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

Domain: biology
Published: 2026-03-23
Editorial outcome: minor_revision

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.

Introduction
Cholangiocarcinomas (CCA) are the second most prevalent primary hepatic tumors and comprise 15% of all liver malignancies. Depending on their location along the bile ducts, CCAs are categorized as intrahepatic (ICC), perihilar or distal adenocarcinomas. CCAs possess different genetic, histologic, and clinical features, but all are characterized by a late onset with diffuse clinical symptoms, frequent recurrences and dismal overall survival1–3.

ICCs are rare cancers, nevertheless their incidence rate is rising worldwide3. Risk factors include bile duct stasis, inflammatory diseases of the liver, obesity and diabetes, parasitic or viral infections, and alcohol or tobacco abuse4,5. Remarkably, approximately 50% of cases occur without identifiable risk factors6.

ICCs arise in small bile ducts and ductules inside the liver. Diagnoses are frequently delayed due to nonspecific symptoms, imprecise imaging criteria, and a lack of reliable, non-invasive tumor markers1,2,7. Consequently, at the moment of primary diagnosis, patients often show an advanced tumor stage and are not eligible for surgery2,7. For a remaining minority, an extensive surgical resection represents the first-line treatment option5,8. However, up to 70% of resected patients develop recurrences and standardized treatment guidelines for adjuvant chemo-, targeted-, or immunologic therapies are rare or entirely absent5–7,9–13. In addition, the benefit of adjuvant chemo- or radiotherapy is nowadays considered minimal8,10,12–15. To enable successful tumor re-resections and personalized therapies, robust prognostic markers are essential.

Pathological assessment of resected ICC adheres to the criteria of the American Joint Committee on Cancer (AJCC) and the Union for International Cancer Control (UICC)2,16,17. Previously, we identified lymph node- and lymphangio-invasion, as well as AJCC tumor staging, as potential prognostic markers for the ICC cohort presented here18. Important histopathological markers for ICC include cytokeratins (KRTs)−7,−17,−19 and MUC1, while KRT20 and napsin A are typically missing19–21. More precise markers for tumor subtyping and predicting ICC recurrence are lacking (Fig. 1a).

Due to its rarity, ICC remains insufficiently described on a molecular level. Of note, comprehensive omics-based studies are scarce2,3. Nevertheless, genomic and transcriptomic studies have described the importance of FGFR fusions and IDH1, ARID1A, BAP1, KRAS, and TP53 mutations for ICC tumorigenesis. Additionally, several publications proposed different molecular classifications into fibrotic, immune-deserted, or innate- and acquired immunity-mediated inflammatory subtypes on a genomic and transcriptomic level22–27. The first integrated proteogenomic profile elaborating on ICC subtypes was presented by Dong et al.28, and was followed by subsequent studies on cohorts collected at the same hospital29,30.

In recent years, interest in the proteomic characterization of tumors has increased, spurred by groundbreaking studies from the Clinical Proteomic Tumor Analysis Consortium (CPTAC)31. Since transcriptome alterations are only partially reflected at the protein level and cannot represent processes such as proteolytic events or other post-translational modifications, proteomic profiling has become an integral component in understanding cancer biology32. Advances in methods such as data-independent acquisition (DIA) have facilitated cohort-wide studies with deep proteome coverage33.

Here, we present the proteomic characterization of ICC samples from the Memorial Sloan Kettering Cancer Center (MSKCC), comprising 80 treatment-naive ICC tumors and 69 patient-matched, tumor-adjacent non-malignant tissues (TANM), highlighting tumor-specific proteome alterations. We identified two predominant proteomic subtypes of ICC, which are strongly associated with recurrence-free survival. Using a machine learning-based classification model, we transferred these clusters to an independent cohort from the University Medical Center Freiburg (UKF), consisting of 62 treatment-naive tumors with 61 matching TANM tissues, and to the dataset published by Dong et al.28, uncovering similar proteomic effects. Further analyses of proteolytic activity and patient-derived xenograft models (PDX) emphasize the importance of tumor-stroma interactions in ICC progression. Finally, we introduce EIF4A1 as a potential drug target and assess the effect of its novel inhibitor eFT226 (Zotatifin) on ICC growth in PDX.

Results
Description of the cohorts
Our MSKCC-ICC cohort comprised 80 treatment-naive tumors and 69 patient-matched, tumor-adjacent non-malignant tissues (TANM) from a total of 80 ICC patients. Patient characteristics are provided in Supplementary Table 1 and Supplementary Data 1. Our independent UKF-ICC cohort included 62 treatment-naive tumors and 61 patient-matched TANM tissues; patient characteristics are summarized in Supplementary Table 2. The UKF-ICC cohort is solely used for validation of the subtype-specific classifier. The size of our ICC cohort is smaller than the Fudan University ICC (FU-ICC) cohort, for which comprehensive proteogenomic characterizations have recently been published by Dong et al. (262 patients), Lin et al. (255 patients), and Deng et al. (217 patients)28–30. However, disease etiology and incidence are assumed to differ across world regions, and, to our knowledge, this study represents one of the first large proteomic analyses of ICC in Western countries2,3,34.

Proteome coverage
Across the MSKCC-ICC cohort, on average, 7020 proteins were identified per sample through DIA-PASEF measurements on a Bruker Tims TOF Flex mass spectrometer. Tumor tissues showed significantly higher identification numbers, perhaps due to more complex proteomic profiles (unpaired t-test, p<0.0001, Fig. 1d Supplementary Data 2). We have used DIA-NN 1.9.2 for data analysis. More than 5500 proteins presented with less than 20% missing values throughout the entire cohort and were used for statistical analysis (Fig. 1c). Six outliers were reevaluated by a pathologist, reclassified as hepatocellular carcinomas or fibrosis, and removed from the study, yielding the aforementioned numbers of 80 ICC tumors and 69 TANM tissues.

