# Proteomic characterization of intrahepatic cholangiocarcinoma identifies risk-stratifying subgroups and EIF4A1 as a therapeutic target

## TL;DR

This study identifies two distinct proteomic subgroups of intrahepatic cholangiocarcinoma and shows that targeting EIF4A1 could improve treatment outcomes.

## Contribution

The study introduces a proteomic classifier for ICC prognosis and identifies EIF4A1 as a novel therapeutic target.

## Key findings

- Two ICC proteomic clusters with distinct recurrence times were identified using mass spectrometry.
- A 4-protein classifier accurately stratifies ICC subgroups across independent datasets.
- Inhibition of EIF4A1 significantly reduces tumor growth in a PDX model of ICC.

## Abstract

Intrahepatic cholangiocarcinoma (ICC) features poor survival due to frequent recurrences and limited prognostic markers. Using mass spectrometry-based proteomics, we analyze two independent cohorts comprising 80 and 62 treatment-naive ICC tumors, along with 9 independent patient-derived xenografts (PDX). In the first cohort, we identify two subclusters with distinct times-to-recurrence (TTR): An extracellular matrix (ECM)-enriched cluster (mean TTR 859 days) and a proliferation cluster (mean TTR 229 days). A 4-protein classifier trained on our cohort accurately stratifies these clusters in the Dong et al. dataset (2022) and in our second cohort, revealing similar proteomic motifs and clinical outcomes. The translation regulator EIF4A1, enriched in ICCs of both clusters, emerges as a therapeutic target, as its inhibition with eFT226 significantly reduces tumor growth in an ICC PDX model. Proteomic analyses of various PDX models also emphasize the critical role of tumor-stroma interactions in ICC. Overall, this study establishes two prognostic proteomic clusters, validates their relevance across datasets, and highlights EIF4A1 inhibition as a potential therapeutic strategy.

Intrahepatic cholangiocarcinoma has poor survival due to frequent recurrence and limited prognostic markers. Here, the authors identify two prognostic proteomic tumor clusters, validate a classifier across independent cohorts, and introduce EIF4A1 inhibition as a potential therapeutic strategy.