Dataset Information

Multi-omic analysis reveals a CAF-stemness-governed classification in HCC liver transplant recipients

ABSTRACT: In patients with hepatocellular carcinoma (HCC) meeting the Milan criteria, liver transplantation (LT) is an effective therapy. This study aims to define the survival-related molecular biological features helping precisely identifying the patients with HCC beyond the Milan criteria who have acceptable outcomes. In the derivation cohort (n = 122), integrated analyses of tumor tissues are conducted using RNA sequencing (RNA-seq), proteomic landscape and transposase-accessible chromatin sequencing (ATAC-seq). Based on transcriptomics, three subgroups that significantly differ in overall survival were identified in the derivation cohort, and these findings are validated in an independent cohort. In-depth bioinformatics analysis using RNA-seq and proteomics reveals that the promotion of cancer stemness by cancer-associated fibroblasts (CAFs) can be responsible for the negative biological characteristics observed in high-risk HCC patients. The ATAC-seq identifies key factors regulating transcription, which may bridge CAF infiltration and stemness. Finally, we demonstrate that the CAF-derived CXCL12 sustains the stemness of HCC cells by promoting XRCC5 through CXCR4.

ORGANISM(S): Homo Sapiens

SUBMITTER: Sunbin Ling

PROVIDER: PXD061119 | iProX | Mon Feb 24 00:00:00 GMT 2025

REPOSITORIES: iProX

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Multi-omic analysis reveals a CAF-stemness-governed classification in HCC liver transplant recipients beyond the Milan criteria.

Ling Sunbin S Yu Jiongjie J Zhan Qifan Q Gao Mingwei M Liu Peng P Wu Yongfeng Y Zhang Lincheng L Shan Qiaonan Q Liu Huan H Wang Jiapei J Cai Shuqi S Zhou Wei W Que Qingyang Q Wang Shuo S Hong Jiachen J Xiang Jianan J Xu Shengjun S Liu Jimin J Peng Xiaojun X Wang Nan N Wang Weixin W Xie Haiyang H Cai Jinzhen J Wang Liming L Zheng Shusen S Xu Xiao X

Nature communications 20250512 1

In patients with hepatocellular carcinoma (HCC) meeting the Milan criteria, liver transplantation (LT) is an effective therapy. This study aims to define the survival-related molecular biological features helping precisely identifying the patients with HCC beyond the Milan criteria who have acceptable outcomes. In the derivation cohort, integrated analyses of tumor tissues are conducted using RNA sequencing (RNA-seq), proteomic landscape, and transposase-accessible chromatin sequencing (ATAC-seq ...[more]

PMID: 40355422

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Project description:Kilian2024 - Immune cell dynamics in Cue-Induced Extended Human Colitis Model Single-cell technologies such as scRNA-seq and flow cytometry provide critical insights into immune cell behavior in inflammatory bowel disease (IBD). However, integrating these datasets into computational models for dynamic analysis remains challenging. Here, Kilian et al., (2024) developed a deterministic ODE-based model that incorporates these technologies to study immune cell population changes in murine colitis. The model parameters were optimized to fit experimental data, ensuring an accurate representation of immune cell behavior over time. It was then validated by comparing simulations with experimental data using Pearson’s correlation and further tested on independent datasets to confirm its robustness. Additionally, the model was applied to clinical bulk RNA-seq data from human IBD patients, providing valuable insights into immune system dynamics and potential therapeutic strategies. Figure 4c, obtained from the simulation of human colitis model is highlighted here. This model is described in the article: Kilian, C., Ulrich, H., Zouboulis, V.A. et al. Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease. npj Syst Biol Appl 10, 69 (2024). https://doi.org/10.1038/s41540-024-00395-9 Abstract: Single-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease. This model was curated during the Hackathon hosted by BioMed X GmbH in 2024.

Project description:Introduction: Several attempts have been made to expand the indications of liver transplantation for hepatocellular carcinoma (HCC) beyond Milan criteria. We aimed to define genomic features that enable the identification of patients with HCC beyond Milan criteria who have acceptable transplant outcomes. Methods: Among 770 consecutive HCC patients transplanted at Mount Sinai Hospital and Mayo Clinic between 1990 and 2013, 132 had tumors exceeding Milan criteria on pathology and were enrolled in the study. Explant tumors were analyzed to assess genomic signatures of poor prognosis and immunohistochemical markers associated with tumor recurrence and overall survival. Results: Most of the patients were males (80%) and HCV positive (71%) with a median age of 56. HCC beyond Milan criteria was defined pre-operative in 67%. On pathology, 44% of the patients satisfied the ‘up-to-7 rule’. At a median follow-up of 88 months, 64 patients had died and 45 recurred; the 5-year overall survival (OS) and recurrence rates were 57% and 35%, respectively. CK19 gene signature was independently associated with recurrence (HR=2.95, p<0.001), along with tumor size >5 cm (HR=3.37, p=0.023) and presence of satellite lesions (HR=2.98, p=0.001). S2 subclass signature, which is known to be associated with progenitor cell markers, was independently associated with poor OS (HR=3.18, p=0.001), along with tumor size >5 cm (HR=5.06, p<0.001) and outside up-to-seven rule (HR=2.50, p=0.002). Using the presence of markers of progenitor cells (either CK19 or S2 subclass signatures) patients may be classified into poor-prognosis (n=58; 5-year recurrence 53%, survival 45%) and good-prognosis subgroups (n=74; 5-year recurrence 19%, survival 67%) (HR=3.16, p<0.001 for recurrence, and HR=1.72, p=0.04 for OS). Conclusion: Gene signatures associated with progenitor cell markers (CK19 and S2 subclass signatures) define outcome of HCC patients beyond Milan criteria after transplantation. Patients without these signatures achieve survival rates similar as those patients within Milan criteria. Once prospectively validated, these markers may provide the rationale for a limited expansion of liver transplantation indications.

