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A human liver cell-based system modeling a clinical prognostic liver signature for therapeutic discovery.

ABSTRACT: Chronic liver disease and hepatocellular carcinoma (HCC) are life-threatening diseases with limited treatment options. The lack of clinically relevant/tractable experimental models hampers therapeutic discovery. Here, we develop a simple and robust human liver cell-based system modeling a clinical prognostic liver signature (PLS) predicting long-term liver disease progression toward HCC. Using the PLS as a readout, followed by validation in nonalcoholic steatohepatitis/fibrosis/HCC animal models and patient-derived liver spheroids, we identify nizatidine, a histamine receptor H2 (HRH2) blocker, for treatment of advanced liver disease and HCC chemoprevention. Moreover, perturbation studies combined with single cell RNA-Seq analyses of patient liver tissues uncover hepatocytes and HRH2⁺, CLEC5A^high, MARCO^low liver macrophages as potential nizatidine targets. The PLS model combined with single cell RNA-Seq of patient tissues enables discovery of urgently needed targets and therapeutics for treatment of advanced liver disease and cancer prevention.

SUBMITTER: Crouchet E

PROVIDER: S-EPMC8448834 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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A human liver cell-based system modeling a clinical prognostic liver signature for therapeutic discovery.

Crouchet Emilie E Bandiera Simonetta S Fujiwara Naoto N Li Shen S El Saghire Hussein H Fernández-Vaquero Mirian M Riedl Tobias T Sun Xiaochen X Hirschfield Hadassa H Jühling Frank F Zhu Shijia S Roehlen Natascha N Ponsolles Clara C Heydmann Laura L Saviano Antonio A Qian Tongqi T Venkatesh Anu A Lupberger Joachim J Verrier Eloi R ER Sojoodi Mozhdeh M Oudot Marine A MA Duong François H T FHT Masia Ricard R Wei Lan L Thumann Christine C Durand Sarah C SC González-Motos Victor V Heide Danijela D Hetzer Jenny J Nakagawa Shigeki S Ono Atsushi A Song Won-Min WM Higashi Takaaki T Sanchez Roberto R Kim Rosa S RS Bian C Billie CB Kiani Karun K Croonenborghs Tom T Subramanian Aravind A Chung Raymond T RT Straub Beate K BK Schuppan Detlef D Ankavay Maliki M Cocquerel Laurence L Schaeffer Evelyne E Goossens Nicolas N Koh Anna P AP Mahajan Milind M Nair Venugopalan D VD Gunasekaran Ganesh G Schwartz Myron E ME Bardeesy Nabeel N Shalek Alex K AK Rozenblatt-Rosen Orit O Regev Aviv A Felli Emanuele E Pessaux Patrick P Tanabe Kenneth K KK Heikenwälder Mathias M Schuster Catherine C Pochet Nathalie N Zeisel Mirjam B MB Fuchs Bryan C BC Hoshida Yujin Y Baumert Thomas F TF

Nature communications 20210917 1

Chronic liver disease and hepatocellular carcinoma (HCC) are life-threatening diseases with limited treatment options. The lack of clinically relevant/tractable experimental models hampers therapeutic discovery. Here, we develop a simple and robust human liver cell-based system modeling a clinical prognostic liver signature (PLS) predicting long-term liver disease progression toward HCC. Using the PLS as a readout, followed by validation in nonalcoholic steatohepatitis/fibrosis/HCC animal models ...[more]

PMID: 34535664

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Project description:Background: The increasing prevalence and mortality of gastric cancer (GC) has promoted the urgent need for prognostic signatures to predict the long-term risk and search for therapeutic biomarkers. Methods and materials: A total of 921 GC patients from three GEO cohorts were enrolled in the current study. The GSE15459 and GSE62254 cohorts were used to select the top prognostic gene via the evaluation of the area under the receiver operating characteristic (ROC) curve (AUC) values. The GSE84437 cohort was used as the external validation cohort. Least absolute shrinkage and selector operation (LASSO) regression analysis was applied to reduce the feature dimension and construct the prognostic signature. Furthermore, a nomogram was constructed by integrating the independent prognostic analysis and validated by calibration plot, decision curve analysis and clinical impact curve. The molecular features and response to chemo-/immunotherapy among risk subgroups were evaluated by the "MOVICS" and "ESTAMATE" R packages and the SubMap algorithm. Lauren classification and ACRG molecular subtype were obtained to compare with the risk model. Results: Forty-four prognosis-associated genes were identified with a preset cutoff AUC value of 0.65 in both the GSE62254 and GSE15459 cohorts. With the 10-fold cross validation analysis of LASSO, nine genes were selected to construct the nine-consensus-prognostic-gene signature. The signature showed good prognostic value in the GSE62254 (p < 0.001, HR: 3.81, 95% CI: 2.44-5.956) and GSE15459 (p < 0.001, HR: 2.65, 95% CI: 1.892-3.709) cohorts and the external validation GSE84437 cohort (p < 0.001, HR: 2.06, 95% CI: 1.554-2.735). The nomogram constructed based on two independent predictive factors, tumor stage and the signature, predicted events tightly consistent with the actual (Hosmer-Lemeshow p value: 1-year, 0.624; 3-years, 0.795; 5-years, 0.824). For the molecular features, we observed the activation of apical junction, epithelial mesenchymal transition, and immune pathways in the high-risk group, while in the low-risk group, cell cycle associated G2M, E2F and MYC target pathways were activated. Based on the results we obtained, we indicated that gastric patients in the low-risk group are more suitable for 5-fluorouracil therapy, while high-risk group patients are more suitable for anti-CTLA4 immunotherapy, these results need more support in the further studies. After compare with proposed molecular subtypes, we realized that the nine-consensus prognostic gene signature is a powerful addition to identify the gastric patients with poor prognosis. Conclusion: In summary, we constructed a robust nine-consensus-prognostic-gene signature for the prediction of GC prognosis, which can also predict the personalized treatment of GC patients.

Dataset Information

A human liver cell-based system modeling a clinical prognostic liver signature for therapeutic discovery.

Publications

A human liver cell-based system modeling a clinical prognostic liver signature for therapeutic discovery.

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