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

Journal name:
Nature Medicine
Year published:
DOI:
doi:10.1038/nm.3954
Received
Accepted
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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.

At a glance

Figures

  1. The Novartis Institutes for Biomedical Research patient-derived tumor xenograft encyclopedia (NIBR PDXE).
    Figure 1: The Novartis Institutes for Biomedical Research patient-derived tumor xenograft encyclopedia (NIBR PDXE).

    (a) Distribution of cancer types in the PDXE by lineage (n = 1,075). (b) Similarity of PDXs between passages and lineages using Affymetrix mRNA expression data (MAS5 normalized). x axis, Pearson correlation coefficient (bar, median; box, first and third quartile; whiskers, data within 1.5*IQR of lower or upper quartile; circles: data outside whisker range). y axis, passage distance (defined in Supplementary Fig. 1); numbers in parentheses, number of PDX pairs in each passage distance. (c) Somatic mutation frequencies in PDXs. Points, individual PDX models; parenthesis, number of models per indication. Tumor types are ordered by median somatic mutation frequency, and colored by chromosomal instability (CIN) score. Lower panel, relative proportions of the six different possible base-pair substitutions. SS, soft tissue sarcoma; PDAC, pancreatic ductal carcinoma; EC, esophageal cancer; OVC, ovarian carcinoma; RCC, renal cell carcinoma; BRCA, breast carcinoma; CRC, colorectal cancer; NSCLC, non-small cell lung carcinoma; CM, cutaneous melanoma. (d) Genomic landscape analysis of melanoma across PDXE, TCGA and CCLE data sets. Parenthesis, number of models per indication; blue, homozygous deletions; salmon, amplification >5 copies; red, amplification > 8 copies; light green, known COSMIC (Catalog of Somatic Mutations in Cancer) gain-of-function mutations; dark green, truncating mutations/frameshift or known COSMIC loss-of-function; mustard, novel mutation; purple, pathway altered in at least one gene; gene names colored black or gray to indicate inclusion in same-colored pathway listed above; percentages indicate percentage of samples altered for the given gene or pathway.

  2. Systematic approach for in vivo compound profiling using PDXs (PCT), and its reproducibility.
    Figure 2: Systematic approach for in vivo compound profiling using PDXs (PCT), and its reproducibility.

    (a) Feasibility assessment of 1 × 1 × 1 PCT approach by Pearson correlation analysis. x axis, number of majority response from each response category; y axis, fraction of individual animal response relative to the majority (average ± s.e.m.). A total of 2,138 single-animal response data were collected and categorized from 440 unique treatment models (Online Methods). CR: complete response; PR: partial response; SD: stable disease; PD: progressive disease. (b) Summary of compound sensitivity in the PCTs. The BestAvgResponse was used to make response calls (Online Methods), and each square represents a PDX. A total of 62 treatment groups were tested in 277 PDXs across six indications (BRCA (breast cancer, n = 43), CM (cutaneous melanoma, n = 33), CRC (colorectal carcinoma, n = 59), GC (gastric cancer, n = 64), NSCLC (non-small cell lung carcinoma, n = 36) and PDAC (pancreatic ductal adenocarcinoma, n = 42)). Arrow (CRright arrowPD, PRright arrowPD, SDright arrowPD, and CR>PD, PR>PD, SD>PD) indicates disease progression; > indicates progression seen after 64 d; > pindicates progression in <64 d. (c) Waterfall plot of responses to the PI3K inhibitors CLR457 (n = 205) and BKM120 (n = 213) across all indications; each bar represents an individual PDX. (d) Kaplan-Meier progression-free survival curve of PDXs treated with CLR457 (n = 205) and BKM120 (n = 213) across all indications.

  3. PCT predicts targeted therapy response and validates predictive gene signature.
    Figure 3: PCT predicts targeted therapy response and validates predictive gene signature.

    (a,b) Waterfall plot of response to the BRAF inhibitor encorafenib (n = 33) (a) and encorafenib in combination with the MEK inhibitor binimetinib (n = 33) (b) among melanoma PDXs. GOF, gain of function; WT, wild type. (c) Waterfall plot of response to DR5 agonist TAS266 among DR5 signature–positive and DR5 signature–negative melanoma PDXs (n = 33). (d) Kaplan-Meier PFS curve with TAS266 treatment in melanoma, stratified by DR5 predictive signature (n = 33). (e) Waterfall plot of response to PI3Kα inhibitor BYL719 among PIK3CA- and PTEN- mutated PDXs across five indications (n = 205). LOF, loss of function; blue, PIK3CA GOF/PTEN WT (n = 21); green, PIK3CA GOF/PTEN LOF (n = 6); brown, PIK3CA WT/PTEN LOF (n = 9); gray, PIK3CA WT/PTEN WT (n = 169).

