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Journal articles on the topic 'Preclinical cancer models'

Author: Grafiati

Published: 1 March 2025

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1

KARNEZIS, ANTHONY N., and KATHLEEN R. CHO. "Preclinical Models of Ovarian Cancer." Clinical Obstetrics and Gynecology 60, no. 4 (December 2017): 789–800. http://dx.doi.org/10.1097/grf.0000000000000312.

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2

Sedlack, Andrew J. H., Samual J. Hatfield, Suresh Kumar, Yasuhiro Arakawa, Nitin Roper, Nai-Yun Sun, Naris Nilubol, et al. "Preclinical Models of Adrenocortical Cancer." Cancers 15, no. 11 (May 23, 2023): 2873. http://dx.doi.org/10.3390/cancers15112873.

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Adrenocortical cancer is an aggressive endocrine malignancy with an incidence of 0.72 to 1.02 per million people/year, and a very poor prognosis with a five-year survival rate of 22%. As an orphan disease, clinical data are scarce, meaning that drug development and mechanistic research depend especially on preclinical models. While a single human ACC cell line was available for the last three decades, over the last five years, many new in vitro and in vivo preclinical models have been generated. Herein, we review both in vitro (cell lines, spheroids, and organoids) and in vivo (xenograft and genetically engineered mouse) models. Striking leaps have been made in terms of the preclinical models of ACC, and there are now several modern models available publicly and in repositories for research in this area.

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Sewduth, Raj N., and Konstantina Georgelou. "Relevance of Carcinogen-Induced Preclinical Cancer Models." Journal of Xenobiotics 14, no. 1 (January 5, 2024): 96–109. http://dx.doi.org/10.3390/jox14010006.

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Chemical agents can cause cancer in animals by damaging their DNA, mutating their genes, and modifying their epigenetic signatures. Carcinogen-induced preclinical cancer models are useful for understanding carcinogen-induced human cancers, as they can reproduce the diversity and complexity of tumor types, as well as the interactions with the host environment. However, these models also have some drawbacks that limit their applicability and validity. For instance, some chemicals may be more effective or toxic in animals than in humans, and the tumors may differ in their genetics and phenotypes. Some chemicals may also affect normal cells and tissues, such as by causing oxidative stress, inflammation, and cell death, which may alter the tumor behavior and response to therapy. Furthermore, some chemicals may have variable effects depending on the exposure conditions, such as dose, route, and duration, as well as the animal characteristics, such as genetics and hormones. Therefore, these models should be carefully chosen, validated, and standardized, and the results should be cautiously interpreted and compared with other models. This review covers the main features of chemically induced cancer models, such as genetic and epigenetic changes, tumor environment, angiogenesis, invasion and metastasis, and immune response. We also address the pros and cons of these models and the current and future challenges for their improvement. This review offers a comprehensive overview of the state of the art of carcinogen-induced cancer models and provides new perspectives for cancer research.

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Jeon, Min Ji, and Bryan R. Haugen. "Preclinical Models of Follicular Cell-Derived Thyroid Cancer: An Overview from Cancer Cell Lines to Mouse Models." Endocrinology and Metabolism 37, no. 6 (December 31, 2022): 830–38. http://dx.doi.org/10.3803/enm.2022.1636.

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The overall prognosis of thyroid cancer is excellent, but some patients have grossly invasive disease and distant metastases with limited responses to systemic therapies. Thus, relevant preclinical models are needed to investigate thyroid cancer biology and novel treatments. Different preclinical models have recently emerged with advances in thyroid cancer genetics, mouse modeling and new cell lines. Choosing the appropriate model according to the research question is crucial to studying thyroid cancer. This review will discuss the current preclinical models frequently used in thyroid cancer research, from cell lines to mouse models, and future perspectives on patient-derived and humanized preclinical models in this field.

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McIntyre, Rebecca E., Simon J. A. Buczacki, Mark J. Arends, and David J. Adams. "Mouse models of colorectal cancer as preclinical models." BioEssays 37, no. 8 (June 26, 2015): 909–20. http://dx.doi.org/10.1002/bies.201500032.

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Zhu, Shaoming, Zheng Zhu, Ai-Hong Ma, Guru P. Sonpavde, Fan Cheng, and Chong-xian Pan. "Preclinical Models for Bladder Cancer Research." Hematology/Oncology Clinics of North America 35, no. 3 (June 2021): 613–32. http://dx.doi.org/10.1016/j.hoc.2021.02.007.

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Rodriguez, Cynthia, Paloma Valenzuela, Natzidielly Lerma, and Giulio Francia. "Preclinical Models to Study Breast Cancer." Clinical Cancer Drugs 1, no. 2 (May 31, 2014): 90–99. http://dx.doi.org/10.2174/2212697x113026660005.

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Kim, Minlee, Andrea L. Kasinski, and Frank J. Slack. "MicroRNA therapeutics in preclinical cancer models." Lancet Oncology 12, no. 4 (April 2011): 319–21. http://dx.doi.org/10.1016/s1470-2045(11)70067-5.

