A platform for efficient establishment and drug-response profiling of high-grade serous ovarian cancer organoids

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  • Matías Marín Falco
  • Yilin Li
  • Kari Lavikka
  • Mette C. Kriegbaum
  • Jaana Oikkonen
  • Elin J. Pietras
  • Erdogan Pekcan Erkan
  • Jun Dai
  • Anastasia Lundgren
  • Tarja Lamminen
  • Katja Kaipio
  • Jutta Huvila
  • Anni Virtanen
  • Pernille Christiansen
  • Kaisa Huhtinen
  • Olli Carpén
  • Johanna Hynninen
  • Sampsa Hautaniemi
  • Anna Vähärautio

The broad research use of organoids from high-grade serous ovarian cancer (HGSC) has been hampered by low culture success rates and limited availability of fresh tumor material. Here, we describe a method for generation and long-term expansion of HGSC organoids with efficacy markedly improved over previous reports (53% vs. 23%–38%). We established organoids from cryopreserved material, demonstrating the feasibility of using viably biobanked tissue for HGSC organoid derivation. Genomic, histologic, and single-cell transcriptomic analyses revealed that organoids recapitulated genetic and phenotypic features of original tumors. Organoid drug responses correlated with clinical treatment outcomes, although in a culture conditions-dependent manner and only in organoids maintained in human plasma-like medium (HPLM). Organoids from consenting patients are available to the research community through a public biobank and organoid genomic data are explorable through an interactive online tool. Taken together, this resource facilitates the application of HGSC organoids in basic and translational ovarian cancer research.

Original languageEnglish
JournalDevelopmental Cell
Volume58
Issue number12
Pages (from-to)1106-1121.e7
Number of pages24
ISSN1534-5807
DOIs
Publication statusPublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

    Research areas

  • 3D cell culture, culture conditions, functional precision medicine, high-grade serous ovarian cancer, HPLM, organoid biobank, organoids, personalized medicine, scRNA-seq, tumor models

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