Patient-derived xenografts undergo mouse-specific tumor evolution

Subjects

Abstract

Patient-derived xenografts (PDXs) have become a prominent cancer model system, as they are presumed to faithfully represent the genomic features of primary tumors. Here we monitored the dynamics of copy number alterations (CNAs) in 1,110 PDX samples across 24 cancer types. We observed rapid accumulation of CNAs during PDX passaging, often due to selection of preexisting minor clones. CNA acquisition in PDXs was correlated with the tissue-specific levels of aneuploidy and genetic heterogeneity observed in primary tumors. However, the particular CNAs acquired during PDX passaging differed from those acquired during tumor evolution in patients. Several CNAs recurrently observed in primary tumors gradually disappeared in PDXs, indicating that events undergoing positive selection in humans can become dispensable during propagation in mice. Notably, the genomic stability of PDXs was associated with their response to chemotherapy and targeted drugs. These findings have major implications for PDX-based modeling of human cancer.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: The landscape of aneuploidy and copy number alterations in PDXs.
Figure 2: Selection of preexisting subclones underlies CNA dynamics.
Figure 3: The genomic instability of PDXs mirrors that of primary tumors.
Figure 4: Tumor evolution of PDXs diverges from that of primary tumors.
Figure 5: The genomic instability of PDXs is comparable to that of cell lines and CLDXs.
Figure 6: CNA dynamics affect PDX drug response.

Accession codes

Accessions

Gene Expression Omnibus

References

  1. 1

    Tentler, J.J. et al. Patient-derived tumour xenografts as models for oncology drug development. Nat. Rev. Clin. Oncol. 9, 338–350 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Siolas, D. & Hannon, G.J. Patient-derived tumor xenografts: transforming clinical samples into mouse models. Cancer Res. 73, 5315–5319 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

    Gao, H. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat. Med. 21, 1318–1325 (2015).

    CAS  PubMed  Google Scholar 

  4. 4

    Hidalgo, M. et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 4, 998–1013 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Zhang, X. et al. A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models. Cancer Res. 73, 4885–4897 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Hodgson, J.G. et al. Comparative analyses of gene copy number and mRNA expression in glioblastoma multiforme tumors and xenografts. Neuro-oncol. 11, 477–487 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Eirew, P. et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015).

    CAS  Google Scholar 

  8. 8

    Bruna, A. et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell 167, 260–274.e22 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Kresse, S.H., Meza-Zepeda, L.A., Machado, I., Llombart-Bosch, A. & Myklebost, O. Preclinical xenograft models of human sarcoma show nonrandom loss of aberrations. Cancer 118, 558–570 (2012).

    PubMed  Google Scholar 

  10. 10

    DeRose, Y.S. et al. Tumor grafts derived from women with breast cancer authentically reflect tumor pathology, growth, metastasis and disease outcomes. Nat. Med. 17, 1514–1520 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Lin, D. et al. High fidelity patient-derived xenografts for accelerating prostate cancer discovery and drug development. Cancer Res. 74, 1272–1283 (2014).

    CAS  PubMed  Google Scholar 

  12. 12

    Huynh, H. et al. Bevacizumab and rapamycin induce growth suppression in mouse models of hepatocellular carcinoma. J. Hepatol. 49, 52–60 (2008).

    CAS  PubMed  Google Scholar 

  13. 13

    Zhang, X. et al. SRRM4 expression and the loss of REST activity may promote the emergence of the neuroendocrine phenotype in castration-resistant prostate cancer. Clin. Cancer Res. 21, 4698–4708 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Oakes, S.R. et al. Sensitization of BCL-2-expressing breast tumors to chemotherapy by the BH3 mimetic ABT-737. Proc. Natl. Acad. Sci. USA 109, 2766–2771 (2012).

    CAS  PubMed  Google Scholar 

  15. 15

    Jäger, W. et al. Patient-derived bladder cancer xenografts in the preclinical development of novel targeted therapies. Oncotarget 6, 21522–21532 (2015).

    PubMed  PubMed Central  Google Scholar 

  16. 16

    Monsma, D.J. et al. Using a rhabdomyosarcoma patient-derived xenograft to examine precision medicine approaches and model acquired resistance. Pediatr. Blood Cancer 61, 1570–1577 (2014).

