Radiogenomics of intrahepatic cholangiocarcinoma predicts immunochemotherapy response and identifies therapeutic target

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.

Conclusion: 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; Machine learning; Multi-omics profiling; Prediction model; Radiogenomics.

Figure 1.

Schematic representation of the study design. (A) IRS identification. (B) Mapping spatial radiomics onto genomics. (C) Multilevel validation of the radiotranscriptomic signature. (D) Experimental investigations to determine the potential of a novel therapeutic target. IRS, immune-related score; RNA-seq, RNA sequencing.

Figure 2.

Development and validation of IRS via an integrative procedure. (A) Consensus score matrix of the Fudan-ICC cohort for the optimal 3-cluster solution with the abundance of 28 infiltrating immune cell subsets. (B) Gene co-expression modules identified by WGCNA dendrogram and four key modules that exhibited high correlation with immune clusters. (C) Overlapping genes between WGCNA and ImmPort. filtered by RSF and LASSO-Cox algorithms. (D) Forest plot with hazard ratios of retained genes in the multivariate backward stepwise Cox regression analysis. (E) Kaplan–Meier plots of IRS risk groups in different cohorts. (F) Correlations between IRS and immune infiltration. IRS, immune-related score; LASSO, least absolute shrinkage and selection operator; NS, not significant; RSF, random survival forest; VIMP, variable importance; WGCNA, weighted correlation network analysis. *P<0.05, **P<0.01, ***P<0.001.

Figure 3.

Analysis of IRS using single-cell RNA-seq data. (A) t-SNE plots for cell-type identification and all single cells colored by IRS as well as constitutive genes. (B) Weighted cell-cell interaction network between the identified cell types. (C) Inferred ligand-receptor interactions between malignant cells, monocyte-macrophages and T cells. (D) t-SNE plots for PLAUR and FGFR4 expressions in distinct malignant sub-clusters. (E) t-SNE plots for CD40LG expression in distinct T sub-clusters. (F) t-SNE plots for PLAUR expression, M1 and M2 phenotypes as well as violin plots of marker gene expression in distinct monocyte-macrophages sub-clusters. IRS, immune-related score; RNA-seq, RNA sequencing.

Figure 4.

Training-validation-testing of spatially-resolved radiotranscriptomic signature using machine learning. (A) DSP of formalin-fixed paraffin-embedded tissue sections harvested from representative spatially separated regions. (B) Violin plots for the spatial distribution of IRS and constitutive genes. (C) Correlation analysis between defined IRS and immune infiltration estimated by CIBERSORT in DSP. (D) Machine learning pipeline with wrapped feature selection based on recursive feature elimination method, selection of the best classifier, weights of radiomics features and receiver operating characteristic curves for the exported models. (E) Correlation analysis between radiomics, IRS and immune infiltration. AUC, area under the curve; DSP, Digital Spatial Profiling; IRS, immune-related score; ROI, regions of interest. *P<0.05, **P<0.01, ***P<0.001.

Figure 5.

Multi-level validation of radiotranscriptomic signature. (A) Associations between computationally derived signature and IRS determined by quantitative analysis of IHC slides. (B) Kaplan–Meier plots showing survival of patients following resection. (C) Comparison of treatment response proportions stratified by PD-L1 expression and radiotranscriptomic signature by using χ² test. (D) Performance of PD-L1 expression and radiotranscriptomic signature in predicting treatment response. (E) Kaplan–Meier plots stratified by the signature. (F) Example implementation of the radiogenomics signature. CAPOX, capecitabine and oxaliplatin; GEMOX, gemcitabine and oxaliplatin; GC, gemcitabine and cisplatin; IHC, immunohistochemistry; IRS, immune-related score; PD-L1, programmed death-ligand 1. *P<0.05, **P<0.01, ***P<0.001.

Figure 6.

uPAR promotes cell proliferation and migration by activating PI3K-Akt signaling pathway in vitro. (A) The mRNA and protein expression levels of uPAR in HiBEC and 3 ICC cell lines were measured by qRT-PCR and Western blotting while the efficiency of uPAR plasmid in RBE cells was certified by qRT-PCR. (B) CCK8, colony formation, transwell and wound healing assays were performed in RBE cells transfected with uPAR plasmid. (C) The efficiency of uPAR plasmid or siRNAs in THP-1 cells was certified by qRT-PCR while transwell assay was performed in THP-1 cells transfected with uPAR siRNAs or plasmid. (D) KEGG pathway analysis of differentially expressed genes in RBE and THP-1 cells with uPAR OE and control group. (E) Western blotting analysis showed the levels of PI3K, p- PI3K, AKT and p-AKT in RBE cells before and after PI3K inhibitor (LY294002) or anti-uPAR treatment. (F) CCK8 and Transwell assays were performed in RBE cells and TAMs before and after indicated treatments. Data are shown as mean±standard deviation. ICC, intrahepatic cholangiocarcinoma; OE, over-expression; qRT-PCR, quantitative reverse-transcription polymerase chain reaction; siRNA, small interfering RNA; TAM, tumor-associated macrophage. *P<0.05, **P<0.01, ***P<0.001.

Figure 7.

Anti-uPAR alone or in combination with anti-PD-1 treatment in vitro and in vivo. (A) Colony formation and transwell assays of RBE and THP-1 cells treated with anti-uPAR mAb before and after plasmid-mediated uPAR OE with mRNA expression levels of T cellassociated chemokines in THP-1 macrophages before and after anti-uPAR treatment. (B) The uPAR IHC staining of PDX tumor. (C) Schematic diagram of experimental procedure. (D) Tumor images of humanized PDX mice in each group at the end of treatment. (E) IHC staining of Ki-67, CD68⁺ macrophages and CD8⁺ T cells. (F) The huPBMC reconstitution efficiency, tumor growth, and tumor weight as well as IHC quantification of Ki-67, CD68⁺ macrophages and CD8⁺ T cells at the end of treatment in the respective group. Data are shown as mean±standard deviation (A) or standard error of the mean (F). huPBMC, human peripheral blood mononuclear cell; IHC, immunohistochemistry; OE, over-expression; PD-1, death protein 1; PDX, patient-derived xenograft; *P<0.05, **P<0.01, ***P<0.001.