinct signatures after exposure to environmental chemicals. Proc Natl Acad Sci U S A. 2016;113(10):E1343–51. https://doi.org/10.1073/pnas.1600645113.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Moss JI, Barjat H, Emmas SA, Strittmatter N, Maynard J, Goodwin RJA, et al. High-resolution 3D visualization of nanomedicine distribution in tumors. Theranostics. 2020;10(2):880–97. https://doi.org/10.7150/thno.37178.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Santagata S, Eberlin LS, Norton I, Calligaris D, Feldman DR, Ide JL, et al. Intraoperative mass spectrometry mapping of an onco-metabolite to guide brain tumor surgery. Proc Natl Acad Sci U S A. 2014;111(30):11121–6. https://doi.org/10.1073/pnas.1404724111.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Koundouros N, Karali E, Tripp A, Valle A, Inglese P, Perry NJS, et al. Metabolic fingerprinting links oncogenic PIK3CA with enhanced arachidonic acid-derived eicosanoids. Cell. 2020;181(7):1596–611 e27. https://doi.org/10.1016/j.cell.2020.05.053.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Techna Institute for the Advancement of Technology for Health, University Health Network, 100 College Street, Toronto, ON, M5G 1P5, Canada

    Lauren Katz, Michael Woolman & Arash Zarrine-Afsar

  2. Department of Medical Biophysics, University of Toronto, 101 College Street, Toronto, ON, M5G 1L7, Canada

    Lauren Katz, Michael Woolman & Arash Zarrine-Afsar

  3. Laboratorio di Chimica Sperimentale, Istituto Zooprofilattico delle Venezie, Viale Fiume 78, 36100, Vicenza, Italy

    Alessandra Tata

  4. Department of Surgery, University of Toronto, 149 College Street, Toronto, ON, M5T 1P5, Canada

    Arash Zarrine-Afsar

  5. Keenan Research Center for Biomedical Science & the Li Ka Shing Knowledge Institute, St. Michael’s Hospital, 30 Bond Street, Toronto, ON, M5B 1W8, Canada

    Arash Zarrine-Afsar

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  1. Lauren Katz
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Correspondence to Arash Zarrine-Afsar.

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Conflict of interest

Mass spectrometry research in the Zarrine-Afsar group is currently supported by grants from Princess Margaret Hospital Foundation, Natural Sciences and Engineering Research Council of Canada and Canadian Institutes of Health Research.

Arash Zarrine-Afsar is a consultant with Point Surgical Inc.

Arash Zarrine-Afsar and Michael Woolman are co-inventors of soft ionization for PIRL-MS analysis.

Michael Woolman is supported by a graduate scholarship from the Natural Sciences and Engineering Research Council of Canada.

Lauren Katz is supported by the Ontario Graduate Scholarship program.

Alessandra Tata has no conflicts to declare.

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Published in the topical collection Mass Spectrometry Imaging 2.0 with guest editors Shane R. Ellis and Tiffany Porta Siegel.

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Katz, L., Woolman, M., Tata, A. et al. Potential impact of tissue molecular heterogeneity on ambient mass spectrometry profiles: a note of caution in choosing the right disease model. Anal Bioanal Chem 413, 2655–2664 (2021). https://doi.org/10.1007/s00216-020-03054-0

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