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Joshi, Kirtan, Tejas Katam, Akshata Hegde, Jianlin Cheng, Randall S. Prather, Kristin Whitworth, Kevin Wells, et al. "Pigs: Large Animal Preclinical Cancer Models." World Journal of Oncology 15, no. 2 (April 2024): 149–68. http://dx.doi.org/10.14740/wjon1763.

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Sedlack, Andrew J. H., Kimia Saleh-Anaraki, Suresh Kumar, Po Hien Ear, Kate E. Lines, Nitin Roper, Karel Pacak, et al. "Preclinical Models of Neuroendocrine Neoplasia." Cancers 14, no. 22 (November 17, 2022): 5646. http://dx.doi.org/10.3390/cancers14225646.

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Neuroendocrine neoplasia (NENs) are a complex and heterogeneous group of cancers that can arise from neuroendocrine tissues throughout the body and differentiate them from other tumors. Their low incidence and high diversity make many of them orphan conditions characterized by a low incidence and few dedicated clinical trials. Study of the molecular and genetic nature of these diseases is limited in comparison to more common cancers and more dependent on preclinical models, including both in vitro models (such as cell lines and 3D models) and in vivo models (such as patient derived xenografts (PDXs) and genetically-engineered mouse models (GEMMs)). While preclinical models do not fully recapitulate the nature of these cancers in patients, they are useful tools in investigation of the basic biology and early-stage investigation for evaluation of treatments for these cancers. We review available preclinical models for each type of NEN and discuss their history as well as their current use and translation.

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Engrácia, Diogo M., Catarina I. G. Pinto, and Filipa Mendes. "Cancer 3D Models for Metallodrug Preclinical Testing." International Journal of Molecular Sciences 24, no. 15 (July 25, 2023): 11915. http://dx.doi.org/10.3390/ijms241511915.

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Despite being standard tools in research, the application of cellular and animal models in drug development is hindered by several limitations, such as limited translational significance, animal ethics, and inter-species physiological differences. In this regard, 3D cellular models can be presented as a step forward in biomedical research, allowing for mimicking tissue complexity more accurately than traditional 2D models, while also contributing to reducing the use of animal models. In cancer research, 3D models have the potential to replicate the tumor microenvironment, which is a key modulator of cancer cell behavior and drug response. These features make cancer 3D models prime tools for the preclinical study of anti-tumoral drugs, especially considering that there is still a need to develop effective anti-cancer drugs with high selectivity, minimal toxicity, and reduced side effects. Metallodrugs, especially transition-metal-based complexes, have been extensively studied for their therapeutic potential in cancer therapy due to their distinctive properties; however, despite the benefits of 3D models, their application in metallodrug testing is currently limited. Thus, this article reviews some of the most common types of 3D models in cancer research, as well as the application of 3D models in metallodrug preclinical studies.

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12

Marchiò, S., and F. Bussolino. "Targeted nanomedicines for applications in preclinical cancer models." NANOMEDICINE, no. 6 (December 30, 2018): 5–13. http://dx.doi.org/10.24075/brsmu.2018.081.

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Despite substantial advancements in cancer management, a considerable proportion of patients cannot yet be cured. Strategies to address this open medical need are actively pursued and include two main approaches: 1) optimizing diagnostic protocols to detect tumors at early stages, and 2) designing personalized therapies to increase efficiency and selectivity of clinical interventions. Our recent work has been directed to a rationally-designed implementation of both approaches. Particularly, we have contributed to the development of nanomedicines that can be targeted to diseased tissues for theranostic purposes in preclinical models of human cancers. Such modular nanoscale systems proved to be versatile platforms to combine imaging and drug delivery for applications in the oncological field and could be a basis for future improvements.

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13

Guo, Haochuan, Xinru Xu, Jiaxi Zhang, Yajing Du, Xinbing Yang, Zhiheng He, Linjie Zhao, Tingming Liang, and Li Guo. "The Pivotal Role of Preclinical Animal Models in Anti-Cancer Drug Discovery and Personalized Cancer Therapy Strategies." Pharmaceuticals 17, no. 8 (August 9, 2024): 1048. http://dx.doi.org/10.3390/ph17081048.

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The establishment and utilization of preclinical animal models constitute a pivotal aspect across all facets of cancer research, indispensably contributing to the comprehension of disease initiation and progression mechanisms, as well as facilitating the development of innovative anti-cancer therapeutic approaches. These models have emerged as crucial bridges between basic and clinical research, offering multifaceted support to clinical investigations. This study initially focuses on the importance and benefits of establishing preclinical animal models, discussing the different types of preclinical animal models and recent advancements in cancer research. It then delves into cancer treatment, studying the characteristics of different stages of tumor development and the development of anti-cancer drugs. By integrating tumor hallmarks and preclinical research, we elaborate on the path of anti-cancer drug development and provide guidance on personalized cancer therapy strategies, including synthetic lethality approaches and novel drugs widely adopted in the field. Ultimately, we summarize a strategic framework for selecting preclinical safety experiments, tailored to experimental modalities and preclinical animal species, and present an outlook on the prospects and challenges associated with preclinical animal models. These models undoubtedly offer new avenues for cancer research, encompassing drug development and personalized anti-cancer protocols. Nevertheless, the road ahead continues to be lengthy and fraught with obstacles. Hence, we encourage researchers to persist in harnessing advanced technologies to refine preclinical animal models, thereby empowering these emerging paradigms to positively impact cancer patient outcomes.

