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Breast Cancer Research and Treatment

, Volume 135, Issue 3, pp 913–922 | Cite as

Xenografts faithfully recapitulate breast cancer-specific gene expression patterns of parent primary breast tumors

  • Laura A. Petrillo
  • Denise M. Wolf
  • Ann M. Kapoun
  • Nicholas J. Wang
  • Andrea Barczak
  • Yuanyuan Xiao
  • Hasan Korkaya
  • Frederick Baehner
  • John Lewicki
  • Max Wicha
  • John W. Park
  • Paul T. Spellman
  • Joe W. Gray
  • Laura van’t Veer
  • Laura J. Esserman
Brief Report

Abstract

Though xenografts are used extensively for drug development in breast cancer, how well xenografts reflect the breadth of primary breast tumor subtypes has not been well characterized. Moreover, few studies have compared the gene expression of xenograft tumors to the primary tumors from which they were derived. Here we investigate whether the ability of human breast tumors (n = 20) to create xenografts in immune-deficient mice is associated with breast cancer immunohistochemical (IHC) and intrinsic subtype. We also characterize how precisely the gene expression of xenografts reprises that of parent breast tumors, using hierarchical clustering and other correlation-based techniques applied to Agilent 44K gene expression data from 16 samples including four matched primary tumor-xenograft pairs. Of the breast tumors studied, 25 % (5/20) generated xenografts. Receptor and intrinsic subtype were significant predictors of xenograft success, with all (4/4) triple-negative (TN) tumors and no (0/12) HR+Her2− tumors forming xenografts (P = 0.0005). Tumor cell expression of ALDH1, a stem cell marker, trended toward successful engraftment (P = 0.14), though CDK5/6, a basal marker, did not. Though hierarchical clustering across the 500 most variable genes segregated human breast tumors from xenograft tumors, when clustering was performed over the PAM50 gene set the primary tumor-xenograft pairs clustered together, with all IHC subtypes clustered in distinct groups. Greater similarity between primary tumor-xenograft pairs relative to random pairings was confirmed by calculation of the within-pair between-pair scatter ratio (WPBPSR) distribution (P = 0.0269), though there was a shift in the xenografts toward more aggressive features including higher proliferation scores relative to the primary. Triple-negative breast tumors demonstrate superior ability to create xenografts compared to HR+ tumors, which may reflect higher proliferation or relatively stroma-independent growth of this subtype. Xenograft tumors’ gene expression faithfully resembles that of their parent tumors, yet also demonstrates a shift toward more aggressive molecular features.

Keywords

Mouse model Breast cancer Xenograft Receptor subtype Intrinsic subtype ALDH1 CDK5/6 PAM50 

Notes

Acknowledgments

The authors would like to thank the patients who participated in the study. We would like to thank Angie Park for her xenograft development work at OncoMed. This work was supported by funding from OncoMed Pharmaceuticals, Inc. the National Cancer Institute Specialized Program of Research Excellence in Breast Cancer, the Doris Duke Charitable Foundation and the NIH/NCRR/OD UCSF-CTSI Grant Number TL1 RR024129. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

Conflicts of interest

Ann Kapoun and John Lewicki are employees and stock holders of OncoMed Pharmaceuticals, Inc.

Supplementary material

10549_2012_2226_MOESM1_ESM.xls (32 kb)
Supplementary material 1 (XLS 32 kb)

