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Autor(en) / Beteiligte
Titel
Abstract 166: Comparative analysis of response to treatments and molecular features of patient-derived tumor organoids versus cell lines and PDX derived from the same ovarian clear cell carcinoma
Ist Teil von
  • Cancer research (Chicago, Ill.), 2023-04, Vol.83 (7_Supplement), p.166-166
Erscheinungsjahr
2023
Quelle
Free E-Journal (出版社公開部分のみ)
Beschreibungen/Notizen
  • Abstract Preclinical models of cancer are essential for understanding cancer biology and developing effective cancer therapeutics, and potentially to guide clinical decision-making in the era of personalized medicine. Therefore, it is required that these preclinical cancer models should closely recapitulate original tumors, including histo- and molecular pathology, and match patient’s drug response. Here we described the establishment and the characterization of 7 models (4 cell lines, 2 PDTO and 1 PDX), all derived from an Ovarian Clear Cell Carcinoma (OCCC) including their responses to treatment and their comparison to the clinical response. This ovarian cancer subtype is considered as a rare tumor, clinically aggressive and chemoresistant, and very few models are yet available. We succeeded in establishing Patient-Derived Xenografts (PDX) from subcutaneous tumor fragment implantation onto nude mice, and a monolayer tumor-derived cell line and Patient-Derived Tumor Organoid (PDTO) after patient tumor dissociation. A PDTO model was derived from the PDX and 3 additional cell lines were established from this PDTO model, from the PDTO derived from the patient tumor and from the PDX model, respectively. In order to determine the relevance of each of these models, comprehensive characterization was performed based on morphological, histological, and transcriptomic analyses as well as on the evaluation of their response to the conventional treatments received by the patient. These results were compared to the features of the original tumor and to the clinical data when it was possible. Only the PDX and PDTO model derived from the patient tumor were able to retain the two components within the patient tumor; one component displaying histological features of OCCC and the other one consisted of undifferentiated cells. The patient from which all these models were derived was refractory to carboplatin as first line chemotherapy and resistant to the next lines of treatments, doxorubicin and gemcitabine, respectively. While tumor cell lines were sensitive to these treatments, PDX and PDTO models displayed resistance to the 3 drugs. The transcriptomic analysis was also consistent with these results since the models recapitulating faithfully the clinical response did cluster together away from the other classical cell culture models. Then we investigated drugs that were not used in the patient clinical management and we identified the HDAC inhibitor Belinostat as a potential effective treatments based on the response of PDTO. In conclusion, PDX and PDTO appear thus to be the most relevant models, but PDTO seem to present all the necessary prerequisites for predictive purposes and could constitute relevant tools for therapeutic decision support in the context of these particularly aggressive cancers refractory to conventional treatments. Citation Format: Lucie Thorel, Pierre-Marie Morice, Hippolyte Paysant, Romane Florent, Guillaume Babin, Cecilia Thomine, Marion Perreard, Edwige Abeilard, Florence Giffard, Emilie Brotin, Christophe Denoyelle, Celine Villenet, Melanie Briand, Alexandra Leconte, Florence Joly, Enora Dolivet, Didier Goux, Cecile Blanc-Fournier, Corinne Jeanne, Marie Villedieu, Matthieu Meryet-Figuiere, Martin Figeac, Laurent Poulain, Louis-Bastien Weiswald. Comparative analysis of response to treatments and molecular features of patient-derived tumor organoids versus cell lines and PDX derived from the same ovarian clear cell carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 166.
Sprache
Englisch
Identifikatoren
ISSN: 1538-7445
eISSN: 1538-7445
DOI: 10.1158/1538-7445.AM2023-166
Titel-ID: cdi_crossref_primary_10_1158_1538_7445_AM2023_166
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