Radiogenomics of intrahepatic cholangiocarcinoma predicts immunochemotherapy response and identifies therapeutic target
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Background/Aims: Identifying patients with intrahepatic cholangiocarcinoma (ICC) likely to benefit from immunochemotherapy, the new front-line treatment, remains challenging. We aimed to unveil a novel radiotranscriptomic signature that can facilitate treatment response prediction by multi-omics integration and multiscale modelling. Methods: We analyzed bulk, single-cell and spatial transcriptomic data comprising 457 ICC patients to identify an immune-related score (IRS), followed by decoding its spatial immune context. We mapped radiomics profiles onto spatial-specific IRS using machine learning to define a novel radiotranscriptomic signature, followed by multi-scale and multi-cohort validation covering 331 ICC patients. The signature was further explored for the potential therapeutic target from in vitro to in vivo. Results: We revealed a novel 3-gene (PLAUR, CD40LG, and FGFR4) IRS whose down-regulation correlated with better survival and improved sensitivity to immunochemotherapy. We highlighted functional IRS-immune interactions within tumor epithelium, rather than stromal compartment, irrespective of geospatial locations. Machine learning pipeline identified the optimal 3-feature radiotranscriptomic signature that was well-validated by immunohistochemical assays in molecular cohort, exhibited favorable external prognostic validity with C-index over 0.64 in resection cohort, and predicted treatment response with an area under the curve of up to 0.84 in immunochemotherapy cohort. We also showed that anti-uPAR/PLAUR alone or in combination with anti-programmed cell death protein 1 therapy remarkably curbed tumor growth, using in vitro ICC cell lines and in vivo humanized ICC patient-derived xenograft mouse models. Conclusions: This proof-of-concept study sheds light on the spatially-resolved radiotranscriptomic signature to improve patient selection for emerging immunochemotherapy and high-order immunotherapy combinations in ICC.
Abstract
Background/Aims: Identifying patients with intrahepatic cholangiocarcinoma (ICC) likely to benefit from immunochemotherapy, the new front-line treatment, remains challenging. We aimed to unveil a novel radiotranscriptomic signature that can facilitate treatment response prediction by multi-omics integration and multiscale modelling. Methods: We analyzed bulk, single-cell and spatial transcriptomic data comprising 457 ICC patients to identify an immune-related score (IRS), followed by decoding its spatial immune context. We mapped radiomics profiles onto spatial-specific IRS using machine learning to define a novel radiotranscriptomic signature, followed by multi-scale and multi-cohort validation covering 331 ICC patients. The signature was further explored for the potential therapeutic target from in vitro to in vivo. Results: We revealed a novel 3-gene (PLAUR, CD40LG, and FGFR4) IRS whose down-regulation correlated with better survival and improved sensitivity to immunochemotherapy. We highlighted functional IRS-immune interactions within tumor epithelium, rather than stromal compartment, irrespective of geospatial locations. Machine learning pipeline identified the optimal 3-feature radiotranscriptomic signature that was well-validated by immunohistochemical assays in molecular cohort, exhibited favorable external prognostic validity with C-index over 0.64 in resection cohort, and predicted treatment response with an area under the curve of up to 0.84 in immunochemotherapy cohort. We also showed that anti-uPAR/PLAUR alone or in combination with anti-programmed cell death protein 1 therapy remarkably curbed tumor growth, using in vitro ICC cell lines and in vivo humanized ICC patient-derived xenograft mouse models. Conclusions: This proof-of-concept study sheds light on the spatially-resolved radiotranscriptomic signature to improve patient selection for emerging immunochemotherapy and high-order immunotherapy combinations in ICC.