References

  1. 1.
    Giovanella B, Stehlin J, Williams L, Shih-Shun L, Shepard R (1978) Heterotransplantation of human cancers into nude mice. Cancer 42:2269–2281PubMedCrossRefGoogle Scholar
  2. 2.
    Steel GG, Courtenay VD, Peckham MJ (1983) The response to chemotherapy of a variety of human tumour xenografts. Br J Cancer 47:001–013CrossRefGoogle Scholar
  3. 3.
    Rae-Venter B, Reid LM (1980) Growth of human breast carcinomas in nude mice and subsequent establishment in tissue culture. Cancer Res 40:95–100PubMedGoogle Scholar
  4. 4.
    Mehta R, Graves J, Hart G, Shilkaitis A, Das Gupta T (1993) Growth and metastasis of human breast carcinomas with Matrigel in athymic mice. Breast Cancer Res Treat 25:65–71PubMedCrossRefGoogle Scholar
  5. 5.
    Clarke R (1996) Human breast cancer cell line xenografts as models of breast cancer—the immunobiologies of recipient mice and the characteristics of several tumorigenic cell lines. Breast Cancer Res Treat 39:69–86PubMedCrossRefGoogle Scholar
  6. 6.
    Pegram M, Ngo D (2006) Application and potential limitations of animal models utilized in the development of trastuzumab (Herceptin®): a case study. Adv Drug Deliv Rev 58(5–6):723–734PubMedCrossRefGoogle Scholar
  7. 7.
    Keller PJ et al (2010) Mapping the cellular and molecular heterogeneity of normal and malignant breast tissues and cultured cell lines. Breast Cancer Res 12(5):R87PubMedCrossRefGoogle Scholar
  8. 8.
    Reyal F et al (2012) Molecular profiling of patient-derived breast cancer xenografts. Breast Cancer Res 14(1):R11PubMedCrossRefGoogle Scholar
  9. 9.
    Valdez KE et al (2011) Human primary ductal carcinoma in situ (DCIS) subtype-specific pathology is preserved in a mouse intraductal (MIND) xenograft model. J Pathol 225(4):565–573PubMedCrossRefGoogle Scholar
  10. 10.
    Moestue SA et al (2010) Distinct choline metabolic profiles are associated with differences in gene expression for basal-like and luminal-like breast cancer xenograft models. BMC Cancer 10:433PubMedCrossRefGoogle Scholar
  11. 11.
    Bergamaschi A et al (2009) Molecular profiling and characterization of luminal-like and basal-like in vivo breast cancer xenograft models. Mol Oncol 3(5–6):469–482PubMedCrossRefGoogle Scholar
  12. 12.
    Ding L et al (2010) Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 464(7291):999–1005PubMedCrossRefGoogle Scholar
  13. 13.
    Foulkes WD, Smith IE, Reis-Filho JS (2010) Triple negative breast cancer. N Engl J Med 363(20):1938–1948PubMedCrossRefGoogle Scholar
  14. 14.
    Perou CM et al (2000) Molecular portraits of human breast tumours. Nature 406(6797):747–752PubMedCrossRefGoogle Scholar
  15. 15.
    Resetkova E, Reis-Filho J, Jain RK, Mehta R, Thorat MA, Nakshatri H, Badve S (2010) Prognostic impact of ALDH1 in breast cancer: a story of stem cells and tumor microenvironment. Breast Cancer Res Treat 123(1):97–108PubMedCrossRefGoogle Scholar
  16. 16.
    Ginestier C, Hur M, Charafe-Jauffret E, Monville F, Dutcher J, Brown M, Jacquemier J, Viens P, Kleer CG, Liu S, Schott A, Hayes D, Birnbaum D, Wicha MS, Dontu G (2007) ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell Stem Cell 1:555–567PubMedCrossRefGoogle Scholar
  17. 17.
    Dalerba P, Dylla SJ, Park I, Liu R, Wang X, Cho R, Hoey T, Gurney A, Huang E, Simeone D, Shelton A, Parmiani G, Castelli C, Clarke M (2007) Phenotypic characterization of human colorectal cancer stem cells. Proc Natl Acad Sci 104(24):10158–10163PubMedCrossRefGoogle Scholar
  18. 18.
    Bolstad BM et al (2003) A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19(2):185–193PubMedCrossRefGoogle Scholar
  19. 19.
    Smyth GK (2005) Limma: linear models for microarray data. In: Gentleman R et al (eds) Bioinformatics and computational biology solutions using R and bioconductor. Springer, New YorkGoogle Scholar
  20. 20.
    Parker JS, Michael M, Cheang MC, Leung S, Voduc D, Vickery T, Davies S, Fauron C, He X, Hu Z, Quackenbush JF, Stijleman IJ, Palazzo J, Marron JS, Nobel AB, Mardis E, Nielsen TO, Ellis MJ, Perou CM, Bernard PS (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27(8):1160–1167PubMedCrossRefGoogle Scholar
  21. 21.
    Seber GAF (1984) Multivariate observations. John Wiley & Sons, Hoboken, NJCrossRefGoogle Scholar
  22. 22.
    Weigelt B, Hu Z, He X, Livasy C, Carey LA, Ewend MG, Glas AM, Perou CM, Van’t Veer LJ (2005) Molecular portraits and 70-gene prognosis signature are preserved throughout the metastatic process of breast cancer. Cancer Res 65(20):9155–9158PubMedCrossRefGoogle Scholar
  23. 23.
    Lonnstedt I, Speed T (2002) Replicated microarray data. Stat Sin 12(1):31–46Google Scholar
  24. 24.
    Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protocol 4(1):44–57CrossRefGoogle Scholar
  25. 25.
    Marangoni E et al (2007) A new model of patient tumor-derived breast cancer xenografts for preclinical assays. Clin Cancer Res 13(13):3989–3998PubMedCrossRefGoogle Scholar
  26. 26.
    de Plater L et al (2010) Establishment and characterisation of a new breast cancer xenograft obtained from a woman carrying a germline BRCA2 mutation. Br J Cancer 103(8):1192–1200PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC. 2012

Authors and Affiliations

  • Laura A. Petrillo
    • 1
  • Denise M. Wolf
    • 2
  • Ann M. Kapoun
    • 3
  • Nicholas J. Wang
    • 6
  • Andrea Barczak
    • 7
  • Yuanyuan Xiao
    • 7
  • Hasan Korkaya
    • 5
  • Frederick Baehner
    • 4
  • John Lewicki
    • 3
  • Max Wicha
    • 5
  • John W. Park
    • 2
  • Paul T. Spellman
    • 6
  • Joe W. Gray
    • 6
  • Laura van’t Veer
    • 2
  • Laura J. Esserman
    • 8
  1. 1.Department of MedicineUniversity of CaliforniaSan FranciscoUSA
  2. 2.Department of Laboratory MedicineUniversity of CaliforniaSan FranciscoUSA
  3. 3.OncoMed Pharmaceuticals, Inc.Redwood CityUSA
  4. 4.Department of PathologyUniversity of CaliforniaSan FranciscoUSA
  5. 5.Department of Internal MedicineUniversity of MichiganAnn ArborUSA
  6. 6.Oregon Health & Science UniversityPortlandUSA
  7. 7.Functional Genomics CoreUniversity of CaliforniaSan FranciscoUSA
  8. 8.Department of SurgeryUniversity of CaliforniaSan FranciscoUSA

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