Functional precision oncology platform of BRAFV600E-mutated colorectal cancer organoids predicts therapy response and reveals RNF43-mediated immunogenicity.
Summary
This study developed a functional precision oncology platform using patient-derived organoids (PDOs) and xenografts (PDOXs) from BRAFV600E-mutant colorectal cancer (CRC). The platform faithfully recapitulated tumor heterogeneity and drug responses. Researchers discovered that RNF43 mutations predict enhanced sensitivity to the encorafenib-cetuximab combination and increase tumor immunogenicity. These findings provide a mechanistic rationale for combining targeted therapies with immunotherapy in this aggressive CRC subtype.
Analysis
🔴 CLINIQUE: This precision platform offers a predictive model for BRAFV600E colorectal cancer, an aggressive subtype. The identification of the RNF43 mutation as a predictive biomarker for response to encorafenib and cetuximab, as well as for immunogenicity, is clinically relevant. This strongly justifies later-phase clinical trials combining BRAF/EGFR inhibition with immunotherapy to improve outcomes in these patients. The impact on clinical practice could be anticipated in the medium term (3-5 years) after validation. 🟢 BIOMOL: The establishment of a biobank of PDOs and PDOXs from BRAFV600E-mutant CRC, characterized by whole-exome sequencing and bulk and single-cell RNA sequencing, represents a significant advance. The discovery that RNF43 mutations are associated with upregulation of E2F, G2M, interferon-α/γ, and inflammatory pathways, along with elevated MHC-I expression, provides a clear molecular mechanism for sensitivity to targeted therapies and immunogenicity. These findings could lead to the development of companion diagnostic tests. 🔵 BIOINFO: The use of single-cell transcriptomics confirmed the high fidelity of PDOs to parental tumors. Integrative transcriptomic analysis of TCGA and PDO cohorts was essential for identifying biological pathways associated with RNF43 mutations and for linking genetic alterations to therapeutic response and immunogenicity phenotypes. These bioinformatic approaches are fundamental for interpreting multi-omics data and biomarker discovery.