Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Clonal populations of a human TNBC model display significant functional heterogeneity and divergent growth dynamics in distinct contexts

Oncogene volume 41, pages 112–124 (2022)Cite this article

Subjects

Abstract

Intratumoral heterogeneity has been described for various tumor types and models of human cancer, and can have profound effects on tumor progression and drug resistance. This study describes an in-depth analysis of molecular and functional heterogeneity among subclonal populations (SCPs) derived from a single triple-negative breast cancer cell line, including copy number analysis, whole-exome and RNA sequencing, proteome analysis, and barcode analysis of clonal dynamics, as well as functional assays. The SCPs were found to have multiple unique genetic alterations and displayed significant variation in anchorage independent growth and tumor forming ability. Analyses of clonal dynamics in SCP mixtures using DNA barcode technology revealed selection for distinct clonal populations in different in vitro and in vivo environmental contexts, demonstrating that in vitro propagation of cancer cell lines using different culture conditions can contribute to the establishment of unique strains. These analyses also revealed strong enrichment of a single SCP during the development of xenograft tumors in immune-compromised mice. This SCP displayed attenuated interferon signaling in vivo and reduced sensitivity to the antiproliferative effects of type I interferons. Reduction in interferon signaling was found to provide a selective advantage within the xenograft microenvironment specifically. In concordance with the previously described role of interferon signaling as tumor suppressor, these findings suggest that similar selective pressures may be operative in human cancer and patient-derived xenograft models.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Genomic and transcriptional heterogeneity among SCPs derived from a single TNBC cell line.
Fig. 2: SCPs display minimal variation in morphology and proliferation rate, but are markedly different with regard to their ability to form colonies in soft agar.
Fig. 3: Barcode analysis of tumors derived from SCP mixtures reveal divergent growth dynamics of SCP mixtures during tumor development and in vitro culture.
Fig. 4: SCP tumors are characterized by induction of IFN target genes with SCP32 showing the lowest activity.
Fig. 5: SCP32 cells display attenuated IFN signaling following IFN treatment.
Fig. 6: IFN signaling acts as a tumor suppressor in SCP tumors.
Fig. 7: PDX tumors display changes in IFN signaling during serial passaging in immune-compromised mice.

Similar content being viewed by others

Data availability

Bulk and single-cell RNA sequencing data have been deposited to the Gene Expression Omnibus (GEO, series GSE184543). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with dataset identifier PXD022325 (TMT1: experiment 1, TMT2: experiment 2). All other data supporting the findings of this study are available within the article, its supplementary information files, or from the corresponding author upon reasonable request.

References

  1. Nowell PC. The clonal evolution of tumor cell populations. Science. 1976;194:23–8. 1

    Article  CAS  PubMed  Google Scholar 

  2. Greaves M, Maley CC. Clonal evolution in cancer. Nature. 2012;481:306–13. 18

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. McGranahan N, Swanton C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell. 2017;168:613–28. 09

    Article  CAS  PubMed  Google Scholar 

  4. Merlo LMF, Pepper JW, Reid BJ, Maley CC. Cancer as an evolutionary and ecological process. Nat Rev Cancer. 2006;6:924–35.

    Article  CAS  PubMed  Google Scholar 

  5. Tabassum DP, Polyak K. Tumorigenesis: it takes a village. Nat Rev Cancer. 2015;15:473–83.

    Article  CAS  PubMed  Google Scholar 

  6. Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15:81–94.

    Article  CAS  Google Scholar 

  7. Marusyk A, Janiszewska M, Polyak K. Intratumor heterogeneity: The Rosetta Stone of therapy resistance. Cancer Cell. 2020;37:471–84. 13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zardavas D, Irrthum A, Swanton C, Piccart M. Clinical management of breast cancer heterogeneity. Nat Rev Clin Oncol. 2015;12:381–94.

    Article  CAS  PubMed  Google Scholar 

  9. Baslan T, Hicks J. Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat Rev Cancer. 2017;17:557–69. 24

    Article  CAS  PubMed  Google Scholar 

  10. Navin N, Kendall J, Troge J, Andrews P, Rodgers L, McIndoo J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472:90–4. 7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Nik-Zainal S, Van Loo P, Wedge DC, Alexandrov LB, Greenman CD, Lau KW, et al. The life history of 21 breast cancers. Cell. 2012;149:994–1007. 25

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Yates LR, Gerstung M, Knappskog S, Desmedt C, Gundem G, Van Loo P, et al. Subclonal diversification of primary breast cancer revealed by multiregion sequencing. Nat Med. 2015;21:751–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Baslan T, Kendall J, Volyanskyy K, McNamara K, Cox H, D’Italia S, et al. Novel insights into breast cancer copy number genetic heterogeneity revealed by single-cell genome sequencing. eLife. 2020;13;9.