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Ullo, Maria F., and Jeremy S. Logue. "Re-thinking preclinical models of cancer metastasis." Oncoscience 5, no. 9-10 (August 22, 2018): 252–53. http://dx.doi.org/10.18632/oncoscience.450.

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15

Bennett, Christina. "Building Preclinical Breast Cancer Models for All." Clinical OMICs 6, no. 3 (May 2019): 33. http://dx.doi.org/10.1089/clinomi.06.03.23.

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16

LIPKIN, MARTIN, KAN YANG, WINFRIED EDELMANN, LEXUN XUE, KUNHUA FAN, MAURO RISIO, HAROLD NEWMARK, and RAJU KUCHERAPATI. "Preclinical Mouse Models for Cancer Chemoprevention Studies." Annals of the New York Academy of Sciences 889, no. 1 CANCER PREVEN (October 1999): 14–19. http://dx.doi.org/10.1111/j.1749-6632.1999.tb08719.x.

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17

Weber, Achim, Tracy O'Connor, and Mathias Heikenwalder. "Next Generation of Preclinical Liver Cancer Models." Clinical Cancer Research 21, no. 19 (July 13, 2015): 4254–56. http://dx.doi.org/10.1158/1078-0432.ccr-15-1152.

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18

Pedroza, Diego A., Yang Gao, Xiang H. F. Zhang, and Jeffrey M. Rosen. "Leveraging preclinical models of metastatic breast cancer." Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1879, no. 5 (September 2024): 189163. http://dx.doi.org/10.1016/j.bbcan.2024.189163.

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19

Ibarrola-Villava, Maider, Andrés Cervantes, and Alberto Bardelli. "Preclinical models for precision oncology." Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1870, no. 2 (December 2018): 239–46. http://dx.doi.org/10.1016/j.bbcan.2018.06.004.

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20

Di Renzo, Maria Flavia, and Simona Corso. "Patient-Derived Cancer Models." Cancers 12, no. 12 (December 15, 2020): 3779. http://dx.doi.org/10.3390/cancers12123779.

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For many decades, basic and preclinical cancer research has been based on the use of established, commercially available cell lines, originally derived from patients’ samples but adapted to grow indefinitely in artificial culture conditions, and on xenograft models developed by injection of these cells in immunocompromised animals [...]

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21

Krawczyk, Ewa, and Joanna Kitlińska. "Preclinical Models of Neuroblastoma—Current Status and Perspectives." Cancers 15, no. 13 (June 23, 2023): 3314. http://dx.doi.org/10.3390/cancers15133314.

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Preclinical in vitro and in vivo models remain indispensable tools in cancer research. These classic models, including two- and three-dimensional cell culture techniques and animal models, are crucial for basic and translational studies. However, each model has its own limitations and typically does not fully recapitulate the course of the human disease. Therefore, there is an urgent need for the development of novel, advanced systems that can allow for efficient evaluation of the mechanisms underlying cancer development and progression, more accurately reflect the disease pathophysiology and complexity, and effectively inform therapeutic decisions for patients. Preclinical models are especially important for rare cancers, such as neuroblastoma, where the availability of patient-derived specimens that could be used for potential therapy evaluation and screening is limited. Neuroblastoma modeling is further complicated by the disease heterogeneity. In this review, we present the current status of preclinical models for neuroblastoma research, discuss their development and characteristics emphasizing strengths and limitations, and describe the necessity of the development of novel, more advanced and clinically relevant approaches.

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22

Knier, Natasha N., Sierra Pellizzari, Jiangbing Zhou, Paula J. Foster, and Armen Parsyan. "Preclinical Models of Brain Metastases in Breast Cancer." Biomedicines 10, no. 3 (March 13, 2022): 667. http://dx.doi.org/10.3390/biomedicines10030667.

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Breast cancer remains a leading cause of mortality among women worldwide. Brain metastases confer extremely poor prognosis due to a lack of understanding of their specific biology, unique physiologic and anatomic features of the brain, and limited treatment strategies. A major roadblock in advancing the treatment of breast cancer brain metastases (BCBM) is the scarcity of representative experimental preclinical models. Current models are predominantly based on the use of animal xenograft models with immortalized breast cancer cell lines that poorly capture the disease’s heterogeneity. Recent years have witnessed the development of patient-derived in vitro and in vivo breast cancer culturing systems that more closely recapitulate the biology from individual patients. These advances led to the development of modern patient-tissue-based experimental models for BCBM. The success of preclinical models is also based on the imaging technologies used to detect metastases. Advances in animal brain imaging, including cellular MRI and multimodality imaging, allow sensitive and specific detection of brain metastases and monitoring treatment responses. These imaging technologies, together with novel translational breast cancer models based on patient-derived cancer tissues, represent a unique opportunity to advance our understanding of brain metastases biology and develop novel treatment approaches. This review discusses the state-of-the-art knowledge in preclinical models of this disease.

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Yu, Jie-Zeng, Zsofia Kiss, Weijie Ma, Ruqiang Liang, and Tianhong Li. "Preclinical Models for Functional Precision Lung Cancer Research." Cancers 17, no. 1 (December 25, 2024): 22. https://doi.org/10.3390/cancers17010022.