  4. Combination therapies increase the overall response rate and progression-free survival.
    Figure 4: Combination therapies increase the overall response rate and progression-free survival.

    (a) Box plot of anti-tumor activity of single agents (36) and combinations (26) across six indications (n = 277) (BRCA, CM, CRC, GC, NSCLC and PDAC) in PCTs. Middle bar, median; box, first and third quartile; whiskers, data within 1.5 = IQR of lower or upper quartile. (b) Kaplan-Meier PFS curve of the single agents (36) and combinations (26) across six indications in the PCTs (n = 277). The y axis is the percentage of animals on study as calculated by tumor doubling (when a tumor volume has doubled compared to its baseline tumor volume), after which a model is considered to have progressed on treatment. (c) Waterfall plot of response to LEE011 among melanoma PDXs (n = 33). (d) Waterfall plot of response to the encorafenib-LEE011 combination among melanoma PDXs (n = 33). (e) Kaplan-Meier PFS curve of encorafenib and LEE011 single agents and encorafenib-LEE011 combination among BRAF-mutated melanoma PDXs (n = 15).

  5. IGF1R inhibitor does not potentiate anti-tumor activities of targeted therapy in vivo.
    Figure 5: IGF1R inhibitor does not potentiate anti-tumor activities of targeted therapy in vivo.

    (a) Waterfall plot of response to 500 combinations among 45 CRC cell lines. A hit is defined as a combination with a synergy score above 2 and a maximum growth inhibition above 0.7 in individual cell line. (b) Score plots for the combination of LFW527-binimetinib. Circle, hit; square, miss. Symbol size translates the increase of effect between the combination and either agent. (c) Waterfall plot of response to binimetinib among CRC PDXs (n = 43). Binimetinib was dosed at 10 mg/kg twice daily. (d) Waterfall plot of response to LFW527-binimetinib combination among CRC PDXs (n = 33). LFW527 was dosed at 12.5 mg/kg daily, and binimetinib was dosed at 10 mg/kg twice daily in the combination. (e) Kaplan-Meier PFS curve of binimetinib single-agent (n = 43) and LFW527-binimetinib combination (n = 33) among CRC PDXs.