  14. Gao R, Davis A, McDonald TO, Sei E, Shi X, Wang Y, et al. Punctuated copy number evolution and clonal stasis in triple-negative breast cancer. Nat Genet. 2016;48:1119–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Laks E, McPherson A, Zahn H, Lai D, Steif A, Brimhall J, et al. Clonal decomposition and DNA replication states defined by scaled single-cell genome sequencing. Cell. 2019;179:1207–1221.e22. 14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Wang Y, Waters J, Leung ML, Unruh A, Roh W, Shi X, et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature. 2014;512:155–60. 14

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Lim B, Lin Y, Navin N. Advancing cancer research and medicine with single-cell genomics. Cancer Cell. 2020;37:456–70. 13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci USA. 2003;100:3983–8. 1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Dongre A, Weinberg RA. New insights into the mechanisms of epithelial-mesenchymal transition and implications for cancer. Nat Rev Mol Cell Biol. 2019;20:69–84.

    Article  CAS  PubMed  Google Scholar 

  20. Mani SA, Guo W, Liao M-J, Eaton EN, Ayyanan A, Zhou AY, et al. The epithelial-mesenchymal transition generates cells with properties of stem cells. Cell. 2008;133:704–15. 16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chung W, Eum HH, Lee H-O, Lee K-M, Lee H-B, Kim K-T, et al. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat Commun. 2017;8:15081. 05

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gao R, Kim C, Sei E, Foukakis T, Crosetto N, Chan L-K, et al. Nanogrid single-nucleus RNA sequencing reveals phenotypic diversity in breast cancer. Nat Commun. 2017;8:228. 9

    Article  PubMed  PubMed Central  Google Scholar 

  23. Karaayvaz M, Cristea S, Gillespie SM, Patel AP, Mylvaganam R, Luo CC, et al. Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq. Nat Commun. 2018;9:3588. 04

    Article  PubMed  PubMed Central  Google Scholar 

  24. Minn AJ, Kang Y, Serganova I, Gupta GP, Giri DD, Doubrovin M, et al. Distinct organ-specific metastatic potential of individual breast cancer cells and primary tumors. J Clin Invest. 2005;115:44–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Olive JF, Qin Y, DeCristo MJ, Laszewski T, Greathouse F, McAllister SS. Accounting for tumor heterogeneity when using CRISPR-Cas9 for cancer progression and drug sensitivity studies. PloS One. 2018;13:e0198790.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wagenblast E, Soto M, Gutiérrez-Ángel S, Hartl CA, Gable AL, Maceli AR, et al. A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis. Nature. 2015;520:358–62. 16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Echeverria GV, Powell E, Seth S, Ge Z, Carugo A, Bristow C, et al. High-resolution clonal mapping of multi-organ metastasis in triple-negative breast cancer. Nat Commun. 2018;9:5079. 29

    Article  PubMed  PubMed Central  Google Scholar 

  28. Marusyk A, Tabassum DP, Altrock PM, Almendro V, Michor F, Polyak K. Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity. Nature 2014;514:54–8. 2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Naffar-Abu Amara S, Kuiken HJ, Selfors LM, Butler T, Leung ML, Leung CT, et al. Transient commensal clonal interactions can drive tumor metastasis. Nat Commun. 2020;11:5799. 16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Fidler IJ, Kripke ML. Metastasis results from preexisting variant cells within a malignant tumor. Science. 1977;197:893–5. 26

    Article  CAS  PubMed  Google Scholar 

  31. Meyer M, Reimand J, Lan X, Head R, Zhu X, Kushida M, et al. Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc Natl Acad Sci USA. 2015;112:851–6. 20

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Cailleau R, Olivé M, Cruciger QV. Long-term human breast carcinoma cell lines of metastatic origin: preliminary characterization. Vitro. 1978;14:911–5.