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Patient-centered precision oncology strives to deliver individualized cancer care. In lung cancer, preclinical models and technological innovations have become critical in advancing this approach. Preclinical models enable deeper insights into tumor biology and enhance the selection of appropriate systemic therapies across chemotherapy, targeted therapies, immunotherapies, antibody–drug conjugates, and emerging investigational treatments. While traditional human lung cancer cell lines offer a basic framework for cancer research, they often lack the tumor heterogeneity and intricate tumor–stromal interactions necessary to accurately predict patient-specific clinical outcomes. Patient-derived xenografts (PDXs), however, retain the original tumor’s histopathology and genetic features, providing a more reliable model for predicting responses to systemic therapeutics, especially molecularly targeted therapies. For studying immunotherapies and antibody–drug conjugates, humanized PDX mouse models, syngeneic mouse models, and genetically engineered mouse models (GEMMs) are increasingly utilized. Despite their value, these in vivo models are costly, labor-intensive, and time-consuming. Recently, patient-derived lung cancer organoids (LCOs) have emerged as a promising in vitro tool for functional precision oncology studies. These LCOs demonstrate high success rates in growth and maintenance, accurately represent the histology and genomics of the original tumors and exhibit strong correlations with clinical treatment responses. Further supported by advancements in imaging, spatial and single-cell transcriptomics, proteomics, and artificial intelligence, these preclinical models are reshaping the landscape of drug development and functional precision lung cancer research. This integrated approach holds the potential to deliver increasingly accurate, personalized treatment strategies, ultimately enhancing patient outcomes in lung cancer.

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24

Hamacher, Rainer, and Sebastian Bauer. "Preclinical models for translational sarcoma research." Current Opinion in Oncology 29, no. 4 (July 2017): 275–85. http://dx.doi.org/10.1097/cco.0000000000000373.

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Dasgupta, Pushan, Veerakumar Balasubramanyian, John F. de Groot, and Nazanin K. Majd. "Preclinical Models of Low-Grade Gliomas." Cancers 15, no. 3 (January 18, 2023): 596. http://dx.doi.org/10.3390/cancers15030596.

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Diffuse infiltrating low-grade glioma (LGG) is classified as WHO grade 2 astrocytoma with isocitrate dehydrogenase (IDH) mutation and oligodendroglioma with IDH1 mutation and 1p/19q codeletion. Despite their better prognosis compared with glioblastoma, LGGs invariably recur, leading to disability and premature death. There is an unmet need to discover new therapeutics for LGG, which necessitates preclinical models that closely resemble the human disease. Basic scientific efforts in the field of neuro-oncology are mostly focused on high-grade glioma, due to the ease of maintaining rapidly growing cell cultures and highly reproducible murine tumors. Development of preclinical models of LGG, on the other hand, has been difficult due to the slow-growing nature of these tumors as well as challenges involved in recapitulating the widespread genomic and epigenomic effects of IDH mutation. The most recent WHO classification of CNS tumors emphasizes the importance of the role of IDH mutation in the classification of gliomas, yet there are relatively few IDH-mutant preclinical models available. Here, we review the in vitro and in vivo preclinical models of LGG and discuss the mechanistic challenges involved in generating such models and potential strategies to overcome these hurdles.

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McCloskey, Curtis, Galaxia Rodriguez, Kristianne Galpin, and Barbara Vanderhyden. "Ovarian Cancer Immunotherapy: Preclinical Models and Emerging Therapeutics." Cancers 10, no. 8 (July 26, 2018): 244. http://dx.doi.org/10.3390/cancers10080244.

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Immunotherapy has emerged as one of the most promising approaches for ovarian cancer treatment. The tumor microenvironment (TME) is a key factor to consider when stimulating antitumoral responses as it consists largely of tumor promoting immunosuppressive cell types that attenuate antitumor immunity. As our understanding of the determinants of the TME composition grows, we have begun to appreciate the need to address both inter- and intra-tumor heterogeneity, mutation/neoantigen burden, immune landscape, and stromal cell contributions. The majority of immunotherapy studies in ovarian cancer have been performed using the well-characterized murine ID8 ovarian carcinoma model. Numerous other animal models of ovarian cancer exist, but have been underutilized because of their narrow initial characterizations in this context. Here, we describe animal models that may be untapped resources for the immunotherapy field because of their shared genomic alterations and histopathology with human ovarian cancer. We also shed light on the strengths and limitations of these models, and the knowledge gaps that need to be addressed to enhance the utility of preclinical models for testing novel immunotherapeutic approaches.

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27

Tosca, Elena M., Davide Ronchi, Daniele Facciolo, and Paolo Magni. "Replacement, Reduction, and Refinement of Animal Experiments in Anticancer Drug Development: The Contribution of 3D In Vitro Cancer Models in the Drug Efficacy Assessment." Biomedicines 11, no. 4 (March 30, 2023): 1058. http://dx.doi.org/10.3390/biomedicines11041058.

