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:

Studying clonal dynamics in response to cancer therapy using high-complexity barcoding

Nature Medicine volume 21, pages 440–448 (2015)Cite this article

Subjects

Abstract

Resistance to cancer therapies presents a significant clinical challenge. Recent studies have revealed intratumoral heterogeneity as a source of therapeutic resistance. However, it is unclear whether resistance is driven predominantly by pre-existing or de novo alterations, in part because of the resolution limits of next-generation sequencing. To address this, we developed a high-complexity barcode library, ClonTracer, which enables the high-resolution tracking of more than 1 million cancer cells under drug treatment. In two clinically relevant models, ClonTracer studies showed that the majority of resistant clones were part of small, pre-existing subpopulations that selectively escaped under therapeutic challenge. Moreover, the ClonTracer approach enabled quantitative assessment of the ability of combination treatments to suppress resistant clones. These findings suggest that resistant clones are present before treatment, which would make up-front therapeutic combinations that target non-overlapping resistance a preferred approach. Thus, ClonTracer barcoding may be a valuable tool for optimizing therapeutic regimens with the goal of curative combination therapies for cancer.

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

Figure 1: Design and characterization of the high-complexity ClonTracer DNA-barcoding technology.
Figure 2: ClonTracer barcoding technology demonstrates the pre-existence of erlotinib-resistant subpopulations in the HCC827 cell line.
Figure 3: The erlotinib–crizotinib dual-resistant subpopulations in HCC827 cells are pre-existing and predetermined and display features of epithelial–mesenchymal transition (EMT).
Figure 4: ClonTracer barcoding demonstrates the presence of distinct pre-existing resistant populations in response to ATP-competitive versus allosteric ABL1 inhibitors.
Figure 5: Genomic and functional characterization of barcoded KCL-22 clones that exhibited divergent responses to catalytic and allosteric inhibitors.
Figure 6: Mathematical modeling of KCL-22 clonal dynamics under treatment with ABL1 inhibitors.

Similar content being viewed by others

References

  1. Ohashi, K., Maruvka, Y.E., Michor, F. & Pao, W. Epidermal growth factor receptor tyrosine kinase inhibitor-resistant disease. J. Clin. Oncol. 31, 1070–1080 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Holohan, C., Van Schaeybroeck, S., Longley, D.B. & Johnston, P.G. Cancer drug resistance: an evolving paradigm. Nat. Rev. Cancer 13, 714–726 (2013).

    CAS  PubMed  Google Scholar 

  3. Weisberg, E., Manley, P.W., Cowan-Jacob, S.W., Hochhaus, A. & Griffin, J.D. Second generation inhibitors of BCR-ABL for the treatment of imatinib-resistant chronic myeloid leukaemia. Nat. Rev. Cancer 7, 345–356 (2007).

    CAS  PubMed  Google Scholar 

  4. Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Morrison, C.D. et al. Whole-genome sequencing identifies genomic heterogeneity at a nucleotide and chromosomal level in bladder cancer. Proc. Natl. Acad. Sci. USA 111, E672–E681 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Diaz, L.A. Jr. et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486, 537–540 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Turke, A.B. et al. Preexistence and clonal selection of MET amplification in EGFR mutant NSCLC. Cancer Cell 17, 77–88 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Landau, D.A. et al. Evolution and impact of subclonal mutations in chronic lymphocytic leukemia. Cell 152, 714–726 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532–536 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Robasky, K., Lewis, N.E. & Church, G.M. The role of replicates for error mitigation in next-generation sequencing. Nat. Rev. Genet. 15, 56–62 (2014).

    CAS  PubMed  Google Scholar 

  12. Lu, R., Neff, N.F., Quake, S.R. & Weissman, I.L. Tracking single hematopoietic stem cells in vivo using high-throughput sequencing in conjunction with viral genetic barcoding. Nat. Biotechnol. 29, 928–933 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Naik, S.H. et al. Diverse and heritable lineage imprinting of early haematopoietic progenitors. Nature 496, 229–232 (2013).