    Article  CAS  Google Scholar 

  33. Grigoriadis A, Mackay A, Noel E, Wu PJ, Natrajan R, Frankum J, et al. Molecular characterisation of cell line models for triple-negative breast cancers. BMC Genom. 2012;13:619. 14

    Article  CAS  Google Scholar 

  34. Reis-Filho JS, Tutt ANJ. Triple negative tumours: a critical review. Histopathology. 2008;52:108–18.

    Article  CAS  PubMed  Google Scholar 

  35. Agelopoulos K, Greve B, Schmidt H, Pospisil H, Kurtz S, Bartkowiak K, et al. Selective regain of EGFR gene copies in CD44+/CD24-/low breast cancer cellular model MDA-MB-468. BMC Cancer. 2010;10:78. 3

    Article  PubMed  PubMed Central  Google Scholar 

  36. Filmus J, Trent JM, Pollak MN, Buick RN. Epidermal growth factor receptor gene-amplified MDA-468 breast cancer cell line and its nonamplified variants. Mol Cell Biol. 1987;7:251–7.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Li W, Ma H, Zhang J, Zhu L, Wang C, Yang Y. Unraveling the roles of CD44/CD24 and ALDH1 as cancer stem cell markers in tumorigenesis and metastasis. Sci Rep. 2017;7:13856. 23

    Article  PubMed  PubMed Central  Google Scholar 

  38. Nolan-Stevaux O, Tedesco D, Ragan S, Makhanov M, Chenchik A, Ruefli-Brasse A, et al. Measurement of cancer cell growth heterogeneity through lentiviral barcoding identifies clonal dominance as a characteristic of in vivo tumor engraftment. PloS One. 2013;8:e67316.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bhang HC, Ruddy DA, Krishnamurthy Radhakrishna V, Caushi JX, Zhao R, Hims MM, et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat Med. 2015;21:440–8.

    Article  CAS  PubMed  Google Scholar 

  40. Shah SP, Roth A, Goya R, Oloumi A, Ha G, Zhao Y, et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature. 2012;486:395–9. 4

    Article  CAS  PubMed  Google Scholar 

  41. Klijn C, Durinck S, Stawiski EW, Haverty PM, Jiang Z, Liu H, et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat Biotechnol. 2015;33:306–12.

    Article  CAS  PubMed  Google Scholar 

  42. Sondka Z, Bamford S, Cole CG, Ward SA, Dunham I, Forbes SA. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat Rev Cancer. 2018;18:696–705.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Callari M, Musella V, Di Buduo E, Sensi M, Miodini P, Dugo M, et al. Subtype-dependent prognostic relevance of an interferon-induced pathway metagene in node-negative breast cancer. Mol Oncol. 2014;8:1278–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Saleiro D, Platanias LC. Interferon signaling in cancer. Non-canonical pathways and control of intracellular immune checkpoints. Semin Immunol. 2019;43:101299. 1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Zitvogel L, Galluzzi L, Kepp O, Smyth MJ, Kroemer G. Type I interferons in anticancer immunity. Nat Rev Immunol. 2015;15:405–14.

    Article  CAS  PubMed  Google Scholar 

  46. Gibbs VC, Williams SR, Gray PW, Schreiber RD, Pennica D, Rice G, et al. The extracellular domain of the human interferon gamma receptor interacts with a species-specific signal transducer. Mol Cell Biol. 1991;11:5860–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Weber H, Valenzuela D, Lujber G, Gubler M, Weissmann C. Single amino acid changes that render human IFN-alpha 2 biologically active on mouse cells. EMBO J. 1987;6:591–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Bruna A, Rueda OM, Greenwood W, Batra AS, Callari M, Batra RN, et al. A biobank of breast cancer explants with preserved intra-tumor heterogeneity to screen anticancer compounds. Cell. 2016;167:260–274.e22. 22

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Georgopoulou D, Callari M, Rueda OM, Shea A, Martin A, Giovannetti A, et al. Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response. Nat Commun. 2021;12:1998. 31

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Callari M, Cappelletti V, D’Aiuto F, Musella V, Lembo A, Petel F, et al. Subtype-specific metagene-based prediction of outcome after neoadjuvant and adjuvant treatment in breast cancer. Clin Cancer Res J Am Assoc Cancer Res. 2016;22:337–45. 15

    Article  CAS  Google Scholar 

  51. Ben-David U, Siranosian B, Ha G, Tang H, Oren Y, Hinohara K, et al. Genetic and transcriptional evolution alters cancer cell line drug response. Nature. 2018;560:325–30.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Liu Y, Mi Y, Mueller T, Kreibich S, Williams EG, Van Drogen A, et al. Multi-omic measurements of heterogeneity in HeLa cells across laboratories. Nat Biotechnol. 2019;37:314–22.