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In the last decades three-dimensional (3D) in vitro cancer models have been proposed as a bridge between bidimensional (2D) cell cultures and in vivo animal models, the gold standards in the preclinical assessment of anticancer drug efficacy. 3D in vitro cancer models can be generated through a multitude of techniques, from both immortalized cancer cell lines and primary patient-derived tumor tissue. Among them, spheroids and organoids represent the most versatile and promising models, as they faithfully recapitulate the complexity and heterogeneity of human cancers. Although their recent applications include drug screening programs and personalized medicine, 3D in vitro cancer models have not yet been established as preclinical tools for studying anticancer drug efficacy and supporting preclinical-to-clinical translation, which remains mainly based on animal experimentation. In this review, we describe the state-of-the-art of 3D in vitro cancer models for the efficacy evaluation of anticancer agents, focusing on their potential contribution to replace, reduce and refine animal experimentations, highlighting their strength and weakness, and discussing possible perspectives to overcome current challenges.

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28

Pasupuleti, Vasavi, Lalitkumar Vora, Renuka Prasad, D. N. Nandakumar, and Dharmendra Kumar Khatri. "Glioblastoma preclinical models: Strengths and weaknesses." Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1879, no. 1 (January 2024): 189059. http://dx.doi.org/10.1016/j.bbcan.2023.189059.

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29

Park, Mi Kyung, Chang Hoon Lee, and Ho Lee. "Mouse models of breast cancer in preclinical research." Laboratory Animal Research 34, no. 4 (2018): 160. http://dx.doi.org/10.5625/lar.2018.34.4.160.

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30

Burris, Howard A. "Preclinical Investigations With Epothilones in Breast Cancer Models." Seminars in Oncology 35 (April 2008): S15—S21. http://dx.doi.org/10.1053/j.seminoncol.2008.02.002.

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31

You, Ming, and Gerald Bergman. "PRECLINICAL AND CLINICAL MODELS OF LUNG CANCER CHEMOPREVENTION." Hematology/Oncology Clinics of North America 12, no. 5 (October 1998): 1037–53. http://dx.doi.org/10.1016/s0889-8588(05)70040-x.

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32

Drost, R. M., and J. Jonkers. "Preclinical mouse models for BRCA1-associated breast cancer." British Journal of Cancer 101, no. 10 (September 29, 2009): 1651–57. http://dx.doi.org/10.1038/sj.bjc.6605350.

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33

Bibby, M. C. "Orthotopic models of cancer for preclinical drug evaluation." European Journal of Cancer 40, no. 6 (April 2004): 852–57. http://dx.doi.org/10.1016/j.ejca.2003.11.021.

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Sausville, E. A., and David R. Newell. "Preclinical models in cancer drug discovery and development." European Journal of Cancer 40, no. 6 (April 2004): 783–84. http://dx.doi.org/10.1016/j.ejca.2004.01.010.

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35

Mahmoudian, Reihaneh Alsadat, Moein Farshchian, Fatemeh Fardi Golyan, Parvaneh Mahmoudian, Ali Alasti, Vahid Moghimi, Mina Maftooh, et al. "Preclinical tumor mouse models for studying esophageal cancer." Critical Reviews in Oncology/Hematology 189 (September 2023): 104068. http://dx.doi.org/10.1016/j.critrevonc.2023.104068.

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Johnson, Hillary R., Laura C. Gunder, Amani Gillette, Hana Sleiman, Brooks L. Rademacher, Louise M. Meske, Wesley S. Culberson, et al. "Preclinical Models of Anal Cancer Combined-Modality Therapy." Journal of Surgical Research 294 (February 2024): 82–92. http://dx.doi.org/10.1016/j.jss.2023.09.053.

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Bresnahan, Erin, Pierluigi Ramadori, Mathias Heikenwalder, Lars Zender, and Amaia Lujambio. "Novel patient-derived preclinical models of liver cancer." Journal of Hepatology 72, no. 2 (February 2020): 239–49. http://dx.doi.org/10.1016/j.jhep.2019.09.028.

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38

Tétreault, Marie-Pier. "Esophageal Cancer: Insights from Mouse Models." Cancer Growth and Metastasis 8s1 (January 2015): CGM.S21218. http://dx.doi.org/10.4137/cgm.s21218.

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Esophageal cancer is the eighth leading cause of cancer and the sixth most common cause of cancer-related death worldwide. Despite recent advances in the development of surgical techniques in combination with the use of radiotherapy and chemotherapy, the prognosis for esophageal cancer remains poor. The cellular and molecular mechanisms that drive the pathogenesis of esophageal cancer are still poorly understood. Hence, understanding these mechanisms is crucial to improving outcomes for patients with esophageal cancer. Mouse models constitute valuable tools for modeling human cancers and for the preclinical testing of therapeutic strategies in a manner not possible in human subjects. Mice are excellent models for studying human cancers because they are similar to humans at the physiological and molecular levels and because they have a shorter gestation time and life cycle. Moreover, a wide range of well-developed technologies for introducing genetic modifications into mice are currently available. In this review, we describe how different mouse models are used to study esophageal cancer.

