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High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response

Abstract

Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established 1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

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Figure 1: The Novartis Institutes for Biomedical Research patient-derived tumor xenograft encyclopedia (NIBR PDXE).
Figure 2: Systematic approach for in vivo compound profiling using PDXs (PCT), and its reproducibility.
Figure 3: PCT predicts targeted therapy response and validates predictive gene signature.
Figure 4: Combination therapies increase the overall response rate and progression-free survival.
Figure 5: IGF1R inhibitor does not potentiate anti-tumor activities of targeted therapy in vivo.

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Acknowledgements

We thank B. Gruenenfelder, M. Lechevalier and D. Thomis for project management; R. Mosher and M. Murakami for their advice on the pathology of PDXs; L. Barys, P. Fordjour, M. Gallagher, B. Gorbatcheva, N. Houde, E. Kurth, J.A. Kwon, Y. Oei, K. O'Malley, D. Rakiec and C. Tauras for their technical support; D. Fox for IT support; S.-M. Maira, C. Fritsch and M. Yao for their helpful discussion; M. Stump, L. Kifule and P. Zhu for support with in vitro proliferation screens; J. Ledell for Chalice software development; and J. Steiger for data interpretation and project management. We received tumor specimens from the US National Disease Research Interchange, the US National Cancer Institute, the Maine Medical Center, the Tufts Medical Center, the Mt Group Inc. and GenenDesign, and we are grateful to the people who consented to donate their tissues to support this work.

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Contributions

H.G., S.F., Y.W., M.S., C.Z., C.S., G.Y., S.B., H.C., S. Chatterjee, S.M.C., S.D.C., N.E., M.E., C.K., E. Lorenzana, M.P., C.R., F. Salangsang, F. Santacroce, Y.T., W.T., S.T., R.V., F.V.A., Z.W., D.W. and F.X. performed the PCT trials; H.G., G.Y., Y.Z., S.M.C., J.G., C.K., A.L., R.V., Z.W. and F.X. performed PDX model development; E.D., Y.L., M.E.M. and R.M. performed histopathologic analysis; J.M.K. and E.R.M. led the genomic landscape analysis; J.M.K., F.C., S. Chuai, A.K., J.M., J.L., A.R. and K.V. performed computational biology and bioinformatics analysis; O.A.B., D.Y.C., R.J.L. and A.P.S. performed pan-cancer panel analysis for melanoma resistance; J.E.M., J.G., T.L.N. and D.A.R. performed or directed nuclear acid extraction, quality control and genomic data generation; H.Q.W. performed the PK analysis of the encorafenib and LEE011 combination; M.W. led the in vitro combination screens; H.G., J.M.K., J.M., A.R., O.A.B., D.Y.C. prepared figures and tables for the main text and supplementary information; H.G., J.M.K., J.E.M., S.F., M.S., C.S., O.A.B., A.P.S., D.Y.C., M.W., H.B., J.A.W. and W.R.S. wrote and edited the main text and supplementary information; P.A., R.C., M.R.J., N.K.P., J.A.W., E. Li, E. Lees, F.H., N.K. and W.R.S. contributed to project oversight and advisory roles; J.A.W. and W.R.S. provided overall project leadership.

Corresponding author

Correspondence to Juliet A Williams.

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This research was funded by Novartis, Inc. and all authors were employees thereof at the time the study was performed. The authors declare no other competing financial interests.

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Supplementary Text and Figures

Supplementary Figures 1–13 (PDF 1451 kb)

Supplementary Table 1

Genomic profiling of PDXs and raw response and curve metrics of PCTs. (XLSX 119485 kb)

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Gao, H., Korn, J., Ferretti, S. et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response. Nat Med 21, 1318–1325 (2015). https://doi.org/10.1038/nm.3954

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