Models of Intrahepatic Cholangiocarcinoma: Novel Tools and Therapeutic Applications
the ONA take:
Treatment options for intrahepatic cholangiocarcinoma (iCCA) are limited; its aggressive nature leads to early metastasis limiting the applicability of surgical resection, and is typically not responsive to chemotherapy. The prognosis for patients with iCCA is very poor, and recent studies show that the incidence and mortality is on the rise.
In order to develop novel early detection methods and therapeutic interventions, researchers have been utilizing various strategies to model iCCA. An array of models in both genetically engineered mouse models (GEMMs) and human cancer cell lines — chemical/surgical, Cre-Lox, SB, CRISPR/Cas9, and Patient-derived — enable researchers to study oncogenic gene activation or inactivation, downstream pathways, and the testing of potential therapies.
For this review, the authors present a detailed look of the models used to study iCCA, and concluded that “these advanced systems will enable a more thorough understanding of the development and progression of the disease and will provide flexible platforms for the evaluation of new treatments in a precise manner.”
Gastrointestinal Cancer: Targets and Therapy
Abstract: Intrahepatic cholangiocarcinoma (iCCA) is the second most common hepatic malignancy, and a number of recent studies have identified an increasing trend in incidence and mortality. As an aggressive disease characterized by early metastasis, surgical resection is not an option for most patients, and chemotherapy has limited benefit. Thus, the prognosis is extremely poor, warranting the development of novel models to improve detection and treatment strategies for this lethal cancer. In this regard, significant technological advancements have provided key tools to model and study iCCA. Furthermore, these technologies are addressing the need for models that can readily be adapted to address different genetic contexts, an important consideration for genetically diverse cancers such as iCCA. In this review, we outline these various available tools, discussing specifically how they have been employed to study iCCA while highlighting important therapeutic implications. Finally, we discuss novel strategies utilizing patient-derived tumor tissue which have promising translational applications.
Keywords: cholangiocarcinoma, biliary cancer, models, cell of origin, Cre-Lox, sleeping beauty
Intrahepatic cholangiocarcinoma (iCCA) is an extremely lethal cancer with limited therapeutic options; therefore, novel approaches to early detection and treatment are desperately needed.1 Key to this is the development of models that accurately recapitulate critical aspects of human iCCA to both better understand the underlying biology of the disease and provide systems in which new therapeutic strategies can be explored. Furthermore, iCCA is a genetically diverse cancer, and designing flexible models that can readily be adapted to reflect this genetic complexity is of paramount importance.
In this review, we discuss the various strategies that have been employed to model iCCA. Throughout, we highlight important biological insights made possible by the specific models and also discuss related therapeutic implications. In deciding how to best utilize these models and how to approach the development of new models, there are a number of important considerations, which are outlined in Box 1. Balancing these often requires a combination of complementary approaches, which is evident in the crossover of technologies in some of the sections of this review. To date, Cre-Lox-based genetically engineered mouse models (GEMMs) have proven to be an invaluable resource for studying iCCA, and therefore, we devote significant space to describing their utility and contributions. We further discuss novel approaches that have been applied to modeling iCCA using non-germline-based technologies. Finally, we focus on recent developments that rely on patient-derived tumor tissue, patient-derived tumor xenografts (PDTXs), and organoids, which have significant potential for translational applications.