    CAS  PubMed  Google Scholar 

  14. Wu, C. et al. Clonal tracking of rhesus macaque hematopoiesis highlights a distinct lineage origin for natural killer cells. Cell Stem Cell 14, 486–499 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Lynch, T.J. et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N. Engl. J. Med. 350, 2129–2139 (2004).

    CAS  PubMed  Google Scholar 

  16. Paez, J.G. et al. EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy. Science 304, 1497–1500 (2004).

    CAS  PubMed  Google Scholar 

  17. Pao, W. et al. EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc. Natl. Acad. Sci. USA 101, 13306–13311 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Amann, J. et al. Aberrant epidermal growth factor receptor signaling and enhanced sensitivity to EGFR inhibitors in lung cancer. Cancer Res. 65, 226–235 (2005).

    CAS  PubMed  Google Scholar 

  19. Sharma, S.V. et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141, 69–80 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Ohashi, K. et al. Lung cancers with acquired resistance to EGFR inhibitors occasionally harbor BRAF gene mutations but lack mutations in KRAS, NRAS, or MEK1. Proc. Natl. Acad. Sci. USA 109, E2127–E2133 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Shien, K. et al. Acquired resistance to EGFR inhibitors is associated with a manifestation of stem cell-like properties in cancer cells. Cancer Res. 73, 3051–3061 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Costa, D.B., Kobayashi, S., Yeo, W.L. & Hamada, A. Serum concentrations of Erlotinib at a dose of 25 mg daily. J. Thorac. Oncol. 5, 1311–1312 (2010).

    PubMed  PubMed Central  Google Scholar 

  23. Bean, J. et al. MET amplification occurs with or without T790M mutations in EGFR mutant lung tumors with acquired resistance to gefitinib or erlotinib. Proc. Natl. Acad. Sci. USA 104, 20932–20937 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Engelman, J.A. et al. MET amplification leads to gefitinib resistance in lung cancer by activating ERBB3 signaling. Science 316, 1039–1043 (2007).

    CAS  PubMed  Google Scholar 

  25. Sequist, L.V. et al. Genotypic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors. Sci. Transl. Med. 3, 75ra26 (2011).

    PubMed  PubMed Central  Google Scholar 

  26. Ware, K.E. et al. A mechanism of resistance to gefitinib mediated by cellular reprogramming and the acquisition of an FGF2-FGFR1 autocrine growth loop. Oncogenesis 2, e39 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Byers, L.A. et al. An epithelial-mesenchymal transition gene signature predicts resistance to EGFR and PI3K inhibitors and identifies Axl as a therapeutic target for overcoming EGFR inhibitor resistance. Clin. Cancer Res. 19, 279–290 (2013).

    CAS  PubMed  Google Scholar 

  28. Kubonishi, I. & Miyoshi, I. Establishment of a Ph1 chromosome-positive cell line from chronic myelogenous leukemia in blast crisis. Int. J. Cell Cloning 1, 105–117 (1983).

    CAS  PubMed  Google Scholar 

  29. Soverini, S. et al. BCR-ABL kinase domain mutation analysis in chronic myeloid leukemia patients treated with tyrosine kinase inhibitors: recommendations from an expert panel on behalf of European LeukemiaNet. Blood 118, 1208–1215 (2011).

    CAS  PubMed  Google Scholar 

  30. Soverini, S. et al. Unraveling the complexity of tyrosine kinase inhibitor-resistant populations by ultra-deep sequencing of the BCR-ABL kinase domain. Blood 122, 1634–1648 (2013).

    CAS  PubMed  Google Scholar 

  31. Adrián, F.J. et al. Allosteric inhibitors of Bcr-abl–dependent cell proliferation. Nat. Chem. Biol. 2, 95–102 (2006).

    PubMed  Google Scholar 

  32. Zhang, J. et al. Targeting Bcr-Abl by combining allosteric with ATP-binding-site inhibitors. Nature 463, 501–506 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Gorre, M.E. et al. Clinical resistance to STI-571 cancer therapy caused by BCR-ABL gene mutation or amplification. Science 293, 876–880 (2001).