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Hamel, Katie M., Trivia P. Frazier, Christopher Williams, Tamika Duplessis, Brian G. Rowan, Jeffrey M. Gimble, and Cecilia G. Sanchez. "Adipose Tissue in Breast Cancer Microphysiological Models to Capture Human Diversity in Preclinical Models." International Journal of Molecular Sciences 25, no. 5 (February 27, 2024): 2728. http://dx.doi.org/10.3390/ijms25052728.

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Female breast cancer accounts for 15.2% of all new cancer cases in the United States, with a continuing increase in incidence despite efforts to discover new targeted therapies. With an approximate failure rate of 85% for therapies in the early phases of clinical trials, there is a need for more translatable, new preclinical in vitro models that include cellular heterogeneity, extracellular matrix, and human-derived biomaterials. Specifically, adipose tissue and its resident cell populations have been identified as necessary attributes for current preclinical models. Adipose-derived stromal/stem cells (ASCs) and mature adipocytes are a normal part of the breast tissue composition and not only contribute to normal breast physiology but also play a significant role in breast cancer pathophysiology. Given the recognized pro-tumorigenic role of adipocytes in tumor progression, there remains a need to enhance the complexity of current models and account for the contribution of the components that exist within the adipose stromal environment to breast tumorigenesis. This review article captures the current landscape of preclinical breast cancer models with a focus on breast cancer microphysiological system (MPS) models and their counterpart patient-derived xenograft (PDX) models to capture patient diversity as they relate to adipose tissue.

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Cigliano, Antonio, Weiting Liao, Giovanni A. Deiana, Davide Rizzo, Xin Chen, and Diego F. Calvisi. "Preclinical Models of Hepatocellular Carcinoma: Current Utility, Limitations, and Challenges." Biomedicines 12, no. 7 (July 22, 2024): 1624. http://dx.doi.org/10.3390/biomedicines12071624.

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Hepatocellular carcinoma (HCC), the predominant primary liver tumor, remains one of the most lethal cancers worldwide, despite the advances in therapy in recent years. In addition to the traditional chemically and dietary-induced HCC models, a broad spectrum of novel preclinical tools have been generated following the advent of transgenic, transposon, organoid, and in silico technologies to overcome this gloomy scenario. These models have become rapidly robust preclinical instruments to unravel the molecular pathogenesis of liver cancer and establish new therapeutic approaches against this deadly disease. The present review article aims to summarize and discuss the commonly used preclinical models for HCC, evaluating their strengths and weaknesses.

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Vanhaezebrouck, Isabelle F., and Matthew L. Scarpelli. "Companion Animals as a Key to Success for Translating Radiation Therapy Research into the Clinic." Cancers 15, no. 13 (June 27, 2023): 3377. http://dx.doi.org/10.3390/cancers15133377.

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Many successful preclinical findings fail to be replicated during translation to human studies. This leads to significant resources being spent on large clinical trials, and in some cases, promising therapeutics not being pursued due to the high costs of clinical translation. These translational failures emphasize the need for improved preclinical models of human cancer so that there is a higher probability of successful clinical translation. Companion-animal cancers offer a potential solution. These cancers are more similar to human cancer than other preclinical models, with a natural evolution over time, genetic alterations, intact immune system, and a permanent adaptation to the microenvironment. These advantages have led pioneers in veterinary radiation oncology to aid human medicine by elucidating basic principles of radiation biology. More recently, the veterinary and human radiation oncology fields have increasingly collaborated to achieve advancements in education, radiotherapy techniques, and trial networks. This review describes these advancements, including significant prior research findings and the evolution of the veterinary radiation oncology discipline. It concludes by describing how companion-animal models can help shape the future of human radiotherapy. Taken as a whole, this review suggests companion-animal cancers may become widely used for preclinical radiotherapy research.

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Stripay, Jennifer L., Thomas E. Merchant, Martine F. Roussel, and Christopher L. Tinkle. "Preclinical Models of Craniospinal Irradiation for Medulloblastoma." Cancers 12, no. 1 (January 5, 2020): 133. http://dx.doi.org/10.3390/cancers12010133.

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Medulloblastoma is an embryonal tumor that shows a predilection for distant metastatic spread and leptomeningeal seeding. For most patients, optimal management of medulloblastoma includes maximum safe resection followed by adjuvant craniospinal irradiation (CSI) and chemotherapy. Although CSI is crucial in treating medulloblastoma, the realization that medulloblastoma is a heterogeneous disease comprising four distinct molecular subgroups (wingless [WNT], sonic hedgehog [SHH], Group 3 [G3], and Group 4 [G4]) with distinct clinical characteristics and prognoses has refocused efforts to better define the optimal role of CSI within and across disease subgroups. The ability to deliver clinically relevant CSI to preclinical models of medulloblastoma offers the potential to study radiation dose and volume effects on tumor control and toxicity in these subgroups and to identify subgroup-specific combination adjuvant therapies. Recent efforts have employed commercial image-guided small animal irradiation systems as well as custom approaches to deliver accurate and reproducible fractionated CSI in various preclinical models of medulloblastoma. Here, we provide an overview of the current clinical indications for, and technical aspects of, irradiation of pediatric medulloblastoma. We then review the current literature on preclinical modeling of and treatment interventions for medulloblastoma and conclude with a summary of challenges in the field of preclinical modeling of CSI for the treatment of leptomeningeal seeding tumors.

