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Multiparameter Modalities for the Study of Patients in the Setting of Individualized Medicine

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Pancreatic Cancer

Abstract

The recent revolution in cancer genetics offers the promise of using genetic information to individualize patient treatment. In pancreatic cancer, numerous studies have described a genetic landscape characterized by a set of commonly mutated genes aggregated into core molecular pathways accompanied by numerous but infrequently mutated genes. Studies have also demonstrated significant intratumoral heterogeneity. Resistance against chemotherapeutic agents has also been attributed to difficulty of drug delivery through a rich stromal microenvironment. For these reasons, therapeutic development against pancreatic cancer has been challenging, and a number of promising agents have failed clinical trial testing. Personalized models have been studied as a tool for testing candidate drugs to select the most efficacious treatment. The patient-derived xenograft (PDX) is a well-established preclinical tool to improve the drug screening and development. The PDX model requires adequate tissue for transplantation, and failure is common. A recently described, innovative three-dimensional organoid culture platform can be exploited for genomic and functional studies at the level of the individual patient for personalized treatment approach. Organoid technology may fill the gap between cancer genetics and patient trials and allow personalized therapy design. Combination of genome-based medicine and individualized model-based drug screening may fulfill the promise of precision medicine for pancreatic cancer.

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References

  1. Kaufman B, et al. Olaparib monotherapy in patients with advanced cancer and a germline BRCA1/2 mutation. J Clin Oncol. 2015;33:244–50.

    Article  CAS  PubMed  Google Scholar 

  2. Bailey P, et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature. 2016;531:47–52.

    Article  CAS  PubMed  Google Scholar 

  3. Jones S, et al. Exomic sequencing identifies PALB2 as a pancreatic cancer susceptibility gene. Science. 2009;324:217.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Villarroel MC, et al. Personalizing cancer treatment in the age of global genomic analyses: PALB2 gene mutations and the response to DNA damaging agents in pancreatic cancer. Mol Cancer Ther. 2011;10:3–8.

    Article  CAS  PubMed  Google Scholar 

  5. Waddell N, et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature. 2015;518:495–501.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Chantrill LA, et al. Precision medicine for advanced pancreas cancer: the individualized molecular pancreatic cancer therapy (IMPaCT) trial. Clin Cancer Res. 2015;21:2029–37.

    Article  CAS  PubMed  Google Scholar 

  7. Biankin AV, et al. Pancreatic cancer genomes reveal aberrations in axon guidance pathway genes. Nature. 2012;491:399–405.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Jones S, et al. Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science. 2008;321:1801–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wang L, et al. Whole-exome sequencing of human pancreatic cancers and characterization of genomic instability caused by MLH1 haploinsufficiency and complete deficiency. Genome Res. 2012;22:208–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Nones K, et al. Genome-wide DNA methylation patterns in pancreatic ductal adenocarcinoma reveal epigenetic deregulation of SLIT-ROBO, ITGA2 and MET signaling. Int J Cancer. 2014;135:1110–8.

    Article  CAS  PubMed  Google Scholar 

  11. Garcia PL, et al. The BET bromodomain inhibitor JQ1 suppresses growth of pancreatic ductal adenocarcinoma in patient-derived xenograft models. Oncogene. 2016;35:833–45.

    Article  CAS  PubMed  Google Scholar 

  12. Mazur PK, et al. Combined inhibition of BET family proteins and histone deacetylases as a potential epigenetics-based therapy for pancreatic ductal adenocarcinoma. Nat Med. 2015;21:1163–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Collisson EA, et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat Med. 2011;17:500–3. doi:10.1038/nm.2344.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Moffitt RA, et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat Genet. 2015;47:1168–78.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Noll EM, et al. CYP3A5 mediates basal and acquired therapy resistance in different subtypes of pancreatic ductal adenocarcinoma. Nat Med. 2016;22:278–87.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Rosty C, Goggins M. Early detection of pancreatic carcinoma. Hematol Oncol Clin North Am. 2002;16:37–52.