    CAS  PubMed  Google Scholar 

  34. Lange, T. et al. The quantitative level of T315I mutated BCR-ABL predicts for major molecular response to second-line nilotinib or dasatinib treatment in patients with chronic myeloid leukemia. Haematologica 98, 714–717 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Kunkel, T.A. & Bebenek, K. DNA replication fidelity. Annu. Rev. Biochem. 69, 497–529 (2000).

    CAS  PubMed  Google Scholar 

  36. Robbins, C.M. et al. Copy number and targeted mutational analysis reveals novel somatic events in metastatic prostate tumors. Genome Res. 21, 47–55 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Tewhey, R. et al. Microdroplet-based PCR enrichment for large-scale targeted sequencing. Nat. Biotechnol. 27, 1025–1031 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Zhong, Q. et al. Multiplex digital PCR: breaking the one target per color barrier of quantitative PCR. Lab Chip 11, 2167–2174 (2011).

    CAS  PubMed  Google Scholar 

  39. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401–404 (2012).

    Article  PubMed  Google Scholar 

  40. 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 

  41. Garnett, M.J. et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483, 570–575 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    PubMed  PubMed Central  Google Scholar 

  43. Schmitt, M.W. et al. Detection of ultra-rare mutations by next-generation sequencing. Proc. Natl. Acad. Sci. USA 109, 14508–14513 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Shiroguchi, K., Jia, T.Z., Sims, P.A. & Xie, X.S. Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. Proc. Natl. Acad. Sci. USA 109, 1347–1352 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Kinde, I. et al. Evaluation of DNA from the Papanicolaou test to detect ovarian and endometrial cancers. Sci. Transl. Med. 5, 167ra164 (2013).

    Google Scholar 

  46. Kinde, I., Wu, J., Papadopoulos, N., Kinzler, K.W. & Vogelstein, B. Detection and quantification of rare mutations with massively parallel sequencing. Proc. Natl. Acad. Sci. USA 108, 9530–9535 (2011).

    PubMed  PubMed Central  Google Scholar 

  47. Diehl, F. et al. BEAMing: single-molecule PCR on microparticles in water-in-oil emulsions. Nat. Methods 3, 551–559 (2006).

    CAS  PubMed  Google Scholar 

  48. Bozic, I. et al. Evolutionary dynamics of cancer in response to targeted combination therapy. Elife 2, e00747 (2013).

    PubMed  PubMed Central  Google Scholar 

  49. Komarova, N.L. & Wodarz, D. Drug resistance in cancer: principles of emergence and prevention. Proc. Natl. Acad. Sci. USA 102, 9714–9719 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Iwasa, Y., Michor, F. & Nowak, M.A. Evolutionary dynamics of escape from biomedical intervention. Proc. Biol. Sci. 270, 2573–2578 (2003).

    PubMed  PubMed Central  Google Scholar 

  51. Korpal, M. et al. An F876L mutation in androgen receptor confers genetic and phenotypic resistance to MDV3100 (enzalutamide). Cancer Discov. 3, 1030–1043 (2013).

    CAS  PubMed  Google Scholar 

  52. Rahal, R. et al. Pharmacological and genomic profiling identifies NF-κB-targeted treatment strategies for mantle cell lymphoma. Nat. Med. 20, 87–92 (2014).

    CAS  PubMed  Google Scholar 

  53. Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Trapnell, C., Pachter, L. & Salzberg, S.L. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25, 1105–1111 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491–498 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Trapnell, C. et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Wiederschain, D. et al. Contribution of polycomb homologues Bmi-1 and Mel-18 to medulloblastoma pathogenesis. Mol. Cell. Biol. 27, 4968–4979 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank A. Wylie, J. Donovan, S. Zhu, E.R. McDonald, Z. Jagani and B. Firestone for helpful discussion. We also thank J. Brugge, K. Polyak, J. Williams and J. Kuiken for critical reading of the manuscript and K. Yu for technical assistance. R.Z. and F.M. gratefully acknowledge support from the Dana-Farber Cancer Institute Physical Sciences-Oncology Center (U54CA143798).