    Article  CAS  PubMed  Google Scholar 

  53. Cantor JR, Abu-Remaileh M, Kanarek N, Freinkman E, Gao X, Louissaint A, et al. Physiologic medium rewires cellular metabolism and reveals uric acid as an endogenous inhibitor of UMP synthase. Cell. 2017;169:258–272.e17. 6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Waclaw B, Bozic I, Pittman ME, Hruban RH, Vogelstein B, Nowak MA. A spatial model predicts that dispersal and cell turnover limit intratumour heterogeneity. Nature. 2015;525:261–4. 10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Krysko DV, Garg AD, Kaczmarek A, Krysko O, Agostinis P, Vandenabeele P. Immunogenic cell death and DAMPs in cancer therapy. Nat Rev Cancer. 2012;12:860–75.

    Article  CAS  PubMed  Google Scholar 

  56. Kaczmarek A, Vandenabeele P, Krysko DV. Necroptosis: the release of damage-associated molecular patterns and its physiological relevance. Immunity. 2013;38:209–23. 21

    Article  CAS  PubMed  Google Scholar 

  57. Roh JS, Sohn DH. Damage-associated molecular patterns in inflammatory diseases. Immune Netw. 2018;18:e27.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Bakhoum SF, Cantley LC. The multifaceted role of chromosomal instability in cancer and its microenvironment. Cell. 2018;174:1347–60. 06

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Paludan SR, Reinert LS, Hornung V. DNA-stimulated cell death: implications for host defence, inflammatory diseases and cancer. Nat Rev Immunol. 2019;19:141–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Byrne AT, Alférez DG, Amant F, Annibali D, Arribas J, Biankin AV, et al. Interrogating open issues in cancer precision medicine with patient-derived xenografts. Nat Rev Cancer. 2017;17:254–68.

    Article  CAS  PubMed  Google Scholar 

  61. Cassidy JW, Caldas C, Bruna A. Maintaining tumor heterogeneity in patient-derived tumor xenografts. Cancer Res. 2015;75:2963–8. 1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Hidalgo M, Amant F, Biankin AV, Budinská E, Byrne AT, Caldas C, et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 2014;4:998–1013.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Ben-David U, Ha G, Tseng Y-Y, Greenwald NF, Oh C, Shih J, et al. Patient-derived xenografts undergo mouse-specific tumor evolution. Nat Genet. 2017;49:1567–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Ding L, Ellis MJ, Li S, Larson DE, Chen K, Wallis JW, et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature. 2010;464:999–1005. 15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Eirew P, Steif A, Khattra J, Ha G, Yap D, Farahani H, et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature. 2015;518:422–6. 19

    Article  CAS  PubMed  Google Scholar 

  66. Kreso A, O’Brien CA, van Galen P, Gan OI, Notta F, Brown AMK, et al. Variable clonal repopulation dynamics influence chemotherapy response in colorectal cancer. Science. 2013;339:543–8. 1

    Article  CAS  PubMed  Google Scholar 

  67. Merino D, Weber TS, Serrano A, Vaillant F, Liu K, Pal B, et al. Barcoding reveals complex clonal behavior in patient-derived xenografts of metastatic triple negative breast cancer. Nat Commun. 2019;10:766. 15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Sato K, Niida A, Masuda T, Shimizu D, Tobo T, Kuroda Y, et al. Multiregion genomic analysis of serially transplanted patient-derived xenograft tumors. Cancer Genom Proteom. 2019;16:21–7.

    Article  CAS  Google Scholar 

  69. Woo XY, Giordano J, Srivastava A, Zhao Z-M, Lloyd MW, de Bruijn R, et al. Conservation of copy number profiles during engraftment and passaging of patient-derived cancer xenografts. Nat Genet. 2021;53:86–99.