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Eggermont, A. M. M. "Isolated limb perfusion: Lessons from preclinical models." European Journal of Cancer 37 (April 2001): S253. http://dx.doi.org/10.1016/s0959-8049(01)81429-5.

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de Jong, Marion, Jeroen Essers, and Wytske M. van Weerden. "Imaging preclinical tumour models: improving translational power." Nature Reviews Cancer 14, no. 7 (June 19, 2014): 481–93. http://dx.doi.org/10.1038/nrc3751.

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Nolan, J. C., T. Frawley, J. Tighe, H. Soh, C. Curtin, and O. Piskareva. "Preclinical models for neuroblastoma: Advances and challenges." Cancer Letters 474 (April 2020): 53–62. http://dx.doi.org/10.1016/j.canlet.2020.01.015.

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Loree, Jonathan M., Arvind Dasari, Jason Willis, Himanish Gothwal, Mena Shaheed, Kulwinder Singh, Hewad Shaheed, et al. "Racial/ethnic representation and disparities in preclinical cancer models." Journal of Clinical Oncology 42, no. 16_suppl (June 1, 2024): 1601. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.1601.

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1601 Background: Patient-derived xenograft models (PDXs) recapitulate tumor characteristics credibly and have become a standard for preclinical inquiries that form the basis of clinical trials of novel therapies in oncology. While ample evidence reveals racial/ethnic disparities in cancer care delivery and clinical research, limited data exists regarding racial composition of available PDXs. We sought to define the extent of racial/ethnic representation and disparities among existing PDXs. Methods: Data regarding available PDXs was gathered from the publicly accessible CancerModels.org website ( https://www.cancermodels.org/overview ). Seven members of the research team were involved in data extraction. Information on race/ethnicity (White, Black, Hispanic, Asian), sex, age, and cancer type were recorded. The primary objective was to determine the racial/ethnic composition of PDX models and compare this to racial/ethnic demographics of cancer patients, for which we used US population-based cancer estimates calculated using National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER Incidence Data, 11/2022 Submission (1975 - 2020), SEER 22 registries). Descriptive statistics were used. Proportions were compared using Fischer’s exact test or Chi-squared tests with Yates' correction (odds-ratio [OR] and 95% confidence intervals [95%CI] or Woolf logit interval) were reported. Results: We reviewed 4597 unique PDXs across 33 SEER cancer sites spanning 11 oncology sub-specialties. Of these, 55% models were derived from males and age groups were (years): < 20: 6%; 20-70: 69% and ≥ 70: 25%. Most common cancer sites represented were colorectal (26%), lung (12%), breast (9%), melanoma (9%) and leukemia (7%). Race/ethnicity was not reported in 3395 (73.9%) cases. Racial/ethnic composition of the remaining models was Whites (80.9%), Blacks (7.3%), Hispanics (6.4%) and Asians (5.4%). Compared with their respective proportion of US cancer incidence (69.9%, 10.9%, 13.2% and 5.9%, respectively), these models were over-representative for Whites (OR: 1.82, 95%CI: 1.6-2.1, P < 0.001) and under-represented Blacks (OR: 0.64, 95%CI: 0.5-0.8, P < 0.001) and Hispanics (OR: 0.45, 95%CI: 0.4-0.6, P < 0.001) but not Asians (OR: 0.89, 95%CI: 0.7-1.2, P = 0.43). Similar trends were seen in subgroups focused by cancer site. Among colorectal (N = 1201) models, race/ethnicity was reported for 17.2% of cases and Blacks (OR: 0.52; 55.7% of expected; P = 0.014) and Hispanics (OR: 0.57; 60.8% of expected; P = 0.015) were underrepresented compared to Whites (OR: 1.82; 116% of expected; P < 0.001). Conclusions: Race/ethnicity are infrequently reported for PDX models. Minority races/ethnicities (Blacks and Hispanics) are underrepresented in preclinical models compared to their burden of cancer incidence. There is a need to develop a diverse repertoire of preclinical models to ensure inclusivity and guide equitable research.

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Kindt, Nadège, Nuria Kotecki, and Ahmad Awada. "Preclinical models to understand the biology and to discover new targets in brain metastases." Current Opinion in Oncology 35, no. 5 (June 30, 2023): 436–40. http://dx.doi.org/10.1097/cco.0000000000000963.

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Purpose of review Incidence of brain metastases increases overtime therefore it is important to rapidly progress in the discovery of new strategies of treatment for these patients. In consequence, more and more preclinical models of brain metastases (BM) are established to study new treatments for melanoma, lung, and breast cancer BM. Here, we reviewed the most recent findings of new drugs assessed in BM mouse preclinical models. Recent findings BM are a common metastatic site of several types of solid cancers and can be difficult to treat due to the unique environment of the brain and the blood-brain barrier. Currently, several preclinical models of BM have been demonstrated that new molecular targeted therapies, small metabolic inhibitors, immunotherapies or a combination of these drugs with radiotherapy lead to a reduction of BM growth and an improvement of mouse survival. Summary The use of preclinical models of BM is crucial to discover new treatment strategies for patients with BM. In the last years, some new drugs have been highlighted in preclinical models and are now tested in clinical trials including patients with brain metastases.

