Home » Abstracts » Biomarkers & Drug discovery » Proteomic Characterization of Intrahepatic Cholangiocarcinoma Identifies Risk-Stratifying Subclusters, Proteins Linked to Time-To-Recurrence, and a Druggable Therapeutic Target

Proteomic Characterization of Intrahepatic Cholangiocarcinoma Identifies Risk-Stratifying Subclusters, Proteins Linked to Time-To-Recurrence, and a Druggable Therapeutic Target

Abstract #41 Posted on25 February 202512 May 2025 12 May 2025 13h29

Author(s)

Tilman Werner (Presenting Author) | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany
Klara-Luisa Budau | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany
Miguel Cosenza Contreras | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany
Frank Hause | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany
Johanna Thiery | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany
Annika Topitsch | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany
Gaia Gentile | Charles River Germany | Am Flughafen 12, 79108, Freiburg, Germany
Konrad Kurowski | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany
Niko Pinter | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany
Julia Schüler | Charles River Germany | Am Flughafen 12, 79108, Freiburg, Germany
Martin Werner | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany
Carlie Sigel | Memorial Sloan Kettering Cancer Center | 1275 York Avenue , NY 10065, New York, United States
Laura Tang | Memorial Sloan Kettering Cancer Center | 1275 York Avenue , NY 10065, New York, United States
Peter Bronsert | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany
Oliver Schilling | Institut für Klinische Pathologie | Breisacher Straße 115a, 79106, Freiburg, Germany

Abstract

Intrahepatic cholangiocarcinoma (ICC) is associated with poor patient survival due to frequent recurrences and the lack of reliable prognostic markers. In this study, we analyzed 80 treatment-naïve ICC tumors, 77 adjacent non-malignant tissues, and 9 patient-derived xenografts (PDX) via mass spectrometry-based proteomics to identify proteomic profiles linked to patient outcomes.
We found two distinct subclusters with significantly different times to recurrence (TTR). The first cluster, characterized by a high extracellular matrix (ECM) content and increased proteolytic processing, had a mean TTR of 1,125 days. The second cluster, associated with increased mRNA and protein turnover, had a mean TTR of 306 days. A four-protein classifier trained on our data successfully stratified these ICC clusters in the Dong et al. dataset (2022), revealing similar associations between proteomic signatures and clinical outcomes. Furthermore, a mixed-species proteomic analysis of various PDX models underscored the role of tumor-stroma interactions in ICC. Notably, we also identified translation regulator EIF4A1 as enriched in the protein-turnover cluster. Inhibition of EIF4A1 with the novel drug eFT226 significantly reduced tumor growth in an ICC PDX model.
Overall, our study establishes two prognostic proteomic clusters, validates their relevance across datasets, and introduces EIF4A1 inhibition as a potential strategy to mitigate ICC progression.

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Intrahepatic cholangiocarcinoma (ICC) is associated with poor patient survival due to frequent recurrences and the lack of reliable prognostic markers. In this study, we analyzed 80 treatment-naïve ICC tumors, 77 adjacent non-malignant tissues, and 9 patient-derived xenografts (PDX) via mass spectrometry-based proteomics to identify proteomic profiles linked to patient outcomes. 
We found two distinct subclusters with significantly different times to recurrence (TTR). The first cluster, characterized by a high extracellular matrix (ECM) content and increased proteolytic processing, had a mean TTR of 1,125 days. The second cluster, associated with increased mRNA and protein turnover, had a mean TTR of 306 days. A four-protein classifier trained on our data successfully stratified these ICC clusters in the Dong et al. dataset (2022), revealing similar associations between proteomic signatures and clinical outcomes. Furthermore, a mixed-species proteomic analysis of various PDX models underscored the role of tumor-stroma interactions in ICC. Notably, we also identified translation regulator EIF4A1 as enriched in the protein-turnover cluster. Inhibition of EIF4A1 with the novel drug eFT226 significantly reduced tumor growth in an ICC PDX model. 
Overall, our study establishes two prognostic proteomic clusters, validates their relevance across datasets, and introduces EIF4A1 inhibition as a potential strategy to mitigate ICC progression.

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