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An in vivo multiplexed small-molecule screening platform

Abstract

Phenotype-based small-molecule screening is a powerful method to identify molecules that regulate cellular functions. However, such screens are generally performed in vitro under conditions that do not necessarily model complex physiological conditions or disease states. Here, we use molecular cell barcoding to enable direct in vivo phenotypic screening of small-molecule libraries. The multiplexed nature of this approach allows rapid in vivo analysis of hundreds to thousands of compounds. Using this platform, we screened >700 covalent inhibitors directed toward hydrolases for their effect on pancreatic cancer metastatic seeding. We identified multiple hits and confirmed the relevant target of one compound as the lipase ABHD6. Pharmacological and genetic studies confirmed the role of this enzyme as a regulator of metastatic fitness. Our results highlight the applicability of this multiplexed screening platform for investigating complex processes in vivo.

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Figure 1: Development and application of an in vivo multiplexed small-molecule screening platform to interrogate metastatic seeding.
Figure 2: In vivo dose–response screening in human and mouse PDAC cells for seeding to the lung and liver.
Figure 3: Identification and validation of ABHD6 as a target of JCP-265.
Figure 4: Knockdown of Abhd6 decreases metastatic ability of pancreatic cancer cells.

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References

  1. Schirle, M. & Jenkins, J.L. Identifying compound efficacy targets in phenotypic drug discovery. Drug Discov. Today 21, 82–89 (2016).

    Article  CAS  Google Scholar 

  2. Bassilana, F. et al. Target identification for a Hedgehog pathway inhibitor reveals the receptor GPR39. Nat. Chem. Biol. 10, 343–349 (2014).

    Article  CAS  Google Scholar 

  3. Chen, X., Barclay, J.W., Burgoyne, R.D. & Morgan, A. Using C. elegans to discover therapeutic compounds for ageing-associated neurodegenerative diseases. Chem. Cent. J. 9, 65 (2015).

    Article  CAS  Google Scholar 

  4. Rennekamp, A.J. & Peterson, R.T. 15 years of zebrafish chemical screening. Curr. Opin. Chem. Biol. 24, 58–70 (2015).

    Article  CAS  Google Scholar 

  5. Hannan, S.B., Dräger, N.M., Rasse, T.M., Voigt, A. & Jahn, T.R. Cellular and molecular modifier pathways in tauopathies: the big picture from screening invertebrate models. J. Neurochem. 137, 12–25 (2016).

    Article  CAS  Google Scholar 

  6. Jonas, O. et al. An implantable microdevice to perform high-throughput in vivo drug sensitivity testing in tumors. Sci. Transl. Med. 7, 284ra57 (2015).

    Article  Google Scholar 

  7. Klinghoffer, R.A. et al. A technology platform to assess multiple cancer agents simultaneously within a patient's tumor. Sci. Transl. Med. 7, 284ra58 (2015).

    Article  Google Scholar 

  8. Wagenblast, E. et al. A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis. Nature 520, 358–362 (2015).

    Article  CAS  Google Scholar 

  9. Bhang, H.E. et al. Studying clonal dynamics in response to cancer therapy using high-complexity barcoding. Nat. Med. 21, 440–448 (2015).

    Article  CAS  Google Scholar 

  10. 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).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  12. Fan, H.C., Fu, G.K. & Fodor, S.P. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

    Article  Google Scholar 

  13. Yu, C. et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat. Biotechnol. 34, 419–423 (2016).

    Article  CAS  Google Scholar 

  14. Siegel, R.L., Miller, K.D. & Jemal, A. Cancer statistics, 2015. CA Cancer J. Clin. 65, 5–29 (2015).

    Article  Google Scholar 

  15. Jones, S. et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science 321, 1801–1806 (2008).

    Article  CAS  Google Scholar 

  16. Moffitt, R.A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47, 1168–1178 (2015).

    Article  CAS  Google Scholar 

  17. Knudsen, E.S., O'Reilly, E.M., Brody, J.R. & Witkiewicz, A.K. Genetic diversity of pancreatic ductal adenocarcinoma and opportunities for precision medicine. Gastroenterology 150, 48–63 (2015).

    Article  Google Scholar 

  18. Yachida, S. & Iacobuzio-Donahue, C.A. The pathology and genetics of metastatic pancreatic cancer. Arch. Pathol. Lab. Med. 133, 413–422 (2009).

    PubMed  Google Scholar 

  19. Yachida, S. et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 467, 1114–1117 (2010).

    Article  CAS  Google Scholar 

  20. Whittle, M.C. et al. RUNX3 controls a metastatic switch in pancreatic ductal adenocarcinoma. Cell 161, 1345–1360 (2015).