    Article  PubMed  Google Scholar 

  17. Ching CK, Rhodes JM. Enzyme-linked PNA lectin binding assay compared with CA19-9 and CEA radioimmunoassay as a diagnostic blood test for pancreatic cancer. Br J Cancer. 1989;59:949–53.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Uehara H, et al. Diagnosis of pancreatic cancer by detecting telomerase activity in pancreatic juice: comparison with K-ras mutations. Am J Gastroenterol. 1999;94:2513–8.

    Article  CAS  PubMed  Google Scholar 

  19. Yokoyama M, et al. Matrix metalloproteinase-2 in pancreatic juice for diagnosis of pancreatic cancer. Pancreas. 2002;24:344–7.

    Article  PubMed  Google Scholar 

  20. Bettegowda C, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6:224ra24.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Chen R, Pan S, Aebersold R, Brentnall TA. Proteomics studies of pancreatic cancer. Proteomics Clin Appl. 2007;1:1582–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Yu KH, Rustgi AK, Blair IA. Characterization of proteins in human pancreatic cancer serum using differential gel electrophoresis and tandem mass spectrometry. J Proteome Res. 2005;4:1742–51.

    Article  CAS  PubMed  Google Scholar 

  23. Wehr AY, Furth EE, Sangar V, Blair IA, Yu KH. Analysis of the human pancreatic stellate cell secreted proteome. Pancreas. 2011;40:557–66.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Yu KH, et al. Stable isotope dilution multidimensional liquid chromatography-tandem mass spectrometry for pancreatic cancer serum biomarker discovery. J Proteome Res. 2009;8:1565–76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wehr AY, Hwang W-T, Blair IA, Yu KH. Relative quantification of serum proteins from pancreatic ductal adenocarcinoma patients by stable isotope dilution liquid chromatography-mass spectrometry. J Proteome Res. 2012;11:1749–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Britton D, et al. Quantification of pancreatic cancer proteome and phosphorylome: indicates molecular events likely contributing to cancer and activity of drug targets. PLoS One. 2014;9:e90948.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Humphrey ES, et al. Resolution of novel pancreatic ductal adenocarcinoma subtypes by global phosphotyrosine profiling. Mol Cell Proteomics. 2016;15:2671–85.

    Article  CAS  PubMed  Google Scholar 

  28. Daemen A, et al. Metabolite profiling stratifies pancreatic ductal adenocarcinomas into subtypes with distinct sensitivities to metabolic inhibitors. Proc Natl Acad Sci USA. 2015;112:E4410–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Ying H, et al. Oncogenic Kras maintains pancreatic tumors through regulation of anabolic glucose metabolism. Cell. 2012;149:656–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kottakis F, et al. LKB1 loss links serine metabolism to DNA methylation and tumorigenesis. Nature. 2016;539:390–5.

    Article  CAS  PubMed  Google Scholar 

  31. Zhao H, et al. Tumor microenvironment derived exosomes pleiotropically modulate cancer cell metabolism. eLife. Sciences. 2016;5:e10250.

    Google Scholar 

  32. Sherman MH, et al. Vitamin D receptor-mediated stromal reprogramming suppresses pancreatitis and enhances pancreatic cancer therapy. Cell. 2014;159:80–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kobayashi T, et al. A novel serum metabolomics-based diagnostic approach to pancreatic cancer. Cancer Epidemiol Biomark Prev. 2013;22:571–9.

    Article  CAS  Google Scholar 

  34. Mayers JR, et al. Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development. Nat Med. 2014;20:1193–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Fukutake N, et al. A novel multivariate index for pancreatic cancer detection based on the plasma free amino acid profile. PLoS One. 2015;10:e0132223.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Zhang G, et al. Integration of metabolomics and transcriptomics revealed a fatty acid network exerting growth inhibitory effects in human pancreatic cancer. Clin Cancer Res. 2013;19:4983–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Davis VW, Schiller DE, Eurich D, Bathe OF, Sawyer MB. Pancreatic ductal adenocarcinoma is associated with a distinct urinary metabolomic signature. Ann Surg Oncol. 2013;20(Suppl 3):S415–23.