    CAS  PubMed  Google Scholar 

  17. 17

    Wong, N.C. et al. Stability of gene expression and epigenetic profiles highlights the utility of patient-derived paediatric acute lymphoblastic leukaemia xenografts for investigating molecular mechanisms of drug resistance. BMC Genomics 15, 416 (2014).

    PubMed  PubMed Central  Google Scholar 

  18. 18

    Gu, Q. et al. Genomic characterization of a large panel of patient-derived hepatocellular carcinoma xenograft tumor models for preclinical development. Oncotarget 6, 20160–20176 (2015).

    PubMed  PubMed Central  Google Scholar 

  19. 19

    Smith, K.B. et al. Novel dedifferentiated liposarcoma xenograft models reveal PTEN down-regulation as a malignant signature and response to PI3K pathway inhibition. Am. J. Pathol. 182, 1400–1411 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Ma, C.X. et al. Targeting Chk1 in p53-deficient triple-negative breast cancer is therapeutically beneficial in human-in-mouse tumor models. J. Clin. Invest. 122, 1541–1552 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Morisot, S. et al. High frequencies of leukemia stem cells in poor-outcome childhood precursor-B acute lymphoblastic leukemias. Leukemia 24, 1859–1866 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Peng, S. et al. Tumor grafts derived from patients with head and neck squamous carcinoma authentically maintain the molecular and histologic characteristics of human cancers. J. Transl. Med. 11, 198 (2013).

    PubMed  PubMed Central  Google Scholar 

  23. 23

    Chou, J. et al. Phenotypic and transcriptional fidelity of patient-derived colon cancer xenografts in immune-deficient mice. PLoS One 8, e79874 (2013).

    PubMed  PubMed Central  Google Scholar 

  24. 24

    Ben-David, U., Mayshar, Y. & Benvenisty, N. Virtual karyotyping of pluripotent stem cells on the basis of their global gene expression profiles. Nat. Protoc. 8, 989–997 (2013).

    PubMed  Google Scholar 

  25. 25

    Ben-David, U. et al. The landscape of chromosomal aberrations in breast cancer mouse models reveals driver-specific routes to tumorigenesis. Nat. Commun. 7, 12160 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Fehrmann, R.S. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Zack, T.I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Carter, S.L., Eklund, A.C., Kohane, I.S., Harris, L.N. & Szallasi, Z. A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers. Nat. Genet. 38, 1043–1048 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Clappier, E. et al. Clonal selection in xenografted human T cell acute lymphoblastic leukemia recapitulates gain of malignancy at relapse. J. Exp. Med. 208, 653–661 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Andor, N. et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat. Med. 22, 105–113 (2016).

    CAS  PubMed  Google Scholar 

  31. 31

    Bakker, B. et al. Single-cell sequencing reveals karyotype heterogeneity in murine and human malignancies. Genome Biol. 17, 115 (2016).

    PubMed  PubMed Central  Google Scholar 

  32. 32

    Mroz, E.A., Tward, A.D., Hammon, R.J., Ren, Y. & Rocco, J.W. Intra-tumor genetic heterogeneity and mortality in head and neck cancer: analysis of data from the Cancer Genome Atlas. PLoS Med. 12, e1001786 (2015).

    PubMed  PubMed Central  Google Scholar 

  33. 33

    Gibson, W.J. et al. The genomic landscape and evolution of endometrial carcinoma progression and abdominopelvic metastasis. Nat. Genet. 48, 848–855 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Kim, T.M. et al. Subclonal genomic architectures of primary and metastatic colorectal cancer based on intratumoral genetic heterogeneity. Clin. Cancer Res. 21, 4461–4472 (2015).

    CAS  PubMed  Google Scholar 

  35. 35

    Bai, H. et al. Integrated genomic characterization of IDH1-mutant glioma malignant progression. Nat. Genet. 48, 59–66 (2016).

    CAS  PubMed  Google Scholar 

  36. 36

    Hedberg, M.L. et al. Genetic landscape of metastatic and recurrent head and neck squamous cell carcinoma. J. Clin. Invest. 126, 1606 (2016).