Tumor and TANM tissues show highly divergent proteome profiles
Principal component analysis and hierarchical clustering of the MSKCC cohort demonstrate highly divergent proteomic profiles of ICC and TANM proteomes (Fig. 1e and Supplementary Fig. 1A). To identify differentially regulated proteins in ICC vs. TANM, we employed linear models, which revealed 4865 proteins that were differentially regulated in the MSKCC-ICC cohort (p<0.01, with FDR correction) (Fig. 2a and Supplementary Data 3).

Reassuringly, cytokeratins 7 (KRT7) and 19 (KRT19), commonly used markers to differentiate ICC from hepatocellular carcinoma, as well as epithelial marker melanoma cell adhesion molecule (MCAM/CD146), were strongly upregulated in tumors (KRT7 logFC=4.03, p_adj=2.23E-36; KRT19 logFC=4.75, p_adj=5.01E-56; MCAM logFC=1.85, p_adj=7.99E-37)19–21,35. Likewise, biglycan (BGN), an extracellular matrix proteoglycan frequently upregulated in tumor tissue and tumor microenvironment, was detected with higher abundance in ICC tissues (logFC=2.02, p_adj=1.86E-28)36–38.

We sought to identify common biological themes within the ICC-associated proteins. To this end, we applied Gene Ontology (GO) enrichment analysis. Compared to tumor tissue, proteins belonging to lipid, amino acid, and glucose metabolic processes, and oxidative respiration were upregulated in TANM, likely due to the inclusion of surrounding liver tissue (Fig. 2c and Supplementary Fig. 2). In part, this observation also adheres to the commonly reported Warburg effect within solid tumors39. Conversely, proteins connected to the extracellular matrix (ECM), as well as proteins involved in DNA replication, and RNA transport and processing were upregulated in ICC (Fig. 2c and Supplementary Fig. 2). Enrichment of endocytosis, spliceosome, and DNA repair pathways, together with down-regulation of proteasome components, was previously described by Dong et al. as a trans-effect of a characteristic chromosome 14q loss28. We identified ECM and matrisome components (e.g., prolargin, mimecan, biglycan, lumican, and various collagens) to be among the most prominently increased proteins within ICC tumor tissue, which is in agreement with strong desmoplastic reactions being a hallmark of ICC24. Further, we surveyed the proteome data for proteins likely related to immune cell infiltration or tumor immunity (Fig. 2b and Supplementary Fig. 1B). We found pan-leukocyte marker receptor-type tyrosine-protein phosphatase C (PTPRC, also known as CD45) and neutrophil-specific marker CEACAM8 to be increased in ICC40,41. In contrast, the monocyte/macrophage markers CD14 and CD163 were significantly increased in TANM tissue, which might be related to their high expression in Kupffer cells of the liver (Fig. 2c and Supplementary Fig. 1B)42–44.

ICC tumor proteomes form two clusters with strong proteomic differences and divergent times to recurrence in the MSKCC-ICC cohort
Recent proteome studies on solid tumors, including ICC, have highlighted the existence of proteomic subgroups within a tumor entity; i.e., subgroups that are enriched in proteins stemming from (tumor-) biological processes28,30. Unsupervised PCA of our ICC protein abundance data of the MSKCC-ICC cohort showed homogenous distribution of tumor samples along components 1 and 2 (Fig. 3b). Next, we performed unsupervised hierarchical clustering, which sorted tumors into two proteomic clusters (Supplementary Table 1 and Fig. 3a). The number of clusters was confirmed using a Monte-Carlo-based consensus clustering analysis with hierarchical clustering set as method (Supplementary Fig. 3A). Linear modeling of differentially expressed proteins followed by gene set enrichment analysis (GSEA) via the KEGG and Gene Ontology databases revealed different biological processes to be upregulated in each cluster. In cluster 1, we detected a fingerprint of matrisomal proteins, humoral immune response, and coagulation and complement cascades. Cluster 2 exhibited increased activity in DNA replication and synthesis, and processing of (m)RNA and proteins (Fig. 3c, d, Supplementary Fig. 4, Supplementary Data 3). In line with their proteomic motifs, we refer to cluster 1 as “ECM cluster” and to cluster 2 as “proliferation cluster”. Consistently, the routinely used proliferation markers KI67 and PCNA were both upregulated in the proliferation cluster (KI67 logFC=0.65, p_adj=4.48E-4; PCNA logFC=0.60, p_adj=1.81E-4)45. We further investigated the matrisomal proteins identified in the ECM cluster and found elevated expression to be largely confined to classical ECM glycoproteins, collagens, and proteoglycans (Supplementary Fig. 5). A screen for immunity-related marker proteins did not reveal specific inflammation patterns associated with either cluster (Supplementary Fig. 3B, C).

