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

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.

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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.

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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).

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Authors

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.

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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)

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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

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