Abstract
(1) Background. PDX models have become the preferred tool in research laboratories seeking to improve development and pre-clinical testing of new drugs. PDXs have been shown to capture the cellular and molecular characteristics of human tumors better than simpler cell line-based models. More recently, however, hints that PDXs may change their characteristics over time have begun to emerge, emphasizing the need for comprehensive analysis of PDX evolution. (2) Methods. We established a panel of high-grade serous ovarian carcinoma (HGSOC) PDXs and developed and validated a 300-SNP signature that can be successfully utilized to assess genetic drift across PDX passages and detect PDX contamination with lymphoproliferative tissues. In addition, we performed a detailed histological characterization and functional assessment of multiple PDX passages. (3) Results. Our data show that the PDXs remain largely stable throughout propagation, with marginal genetic drift at the time of PDX initiation and adaptation to mouse host. Importantly, our PDX lines retained the major histological characteristics of the original patients' tumors even after multiple passages in mice, demonstrating a strong concordance with the clinical responses of their corresponding patients. (4) Conclusions. Our data underline the value of defined HGSOC PDXs as a pre-clinical tumor model.
Highlights
High-grade serous ovarian carcinoma is the most aggressive ovarian cancer subtype, and accounts for two-thirds of all ovarian cancer deaths, making it by far the most lethal gynecological malignancy [1,2]
Between May 2015 and July 2019, a total of 43 chemotherapy-naïve tumor samples were collected from patients undergoing debulking surgery for high-grade serous ovarian cancer
We observed a significant increase in single nucleotide polymorphisms (SNPs) alteration rate, reflected as the acquisition of 4.7% new variants in the initial patientderived xenograft (PDX) passage (P1) when compared with an SNP profile of the corresponding patient’s tumor (PT) (Figure 3A)
Summary
High-grade serous ovarian carcinoma is the most aggressive ovarian cancer subtype, and accounts for two-thirds of all ovarian cancer deaths, making it by far the most lethal gynecological malignancy [1,2]. The collective data demonstrate that ~85% of anticancer drugs entering clinical trials fail to demonstrate sufficient safety or efficacy to gain regulatory approval [3]. This high failure rate reflects the weak understanding of the complexity of human cancer and the limitations of existing pre-clinical tumor models [3,4]. A comprehensive study revealed profound molecular differences between the common cell lines used to model high-grade serous ovarian carcinoma and patient tumor specimens of the same cancer subtype, which highlights the serious limitations of these models’ predictive value in terms of clinical efficacy [1]. There is a need for tumor models that better replicate the diversity and heterogeneity of defined ovarian cancer subtypes
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