References

  1. Arrowsmith, J. & Miller, P. Trial watch: phase II and phase III attrition rates 2011–2012. Nat. Rev. Drug Discov. 12, 569 (2013).
  2. Arrowsmith, J. Trial watch: Phase II failures: 2008–2010. Nat. Rev. Drug Discov. 10, 328329 (2011).
  3. DiMasi, J.A., Reichert, J.M., Feldman, L. & Malins, A. Clinical approval success rates for investigational cancer drugs. Clin. Pharmacol. Ther. 94, 329335 (2013).
  4. Paul, S.M. et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov. 9, 203214 (2010).
  5. Tentler, J.J. et al. Patient-derived tumour xenografts as models for oncology drug development. Nat. Rev. Clin. Oncol. 9, 338350 (2012).
  6. Siolas, D. & Hannon, G.J. Patient-derived tumor xenografts: transforming clinical samples into mouse models. Cancer Res. 73, 53155319 (2013).
  7. Rosfjord, E., Lucas, J., Li, G. & Gerber, H.P. Advances in patient-derived tumor xenografts: from target identification to predicting clinical response rates in oncology. Biochem. Pharmacol. 91, 135143 (2014).
  8. Hidalgo, M. et al. Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov. 4, 9981013 (2014).
  9. Bertotti, A. et al. A molecularly annotated platform of patient-derived xenografts (“xenopatients”) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov. 1, 508523 (2011).
  10. Migliardi, G. et al. Inhibition of MEK and PI3K/mTOR suppresses tumor growth but does not cause tumor regression in patient-derived xenografts of RAS-mutant colorectal carcinomas. Clin. Cancer Res. 18, 25152525 (2012).
  11. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603607 (2012).
  12. 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, 15141520 (2011).
  13. Ding, L. et al. Genome remodelling in a basal-like breast cancer metastasis and xenograft. Nature 464, 9991005 (2010).
  14. Eirew, P. et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422426 (2015).
  15. Hennessey, P.T. et al. Promoter methylation in head and neck squamous cell carcinoma cell lines is significantly different than methylation in primary tumors and xenografts. PLoS ONE 6, e20584 (2011).
  16. Julien, S. et al. Characterization of a large panel of patient-derived tumor xenografts representing the clinical heterogeneity of human colorectal cancer. Clin. Cancer Res. 18, 53145328 (2012).
  17. Mattie, M. et al. Molecular characterization of patient-derived human pancreatic tumor xenograft models for preclinical and translational development of cancer therapeutics. Neoplasia 15, 11381150 (2013).
  18. Einarsdottir, B.O. et al. Melanoma patient-derived xenografts accurately model the disease and develop fast enough to guide treatment decisions. Oncotarget 5, 96099618 (2014).
  19. de Plater, L. et al. Establishment and characterisation of a new breast cancer xenograft obtained from a woman carrying a germline BRCA2 mutation. Br. J. Cancer 103, 11921200 (2010).
  20. Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin) 6, 8092 (2012).
  21. Therasse, P. et al. New guidelines to evaluate the response to treatment in solid tumors. European Organization for Research and Treatment of Cancer, National Cancer Institute of the United States, National Cancer Institute of Canada. J. Natl. Cancer Inst. 92, 205216 (2000).
  22. Sosman, J.A. et al. Survival in BRAF V600-mutant advanced melanoma treated with vemurafenib. N. Engl. J. Med. 366, 707714 (2012).
  23. Ascierto, P.A. et al. Phase II trial (BREAK-2) of the BRAF inhibitor dabrafenib (GSK2118436) in patients with metastatic melanoma. J. Clin. Oncol. 31, 32053211 (2013).
  24. Kaplan, F.M., Shao, Y., Mayberry, M.M. & Aplin, A.E. Hyperactivation of MEK-ERK1/2 signaling and resistance to apoptosis induced by the oncogenic B-RAF inhibitor, PLX4720, in mutant N-RAS melanoma cells. Oncogene 30, 366371 (2011).
  25. Halaban, R. et al. PLX4032, a selective BRAF(V600E) kinase inhibitor, activates the ERK pathway and enhances cell migration and proliferation of BRAF melanoma cells. Pigment Cell Melanoma Res. 23, 190200 (2010).
  26. Flaherty, K.T. et al. Combined BRAF and MEK inhibition in melanoma with BRAF V600 mutations. N. Engl. J. Med. 367, 16941703 (2012).
  27. Robert, C. et al. Improved overall survival in melanoma with combined dabrafenib and trametinib. N. Engl. J. Med. 372, 3039 (2015).
  28. Shi, H. et al. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer Discov. 4, 8093 (2014).
  29. Shi, H. et al. Melanoma whole-exome sequencing identifies (V600E)B-RAF amplification-mediated acquired B-RAF inhibitor resistance. Nat. Commun. 3, 724 (2012).
  30. Wagle, N. et al. MAP kinase pathway alterations in BRAF-mutant melanoma patients with acquired resistance to combined RAF/MEK inhibition. Cancer Discov. 4, 6168 (2014).
  31. Rizos, H. et al. BRAF inhibitor resistance mechanisms in metastatic melanoma: spectrum and clinical impact. Clin. Cancer Res. 20, 19651977 (2014).
  32. Papadopoulos, K.P. et al. Unexpected hepatotoxicity in a phase I study of TAS266, a novel tetravalent agonistic nanobody targeting the DR5 receptor. Cancer Chemother. Pharmacol. 75, 887895 (2015).
  33. Fritsch, C. et al. Characterization of the novel and specific PI3Kα inhibitor NVP-BYL719 and development of the patient stratification strategy for clinical trials. Mol. Cancer Ther. 13, 11171129 (2014).
  34. Juric, D. et al. Convergent loss of PTEN leads to clinical resistance to a PI(3)Kα inhibitor. Nature 518, 240244 (2015).
  35. Sasai, K. et al. Shh pathway activity is down-regulated in cultured medulloblastoma cells: implications for preclinical studies. Cancer Res. 66, 42154222 (2006).
  36. Flanigan, S.A. et al. Overcoming IGF1R/IR resistance through inhibition of MEK signaling in colorectal cancer models. Clin. Cancer Res. 19, 62196229 (2013).
  37. Molina-Arcas, M., Hancock, D.C., Sheridan, C., Kumar, M.S. & Downward, J. Coordinate direct input of both KRAS and IGF1 receptor to activation of PI3 kinase in KRAS-mutant lung cancer. Cancer Discov. 3, 548563 (2013).
  38. Ebi, H. et al. Receptor tyrosine kinases exert dominant control over PI3K signaling in human KRAS mutant colorectal cancers. J. Clin. Invest. 121, 43114321 (2011).
  39. Friedbichler, K. et al. Pharmacodynamic and antineoplastic activity of BI 836845, a fully human IGF ligand-neutralizing antibody, and mechanistic rationale for combination with rapamycin. Mol. Cancer Ther. 13, 399409 (2014).
  40. Moran, T. et al. Activity of dalotuzumab, a selective anti-IGF1R antibody, in combination with erlotinib in unselected patients with Non-small-cell lung cancer: a phase I/II randomized trial. Exp. Hematol. Oncol. 3, 26 (2014).
  41. Scagliotti, G.V. et al. Randomized, phase III trial of figitumumab in combination with erlotinib versus erlotinib alone in patients with nonadenocarcinoma nonsmall-cell lung cancer. Ann. Oncol 26, 497504 (2015).
  42. Brana, I. et al. A parallel-arm phase I trial of the humanised anti-IGF-1R antibody dalotuzumab in combination with the AKT inhibitor MK-2206, the mTOR inhibitor ridaforolimus, or the NOTCH inhibitor MK-0752, in patients with advanced solid tumours. Br. J. Cancer 111, 19321944 (2014).
  43. Di Cosimo, S. et al. Combination of the mTOR inhibitor ridaforolimus and the anti-IGF1R monoclonal antibody dalotuzumab: preclinical characterization and phase I clinical trial. Clin. Cancer Res. 21, 4959 (2015).
  44. Das Thakur, M. et al. Modelling vemurafenib resistance in melanoma reveals a strategy to forestall drug resistance. Nature 494, 251255 (2013).
  45. Korpal, M. et al. An F876L mutation in androgen receptor confers genetic and phenotypic resistance to MDV3100 (enzalutamide). Cancer Discov. 3, 10301043 (2013).
  46. Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2, 401404 (2012).
  47. Lawrence, M.S. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214218 (2013).
  48. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 17541760 (2009).
  49. DePristo, M.A. et al. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat. Genet. 43, 491498 (2011).
  50. McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 12971303 (2010).
  51. Sathirapongsasuti, J.F. et al. Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV. Bioinformatics 27, 26482654 (2011).
  52. Lehár, J. et al. Synergistic drug combinations tend to improve therapeutically relevant selectivity. Nat. Biotechnol. 27, 659666 (2009).