    PubMed  PubMed Central  Google Scholar 

  37. 37

    Um, S.W. et al. Molecular evolution patterns in metastatic lymph nodes reflect the differential treatment response of advanced primary lung cancer. Cancer Res. 76, 6568–6576 (2016).

    CAS  PubMed  Google Scholar 

  38. 38

    Sveen, A. et al. Intra-patient inter-metastatic genetic heterogeneity in colorectal cancer as a key determinant of survival after curative liver resection. PLoS Genet. 12, e1006225 (2016).

    PubMed  PubMed Central  Google Scholar 

  39. 39

    Lee, S.Y. et al. Comparative genomic analysis of primary and synchronous metastatic colorectal cancers. PLoS One 9, e90459 (2014).

    PubMed  PubMed Central  Google Scholar 

  40. 40

    Lim, B. et al. Genome-wide mutation profiles of colorectal tumors and associated liver metastases at the exome and transcriptome levels. Oncotarget 6, 22179–22190 (2015).

    PubMed  PubMed Central  Google Scholar 

  41. 41

    Daniel, V.C. et al. A primary xenograft model of small-cell lung cancer reveals irreversible changes in gene expression imposed by culture in vitro. Cancer Res. 69, 3364–3373 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Günther, H.S. et al. Glioblastoma-derived stem cell–enriched cultures form distinct subgroups according to molecular and phenotypic criteria. Oncogene 27, 2897–2909 (2008).

    PubMed  PubMed Central  Google Scholar 

  43. 43

    Schulte, A. et al. A distinct subset of glioma cell lines with stem cell–like properties reflects the transcriptional phenotype of glioblastomas and overexpresses CXCR4 as therapeutic target. Glia 59, 590–602 (2011).

    PubMed  Google Scholar 

  44. 44

    Cifola, I. et al. Renal cell carcinoma primary cultures maintain genomic and phenotypic profile of parental tumor tissues. BMC Cancer 11, 244 (2011).

    PubMed  PubMed Central  Google Scholar 

  45. 45

    Hollingshead, M.G. et al. Gene expression profiling of 49 human tumor xenografts from in vitro culture through multiple in vivo passages—strategies for data mining in support of therapeutic studies. BMC Genomics 15, 393 (2014).

    PubMed  PubMed Central  Google Scholar 

  46. 46

    Gillet, J.P., Varma, S. & Gottesman, M.M. The clinical relevance of cancer cell lines. J. Natl. Cancer Inst. 105, 452–458 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Birkbak, N.J. et al. Paradoxical relationship between chromosomal instability and survival outcome in cancer. Cancer Res. 71, 3447–3452 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Zhang, W. et al. Centromere and kinetochore gene misexpression predicts cancer patient survival and response to radiotherapy and chemotherapy. Nat. Commun. 7, 12619 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Silk, A.D. et al. Chromosome missegregation rate predicts whether aneuploidy will promote or suppress tumors. Proc. Natl. Acad. Sci. USA 110, E4134–E4141 (2013).

    CAS  PubMed  Google Scholar 

  50. 50

    Zasadil, L.M. et al. Cytotoxicity of paclitaxel in breast cancer is due to chromosome missegregation on multipolar spindles. Sci. Transl. Med. 6, 229ra43 (2014).

    PubMed  PubMed Central  Google Scholar 

  51. 51

    Janssen, A., Kops, G.J. & Medema, R.H. Elevating the frequency of chromosome mis-segregation as a strategy to kill tumor cells. Proc. Natl. Acad. Sci. USA 106, 19108–19113 (2009).

    CAS  PubMed  Google Scholar 

  52. 52

    Burrell, R.A. et al. Targeting chromosomal instability and tumour heterogeneity in HER2-positive breast cancer. J. Cell. Biochem. 111, 782–790 (2010).

    CAS  PubMed  Google Scholar 

  53. 53

    Tempest, H.G. et al. Sperm aneuploidy frequencies analysed before and after chemotherapy in testicular cancer and Hodgkin's lymphoma patients. Hum. Reprod. 23, 251–258 (2008).

    CAS  PubMed  Google Scholar 

  54. 54

    Khan, F., Sherwani, A.F. & Afzal, M. Analysis of genotoxic damage induced by dacarbazine: an in vitro study. Toxin Rev. 29, 130–136 (2010).

