Radiogenomics of intrahepatic cholangiocarcinoma predicts immunochemotherapy response and identifies therapeutic target

Ji, Gu-Wei; Xu, Zheng-Gang; Liu, Shuo-Chen; Cao, Shu-Ya; Jiao, Chen-Yu; Lu, Ming; Zhang, Biao; Yang, Yue; Xu, Qing; Wu, Xiao-Feng; Wang, Ke; Xia, Yong-Xiang; Li, Xiang-Cheng; Wang, Xue-Hao

doi:.0..

Original Article

Radiogenomics of intrahepatic cholangiocarcinoma predicts immunochemotherapy response and identifies therapeutic target

Gu-Wei Ji^1,2,3, Zheng-Gang Xu^1,2,3, Shuo-Chen Liu^1,2,3, Shu-Ya Cao^1,2,3, Chen-Yu Jiao^1,2,3, Ming Lu⁴, Biao Zhang⁵, Yue Yang⁶, Qing Xu⁴, Xiao-Feng Wu^1,2,3, Ke Wang^1,2,3, Yong-Xiang Xia^1,2,3, Xiang-Cheng Li^1,2,3, Xue-Hao Wang^1,2,3

¹Hepatobiliary Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, P.R. China
²Key Laboratory of Liver Transplantation, Chinese Academy of Medical Sciences, Nanjing, P.R. China
³NHC Key laboratory of Hepatobiliary cancers, Nanjing, P.R. China
⁴Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, P.R. China
⁵Department of General Surgery, Yancheng No.1 People’s Hospital, Yancheng, P.R. China
⁶Department of General Surgery, The First People’s Hospital of Changzhou, Changzhou, P.R. China

Correspondence :

Yong-Xiang Xia ,
Tel: +86 13815893869, Fax: +86 25 68136450, Email: yx_xia@njmu.edu.cn
Xiang-Cheng Li ,
Tel: +86 18951999088, Fax: +86 68136450, Email: drxcli@njmu.edu.cn
Xue-Hao Wang ,
Tel: +86 13305178713, Fax: +86 68136450, Email: wangxh@njmu.edu.cn

Received: October 9, 2024 Revised: December 29, 2024 Accepted: February 7, 2025

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 multi-scale 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.

KeyWords: Intrahepatic cholangiocarcinoma; Radiogenomics; Multi-omics profiling; Machine learning; Prediction model