Next, we probed whether both clusters differ in clinicopathological features. Most clinical parameters, including sex, age, and duct type classification (large duct type and small duct type), were evenly distributed across both clusters (Supplementary Table 1, p=0.4 Pearson’s Chi-squared test, p>0.9 Wilcoxon rank sum test, p=0.9 Pearson’s Chi-squared test). However, tumor stages differed significantly across clusters (p=0.014): the proliferation cluster contained a higher fraction of AJCC stage T2 specimens, while the ECM cluster included more T1 tumors. Analysis of corresponding tissue images by a trained pathologist revealed that tumors of the proliferation cluster were characterized by significantly increased lymphangioinvasion and a tendency for elevated peritumoral tumor budding and satellite nodules, suggesting more invasive growth (Pearson’s Chi-squared Test, p_lymphangio<0.001, p_peri_tubudding=0.065, p_satellite_nodule=0.083). Most importantly, the ECM cluster is associated with significantly prolonged time-to-recurrence (TTR) compared with the proliferation cluster (median recurrence-free survival: proliferation 229 days vs. ECM 859 days, log rank p=0.00013, total recurrence events 27/48 ECM and 29/32 proliferation cluster patients, Fig. 3e). The clusters are more predictive for recurrence-free survival than AJCC tumor staging (Supplementary Fig. 6A). Moreover, both clusters are significant predictors for overall survival (OS) (median survival: proliferation 872 days vs. ECM 1817 days, log rank p=0.014, total events 19/48 ECM and 20/32 proliferation cluster patients, Supplementary Figs. 6B and 7A, C).

30 out of 80 MSKCC-ICC patients have been treated with adjuvant radiotherapy, with a significantly larger fraction belonging to the proliferation cluster (proliferation: 18/32 vs ECM: 12/48; Chi-Squared Test p=0.005, Supplementary Table 1). Still, radiation therapy remains less predictive for recurrence-free survival than the proteome clusters and was linked to significantly shorter TTRs (Fig. 4a, b and Supplementary Fig. 6A).

Furthermore, we used an image-based classification model to deconvolute different cell types present in tumor samples. The classifier was trained in a supervised manner with more than 150,000 manually annotated nuclei by a surgical pathology resident. Annotated cell categories included immune cells, liver cells, necrotic cells, stromal cells and tumor cells. We found no difference between the two clusters regarding liver and immune cell content, whereas necrotic and stromal cells were significantly enriched in tumors belonging to the ECM cluster. Interestingly, there was no difference in the amount of tumor cells, underlining that the two clusters reflect distinct tumor phenotypes rather than differences in tumor cell content (Pearson’s Chi-Square test, p_Liver Cells=0.2, p_Immune Cells=0.3, p_Necrotic Cells=0.005, p_Stromal Cells=0.013, p_Tumor Cells=0.15, Supplementary Table 1). Importantly, tumor cellularity was not associated with either TTR or OS (Cox proportional hazards model, HR=1.008, p_TTR=0.356, HR_OS=0.995, p_OS=0.656; Supplementary Fig. 6C).

These proteomic results are largely in line with previous multi-omics publications. Dong et al. describe four ICC subclusters based on mRNA, proteomic, and phosphoproteomic data for the FU-ICC cohort28. FU-ICC cluster 1 was enriched in proteins involved in inflammation and glucose metabolism. Conversely, FU-ICC cluster 2 showed high levels of ECM proteins. FU-ICC cluster 3 was enriched with proteins involved in MAP kinase signaling and fatty acid and nucleotide synthesis, while the biology of FU-ICC cluster 4 remained inconclusive. FU-ICC cluster 1 had the worst prognosis, particularly for tumors of AJCC stages I and II. Similarly, Lin et al. report three immune subgroups within the same cohort and link immune-activated cluster IG3 (fibroblast and lymphocyte invasion) to prolonged survival, while other clusters IG1 and IG2 are characterized by a worse prognosis and innate immunity mediated inflammation (IG1) or immune exclusion (IG2)29. A study by Deng et al. was not fully comparable due to the inclusion of extrahepatic cholangiocarcinoma, which were characterized by a very high expression of matrisomal proteins30. Interestingly, Martin-Serrano et al. described contrasting inflamed vs. non-inflamed ICC gene expression profiles in a large re-analysis of several transcriptomic datasets. Inflamed tumors, representing about 40% of patients, were defined by strong stromal enrichment but not clearly associated with survival outcome24.

Cox proportional hazards model highlights individual prognostic ICC proteins in the MSKCC-ICC dataset
To identify biological motifs and individual proteins with expression patterns directly correlating to the TTR, we iteratively applied a Cox proportional hazards model (CPHM) within the MSKCC-ICC cohort. Adjuvant radiotherapy was included as an independent covariate, since it was significantly associated with a shorter TTR (Fig. 4a, b). In total, we identified 188 proteins with expression patterns significantly correlating with the TTR distribution (p.adj<0.05, Fig. 4c). Among these, 84 proteins were associated with an increased risk of early recurrence (hazard ratio (HR)>1), while 104 proteins were associated with a reduced risk (HR<1). Gene set enrichment analysis revealed that proteins with high HRs were enriched in pathways related to proteasomal degradation and autophagy (Fig. 4e). Conversely, proteins with HR<1 were involved in ECM and oxidoreductive processes (Fig. 4d). Taken together, the biological processes associated with high or low recurrence risk were in line with those revealed by hierarchical clustering.

Importantly, CPHM does not consider absolute protein abundance and thus also recognizes subtle expression changes as long as they align with the TTR distribution. To highlight protein hits with meaningful abundance changes across the cohort, we further filtered CPHM results according to their log2 abundance coefficient of variation. Only proteins with a standard deviation >1 were considered, yielding 44 proteins. Of these, 5 proteins with high HR were also significantly enriched (logFC>0.8, p.adj<0.05) in the unfavorable proliferation cluster—for example, DNA topoisomerase 2-alpha (TOP2A) and glucose-6-phosphate 1-dehydrogenase (G6PD). Conversely, 28 proteins were significantly enriched in the favorable ECM cluster (logFC > 0.8, p.adj <0.05), including prolargin (PRELP), mimecan (OGN), vitronectin (VTN), and lumican (LUM; Supplementary Data 4).