    Article  PubMed  Google Scholar 

  38. Canto MI, et al. International cancer of the pancreas screening (CAPS) Consortium summit on the management of patients with increased risk for familial pancreatic cancer. Gut. 2013;62:339–47.

    Google Scholar 

  39. Witt H, et al. Variants in CPA1 are strongly associated with early onset chronic pancreatitis. Nat Genet. 2013;45:1216–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Von Hoff DD, et al. Pilot study using molecular profiling of patients’ tumors to find potential targets and select treatments for their refractory cancers. J Clin Oncol. 2010;28:4877–83.

    Article  Google Scholar 

  41. Yu KH, et al. Pharmacogenomic modeling of circulating tumor and invasive cells for prediction of chemotherapy response and resistance in pancreatic cancer. Clin Cancer Res. 2014;20:5281–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Singh A, et al. A gene expression signature associated with “K-Ras addiction” reveals regulators of EMT and tumor cell survival. Cancer Cell. 2009;15:489–500.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Garnett MJ, et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature. 2012;483:570–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Iorio F, et al. A landscape of pharmacogenomic interactions in cancer. Cell. 2016;166:740–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Voskoglou-Nomikos T, Pater JL, Seymour L. Clinical predictive value of the in vitro cell line, human xenograft, and mouse allograft preclinical cancer models. Clin Cancer Res. 2003;9:4227–39.

    PubMed  Google Scholar 

  46. Abaan OD, et al. The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology. Cancer Res. 2013;73:4372–82.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Bardeesy N, et al. Smad4 is dispensable for normal pancreas development yet critical in progression and tumor biology of pancreas cancer. Genes Dev. 2006;20:3130–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Hruban RH, et al. Pathology of genetically engineered mouse models of pancreatic exocrine cancer: consensus report and recommendations. Cancer Res. 2006;66:95–106.

    Article  CAS  PubMed  Google Scholar 

  49. Olive KP, et al. Inhibition of hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science. 2009;324:1457–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Hingorani SR, et al. Trp53R172H and KrasG12D cooperate to promote chromosomal instability and widely metastatic pancreatic ductal adenocarcinoma in mice. Cancer Cell. 2005;7:469–83.

    Article  CAS  PubMed  Google Scholar 

  51. Jacobetz MA, et al. Hyaluronan impairs vascular function and drug delivery in a mouse model of pancreatic cancer. Gut. 2013;62:112–20.

    Article  CAS  PubMed  Google Scholar 

  52. Toole BP, Slomiany MG. Hyaluronan: a constitutive regulator of chemoresistance and malignancy in cancer cells. Semin Cancer Biol. 2008;18:244–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Rhim AD, et al. Stromal elements act to restrain, rather than support, pancreatic ductal adenocarcinoma. Cancer Cell. 2014;25:735–47.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Feig C, et al. Targeting CXCL12 from FAP-expressing carcinoma-associated fibroblasts synergizes with anti-PD-L1 immunotherapy in pancreatic cancer. Proc Natl Acad Sci USA. 2013;110:20212–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Morran DC, et al. Targeting mTOR dependency in pancreatic cancer. Gut. 2014;63:1481–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Miyabayashi K, et al. Erlotinib prolongs survival in pancreatic cancer by blocking gemcitabine-induced MAPK signals. Cancer Res. 2013;73:2221–34.

    Article  CAS  PubMed  Google Scholar 

  57. Chiou S-H, et al. Pancreatic cancer modeling using retrograde viral vector delivery and in vivo CRISPR/Cas9-mediated somatic genome editing. Genes Dev. 2015;29:1576–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Johnson JI, et al. Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br J Cancer. 2001;84:1424–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Rubio-Viqueira B, et al. An in vivo platform for translational drug development in pancreatic cancer. Clin Cancer Res. 2006;12:4652–61.

