Colorectal cancer (CRC) is a heterogenous disease, and patients have differences in therapeutic response. However, the mechanisms underlying interpatient heterogeneity in the response to chemotherapeutic agents remain to be elucidated, and molecular tumor characteristics are required to select patients for specific therapies. Patient-derived organoids (PDOs) established from CRCs recapitulate various biological characteristics of tumor tissues, including cellular heterogeneity and the response to chemotherapy. Patient-derived organoids established from CRCs show various morphologies, but there are no criteria for defining these morphologies, which hampers the analysis of their biological significance. Here, we developed an artificial intelligence (AI)-based classifier to categorize PDOs based on microscopic images according to their similarity in appearance and classified tubular adenocarcinoma-derived PDOs into six types. Transcriptome analysis identified differential expression of genes related to cell adhesion in some of the morphological types. Genes involved in ribosome biogenesis were also differentially expressed and were most highly expressed in morphological types showing CRC stem cell properties. We identified an RNA polymerase I inhibitor, CX-5641, to be an upstream regulator of these type-specific gene sets. Notably, PDO types with increased expression of genes involved in ribosome biogenesis were resistant to CX-5461 treatment. Taken together, these results uncover the biological significance of the morphology of PDOs and provide novel indicators by which to categorize CRCs. Therefore, the AI-based classifier is a useful tool to support PDO-based cancer research.
Keywords: artificial intelligence; colorectal cancer; integrin; organoid; ribosome biobenesis.
© 2022 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.