    CAS  Google Scholar 

  55. 55

    Ben-David, U. et al. Aneuploidy induces profound changes in gene expression, proliferation and tumorigenicity of human pluripotent stem cells. Nat. Commun. 5, 4825 (2014).

    CAS  PubMed  Google Scholar 

  56. 56

    Cai, Y. et al. Loss of chromosome 8p governs tumor progression and drug response by altering lipid metabolism. Cancer Cell 29, 751–766 (2016).

    CAS  PubMed  Google Scholar 

  57. 57

    Chen, G. et al. Targeting the adaptability of heterogeneous aneuploids. Cell 160, 771–784 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

    Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59

    Rees, M.G. et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat. Chem. Biol. 12, 109–116 (2016).

    CAS  Google Scholar 

  60. 60

    Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576.e16 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61

    Brastianos, P.K. et al. Genomic characterization of brain metastases reveals branched evolution and potential therapeutic targets. Cancer Discov. 5, 1164–1177 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Liu, W. et al. Copy number analysis indicates monoclonal origin of lethal metastatic prostate cancer. Nat. Med. 15, 559–565 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Carter, S.L. et al. Absolute quantification of somatic DNA alterations in human cancer. Nat. Biotechnol. 30, 413–421 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64

    Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    Ben-Porath, I. et al. An embryonic stem cell–like gene expression signature in poorly differentiated aggressive human tumors. Nat. Genet. 40, 499–507 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    Roschke, A.V. et al. Karyotypic complexity of the NCI-60 drug-screening panel. Cancer Res. 63, 8634–8647 (2003).

    CAS  PubMed  Google Scholar 

  67. 67

    Ritchie, M.E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    PubMed  PubMed Central  Google Scholar 

  68. 68

    Larson, J.L. & Owen, A.B. Moment based gene set tests. BMC Bioinformatics 16, 132 (2015).

    PubMed  PubMed Central  Google Scholar 

  69. 69

    Bi, W.L. et al. Genomic landscape of high-grade meningiomas. NPJ Genom. Med. 2, 15 (2017).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank L. Franke for assistance with functional genomic mRNA profiling; A. Bass, K. Ligon, A.J. Aguirre and J. Lorch for providing the clinical samples for cell line derivation; A. Tubelli for assistance with figure preparation; M. Meyerson, A.J. Cherniack, A. Taylor, A. Pearson and Z. Tothova for helpful discussions; and W.J. Gibson for copy number data. U.B.-D. is supported by a Human Frontiers Science Program postdoctoral fellowship, R.B. received support from the US National Institutes of Health (R01 CA188228) and the Gray Matters Brain Cancer Foundation, and T.R.G. received support from the Howard Hughes Medical Institute.

Author information

Affiliations

Authors

Contributions

U.B.-D. conceived the project, collected the data and carried out the analyses. G.H., N.F.G. and J.M.M. assisted with computational analyses. Y.-Y.T. and J.S.B. provided cell line data. C.O. assisted with the copy number analysis of cell lines. B.W. assisted with figure design and preparation. J.S. assisted with the copy number analysis of TCGA samples. R.B. and T.R.G. directed the project. U.B.-D., R.B. and T.R.G. wrote the manuscript.

Corresponding authors

Correspondence to Rameen Beroukhim or Todd R Golub.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–14, Supplementary Tables 1–6 and Supplementary Note (PDF 4534 kb)

Life Sciences Reporting Summary (PDF 158 kb)

Supplementary Data 1

CNA profiles of PDX samples. (XLSX 26968 kb)

Supplementary Data 2

Model-acquired CNAs in PDX samples. (XLSX 1425 kb)

Supplementary Data 3

CNA profiles of CLDX samples. (XLSX 15919 kb)

Supplementary Data 4

Model-acquired CNAs in CLDX samples. (XLSX 791 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ben-David, U., Ha, G., Tseng, Y. et al. Patient-derived xenografts undergo mouse-specific tumor evolution. Nat Genet 49, 1567–1575 (2017). https://doi.org/10.1038/ng.3967

Download citation

Further reading

Search

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing