Patient-Derived Xenograft Models for Intrahepatic Cholangiocarcinoma and Their Application in Guiding Personalized Medicine

Abstract

BackgroundIntrahepatic cholangiocarcinoma (ICC) remains one of the most intractable malignancies. The development of effective drug treatments for ICC is seriously hampered by the lack of reliable tumor models. At present, patient derived xenograft (PDX) models prove to accurately reflect the genetic and biological diversity required to decipher tumor biology and therapeutic vulnerabilities. This study was designed to investigate the establishment and potential application of PDX models for guiding personalized medicine and identifying potential biomarker for lenvatinib resistance.MethodsWe generated PDX models from 89 patients with ICC and compared the morphological and molecular similarities of parental tumors and passaged PDXs. The clinicopathologic features affecting PDX engraftment and the prognostic significance of PDX engraftment were analyzed. Drug treatment responses were analyzed in IMF-138, IMF-114 PDX models and corresponding patients. Finally, lenvatinib treatment response was examined in PDX models and potential drug resistance mechanism was revealed.ResultsForty-nine PDX models were established (take rate: 55.1%). Successful PDX engraftment was associated with negative HbsAg (P = 0.031), presence of mVI (P = 0.001), poorer tumor differentiation (P = 0.023), multiple tumor number (P = 0.003), presence of lymph node metastasis (P = 0.001), and later TNM stage (P = 0.039). Moreover, patients with tumor engraftment had significantly shorter time to recurrence (TTR) (P < 0.001) and worse overall survival (OS) (P < 0.001). Multivariate analysis indicated that PDX engraftment was an independent risk factor for shortened TTR (HR = 1.84; 95% CI, 1.05–3.23; P = 0.034) and OS (HR = 2.13; 95% CI, 1.11–4.11; P = 0.024). PDXs were histologically and genetically similar to their parental tumors. We also applied IMF-138 and IMF-114 PDX drug testing results to guide clinical treatment for patients with ICC and found similar treatment responses. PDX models also facilitated personalized medicine for patients with ICC based on drug screening results using whole exome sequencing data. Additionally, PDX models reflected the heterogeneous sensitivity to lenvatinib treatment and CDH1 might be vital to lenvatinib-resistance.ConclusionPDX models provide a powerful platform for preclinical drug discovery, and potentially facilitate the implementation of personalized medicine and improvement of survival of ICC cancer patient.

AZGP1 activation by lenvatinib suppresses intrahepatic cholangiocarcinoma epithelial-mesenchymal transition through the TGF-β1/Smad3 pathway

“…Direct implantation of human primary tumor cells into immunodeficient mice was used to establish a PDX model [ 49 , 50 ], which showed morphology, histology and immunoreactivity similar to the tumor of the primary patient [ 51 ], making it an excellent model for testing antitumor drugs. Based on the PDX model, we assessed lenvatinib’s effectiveness in treating ICC, and the results were consistent with previous research findings [ 16 ]. Lenvatinib seems to inhibit the proliferation, migration, invasion, and EMT of ICC.…”

“…For example, a study reported that the combination of a PD-1 inhibitor and lenvatinib was effective in the treatment of ICC bone metastasis [ 15 ]. An ICC patient-derived xenograft (PDX) model was also used to reveal the effect of lenvatinib [ 16 ]. However, its role and mechanism in ICC remain unclear.…”

Advancements and application prospects of three-dimensional models for primary liver cancer: a comprehensive review

“…However, Mini-PDX models cannot simulate the tumor microenvironment and the generation of tumor vasculature, resulting in lower sensitivity to targeted drugs compared to cytotoxic drugs. Sorafenib and lenvatinib, as FDA-approved systemic therapies for HCC, showed similar treatment responses in HCC PDX models compared to the primary patients, indicating that PDX models are effective tools for predicting the response to targeted therapy (Tiao et al, 2016;Gao et al, 2021). In a retrospective analysis, comparing the treatment outcomes of 92 patients with advanced solid tumors with the sensitivity of the corresponding PDX models to the same drugs, the sensitivity and specificity of PDX drug screening were 96% and 70%, respectively, with a positive predictive value of 85% and a negative predictive value of 91% (Izumchenko et al, 2017).…”