Preclinical Tumor Model
Preclinical Tumor Model - Related Videos
07:59
Translationally-Relevant Tumor Resection Model for Murine Preclinical Models of Oral Squamous Cell Carcinoma
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2026
A unique method to surgically resect oral squamous cell carcinoma tumors established in the oral cavity is shown here.
06:51
A Preclinical Murine Model of Hepatic Metastases
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Cited by 119 •
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A preclinical, murine model of hepatic metastases performed via a hemispleen injection technique.
09:29
Development and Maintenance of a Preclinical Patient Derived Tumor Xenograft Model for the Investigation of Novel Anti-Cancer Therapies
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Cited by 23 •
2016
Utilizing patient-derived tumors in a subcutaneous preclinical model is an excellent way to study the efficacy of novel therapies, predictive biomarker discovery, and drug resistant pathways. This model, in the drug development process, is essential in determining the fate of many novel anti-cancer therapies prior to clinical investigation.
07:45
Orthotopic Transplantation of Breast Tumors as Preclinical Models for Breast Cancer
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Cited by 15 •
2020
Patient-derived xenograft (PDX) models and transplantable genetically engineered mouse models faithfully recapitulate human disease and are preferred models for basic and translational breast cancer research. Here, a method is described to orthotopically transplant breast tumor fragments into the mammary fat pad to study tumor biology and evaluate drug response.
06:33
Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement
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Cited by 1 •
2013
This paper describes in-house procedures of constructing a preclinical multimodality phantom made of tissue-mimicking (TM) materials for quality assurance (QA) of tumor size measurement in animal imaging modalities such as ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI).
FAQs
Predictive value depends on how closely the model reflects the diversity found in human tumors. A system that captures differences among tumor cells, surrounding tissues, and disease progression may provide more informative evidence about treatment response. Conversely, limited representation of these features can restrict how confidently researchers interpret results before moving a therapy toward clinical testing.
These model categories reproduce cancer biology at different levels of complexity. Cell-based cultures allow controlled study of cancer cells, while engineered models can represent selected biological features. Animal models provide opportunities to examine tumor growth, interactions with surrounding tissues, drug distribution, and therapeutic effects together. Researchers can therefore select a system according to the question being investigated.
Measurements may include tumor growth, molecular changes, drug distribution, and therapeutic effects. Together, these readouts show not only whether a treatment influences tumor progression, but also how the tumor changes and where the drug acts within the experimental system. Such information helps connect observed outcomes with biological mechanisms and supports evaluation of treatment strategies.
Researchers grow cancer cells or tumor tissue under controlled laboratory conditions and then assess the response to a therapy. They track outcomes such as tumor growth, molecular changes, drug distribution, and therapeutic effects. This process can reveal potential benefits and limitations of an approach, providing evidence for whether it warrants further development before clinical evaluation.
These systems allow researchers to examine whether a biological target is associated with measurable changes in tumor behavior and whether candidate drugs produce a therapeutic effect. Their controlled conditions support comparison among treatment strategies during screening. The resulting evidence can help prioritize candidates and identify limitations before resources are directed toward clinical testing.
Models can be used to assess molecular changes alongside treatment effects, helping researchers examine whether particular biological features are associated with response. They also support evaluation of combination therapies by allowing multiple treatment strategies to be studied in the same experimental context. These applications connect measurable tumor biology with decisions about treatment selection and development.