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Author information

  1. Present addresses: Patronus Therapeutics, Inc., San Francisco, California, USA (M.S.); Janssen China R&D and Scientific Affairs, Shanghai, China (C.Z.); BioMarin Pharmaceutical, Inc., Novato, California, USA (N.K.P.); and Duke University, Durham, North Carolina, USA (A.R.).

    • Mallika Singh,
    • Chao Zhang,
    • Anupama Reddy &
    • Nancy K Pryer
  2. These authors contributed equally to this work.

    • Hui Gao,
    • Joshua M Korn &
    • Stéphane Ferretti
  3. These authors jointly directed this work.

    • Juliet A Williams &
    • William R Sellers

Affiliations

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

    • Hui Gao,
    • Joshua M Korn,
    • Guizhi Yang,
    • O Alejandro Balbin,
    • Hongbo Cai,
    • Derek Y Chiang,
    • Shawn M Cogan,
    • Scott D Collins,
    • John Green,
    • Colleen Kowal,
    • Rebecca J Leary,
    • Alice Loo,
    • E Robert McDonald III,
    • Jason Merkin,
    • Angad P Singh,
    • Roberto Velazquez,
    • Kavitha Venkatesan,
    • Hans Bitter,
    • Nicholas Keen,
    • Juliet A Williams &
    • William R Sellers
  2. Oncology Disease Area, Novartis Institutes for Biomedical Research, Basel, Switzerland.

    • Stéphane Ferretti,
    • Christian Schnell,
    • Stéphanie Barbe,
    • Ernesta Dammassa,
    • Nicolas Ebel,
    • Audrey Kauffmann,
    • Claudia Röelli,
    • Francesca Santacroce,
    • Walter Tinetto,
    • Sonja Tobler,
    • Fabian Von Arx,
    • Marion Wiesmann,
    • Daniel Wyss,
    • Francesco Hofmann,
    • Robert Cozens &
    • Michael Rugaard Jensen
  3. Department of Oncology Translational Medicine, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts, USA.

    • John E Monahan,
    • Joseph Lehar,
    • Margaret E McLaughlin,
    • Ronald Meyer,
    • Tara L Naylor,
    • Anupama Reddy,
    • David A Ruddy &
    • Hui Qin Wang
  4. China Novartis Institutes for Biomedical Research, Shanghai, China.

    • Youzhen Wang,
    • Chao Zhang,
    • Yun Zhang,
    • Shannon Chuai,
    • Ying Liang,
    • Zongyao Wang,
    • Fiona Xu,
    • Peter Atadja &
    • En Li
  5. Oncology Disease Area, Novartis Institutes for Biomedical Research, Emeryville, California, USA.

    • Mallika Singh,
    • Fergal Casey,
    • Susmita Chatterjee,
    • Millicent Embry,
    • Edward Lorenzana,
    • Montesa Patawaran,
    • Fernando Salangsang,
    • Yan Tang,
    • Emma Lees &
    • Nancy K Pryer

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.

Competing financial interests

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.

Corresponding author

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Supplementary information

PDF files

  1. Supplementary Text and Figures (1,486 KB)

    Supplementary Figures 1–13

Excel files

  1. Supplementary Table 1 (122 KB)

    Genomic profiling of PDXs and raw response and curve metrics of PCTs.

Additional data