Author information

Author notes

  1. Joshua M Korn and Frank Stegmeier: These authors contributed equally to this work.

Authors and Affiliations

  1. Oncology Disease Area, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA

    Hyo-eun C Bhang, Viveksagar Krishnamurthy Radhakrishna, Justina X Caushi, Matthew M Hims, Angad P Singh, Alina Raza, Nicholas Keen, Michael R Schlabach, Rebecca J Leary, Derek Y Chiang, William R Sellers, Vesselina G Cooke, Joshua M Korn & Frank Stegmeier

  2. Translational Clinical Oncology, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA

    David A Ruddy, Iris Kao, Daniel Rakiec, Pamela Shaw, Marissa Balak, Elizabeth Ackley & Michael Palmer

  3. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Rui Zhao & Franziska Michor

  4. Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts, USA

    Rui Zhao & Franziska Michor

Authors
  1. Hyo-eun C Bhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David A Ruddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Viveksagar Krishnamurthy Radhakrishna
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Justina X Caushi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rui Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Matthew M Hims
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Angad P Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Iris Kao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Daniel Rakiec
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Pamela Shaw
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Marissa Balak
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Alina Raza
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Elizabeth Ackley
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Nicholas Keen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Michael R Schlabach
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Michael Palmer
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Rebecca J Leary
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Derek Y Chiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. William R Sellers
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Franziska Michor
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Vesselina G Cooke
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Joshua M Korn
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Frank Stegmeier
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.C.B. and F.S. conceived the project and designed the experiments. H.C.B., D.A.R., J.M.K., M.R.S. and F.S. designed and developed the ClonTracer cellular barcoding system, and H.C.B. constructed the ClonTracer barcode library. H.C.B. and J.X.C. performed the biological experiments and analyzed the data. H.C.B., V.K.R., J.M.K., A.P.S. and D.Y.C. analyzed NGS and RNA-seq data. D.A.R., I.K., D.R., M.M.H., A.R., E.A. and R.J.L. directed or performed NGS and RNA-seq. V.K.R. and J.M.K. performed bioinformatic analysis. R.Z. and F.M. performed mathematical modeling of clonal dynamics. P.S., M.B. and M.P. performed or directed MET copy-number analysis and ABL1 Sequenom mutation analysis. H.C.B., V.K.R., R.Z. and J.M.K. prepared figures and tables. H.C.B., R.Z., W.R.S., F.M., V.G.C., J.M.K. and F.S. wrote and edited the manuscript. W.R.S. and N.K. contributed to oversight of and advice on the overall project. F.S. and J.M.K. provided overall project leadership.

Corresponding author

Correspondence to Frank Stegmeier.

Ethics declarations

Competing interests

H.C.B., D.A.R., V.K.R., J.X.C., M.M.H., A.P.S., I.K., D.R., P.S., M.B., A.R., E.A., N.K., M.R.S., M.P., R.J.L., D.Y.C., W.R.S., V.G.C., J.M.K. and F.S. are employees of Novartis, Inc., as noted in the affiliations.

Supplementary information

Supplementary Text and Figuress

Supplementary Figures 1–15, Supplementary Tables 1–12, and Supplementary Discussion (PDF 6813 kb)

Supplementary Data Set 1

Mathematical modeling of clonal dynamics in response to ABL1 inhibitor treatment—the distributions of barcode abundance at days 21 and 27. (PDF 952 kb)

Supplementary Data Set 2

Mathematical modeling of clonal dynamics in response to ABL1 inhibitor treatment—the numbers of barcodes with fractions greater than 0.0007 at days 21 and 27. (PDF 201 kb)

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhang, He., Ruddy, D., Krishnamurthy Radhakrishna, V. et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat Med 21, 440–448 (2015). https://doi.org/10.1038/nm.3841

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nm.3841

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer