Skip to main content
Log in

Etablierung einer Living Biobank

Bessere Steuerung der Präzisionsonkologie durch In-vitro- und In-vivo-Krebsmodelle

Establishment of a living biobank

Improved guidance of precision cancer care with in vitro and in vivo cancer models

  • Preisverleihungen: Forschungspreis der DGP 2017
  • Published:
Der Pathologe Aims and scope Submit manuscript

Zusammenfassung

Hintergrund

Präzisionsonkologie ist ein klinischer Ansatz, welcher die Behandlung von Patienten aufgrund des genetischen Profils ihrer Tumorerkrankung optimiert und individualisiert. Die Integration einer Living Biobank mit sog. Tumororganoiden und Xenografts von Patienten mit Next-Generation-Sequenzierung und Hochdurchsatzmedikamentenscreenings hilft, die klinische Entscheidungsfindung zu fördern und klinische Studien zu entwickeln.

Methoden

Verwendung von Tumororganoiden für In-vitro-Hochdurchsatzmedikamentenscreenings (bis zu 160 Medikamte) und die Etablierung von In-vivo Xenotransplantat-Modellen zur Testung von nominierten Medikamenten und von neuen Medikamentenkombinationen.

Ergebnisse

Während 2 Jahren haben wir 56 In-vitro-Tumororganoide und 19 In-vivo-Xenotransplantate aus 18 verschiedenen soliden Tumortypen etabliert. Tumormorphologie und molekulare Profile zeigen eine gute Übereinstimmung zwischen den In-vitro- und In-vivo-Modellen im Vergleich zu ihrem nativen Tumor. Mittels personalisierter Hochdurchsatzmedikamentenscreenings wurden mehrere gezielte kleine Moleküle und neuartige Medikamentenkombinationen nominiert und in entsprechenden Xenotransplantat-Modellen validiert.

Schlussfolgerung

Dieser Ansatz zeigt die Integration von genomischen Daten mit Medikamentenscreenings an personalisierten präklinischen Krebsmodellen, um die individuelle Krebsbehandlung zu präzisieren und neue potenzielle klinische Studien zu etablieren.

Abstract

Background

Precision oncology is a clinical approach aimed towards tailoring treatment strategies for patients based on the genetic profile of each patient’s cancer. The integration of a living biobank, consisting of patient-derived tumor organoids and PDXs, with next generation sequencing approaches and high-throughput drug screening help to guide clinical decision-making and clinical trial development.

Methods

Tumor organoids derived from fresh tumor samples were used for in vitro and in vivo high-throughput drug testing.

Results

Over a period of two years we established 56 in vitro tumor organoids and 19 in vivo xenografts from 18 different solid tumor types. Tumor morphology and molecular profiles show good concordance between the in vitro and in vivo models compared to their native tumor. High-throughput drug screening (up to 160 drugs) has been tested on eight tumor organoid lines. Seven of them underwent an additional combination drug screen. We nominated several targeted small molecules and novel combinations that have been validated in corresponding xenograft models.

Conclusion

This precision medicine approach outlines the integration of genomic data with drug screening from personalized preclinical cancer models to guide precision cancer care. It also fuels next generation research and has been implemented for clinical trial development.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Abb. 1
Abb. 2
Abb. 3
Abb. 4
Abb. 5
Abb. 6

Abbreviations

Mamma-CA:

Brustkrebs

EIPM:

Englander Institute for Precision Medicine

FDA:

U.S. Federal Drug Administration

FFPE:

Formalinfixiert, paraffineingebettet

MEK:

Mitogenaktivierte Proteinkinase-Kinase

NEPC:

Neuroendokrines Prostata Karzinom

PDAC:

Pankreatisches duktales Adenokarzinom

PDX:

„patient-derived xenograft“

RCC:

Renales Klarzellkarzinom

WES:

„whole exome sequencing“

WT:

Wildtyp

Literatur

  1. Pauli C, Hopkins BD, Prandi D et al (2017) Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov 7:462–477

    Article  PubMed  Google Scholar 

  2. Lawrence MS, Stojanov P, Polak P et al (2013) Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499:214–218

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tamborero D, Gonzalez-Perez A, Perez-Llamas C et al (2013) Comprehensive identification of mutational cancer driver genes across 12 tumor types. Sci Rep 3:2650

    Article  PubMed  PubMed Central  Google Scholar 

  4. Rubin MA (2015) Health: make precision medicine work for cancer care. Nature 520:290–291

    Article  CAS  PubMed  Google Scholar 

  5. Beltran H, Eng K, Mosquera JM et al (2015) Whole-exome sequencing of metastatic cancer and biomarkers of treatment response. JAMA Oncol 1:466–474

    Article  PubMed  PubMed Central  Google Scholar 

  6. Arrowsmith J (2011) Trial watch: phase II failures: 2008–2010. Nat Rev Drug Discov 10:328–329

    Article  CAS  PubMed  Google Scholar 

  7. Arrowsmith J (2011) Trial watch: phase III and submission failures: 2007–2010. Nat Rev Drug Discov 10:87

    Article  CAS  PubMed  Google Scholar 

  8. Arrowsmith J, Miller P (2013) Trial watch: phase II and phase III attrition rates 2011–2012. Nat Rev Drug Discov 12:569