Several of the identified high-risk candidate proteins have been previously associated with the tumor biology of cholangiocarcinoma. For example, high TOP2A expression is associated with a poor prognosis46. Similarly, G6PD overexpression has been reported for several gastrointestinal cancers and is also related to clinical outcome47. Conversely, several of the proteins with low HR and enriched in the ECM cluster have been previously associated with tumor suppression and/or better prognosis in other gastrointestinal cancer types, such as cytoglobin, serine protease HTRA1 and septin-448–51. Also, cancer-associated genes TP53 and MUC16/CA125 were more frequently mutated in the proliferation cluster (Fisher’s Exact Test, p_TP53=0.0003, p_MUC16=0.0002, Supplementary Fig. 8F), reflecting the worse survival outcome Dong et al. reported for tumors harboring TP53 mutations28. We observe a similar pattern in the RNA-seq data, with ECM-related genes upregulated in the proteomic ECM cluster and replication-related genes upregulated in the proteomic proliferation cluster (Supplementary Fig. 8G, H). Unfortunately, similar genomic studies were not feasible for the MSKCC-ICC cohort owing to a lack of suitable sample material.

A classifier for ICC clusters detects similar proteomic motifs and survival outcome in the FU-ICC cohort
To transfer the concept of “prognostic ICC subtypes” to new and/or third-party proteomic ICC data, we trained a machine-learning-based classification model. From the 50 most cluster-defining proteins as determined by log2 fold-change and adjusted p-value in the differential expression analysis, we randomly generated 100 combinations of four proteins. Each of these classifier-candidate sets contained two proteins upregulated in the ECM cluster and two proteins upregulated in the proliferation cluster. Notably, this comprised 24 of the 33 proteins we determined as particularly related both to the cluster identities and to the TTR. For each set, we trained a classification model by partitioning the MSKCC-ICC dataset (tumor samples only) into a training and a test set (75% and 25% of samples, respectively; Fig. 5a). Across all 100 tested models, we obtained receiver operating characteristic (ROC) curves with a mean area under the curve (AUC) of 0.89; with only five sets falling slightly below an AUC of 0.8. Mean sensitivity and mean specificity were 0.86 and 0.88, respectively (Fig. 5b). These results emphasize that a broad range of the most differentially abundant proteins between both clusters, and combinations thereof, are well suited for building classifier models.

To validate our classifier approach and the clinical relevance of the clusters, we applied the model to all 214 cases with proteomic data from the FU-ICC dataset published by Dong et al.28, after having adjusted it to the MSKCC-ICC intensity range using the ComBat algorithm. As a classifier, we selected the set of proteins with the highest log2 fold-change and lowest adjusted p-value in the MSKCC-ICC cohort. All four proteins—prolargin (PRELP), mimecan (OGN), structural maintenance of chromosomes protein 2 (SMC2), and importin subunit alpha-1 (KPNA2)—were also consistently identified (<20% missing values) across the FU-ICC dataset. For a more in-depth assessment, we additionally evaluated this classifier’s performance across 100 bootstrapped MSKCC-ICC-based training and test datasets (random 75%/25% partitions), yielding an average AUC of 0.9. When applied to the FU-ICC cohort, the classifier categorized it into 141 ECM and 73 proliferation cluster cases (Fig. 5c, d; Supplementary Fig. 8B; Supplementary Table 3). Differential expression analysis between the assigned clusters in the FU-ICC dataset revealed biological patterns consistent with those observed in our MSKCC-ICC cohort (Supplementary Fig. 8A, C). Moreover, patients assigned to the ECM cluster demonstrated significantly prolonged overall survival as compared to those of the proliferation cluster (survival data available from 137 patients, median survival: ECM not reached vs. proliferation 650 days, log rank p=0.00066, HR_proliferation cluster=2.1; Fig. 5e). Looking at the clinical data, tumors of the assigned proliferation cluster were more often categorized as TNM-stage II (Pearson’s Chi-squared Test, p_TNM=0.007) with significantly higher intrahepatic metastasis and vascular invasion (Pearson’s Chi-squared Test, p_ihep_met=0.007, p_VI=0.022; Supplementary Table 3).

The FU-ICC dataset also included genomic data. Interestingly, the assigned proliferation cluster contained a higher fraction of overall mutations, while the ECM cluster samples carried more (FGFR)-fusions (Supplementary Fig. 8D–F). FGFR2-fusions in ICC have recently been reported to be associated with a favorable diagnosis, which aligns with our findings52.

