This project profiles clonal evolution, intratumoral heterogeneity, and therapy responses in paired colorectal cancer patient-derived organoids (PDOs) and healthy colon organoids using comprehensive multiomics approaches. Doctoral researchers will establish PDO biobank lines from primary tumors, liver metastases, and matched normal mucosa, perform lineage tracing with CRISPR barcoding or mitochondrial heteroplasmy, and track clonal dynamics under chemotherapy (5-FU/oxaliplatin), anti-EGFR therapy, and immunotherapy pressure using single-cell DNA/RNA-seq, spatial transcriptomics, and WGS/WES. High-throughput drug response assays, computational modeling of clonal fitness landscapes, and machine learning-based trajectory inference will map resistant subclones, identify therapy-induced selection pressures, and reveal pre-existing minor populations driving acquired resistance. Patient-correlated multiomics integration generates predictive biomarkers for treatment response, clonal dominance patterns, and metastatic potential, enabling precision stratification strategies to optimize CRC therapy sequencing and overcome heterogeneous resistance mechanisms.