Generation and Application of Patient-Derived Xenograft Models in Pancreatic Cancer Research
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers worldwide, with a 5-year survival rate of approximately 6%. The poor prognosis is largely due to late diagnosis, genomic complexity, and limited effective therapeutic options. To address these challenges, patient-derived xenograft (PDX) models have emerged as a valuable tool in pancreatic cancer research. This article provides a comprehensive overview of the generation, advantages, applications, limitations, and future perspectives of PDX models in PDAC research.
Comparison Between Patient-Derived Xenograft Models and Other Models
Several pre-clinical models have been developed to study PDAC, including pancreatic cancer cell lines (CCLs), organoids, genetically engineered mouse models (GEMMs), circulating tumor cell (CTC)-derived xenografts (CDXs), and PDX models. Each model has its strengths and limitations.
Cell line-derived xenografts (CDXs) are commonly used but have significant drawbacks. CCLs often undergo genetic transformations during in vitro culture, leading to differences from the original tumor. Additionally, CDXs fail to recapitulate the tumor microenvironment, which is crucial for understanding cancer biology and drug response. GEMMs, while useful for studying specific genetic mutations, are limited by their high cost, long latency periods, and inability to fully represent the genetic diversity of human pancreatic cancer.
In contrast, PDX models are derived directly from patient tumors and implanted into immunodeficient mice. These models preserve the histological and genetic characteristics of the original tumor, including the tumor microenvironment. PDX models have been shown to maintain tumor morphology and genetic stability across multiple passages, making them a more reliable tool for drug screening and personalized medicine.
Generation of Pancreatic Cancer PDX Models
The process of generating PDX models involves implanting fresh primary or metastatic human cancer tissues into immunodeficient mice. The tumor tissue can be obtained from surgical resection, biopsy, or malignant ascites. The tissue is then cut into small pieces or dissociated into single-cell suspensions and implanted either subcutaneously (heterotopic) or into the organ of origin (orthotopic).
Orthotopic models are generally preferred as they more closely mimic the tumor microenvironment and metastatic behavior of human cancer. However, subcutaneous implantation is more commonly used due to its higher success rate and simpler procedure. The choice of host mouse strain is critical, with NOD-SCID and NSG mice being the most commonly used due to their severe immunodeficiency.
The success rate of PDX model generation varies, with implantation rates ranging from 42.9% to 60%. Factors such as tumor size, metastatic lesions, and lymphovascular invasion can influence the success of model establishment. Once implanted, tumors are monitored for at least 100 days, and their growth is measured until they reach a volume of 1000 mm³.
Advantages of PDX Models in Cancer Research
PDX models offer several advantages over other pre-clinical models. They preserve the histological and genetic characteristics of the original tumor, including the tumor microenvironment. Studies have shown that PDX models maintain the cellular and histological features, stromal elements, and gene expression profiles of the patient tumor across multiple passages.
PDX models are particularly useful for drug screening and biomarker development. They have been shown to accurately predict patient response to chemotherapy and targeted therapies. For example, a study involving 32 pancreatic cancer patients demonstrated that PDX models treated with gemcitabine showed a high correlation with patient response, with a 90% prediction rate for drug sensitivity and 97% for drug resistance.
PDX models also allow for the study of tumor biology, including the mechanisms of tumorigenesis, metastasis, and drug resistance. They provide a platform for investigating the tumor microenvironment and the interactions between cancer cells and stromal cells, which are critical for understanding cancer progression and developing new therapeutic strategies.
Applications of PDX Models in Pancreatic Cancer Research
Drug Screening and Biomarker Development
PDX models have become an essential tool in drug discovery and development. They are used to screen new therapeutic agents and identify biomarkers of drug response. For example, the efficacy of the centromere protein E inhibitor GSK923295 was evaluated in PDX models of hepatocellular carcinoma, demonstrating its potential as an anti-cancer drug.
In pancreatic cancer, PDX models have been used to test the efficacy of various chemotherapeutic agents, including gemcitabine, oxaliplatin, and lurbinectedin. These studies have shown that PDX models can accurately predict patient response to treatment, providing valuable information for personalized medicine.
PDX models are also used to identify biomarkers of drug response and resistance. For example, the expression of the gemcitabine-activating enzyme deoxycytidine kinase has been identified as a predictor of drug efficacy in PDX models. Similarly, the HER2 receptor has been studied as a biomarker for trastuzumab response in pancreatic cancer.
Study of Tumor Biology
PDX models provide a platform for studying the molecular mechanisms of pancreatic cancer, including tumorigenesis, metastasis, and drug resistance. For example, the role of the CXCL12 chemokine in suppressing tumor growth and metastasis has been investigated in PDX models. Similarly, the Hedgehog signaling pathway has been targeted in PDX models to improve drug delivery and efficacy.
PDX models also allow for the study of the tumor microenvironment and the interactions between cancer cells and stromal cells. This has led to the identification of potential therapeutic targets within the microenvironment, such as tumor-associated macrophages and the Hedgehog signaling pathway.
Personalized Medicine
PDX models are increasingly being used in personalized medicine to guide treatment decisions for individual patients. By preserving the tumor biology of the patient, PDX models can be used to test the efficacy of different therapeutic regimens and identify the most effective treatment for each patient.
For example, PDX models have been used to test the efficacy of combination therapies targeting the epidermal growth factor receptor (EGFR) and HER2 receptors, as well as the downstream KRAS effectors. These studies have provided a rationale for the co-inhibition of these pathways in the treatment of pancreatic cancer.
Limitations and Challenges in Pancreatic Cancer PDX Models
Despite their advantages, PDX models have several limitations. The success rate of model generation can be low, particularly for models derived from biopsy or ascitic fluid. The slow growth of PDX tumors can also be a limitation, as it may take several months to generate a model, which is not feasible for patients with advanced disease.
Another limitation is the replacement of human stromal components by murine elements in PDX models, particularly in subcutaneous implantation. This can affect the accuracy of the model in recapitulating the human tumor microenvironment.
Additionally, PDX models are not suitable for evaluating immune-modulating agents, as they are generated in immunodeficient mice. To address this limitation, humanized PDX models with a human immune system are being developed.
Future Perspectives of Pancreatic Cancer PDX Models
The next generation of PDX models will likely involve the use of genetically modified humanized mice to study immune-modulating agents. These models will allow for the investigation of the interactions between the immune system and cancer cells, providing valuable insights into the mechanisms of immune evasion and resistance to immunotherapy.
New models, such as MiniPDX, are also being developed to increase the efficiency of PDX model generation. These models are currently under investigation and may offer a more rapid and cost-effective alternative to traditional PDX models.
PDX models are also being used in co-clinical trials, where they are developed from patients enrolled in clinical trials and treated with the same regimen. This allows for the monitoring of clinical response and the identification of biomarkers of drug response and resistance.
Conclusion
PDX models have become an essential tool in pancreatic cancer research, offering a more accurate representation of human tumor biology than other pre-clinical models. They are widely used in drug screening, biomarker development, and personalized medicine, and provide valuable insights into the molecular mechanisms of pancreatic cancer. Despite their limitations, PDX models have the potential to improve the prognosis of patients with pancreatic cancer and guide the development of new therapeutic strategies.
doi.org/10.1097/CM9.0000000000000524
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