Classification of the UKF-ICC validation cohort reveals matching prognostic clusters
Encouraged by these results, we integrated an additional ICC cohort consisting of samples collected at the University Medical Center Freiburg (UKF) as a second validation dataset. This cohort included matching tumor and TANM tissues from 66 patients provided as FFPE cores. Proteomic measurement was performed on a Thermo Q Exactive Plus, yielding an average of 4540 identified proteins (3060 proteins with less than 20% missing values), following the data analysis strategy described above (Supplementary Fig. 9A; Supplementary Data 5). After consultation with a pathologist, four tumor samples (plus the matched tissue) were excluded due to ambiguous classification, resulting in 62 tumor samples with 61 patient-matched TANM samples. Again, tumor and TANM tissues separated well in clustering analyses (Supplementary Fig. 9C). A total of 2581 proteins were differentially abundant between tumor and TANM tissue (p<0.01, FDR-corrected, Supplementary Fig. 9B). Findings were very similar to the MSKCC-ICC cohort, with established ICC markers such as KRT7 and KRT19 being more abundant in tumors (KRT7 logFC=4.21, p_adj=1.83E-27; KRT19 logFC=4.31, p_adj=1.12E-36). In contrast, TANM tissue showed higher abundance of common liver enzymes, such as aldolase B (ALDOB), alcohol dehydrogenases and arginase 1 (ARG1), confirming the accuracy of pathological sampling (ALDOB logFC=−6.22, p_adj=5.14E-48, ADH1A logFC=−5.93, p_adj=2.22E-43, ARG1 logFC=−4.95, p_adj=1.82E-51). Gene set enrichment analysis revealed upregulation of RNA processing and downregulation of metabolic and catabolic processes in ICC tissue compared to TANM (Supplementary Fig. 9D).

Using the same approach as for the FU-ICC cohort, we classified tumor samples into the ECM and proliferation cluster by using a 4-protein classifier. Again, proteins were selected for consistent identification in both cohorts, as well as their log2-fold changes and adjusted p-values in the MSKCC-cohort. For the UKF-ICC cohort, the classifier set consisted of prolargin (PRELP), mimecan (OGN), DnaJ homolog subfamily C member 7 (DNAJC7), and nucleolar RNA helicase 2 (DDX21). As for the FU-ICC cohort, we evaluated the classifier’s performance across 100 bootstrapped MSKCC-ICC-based training and test datasets, yielding an average AUC of 0.89 (Fig. 5g). The cohort was stratified into 18 ECM and 42 proliferation cluster samples (2 samples were excluded due to incomplete clinical annotation; Fig. 5h; Supplementary Fig. 10B; Supplementary Table 2). Again, the ECM cluster was associated with significantly longer overall survival (median survival: ECM 46 vs. proliferation 25 months, log-rank p=0.029, Fig. 5i). We also observed trends towards more grade 2 tumors and increased perineural sheath invasion in the proliferation cluster (Pearson’s Chi-squared Test, p_TNM=0.076, p_PNI=0.073, Supplementary Table 2). 49 proteins were differentially abundant between the two clusters, and a GSEA revealed upregulation of glycerophospholipids in the ECM cluster and upregulation of translation in the proliferation cluster (Supplementary Fig. 10A, C). Compared to the other cohorts, protein expression differences between clusters were less pronounced, likely reflecting the use of FFPE tissue punches that can contain minor amounts of surrounding tissue, diluting proteomic specificity. Whole-exome sequencing (WES) of 19 tumors and matched TANM samples identified 512 somatic mutations, with a low overall mutational burden (mean TMB=0.73 mut/mb; Supplementary Data 6). Due to limited sample size and mutation frequency, the genomic data remained inconclusive (Supplementary Fig. 10D).

Semi-specific peptides are increased in TANM and differ between clusters
Tumors and cells of the tumor microenvironment, such as immune cells, actively modify surrounding ECM through endogenous proteolysis, which may aid tumor progression and metastasis53. This process yields truncated proteins that are amenable to proteomic analysis54. Recently, we have shown that so-called semi-specific approaches in peptide-to-spectrum matching are a valuable approach to grasping endogenous proteolytic processing55,56. Since all samples of the present study were digested with trypsin, which specifically cleaves protein sequences after arginine or lysine, any detected peptide sequence that deviates from this specificity might represent a (patho-) physiological cleavage product. Across the MSKCC-ICC cohort, 16.3% (18,087) of all identified peptides possessed such semi-tryptic properties (Fig. 6a). When looking at the different tissues, we find significantly higher portions of semi-specific peptides in TANM samples, regardless of the matching tumor cluster (unpaired t-tests, p=0.000511, p=0.00015, Fig. 6b).

Interestingly, semi-specific peptides seem to differ between tumor and TANM tissue—and also among both clusters (Fig. 6c). While the abundance of most semi-specific peptides closely correlates with their protein of origin, few semi-specific peptides show a diverging behavior. For example, although dehydrogenase/reductase SDR family member 1 (DHRS1) was decreased in tumor tissues, one semi-specific DHRS1 peptide was significantly enriched (Fig. 6d). Similarly, multiple semi-specific peptides of the functionally related aldo-keto reductase family 1 member C4 (AKR1C4) did not correlate well with whole protein abundance when compared between tissues and clusters (Fig. 6e).

We also found several proteases to be differentially abundant between the different tissues and clusters (Supplementary Fig. 11). Additionally, we considered semi-specific peptides in the UKF-ICC cohort (Supplementary Fig. 12A). Consistent with our main findings, the proportion of semi-specific peptides was higher in TANM compared to tumor samples in both clusters (Supplementary Fig. 12B). We also observed substantial global differences in semi-specific peptide abundances between TANM and tumor tissue, whereas differences between clusters were minimal (Supplementary Fig. 12C–E), possibly due to lower purity of FFPE punch samples and/or higher sparsity in the semi-specific dataset. Interestingly, RTN4 emerged as the only protein with a peptide not correlating with the abundance of its parent protein—consistent with its behaviour in the MSKCC-ICC cohort (Fig. 6D and Supplementary Fig. 12D).

Recently, it has been demonstrated that antibodies may specifically recognize proteolytically generated neo-epitopes in solid tumors57. Identification of tumor-specific patterns of endogenous proteolytic processing may supplement such efforts.

