Abstract
To generate robust patient-derived xenograft (PDX) models for epithelial ovarian cancer (EOC), analyze the resemblance of PDX models to the original tumors, and explore factors affecting engraftment rates, fresh cancer tissues from a consecutive cohort of 158 patients with EOC were collected to construct subcutaneous PDX models. Paired samples of original tumors and PDX tumors were compared at the genome, transcriptome, protein levels, and the platinum-based chemotherapy response was evaluated to ensure the reliability of the PDXs. Univariate and multivariate analyses were used to determine the factors affecting the engraftment rates. The engraftment success rate was 58.23% (92/158) over 3–6 months. The Ki-67 index and receiving neoadjuvant chemotherapy can affect the engraftment rate in primary patients. The PDX models generated in this study were found to retain the histomorphology, protein expression, and genetic alteration patterns of the original tumors, despite the transcriptomic differences observed. Clinically, the PDX models demonstrated a high degree of similarity with patients in terms of the chemotherapy response and could predict prognosis. Thus, the PDX model can be considered a promising and reliable preclinical tool for personalized and precise treatment.
Highlights
Epithelial ovarian cancer (EOC) ranks first in mortality among all gynecologic malignancies
This study aims to develop patient-derived xenograft (PDX) models from a consecutive cohort of epithelial ovarian cancer (EOC) patients to identify whether PDXs could be authentic drug screening tools by comparing morphologic, molecular, and clinical manifestations between PDXs and the original tumors
This consecutive cohort included 130 cases of high-grade serous ovarian cancer (HGSOC), 12 cases of endometrioid ovarian cancer (EC), 11 cases of clear cell ovarian cancer (CC), 3 cases of mucinous ovarian cancer, and two cases of low-grade serous ovarian cancer (LGSOC), 92 (58.23%) of which were successfully developed into PDXs
Summary
Epithelial ovarian cancer (EOC) ranks first in mortality among all gynecologic malignancies. Animal models can help to screen new antitumor drugs and optimize medication regimens [4, 5]. Stable cell lines and cell line-derived xenografts (CDXs) have been used in drug screening tests, but they do not well reflect the patient state due to the lack of cancer heterogeneity and a tumor microenvironment; approximately 88% of new drugs identified by CDXs fail in clinical research [6,7,8]. PDXs for Epithelial Ovarian Cancer immunodeficient mice, most resemble the original tumor, which can help clinicians better understand tumor behavior and facilitate therapeutic exploration [6, 9, 10]. CDXs instead of PDXs were subsequently used as drug-screening tools by the National Cancer Institute (NCI) for a long time because PDXs require fresh samples, are more time consuming, and have a low engraftment rate. PDXs have been successfully developed to model lung cancer, gastrointestinal cancer, breast cancer, and gynecologic cancer [10, 13,14,15,16,17]
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