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Evrard, Yvonne A., Cindy R. Timme, Biswajit Das, Gareth Bliss, Carrie Bonomi, Carley Border, Ting-Chia Chang, et al. "Abstract 6898: Cholangiocarcinoma patient-derived models in the NCI Patient-Derived Models Repository." Cancer Research 84, no. 6_Supplement (March 22, 2024): 6898. http://dx.doi.org/10.1158/1538-7445.am2024-6898.

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Abstract Cholangiocarcinoma (CHOL) is an aggressive rare malignancy arising in the biliary tract with a 5-year relative survival rate of 10% and a 60-70% recurrence rate following surgical intervention. Advances in treatment strategies are hampered by the lack of well annotated and characterized preclinical models. The National Cancer Institute’s Patient-Derived Models Repository (NCI PDMR; https://pdmr.cancer.gov) has developed a national repository of Patient-Derived Models (PDMs) comprised of patient-derived xenografts (PDXs), in vitro patient-derived tumor cell cultures (PDCs) and cancer associated fibroblasts (CAFs) as well as patient-derived organoids (PDOrg). These PDMs are clinically annotated with molecular information available in an easily accessible database for the extramural community. To date, 94 patient CHOL tumor specimens (resections, biopsies, rapid autopsy) have been received from 55 unique patients with an overall PDX take rate of 36.3% (80 assessable specimens). There are currently 15 CHOL PDX models available for the research community to request with 14 more in final QC. Nine of the 15 public PDX models were generated from unique lesions in two rapid autopsy patients and while histo-morphologically consistent, some genomic heterogeneity is observed between the unique anatomical locations likely due to tumor evolution during metastasis. In addition, 27 in vitro PDOrgs, PDCs, and CAFs have been generated from patient or PDX material matched to most of the existing PDX models to allow for comparative translation research. The genetic landscape of the CHOL PDXs includes oncogenic drivers in NRAS, IDH1, ERBB2, TP53, KRAS and FGFR2 fusions; OncoKB likely oncogenic variants including ARID1A, AXIN1 and BAP1; and CDKN2A deep deletions representing most of the common alterations in CHOL. Early passage preclinical PDX and in vitro models for CHOL, and many other cancer types, with translational relevant features along with associated NextGen sequencing and patient treatment history are available from the NCI PDMR. Availability of preclinical models that can be used to study these cancers and improve preclinical drug screening is of high importance to better understand the biology of these cancers and prioritize novel therapeutics from bench to clinic. Funded by NCI Contract No. HHSN261200800001E Citation Format: Yvonne A. Evrard, Cindy R. Timme, Biswajit Das, Gareth Bliss, Carrie Bonomi, Carley Border, Ting-Chia Chang, Alice Chen, Li Chen, Michelle A. Crespo-Eugeni, Kevin Cooper, Natalie Czarra, Isabella Czernia, Kelly Dougherty, Marion Gibson, Tara Grinnage-Pulley, Shahanawaz Jiwani, Keegan Kalmbach, Chris A. Karlovich, Chelsea McGlynn, Michael Mullendore, Matthew Murphy, Rini Pauly, Kevin Plater, Jessica Steed, Luke Stockwin, Shannon Uzelac, Peter I-Fan Wu, Dianne L. Newton, P. Mickey Williams, Melinda G. Hollingshead, James H. Doroshow. Cholangiocarcinoma patient-derived models in the NCI Patient-Derived Models Repository [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6898.

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Kellar, Amelia, Cay Egan, and Don Morris. "Preclinical Murine Models for Lung Cancer: Clinical Trial Applications." BioMed Research International 2015 (2015): 1–17. http://dx.doi.org/10.1155/2015/621324.

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Murine models for the study of lung cancer have historically been the backbone of preliminary preclinical data to support early human clinical trials. However, the availability of multiple experimental systems leads to debate concerning which model, if any, is best suited for a particular therapeutic strategy. It is imperative that these models accurately predict clinical benefit of therapy. This review provides an overview of the current murine models used to study lung cancer and the advantages and limitations of each model, as well as a retrospective evaluation of the uses of each model with respect to accuracy in predicting clinical benefit of therapy. A better understanding of murine models and their uses, as well as their limitations may aid future research concerning the development and implementation of new targeted therapies and chemotherapeutic agents for lung cancer.

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Devlies, Wout, Florian Handle, Gaëtan Devos, Steven Joniau, and Frank Claessens. "Preclinical Models in Prostate Cancer: Resistance to AR Targeting Therapies in Prostate Cancer." Cancers 13, no. 4 (February 22, 2021): 915. http://dx.doi.org/10.3390/cancers13040915.

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Prostate cancer is an androgen-driven tumor. Different prostate cancer therapies consequently focus on blocking the androgen receptor pathway. Clinical studies reported tumor resistance mechanisms by reactivating and bypassing the androgen pathway. Preclinical models allowed the identification, confirmation, and thorough study of these pathways. This review looks into the current and future role of preclinical models to understand resistance to androgen receptor-targeted therapies. Increasing knowledge on this resistance will greatly improve insights into tumor pathophysiology and future treatment strategies in prostate cancer.

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