Proteomics of ICC PDX models enables insight into tumor-stroma co-regulation
Since ICC is characterized by strong desmoplastic reactions, the differences in ECM patterns and recurrence-free survival between clusters highlight the need to better understand tumor-stroma co-regulation24. Patient-derived xenograft (PDX) mouse models offer unique opportunities for studying tumor-stroma interactions58. Given that amino acid sequences between murine and human proteins are different, the species origin of individual proteins can be inferred from MS-based proteomic data59.

In this study, we investigated the interplay of ICC tumors and their surrounding stroma on the proteome level in a small cohort of nine PDX, obtained from a commercial source independent of the patient cohorts. Since the transplanted tumor was human, invading stromal cells were supposed to be of murine origin. To unambiguously assign human and murine proteins to our DIA data, we only considered peptides with sequences that are unique to the mouse or human proteome. Data on PDX FFPE specimens were acquired on a Thermo Q Exactive Plus, leading to more than 4600 identified proteins per sample. On average, we detected 1491 murine (stroma) and 3168 human (tumor) proteins per sample (Fig. 7a and Supplementary Data 7). These numbers underscore the feasibility of performing mixed-species proteomics experiments with an acceptable proteome coverage.

We generated an unsupervised partial least squares (PLS) model of the data, defining co- or inversely regulated sets of proteins independently of their species-origin. For the first two components, we focused on the top 200 proteins with the highest absolute factor loadings. With this approach, we could identify proteins sharing similar expression patterns throughout the PDX cohort and functionally compare them to their inversely regulated counterparts. The coregulation is illustrated by a circle plot (Fig. 7b). In brief, proteins aggregating together have similar expression patterns, whereas proteins on opposing ends of either axis are inversely regulated.

Murine and human proteins form contrasting sets along component 1, meaning that the strongest inverse expression pattern is determined by species origin. An enrichment analysis of the negative axis end of component 1 mainly showed murine proteins associated with neutrophil degranulation, ECM organization, cellular respiration, as well as glucose and amino acid metabolism. At the opposing, positive axis end, we found human proteins involved in mitochondrial and cytosolic translation, ribosome biogenesis, and protein metabolism. This divergent expression pattern reflects clustering motifs within the human ICC cohort and support the notion that innate immune reactions and ECM deposition are initiated by stromal cells invading the tumor. Accordingly, we observe a similar contrast in protein expression along component 2. At the positive axis end, human proteins were enriched for oxidative stress response and metabolism (mainly of lipids). At the negative axis end, murine proteins linked to ECM organization and collagens synthesis were increased. Regarding the interpretation of results, it need to be considered that xenograft-carrying mice are deficient in adaptive immunity and thus cannot mount T-cell-mediated antitumor responses. Still, many detected murine ECM components were homologues to ECM cluster-associated proteins in the human cohorts. Since all used mice were isogenic, genomic diversity was confined to grafted tumors. Thus, as observed in our cohort and these xenografts, different translational and metabolic activities of the tumors might result in differing stromal responses.

The mTOR effector EIF4A1 is strongly enriched in tumors of both clusters and its inhibition reduces tumor growth in ICC PDX
In recent years, the translation initiation factor EIF4A1 has gained interest as a downstream effector molecule of mTOR and a novel therapeutic target in various cancers. EIF4A1 plays a critical role in initiating the translation of selected mRNAs, including some that promote cancer, and is often overexpressed in proliferating cells60,61. For instance, elevated levels of EIF4A1 have been observed in hepatocellular carcinoma and pancreatic ductal adenocarcinoma62,63. Inhibiting EIF4A1 in cell lines from these cancers led to reduced proliferation and decreased oncogenic signaling61–63. Currently, a novel EIF4A1 inhibitor eFT226 (Zotatifin, MedChemExpress, NJ, USA) is being evaluated in two phase I/II clinical trials for breast cancer (clinicaltrials.gov NCT05101564 and NCT04092673). A recent study shows transcript-level upregulation of EIF4A1 in ICC and an anti-proliferative impact of Zotatifin in ICC cell models and organoids64. In our study, we noticed protein-level upregulation of EIF4A1 in ICC tumor tissues of both clusters (paired t-tests, adj. p_ECM cluster=9.9E-16, adj. p_proliferation cluster=1.3E-10, Fig. 8a). We performed an eFT226 titration of three ICC cell lines and found that, in two out of the three cases, eFT226 concentrations in the low nanomolar IC50 range reduced cellular viability (Supplementary Fig. 13), consistent with recently published data64. The addition of eFT226 to rapamycin (200nM) yielded an additional reduction of cellular viability. On the other hand, the addition of eFT226 to treatment with gemcitabine (1µM for gemcitabine-sensitive SNU-1079, 10µM for HuCCT-1 and HuH-28) had only a minor effect on viability. Next, we aimed to investigate the compound in a more physiological setting that includes stromal reactions. In accordance with previous publications on eFT226, we conducted a study using ICC PDX in mice to evaluate the effect of 1 mg/kg eFT226 over an observation period of 35 days65–67. We used four mice each for the treatment and control groups, which were dosed with the drug or vehicle once per week for three weeks. We chose LI-002 (TW512) as a model, since stromal proteins were comparatively low-abundant here. Our findings show that eFT226 reduces tumor growth in PDX mice, leading to significantly lower tumor volumes (unpaired t-tests, p_d14=0.025, p_d18=0.006, p_d21=0.012, Fig. 8b; Supplementary Data 8) but not to the killing of tumor cells. Maximum anti-tumor efficacy occurred on study day 25, corresponding to moderate efficacy (T/C = 44.8%). Moreover, except for one animal with an ulceration, treated mice lived longer, tolerated the treatment well, and remained in good general condition throughout the experiment, as evidenced by the relative maintenance of their body weight (unpaired t-tests, p_d18=0.004, p_d21=0.002; Supplementary Data 4 and Supplementary Table 4; Fig. 8c). In contrast, three out of four mice in the control group had to be euthanized due to the tumor load until day 25 of the study (Supplementary Data 4 and Supplementary Table 4). This is in line with previous publications, which report growth inhibition or moderate regression, but no remission, in response to eFT226 treatment in other tumor entities62,66. Zhao et al. and Gerson-Gurwitz et al. also underscore that the effectiveness of eFT226 therapy can be greatly enhanced through synergistic treatment combinations66,67.

