Overcoming drug resistance presents a significant challenge in cancer treatment for both solid and liquid tumors. A comprehensive collection of resistant models, specifically tailored for antibody-drug conjugate (ADC)-pretreated models and liquid tumors, offers deep insights into the underlying causes of resistance, such as genetic mutations, immune evasion, or changes in the tumor microenvironment (TME).
Through the implementation of advanced imaging techniques and metastatic modeling, researchers can achieve a more thorough understanding of resistant phenotypes and their clinical implications, ultimately paving the way for more effective and targeted cancer therapies.
Patient-Derived Xenograft (PDX) Pretreated Models
PDX pretreated models involve implanting tumor tissue from a patient who has relapsed or developed treatment resistance into immunocompromised mice. These models preserve the tumor's genetic and phenotypic characteristics, making them ideal for studying cancer progression and treatment resistance.
PDX models allow for the investigation of resistance mechanisms specific to the patient's tumor biology, especially when combined with serial treatments. They have been used to study resistance to chemotherapy and targeted therapies, identifying changes in tumor genetics, signaling pathways, and mechanisms like epithelial-mesenchymal transition (EMT). PDX models also are valuable for preclinical drug development and personalized medicine, with emerging trends like humanized models, organoid generation, and multi-omics enhancing their predictive power.
Mouse Clinical Trials (MCT)
MCTs use PDX models to enhance preclinical drug discovery by more closely simulating human clinical trials. Unlike traditional approaches, MCTs involve multiple diverse PDX models with fewer subjects per model, improving clinical relevance. Each PDX model acts as a "patient avatar," representing human patient diversity and helping identify responder and non-responder subgroups.
MCTs are designed with randomization, controls, and statistical rigor to test new therapies and combinations. Different MCT designs, such as indication-driven or target-driven, address specific study needs. Expert guidance in model selection, statistical planning, and data interpretation is essential for optimizing MCT outcomes.
Antibody-Drug Conjugate (ADC)
Monoclonal antibodies (mAbs) have improved cancer therapy by targeting tumor surface antigens, but they are less effective than conventional chemotherapy. ADCs combine the specificity of mAbs with the efficiency of cytotoxic drugs. However, ADCs are still subject to resistance, including target-related, drug-related, linker-related, and tumor microenvironment (TME)-related resistance.
In target-related resistance, antigen mutations prevent ADC binding, while drug-related resistance occurs when the tumor resists the cytotoxic drug. Linker-related resistance results from improper drug delivery, and TME changes hinder ADC effectiveness. PDX and genetically engineered mouse models (GEMM) are crucial for studying ADC resistance, as they replicate tumor conditions in patients. These models have revealed resistance mechanisms, such as HER2 downregulation and upregulation of ABC transporters, which affect ADC efficacy.
Overcoming Oncology Drug Resistance
In the ongoing challenge of overcoming drug resistance in cancer treatment, a comprehensive collection of resistant models provides valuable insights into the complexities of resistance. By defining model needs, searching current established models, or creating your model of interest, followed by full characterization, clinically-relevant insights into resistance mechanisms can be uncovered and leveraged to optimize therapies and improve clinical outcomes. Coupled with advanced imaging techniques and metastatic modeling, these approaches enable a deeper understanding of resistant phenotypes and their clinical impact. By integrating these strategies, researchers can drive the development of more effective and targeted cancer therapies, ultimately improving patient outcomes.
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