Project description:Background: Liver transplantation (LT) for Hepatocellular carcinoma (HCC) can be offered to patients beyond Milan criteria. However, there are currently no molecular markers that can be used on HCC explant histology to predict recurrence, which arises in up to 20% of LT recipients. The goal of our study was to identify proteins on HCC explant predictive of recurrence post-transplant, thereby guiding surveillance strategies and identifying patients beyond Milan criteria who would fare well following LT. Methods: LT recipients who had been transplanted at the University Health Network for HCC beyond Milan criteria in the context of Hepatitis B cirrhosis were identified. Snap-frozen samples from the dominant tumors of recurrent (N=7) and non-recurrent (N=4) patients were analyzed using LC-MS/MS on a Q-Exactive Plus mass spectrometer to delineate a distinctive proteomic signature. These tumors were also profiled by a Human Gene 2.0 ST microarray platform to identify a transcriptomic signature predictive of recurrence and analyzed with R packages. STRING database was used to characterize the implicated pathways. Kaplan-Meier estimator was used to generate a combined proteomic/transcriptomic signature predictive of HCC recurrence in patients with HCC beyond Milan criteria at time of LT. Significantly predictive proteins were verified and internally validated by immunoblotting or immunohistochemistry. Results: A total of 79 proteins and 636 genes were significantly differentially expressed in recurrent HCC, compared to non-recurrent (p<0.05). Among these, LGALS3, LGALS3BP, HAL, THBS1, and BLMH, were significantly increased in recurrent HCC at the protein and gene expression level. In turn, ALDH1A1 protein and gene expression were significantly decreased in recurrent HCC. Univariate survival analysis depicted ALDH1A1 (HR=0.084, 95%CI 0.01-0.68, p=0.02), LGALS3BP (HR=7.14, 95%CI 1.20-42.96, p=0.03), and LGALS3 (HR=2.89, 95%CI 1.01-8.3, p=0.049) as the key dysregulated proteins and genes in the patients with HCC recurrence versus those with non-recurrence by both proteomic and transcriptomic analysis. Decreased ALDH1A1 and significantly increased LGALS3 protein expression in recurrent HCC was verified by immunoblotting. Increased LGALS3BP protein expression in recurrent HCC was orthogonally verified and validated by immunohistochemistry in 30 independent HCC samples. Conclusion: Protein and gene expression of the cancer stem cell marker ALDH1A1 was protective against cancer recurrence in patients transplanted for HCC beyond Milan criteria. Conversely, increased expression of LGALS3 and LGALS3BP on explant was significantly predictive of post-transplant recurrence. These findings were internally validated, suggesting potential utility in identifying patients with HCC beyond Milan who would clearly benefit from transplant with limited recurrence risk and guiding post-transplant surveillance.

Project description:Background and aims: Liver transplantation (LT) is the most radical treatment for hepatocellular carcinoma (HCC) with high rates of long-term survival, but tumor recurrence after LT is an unresolved problem. The aim of our study was to identify predictive markers for tumor recurrence after liver transplantation. Methods: In a retrospective single-center study, we included all patients with LT for HCC in our institution (01/2007-12/2012). Beside demographic data, we analyzed course, bridging therapies, Serum-AFP, time point of tumor recurrence, as well as the correlation of imaging and histopathology of our recipients. Additionally, we performed a microarray analysis to identify different miRNA profiles of patients with and without HCC recurrence after LT. Single assay stem-loop real-time PCR (Q-RT-PCR) was used for validation of the results. Results: During the study period, we performed 92 LT in patients with HCC (22 women, 70 men). Twenty-two (23.9%) patients developed a recurrent HCC after LT. Our subgroup with tumor recurrence after LT, presented with a mean disease-free survival of 10 months (3-55 months) and an overall survival of 25.5 months (4-77 months). Milan criteria, AFP levels and pathologic grading had an influence on the tumor recurrence. Performing miRNA analysis, we could identify significant upregulation of 8 miRNAs and downregulation of another 5 miRNAs in patients with tumor recurrence. Consecutively, array data were successfully validated using Q-RT-PCR. Multivariate Cox regression, ROC analysis and Kaplan-Meier showed that a score consisting of two miRNAs and Milan criteria are an independent predictor for tumor recurrence-free survival. Conclusions: Despite careful selection of patients, an early recurrence of HCC after LT cannot be avoided completely. Reliable prognostic markers related to tumor biology are still missing. Analysis and validation of specific miRNAs combined with radiological parameters might lead to a promising strategy for the prediction of tumor recurrence, but prospective studies have to follow. 8 macrodissected hepatocellular carcinoma (recurrent HCC) and 10 macrodissected hepatocellular carcinoma (non-recurrent HCC).

Dataset Information

Multi-omic analysis reveals a CAF-stemness-governed classification in HCC liver transplant recipients

Publications

Multi-omic analysis reveals a CAF-stemness-governed classification in HCC liver transplant recipients beyond the Milan criteria.

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