In line with recent reports, our results encourage further translational research on eFT226 as a potential treatment for ICC patients64. Ongoing clinical studies in humans, eFT226’s good tolerance in mice, and its high efficacy as mono- or combination treatment in models of other cancers underline its promising potential for ICC therapy. Overall, this successful translation of explorative proteomic data into new treatment rationales highlights the great potential of mass spectrometry-based approaches for translational research.

Methods
Patient cohorts and consent
All research complied with relevant ethical regulations, as detailed in the corresponding sections. A detailed description of patients, materials, and methods is provided in the Supplementary file. Both cohort studies comply with the Declaration of Helsinki, and all patients provided written informed consent. The MSKCC study was approved by the MSKCC institutional review board (protocol number 16-1683A(3)), and the UKF study was approved by the Institutional Ethics Committee (protocol number 21-1684).

MSKCC-ICC. Resected ICC tissues from 80 previously untreated patients were collected at Memorial Sloan Kettering Cancer Center (MSKCC) between 2009 and 2018. Detailed patient and clinical information is available in Supplementary Data 1 and in a histopathological evaluation of the same cohort18. Formalin-fixed, paraffin-embedded (FFPE) specimens were prepared and evaluated by the MSKCC department for pathology.

UKF-ICC. As a validation cohort, resected ICC tissues from 62 patients were collected at the Medical Center–University of Freiburg (UKF) between 2000 and 2022. Detailed patient information is provided in Supplementary Data 9. FFPE specimens were prepared and evaluated by the Comprehensive Cancer Center Freiburg tumor tissue bank and provided as punch cores of 1.0 mm diameter.

Sample preparation for LC-MS/MS
MSKCC-ICC tissues were deparaffinized and macrodissected to separate tumor from tumor-adjacent, non-malignant TANM tissue. Proteins were extracted using heat and an acid-labile surfactant. Subsequently, an in-solution digestion protocol was performed with trypsin to generate peptides, essentially as published previously68.

For the UKF-ICC cohort, a single-pot, solid-phase-enhanced sample preparation (SP3) protocol on an automated liquid handling platform was performed, essentially as published previously69.

Patient-derived xenograft proteomics
All animal experiments were approved by the Committee on the Ethics of Animal Experiments of the regional council (Permit Number I-19/02). FFPE tumor sections from nine different patient-derived ICC xenografts (PDX) were provided by Charles River Laboratories and prepared using the S-Trap digestion protocol to generate tryptic peptides.

LC-MS/MS-based proteomics
For the MSKCC-ICC cohort, 500 ng of peptides were measured on a Bruker Tims TOF Flex mass spectrometer coupled to an Evosep One chromatography system. All measurements were performed using the vendor’s 30 samples per day method in data-independent acquisition (DIA) mode. Library prediction and peptide-to-spectrum matching were performed using DIA-NN 1.9.2 with a human proteome database. Semi-specific analyses were conducted with a modified human database containing all possible C- and N-terminal sequence truncations. All expression data were log2-transformed and median-centered.

For the UKF-ICC cohort, 800 ng of peptides were measured on a Thermo Q Exactive Plus mass spectrometer coupled to a Thermo Easy-nLC1000 chromatography system. All measurements were performed in DIA mode. Library generation and peptide-to-spectrum matching for both canonical and semi-specific analyses were performed as described above using DIA-NN 1.9.2.

PDX samples were measured via the same workflow as the UKF-ICC cohort and analyzed via DIA-NN 1.7 using a combined human-mouse proteome database.

Proteomic data is available at the MassIVE repository (MSV000095336 [https://doi.org/10.25345/C5SN01G2V]).

Whole-exome sequencing (WES)
DNA from tumor and TANM samples was extracted, libraries prepared and captured (Agilent v6), and subsequently sequenced on the NovaSeq X Plus platform (PE150). Reads were aligned to GRCh38.p13 with BWA and processed with Sambamba and Picard. Somatic variants were called with MuTect and Strelka and annotated using ANNOVAR. Variants were filtered for coverage and variant allele fraction as described in the Supplementary Methods. WES Data is available via the European Genome-Phenome Archive (EGAD50000001926 [https://ega-archive.org/datasets/EGAD50000001926]).

Cell culture
Three ICC cell lines (SNU-1079, HuCCT-1, HuH-28) were cultured in an incubator at 37 °C and 5% CO2 in RPMI medium containing 10% fetal bovine serum and 1x penicillin/streptomycin. Cells were treated with different concentrations of eFT226 with or without rapamycin or gemcitabine at fixed concentrations of 200 nM rapamycin for all three cell lines or 1 µM gemcitabine for SNU-1079, and 10 µM gemcitabine for HuCCT-1 and HuH-28. The cell’s metabolic activity was measured as a proxy for overall viability using the MTT assay.