    Article  CAS  Google Scholar 

  21. Guan, X. Cancer metastases: challenges and opportunities. Acta Pharm. Sin. B 5, 402–418 (2015).

    Article  Google Scholar 

  22. Zhang, Y., Zhang, W. & Qin, L. Mesenchymal-mode migration assay and antimetastatic drug screening with high-throughput microfluidic channel networks. Angew. Chem. Int. Ed. Engl. 53, 2344–2348 (2014).

    Article  CAS  Google Scholar 

  23. Colas, P. High-throughput screening assays to discover small-molecule inhibitors of protein interactions. Curr. Drug Discov. Technol. 5, 190–199 (2008).

    Article  CAS  Google Scholar 

  24. Budczies, J. et al. The landscape of metastatic progression patterns across major human cancers. Oncotarget 6, 570–583 (2015).

    Article  Google Scholar 

  25. Hingorani, S.R. et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell 7, 469–483 (2005).

    Article  CAS  Google Scholar 

  26. Hingorani, S.R. et al. Preinvasive and invasive ductal pancreatic cancer and its early detection in the mouse. Cancer Cell 4, 437–450 (2003).

    Article  CAS  Google Scholar 

  27. Arastu-Kapur, S. et al. Identification of proteases that regulate erythrocyte rupture by the malaria parasite Plasmodium falciparum. Nat. Chem. Biol. 4, 203–213 (2008).

    Article  CAS  Google Scholar 

  28. Hall, C.I. et al. Chemical genetic screen identifies Toxoplasma DJ-1 as a regulator of parasite secretion, attachment, and invasion. Proc. Natl. Acad. Sci. USA 108, 10568–10573 (2011).

    Article  CAS  Google Scholar 

  29. Child, M.A. et al. Small-molecule inhibition of a depalmitoylase enhances Toxoplasma host-cell invasion. Nat. Chem. Biol. 9, 651–656 (2013).

    Article  CAS  Google Scholar 

  30. Sipos, B. et al. A comprehensive characterization of pancreatic ductal carcinoma cell lines: towards the establishment of an in vitro research platform. Virchows Arch. 442, 444–452 (2003).

    PubMed  Google Scholar 

  31. Jessani, N. et al. A streamlined platform for high-content functional proteomics of primary human specimens. Nat. Methods 2, 691–697 (2005).

    Article  CAS  Google Scholar 

  32. Boersema, P.J., Raijmakers, R., Lemeer, S., Mohammed, S. & Heck, A.J. Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat. Protoc. 4, 484–494 (2009).

    Article  CAS  Google Scholar 

  33. Hsu, K.L. et al. Discovery and optimization of piperidyl-1,2,3-triazole ureas as potent, selective, and in vivo-active inhibitors of α/β-hydrolase domain containing 6 (ABHD6). J. Med. Chem. 56, 8270–8279 (2013).

    Article  CAS  Google Scholar 

  34. Marrs, W.R. et al. The serine hydrolase ABHD6 controls the accumulation and efficacy of 2-AG at cannabinoid receptors. Nat. Neurosci. 13, 951–957 (2010).

    Article  CAS  Google Scholar 

  35. Thomas, G. et al. The serine hydrolase ABHD6 is a critical regulator of the metabolic syndrome. Cell Rep. 5, 508–520 (2013).

    Article  CAS  Google Scholar 

  36. Alhouayek, M., Masquelier, J., Cani, P.D., Lambert, D.M. & Muccioli, G.G. Implication of the anti-inflammatory bioactive lipid prostaglandin D2-glycerol ester in the control of macrophage activation and inflammation by ABHD6. Proc. Natl. Acad. Sci. USA 110, 17558–17563 (2013).

    Article  CAS  Google Scholar 

  37. Zhao, S. et al. α/β-hydrolase domain-6-accessible monoacylglycerol controls glucose-stimulated insulin secretion. Cell Metab. 19, 993–1007 (2014).

    Article  CAS  Google Scholar 

  38. Hsu, K.L. et al. DAGLβ inhibition perturbs a lipid network involved in macrophage inflammatory responses. Nat. Chem. Biol. 8, 999–1007 (2012).

    Article  CAS  Google Scholar 

  39. Thomas, G., Brown, A.L. & Brown, J.M. In vivo metabolite profiling as a means to identify uncharacterized lipase function: recent success stories within the alpha beta hydrolase domain (ABHD) enzyme family. Biochim. Biophys. Acta 1841, 1097–1101 (2014).

    Article  CAS  Google Scholar 

  40. Pribasnig, M.A. et al. α/β hydrolase domain-containing 6 (ABHD6) degrades the late endosomal/lysosomal lipid bis(monoacylglycero)phosphate. J. Biol. Chem. 290, 29869–29881 (2015).

    Article  CAS  Google Scholar 

  41. Nomura, D.K. et al. Monoacylglycerol lipase exerts dual control over endocannabinoid and fatty acid pathways to support prostate cancer. Chem. Biol. 18, 846–856 (2011).

