Preclinical Cancer Models and Biomarkers for Drug Development: New Technologies and Emerging Tools

Area of Science:

• Oncology
• Drug Discovery
• Translational Medicine

Background:

• Predicting anticancer therapy efficacy is vital for drug development and clinical treatment selection.
• Tumor heterogeneity, encompassing malignant cells, stroma, immune cells, and microenvironment, presents a significant challenge for current preclinical models.
• Reliable preclinical models are needed to accurately predict treatment response and resistance.

Purpose of the Study:

• To review emerging preclinical models for anticancer therapy that address tumor heterogeneity.
• To highlight biomarkers associated with treatment response and resistance.
• To provide a perspective on advancing anticancer drug screening and development.

Main Methods:

• Review of 2D and 3D in vitro models, including organoid co-cultures.
• Discussion of in vivo and human explant technologies.
• Focus on models that incorporate intrinsic tumor heterogeneity and native microenvironments.

Main Results:

• Organoid co-cultures accelerate anti-cancer drug screening and enhance understanding of mechanisms of action.
• In vivo and human explant models utilize the native microenvironment to capture tumor heterogeneity.
• These advanced models show potential for understanding cancer immunotherapy.

Conclusions:

• Numerous preclinical models exist, but all have limitations in predicting clinical efficacy.
• Preclinical models incorporating cell heterogeneity are beginning to address challenges in predicting anticancer agent efficacy.
• Advancements in model systems are crucial for improving cancer drug development and patient outcomes.