  70. Post AEM, Smid M, Nagelkerke A, Martens JWM, Bussink J, Sweep FCGJ, et al. Interferon-stimulated genes are involved in cross-resistance to radiotherapy in tamoxifen-resistant breast cancer. Clin Cancer Res J Am Assoc Cancer Res. 2018;24:3397–408. 15

    Article  CAS  Google Scholar 

  71. Sistigu A, Yamazaki T, Vacchelli E, Chaba K, Enot DP, Adam J, et al. Cancer cell-autonomous contribution of type I interferon signaling to the efficacy of chemotherapy. Nat Med. 2014;20:1301–9.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We are grateful to Angie Martinez-Gakidis for scientific editing, the ICCB-Longwood Screening Facility at Harvard Medical School for providing support and access to instruments, and the BioPolymers Facility at Harvard Medical School and Bauer Core Facility at Harvard University for providing support with next-generation sequencing. This work was supported by grants from the NIH (PPG P01CA080111), the Breast Cancer Research Foundation (BCRF-19-021), and the Ludwig Center at Harvard.

Author information

Author notes

  1. Hendrik J. Kuiken

    Present address: Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, 1066 CX, the Netherlands

  2. Sabin Dhakal

    Present address: Inzen Therapeutics, Cambridge, MA, 02142, USA

  3. Ron C. J. Schackmann

    Present address: Merus, Utrecht, 3584 CM, the Netherlands

  4. Jett Crowdis

    Present address: Broad Institute, Cambridge, MA, 02142, USA

  5. Hyo-eun C. Bhang

    Present address: Civetta Therapeutics, Cambridge, MA, 02142, USA

  6. Frank Stegmeier

    Present address: KSQ Therapeutics, Inc., Cambridge, MA, 02139, USA

  7. These authors contributed equally: Hendrik J. Kuiken, Sabin Dhakal.

Authors and Affiliations

  1. Department of Cell Biology, Harvard Medical School, Boston, MA, 02115, USA

    Hendrik J. Kuiken, Sabin Dhakal, Laura M. Selfors, Chandler M. Friend, Tian Zhang, Ron C. J. Schackmann, G. Kenneth Gray, Jett Crowdis, Steven P. Gygi & Joan S. Brugge

  2. Ludwig Center at Harvard, Boston, MA, 02115, USA

    Hendrik J. Kuiken, Sabin Dhakal, Laura M. Selfors, Chandler M. Friend, Ron C. J. Schackmann, G. Kenneth Gray, Jett Crowdis & Joan S. Brugge

  3. Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Robinson Way, Cambridge, CB2 0RE, UK

    Maurizio Callari & Carlos Caldas

  4. Department of Oncology, Novartis Institutes for Biomedical Research, Cambridge, MA, 02139, USA

    Hyo-eun C. Bhang & Frank Stegmeier

  5. Cancer Biology and Genetics Program, Memorial Sloan-Kettering Cancer Center, New York, NY, 10065, USA

    Timour Baslan

Authors
  1. Hendrik J. Kuiken
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sabin Dhakal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura M. Selfors
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chandler M. Friend
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Tian Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maurizio Callari
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ron C. J. Schackmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. G. Kenneth Gray
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jett Crowdis
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Hyo-eun C. Bhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Timour Baslan
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Frank Stegmeier
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Steven P. Gygi
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Carlos Caldas
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Joan S. Brugge
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

HJK and SD performed all experiments with help from CMF and JC. LMS performed all of the bioinformatics, with exception of the gene expression analysis of PDX tumors, which was performed by MC in the laboratory of CC, and the copy number analysis, which was performed in the laboratory of TB. The mass spectrometry analyses were performed by TZ in the laboratory of SPG. The CyTOF analysis was performed by RCJS and GKG. HCB and FS provided the ClonTracer constructs and protocols for the analysis of the DNA barcode experiments. HJK and JSB generated the first draft of the manuscript and all authors contributed to the revisions.

Corresponding author

Correspondence to Joan S. Brugge.

Ethics declarations

Competing interests

JSB is a consultant for Agios Pharmaceuticals, eFFECTOR Therapeutics, and Frontier Medicines. The other authors declare no conflict of interest.

Additional information

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

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuiken, H.J., Dhakal, S., Selfors, L.M. et al. Clonal populations of a human TNBC model display significant functional heterogeneity and divergent growth dynamics in distinct contexts. Oncogene 41, 112–124 (2022). https://doi.org/10.1038/s41388-021-02075-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41388-021-02075-y

This article is cited by

Search

Advanced search

Quick links