Patient-derived cancer models: Valuable platforms for anticancer drug testing

Area of Science:

• Oncology
• Translational Cancer Research
• Drug Discovery

Background:

• Molecularly targeted treatments and immunotherapy are vital in oncology but often fail due to drug resistance in metastatic cancer.
• Preclinical models are essential for understanding cancer progression and predicting drug efficacy.
• Current models face limitations due to tumor heterogeneity and microenvironment complexities, impacting predictive accuracy.

Purpose of the Study:

• To review the advantages and limitations of various preclinical cancer models.
• To assess the applicability of these models for drug testing and co-clinical trials.
• To explore strategies for enhancing the reliability of preclinical models in predicting treatment response.

Main Methods:

• Review of existing literature on preclinical cancer models (genetically engineered mouse models, patient-derived xenografts, 2D/3D cell cultures).
• Analysis of factors affecting model faithfulness, including tumor heterogeneity and microenvironment.
• Discussion of applications in drug efficacy prediction and co-clinical trial design.

Main Results:

• Preclinical models offer valuable insights but are limited by biological complexity.
• Tumor heterogeneity and microenvironment variables can reduce the translational validity of these models.
• Co-clinical trials and advanced model development are key areas for improvement.

Conclusions:

• Preclinical models are indispensable tools in oncology research, despite inherent limitations.
• Enhancing model fidelity is critical for accurately predicting patient response to novel anticancer therapies.
• Further development and validation of preclinical platforms are needed to overcome drug resistance and improve patient outcomes.