    Article  CAS  PubMed  Google Scholar 

  9. Pampaloni F, Reynaud EG, Stelzer EH (2007) The third dimension bridges the gap between cell culture and live tissue. Nat Rev Mol Cell Biol 8:839–845

    Article  CAS  PubMed  Google Scholar 

  10. Nash CE, Mavria G, Baxter EW et al (2015) Development and characterisation of a 3D multi-cellular in vitro model of normal human breast: a tool for cancer initiation studies. Oncotarget 6:13731–13741

    Article  PubMed  PubMed Central  Google Scholar 

  11. Clevers H (2016) Modeling development and disease with organoids. Cell 165:1586–1597

    Article  CAS  PubMed  Google Scholar 

  12. Mueller-Klieser W (1997) Three-dimensional cell cultures: from molecular mechanisms to clinical applications. Am J Physiol 273:C1109–C1123

    CAS  PubMed  Google Scholar 

  13. Mueller-Klieser W (2000) Tumor biology and experimental therapeutics. Crit Rev Oncol Hematol 36:123–139

    Article  CAS  PubMed  Google Scholar 

  14. Pickl M, Ries CH (2009) Comparison of 3D and 2D tumor models reveals enhanced HER2 activation in 3D associated with an increased response to trastuzumab. Oncogene 28:461–468

    Article  CAS  PubMed  Google Scholar 

  15. Gao D, Vela I, Sboner A et al (2014) Organoid cultures derived from patients with advanced prostate cancer. Cell 159:176–187

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Huang L, Holtzinger A, Jagan I et al (2015) Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell- and patient-derived tumor organoids. Nat Med 21:1364–1371

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. van de Wetering M, Francies HE, Francis JM et al (2015) Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161:933–945

    Article  PubMed  Google Scholar 

  18. Pauli C, Puca L, Mosquera JM et al (2016) An emerging role for cytopathology in precision oncology. Cancer Cytopathol 124:167–173

    Article  PubMed  Google Scholar 

  19. Cayrefourcq L, Mazard T, Joosse S et al (2015) Establishment and characterization of a cell line from human circulating colon cancer cells. Cancer Res 75:892–901

    Article  CAS  PubMed  Google Scholar 

  20. Alix-Panabieres C, Pantel K (2016) Clinical applications of circulating tumor cells and circulating tumor DNA as liquid biopsy. Cancer Discov 6:479–491

    Article  CAS  PubMed  Google Scholar 

  21. Ito R, Takahashi T, Ito M (2017) Humanized mouse models: application to human diseases. J Cell Physiol. https://doi.org/10.1002/jcp.26045

    PubMed Central  Google Scholar 

  22. Hidalgo M, Amant F, Biankin AV et al (2014) Patient-derived xenograft models: an emerging platform for translational cancer research. Cancer Discov 4:998–1013

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Caplan AL, Bateman-House A, Waldstreicher J (2016) Compassionate use: a modest proposal. Am Soc Clin Oncol Educ Book 35:e2–e4

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Pauli.

Ethics declarations

Interessenkonflikt

C. Pauli, H. Moch und M.A. Rubin geben an, dass kein Interessenkonflikt besteht.

Alle Gewebeproben in den USA stammen von Patienten, welche eine informierte Einwilligung entsprechend dem genehmigten Institutions Review Board (IRB) bei Weill Cornell unterzeichnet haben. Alle Tierexperimente waren durch das Institutionale Tierschutzkomitee genehmigt (Protokoll 2013-0016). Im Institut für Pathologie und Molekularpathologie wurde der Living Biobank/Zellkulturbank eine Zustimmung durch die kantonale Ethikkommission erteilt (KEK-ZH-Nr. 2014-0619, BASEC-Nr. PB_2016-0057). Es werden nur Proben für die Living Biobank/Zellkulturbank verarbeitet von Patienten, welche den universalen Generalkonsent unterzeichnet und somit dazu eingewilligt haben.

The supplement containing this article is not sponsored by industry.

Additional information

Dieser Beitrag basiert auf der Originalpublikation „Personalized in vitro and in vivo cancer models to guide precision medicine“, erschienen in Cancer Discovery [1]. Translations of any AACR materials into languages other than English are intended solely as a convenience to the non-English-reading public. Translation accuracy is neither guaranteed nor implied. If any questions arise related to the accuracy of the information contained in the translation, please refer to the English version of the AACR journal that is the Version of Record (VoR).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pauli, C., Moch, H. & Rubin, M.A. Etablierung einer Living Biobank. Pathologe 38 (Suppl 2), 160–168 (2017). https://doi.org/10.1007/s00292-017-0346-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00292-017-0346-1

Schlüsselwörter

Keywords

Navigation