Université de Caen Normandie, INSERM U1086 ANTICIPE (Interdisciplinary Research Unit for Cancers Prevention and Treatment), BioTICLA Laboratory (Precision Medicine for Ovarian Cancers), 3 Avenue du Général Harris, BP 45026, 14 076, Caen, Cedex 05, France.
Université de Caen Normandie, Services Unit PLATON, ORGAPRED Core Facility, Caen, France.
J Exp Clin Cancer Res. 2023 Oct 7;42(1):260. doi: 10.1186/s13046-023-02809-8.
In the era of personalized medicine, the establishment of preclinical models of cancer that faithfully recapitulate original tumors is essential to potentially guide clinical decisions.
We established 7 models [4 cell lines, 2 Patient-Derived Tumor Organoids (PDTO) and 1 Patient-Derived Xenograft (PDX)], all derived from the same Ovarian Clear Cell Carcinoma (OCCC). 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 treatments received by the patient. These results were compared to the clinical data.
Only the PDX and PDTO models derived from the patient tumor were able to recapitulate the patient tumor heterogeneity. The patient was refractory to carboplatin, doxorubicin and gemcitabine, while tumor cell lines were sensitive to these treatments. In contrast, PDX and PDTO models displayed resistance to the 3 drugs. The transcriptomic analysis was consistent with these results since the models recapitulating faithfully the clinical response grouped together away from the other classical 2D cell culture models. We next investigated the potential of drugs that have not been used in the patient clinical management and we identified the HDAC inhibitor belinostat as a potential effective treatment based on PDTO response.
PDX and PDTO appear to be the most relevant models, but only 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.
在个性化医学时代，建立能够真实重现原始肿瘤的癌症临床前模型对于指导临床决策至关重要。
我们建立了 7 种模型[4 种细胞系、2 种患者来源的肿瘤类器官（PDTO）和 1 种患者来源的异种移植瘤（PDX）]，均源自同一例卵巢透明细胞癌（OCCC）患者。为了确定这些模型中的每一种的相关性，我们基于形态学、组织学和转录组学分析以及对它们对患者接受的治疗的反应评估进行了全面表征，并将这些结果与临床数据进行了比较。
只有源自患者肿瘤的 PDX 和 PDTO 模型能够重现患者肿瘤的异质性。患者对卡铂、多柔比星和吉西他滨耐药，而肿瘤细胞系对这些治疗敏感。相比之下，PDX 和 PDTO 模型对这 3 种药物均有耐药性。转录组学分析与这些结果一致，因为真实重现临床反应的模型与其他经典的 2D 细胞培养模型分组不同。我们接下来研究了尚未用于患者临床管理的药物的潜力，并根据 PDTO 的反应确定了 HDAC 抑制剂 belinostat 作为一种潜在有效的治疗方法。
PDX 和 PDTO 似乎是最相关的模型，但只有 PDTO 似乎具有预测目的所需的所有必要前提条件，并且可以在这些对常规治疗耐药的特别侵袭性癌症的治疗决策支持中构成相关工具。
