Prediction of Clinical Precision Chemotherapy by Patient-Derived 3D Bioprinting Models of Colorectal Cancer and Its Liver Metastases

Methods accurately predicting the responses of colorectal cancer (CRC) and colorectal cancer liver metastasis (CRLM) to personalized chemotherapy remain limited due to tumor heterogeneity. This study introduces an innovative patient-derived CRC and CRLM tumor model for preclinical investigation, utilizing 3d-bioprinting (3DP) technology. Efficient construction of homogeneous in vitro 3D models of CRC/CRLM is achieved through the application of patient-derived primary tumor cells and 3D bioprinting with bioink. Genomic and histological analyses affirm that the CRC/CRLM 3DP tumor models effectively retain parental tumor biomarkers and mutation profiles. In vitro tests evaluating chemotherapeutic drug sensitivities reveal substantial tumor heterogeneity in chemotherapy responses within the 3DP CRC/CRLM models. Furthermore, a robust correlation is evident between the drug response in the CRLM 3DP model and the clinical outcomes of neoadjuvant chemotherapy. These findings imply a significant potential for the application of patient-derived 3DP cancer models in precision chemotherapy prediction and preclinical research for CRC/CRLM. The study showcases the establishment of patient-derived 3D bioprinting models for colorectal cancer and its liver metastases. These models highly retain parent tumor biomarkers and mutation profiles, revealing substantial heterogeneity. Crucially, drug testing correlates strongly with clinical response, highlighting the great potential of 3D bioprinting tumor model as a preclinical platform for personalized cancer therapy.image

Automated summary

This study introduces an innovative patient-derived tumor model for colorectal cancer and its liver metastases using 3D bioprinting technology. The tumor models effectively retain parental tumor biomarkers and mutation profiles and demonstrate substantial tumor heterogeneity. In vitro tests reveal a strong correlation between drug testing and clinical response, highlighting the potential of 3D bioprinting tumor models for personalized cancer therapy.