Patient-Derived Functional Models for Prediction of Radiotherapy Response in Rectal Cancer: A Systematic Review and...

Area of Science:

• Oncology
• Translational Research
• Preclinical Modeling

Background:

• Patient-derived functional models offer a platform for predicting tumor-specific treatment sensitivity.
• Personalized neoadjuvant treatment strategies are crucial for rectal cancer patients.
• Evaluating preclinical models against patient data is essential for translational accuracy.

Purpose of the Study:

• To systematically review and meta-analyze comparative evidence on radiotherapy response prediction.
• To assess the concordance between patient-derived functional models and matched patient data in rectal cancer.
• To evaluate the predictive performance of preclinical platforms for neoadjuvant chemoradiotherapy response.

Main Methods:

• Systematic review adhering to PRISMA principles.
• Inclusion of studies on patient-derived organoids and zebrafish patient-derived xenograft models for rectal cancer.
• Exploratory hierarchical summary receiver operating characteristic (HSROC) meta-analysis for predictive performance assessment.

Main Results:

• Eight studies involving patient-derived organoids and zebrafish models were included, primarily focusing on locally advanced rectal cancer.
• Concordance rates between ex vivo functional responses and clinical outcomes ranged from 78% to 100%.
• Favorable predictive performance was reported, with high sensitivity and specificity in predicting treatment response and resistance.

Conclusions:

• Patient-derived functional models, especially patient-derived organoids (PDOs), show significant potential as predictive biomarkers for rectal cancer radiotherapy response.
• Current evidence is exploratory, limited by heterogeneity and small cohorts, necessitating prospective validation.
• These models represent a promising translational strategy for precision radiation oncology in rectal cancer.