    Article  CAS  Google Scholar 

  42. Nomura, D.K. et al. Monoacylglycerol lipase regulates a fatty acid network that promotes cancer pathogenesis. Cell 140, 49–61 (2010).

    Article  CAS  Google Scholar 

  43. Max, D., Hesse, M., Volkmer, I. & Staege, M.S. High expression of the evolutionarily conserved alpha/beta hydrolase domain containing 6 (ABHD6) in Ewing tumors. Cancer Sci. 100, 2383–2389 (2009).

    Article  CAS  Google Scholar 

  44. Steeg, P.S. Targeting metastasis. Nat. Rev. Cancer 16, 201–218 (2016).

    Article  CAS  Google Scholar 

  45. Li, C.M. et al. Differential Tks5 isoform expression contributes to metastatic invasion of lung adenocarcinoma. Genes Dev. 27, 1557–1567 (2013).

    Article  CAS  Google Scholar 

  46. Inloes, J.M. et al. The hereditary spastic paraplegia-related enzyme DDHD2 is a principal brain triglyceride lipase. Proc. Natl. Acad. Sci. USA 111, 14924–14929 (2014).

    Article  CAS  Google Scholar 

  47. Washburn, M.P., Wolters, D. & Yates, J.R. III. Large-scale analysis of the yeast proteome by multidimensional protein identification technology. Nat. Biotechnol. 19, 242–247 (2001).

    Article  CAS  Google Scholar 

  48. Weerapana, E. et al. Quantitative reactivity profiling predicts functional cysteines in proteomes. Nature 468, 790–795 (2010).

    Article  CAS  Google Scholar 

  49. Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank P. Chu, A. Winters, and S. Naranjo for technical assistance; the Stanford Shared FACS facility as well as the Stanford High-Throughput Bioscience Center and its director D. Solow-Cordero for technical support; S. Dolan and A. Orantes for administrative support; A. Hayer (Stanford University) for providing reagents; D. Feldser, M. Child, the Stanford Pancreatic Cancer Research community, and members of the Winslow and Bogyo laboratories for helpful comments. We thank J.C. Powers (Georgia Tech University) for providing the covalent serine and cysteine protease inhibitors used for the screening. B.M.G. was supported by the Pancreatic Cancer Action Network AACR Fellowship in memory of Samuel Stroum (14-40-25-GRUE). B.M.G. is a Hope Funds for Cancer Research Fellow supported by the Hope Funds for Cancer Research (HFCR-15-06-07). C.J.S. is supported by an NIH NRSA fellowship (F32CA200078). D.Y. was supported by a Stanford Graduate Fellowship and a Tobacco Related Diseases Research Program (TRDRP) Dissertation Award (24DT-0001). Z.N.R. was supported by a Stanford Graduate Fellowship and the National Science Foundation Graduate Research Fellowship Program (GRFP). C.-H.C. was funded by a Stanford Dean's Fellowship and an American Lung Association Fellowship. This work was supported by NIH R01-HL122283 (J.M.B.), NIH R01-CA132630 and R01-DA033760 (B.F.C.), NIH R21-CA188863 (M.M.W. and M.B.), a Pardee Foundation Research Grant (M.M.W. and M.B.), funding from Len and Kimberly Almalech, and in part by the Stanford Cancer Institute support grant (NIH P30-CA124435).

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Authors and Affiliations

Authors

Contributions

B.M.G., C.J.S., M.B., and M.M.W. conceived the study and designed the experiments. B.M.G., C.J.S., D.Y., M.M.D. C.-H.C. and S.-H.C. performed experiments; C.D.M. and Z.N.R. conducted bioinformatics; D.O. synthesized KT-203; and B.M.G. and C.J.S. analyzed the data. J.M.B. contributed reagents. B.F.C. provided reagents and critical insight. M.B. and M.M.W. oversaw the project. B.M.G., C.J.S., M.B., and M.M.W. wrote the manuscript with comments from all authors.

Corresponding authors

Correspondence to Matthew Bogyo or Monte M Winslow.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–15 and Supplementary Note 1 (PDF 7347 kb)

Supplementary Table 1

Summary 1st screen (XLSX 130 kb)

Supplementary Table 2

Data 2nd screen Figure 2 (XLSX 356 kb)

Supplementary Table 3

Data 3rd screen Supplementary Figure 4 (XLSX 20 kb)

Supplementary Table 4

Data intrasplenic screen Figure 3 (XLSX 19 kb)

Supplementary Table 5

Peptides identified in the ABPP-MudPIT runs Figure 4 (XLSX 1388 kb)

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Grüner, B., Schulze, C., Yang, D. et al. An in vivo multiplexed small-molecule screening platform. Nat Methods 13, 883–889 (2016). https://doi.org/10.1038/nmeth.3992

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