    Article  CAS  PubMed  Google Scholar 

  60. 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. 2011;1:508–23.

    Article  CAS  PubMed  Google Scholar 

  61. Garrido-Laguna I, et al. Integrated preclinical and clinical development of mTOR inhibitors in pancreatic cancer. Br J Cancer. 2010;103:649–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Garrido-Laguna I, et al. Tumor engraftment in nude mice and enrichment in stroma-related gene pathways predict poor survival and resistance to gemcitabine in patients with pancreatic cancer. Clin Cancer Res. 2011;17:5793–800.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Hidalgo M, et al. A pilot clinical study of treatment guided by personalized tumorgrafts in patients with advanced cancer. Mol Cancer Ther. 2011;10:1311–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Sebastiani V. Immunohistochemical and genetic evaluation of deoxycytidine kinase in pancreatic cancer: relationship to molecular mechanisms of gemcitabine resistance and survival. Clin Cancer Res. 2006;12:2492–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Sato T, et al. Single Lgr5 stem cells build crypt-villus structures in vitro without a mesenchymal niche. Nature. 2009;459:262–5.

    Article  CAS  PubMed  Google Scholar 

  66. Ootani A, et al. Sustained in vitro intestinal epithelial culture within a Wnt-dependent stem cell niche. Nat Med. 2009;15:701–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Boj SF, et al. Organoid models of human and mouse ductal pancreatic cancer. Cell. 2015;160:324–38.

    Article  CAS  PubMed  Google Scholar 

  68. Huang L, et al. Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell- and patient-derived tumor organoids. Nat Med. 2015;21:1364–71. doi:10.1038/nm.3973.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Walsh AJ, Castellanos JA, Nagathihalli NS, Merchant NB, Skala MC. Optical imaging of drug-induced metabolism changes in murine and human pancreatic cancer organoids reveals heterogeneous drug response. Pancreas. 2016;45:863–9.

    Article  CAS  PubMed  Google Scholar 

  70. Li X, et al. Oncogenic transformation of diverse gastrointestinal tissues in primary organoid culture. Nat Med. 2014;20:769–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. van de Wetering M, et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell. 2015;161:933–45.

    Article  PubMed  Google Scholar 

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Author information

Authors and Affiliations

  1. Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA

    Koji Miyabayashi, David A. Tuveson & Kenneth H. Yu

  2. Gastrointestinal Oncology Service, Memorial Sloan Kettering Cancer Center and Weill Cornell Medical College, 300 E 66th St, New York, NY, 10065, USA

    Kenneth H. Yu

Authors
  1. Koji Miyabayashi
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  2. David A. Tuveson
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  3. Kenneth H. Yu
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Corresponding author

Correspondence to Koji Miyabayashi .

Editor information

Editors and Affiliations

  1. DMCCMC, University of Liverpool DMCCMC, Liverpool, United Kingdom

    John P. Neoptolemos

  2. Rochester, Minnesota, USA

    Raul Urrutia

  3. Dept. Gastrointestinal Medical Onco, Duke University Medical Center Dept. Gastrointestinal Medical Onco, Durham, North Carolina, USA

    James Abbruzzese

  4. Heidelberg, Germany

    Markus W. Büchler

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Miyabayashi, K., Tuveson, D.A., Yu, K.H. (2017). Multiparameter Modalities for the Study of Patients in the Setting of Individualized Medicine. In: Neoptolemos, J., Urrutia, R., Abbruzzese, J., Büchler, M. (eds) Pancreatic Cancer. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-6631-8_65-1

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  • DOI: https://doi.org/10.1007/978-1-4939-6631-8_65-1

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  • Print ISBN: 978-1-4939-6631-8

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