PDX treatment study
All animal experiments were approved by the Committee on the Ethics of Animal Experiments of the regional council (Permit Number G-20/163). NMRInu/nu mice were implanted with ICC tumors and randomized into experimental groups (n=4 per group) once tumors reached volumes between 75–240 mm3. eFT226 was administered intravenously at a dose of 1 mg/kg once weekly for three weeks. Tumor volumes were measured using a caliper, and animals were sacrificed if tumor volumes exceeded 2000 mm3.

Data analysis
All statistical analysis was performed in R using R Studio and custom-built scripts. Unsupervised statistical analyses including hierarchical clustering and principal component analysis (PCA) were performed in the MixOmics environment. Differentially expressed proteins were identified using linear models via the Limma package. Enrichment analyses were performed using ClusterProfiler with KEGG, Reactome, and Gene Ontology databases. All classifiers were built with the xgboost package. Survival statistics, including Kaplan-Meier and Cox proportional hazards models, were applied via the survival and survminer packages. In all statistical tests, p-values below 0.05 were considered significant, if not indicated otherwise. Scripts are available via GitHub (https://github.com/SchillingLabProteomics/ICC)70.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Please refer to the attached file", "reviewer_2": "The study by Tilman et al. provides a proteomic characterization of intrahepatic cholangiocarcinoma (ICC) in a Western cohort, identifying two subclusters with distinct times-to-recurrence (TTR) and proposing EIF4A1 inhibition as a potential therapeutic strategy for ICC progression. While the study may contribute to the understanding of ICC molecular heterogeneity, it falls short of meeting the high standards expected for publication in Nature Communications, particularly in terms of technical depth, biological novelty, and clinical relevance. Below are the key concerns.\n1. The study reports an average of 2,330 identified proteins per sample, with only 1,700 proteins used for downstream analysis. This level of proteomic depth is significantly lower than current standards in the field and may result in the loss of critical protein information, potentially compromising the biological conclusions. Although the authors mention that PDX FFPE samples yielded ~4,600 proteins, this still falls short of the depth achievable with modern proteomic technologies (e.g., TMT or DIA-based approaches). Given the limited depth of identification, the conclusions drawn may not be entirely credible. It is recommended to employ more advanced proteomic methods to improve coverage and ensure robust biological insights.\n2. The figures in the manuscript, including main and supplementary figures, appear rudimentary and lack the level of detail and polish. Panels are sparse, and the overall presentation does not meet publication standards.\n3. While the study claims to focus on a Western ICC cohort, it fails to provide a meaningful comparison or discussion of how the molecular features of Western ICC differ from those in Asian or other populations.\n4. The identification of EIF4A1 as a potential therapeutic target lacks novelty, as the mTOR pathway and its downstream effectors, including EIF4A1, have been extensively studied in various cancers. The study does not provide new mechanistic insights into EIF4A1’s role in ICC.\n5. The authors utilized PDX models to investigate the interactions between tumor cells and stromal cells in the ICC tumor microenvironment. However, since the stromal cells are derived from mice and the PDX model mice are immunodeficient, this significantly differs from the tumor microenvironment in human patients. Thus, it does not accurately recapitulate the tumor microenvironment in human ICC.\n6. The methods section is overly concise and lacks the necessary detail.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "In this manuscript, Werner et al. present a DIA proteomic analysis of 80 treatment-naive ICC tumors and adjacent tissues, identifying two prognostic proteomic subtypes, validating their relevance in external datasets, and proposing EIF4A1 as a potential therapeutic target through functional studies in ICC cell lines and patient-derived xenografts.\nThis study presents an important and valuable dataset, marking the first large-scale proteomic profiling of intrahepatic cholangiocarcinoma in a European ancestry-dominated cohort. The authors' effort in collecting and analyzing these unique samples is commendable, and the data generated has significant potential to advance the understanding of ICC biology.\nHowever, I found the manuscript challenging to read and follow. The central research question remains unclear, and it is not evident how this study addresses a gap that has not already been explored elsewhere. Furthermore, many transitions between sections are abrupt, making the narrative difficult to follow. Strengthening the clarity and flow of the manuscript, along with providing a clearer rationale and interpretation of the findings, would greatly enhance its impact.\nThe section on TANMs feels abrupt. The authors note that the Dong et al. (2022) FU-ICC paper excluded TANMs, but the findings in this study seem subtle. It appears to support the idea that omitting TANMs does not make much difference, rather than offering new insights.\nIn section 3.5, the description of how the four proteins were selected is surprisingly vague. The criteria include \"large quantitative differences\"—but how large? \"Low adjusted p-values\"—how low? What statistical method was used to compare their abundances between clusters? Were confounding variables adjusted? What thresholds were applied? Additionally, the rationale for using these four proteins to predict the ECM/turnover clusters requires clarification.\nThe proteome-wide survival analysis also appears suddenly. The authors mention a \"second analysis,\" but it is unclear why this additional analysis was necessary, given that patient clustering already demonstrated significant clinical relevance. This section lacks a clear conclusion, offering only a list of individual significant proteins.\nI found the semi-specific peptide analysis compelling. It suggests altered protease activity in tumors of the ECM cluster, with a strong signal. However, this paragraph also appears abruptly and would benefit from better integration with the prior section. Several important questions remain:\n1. What are the proteins from which the tumor-only truncated peptides are derived?\n2. Are they primarily ECM-related proteins? If so, is the observation simply that ECM tumors have more ECM proteins vulnerable to protease activity, rather than reflecting perturbed protease activity itself?\n3. Which proteases are responsible? Are their abundances different between groups?\n4. Is the abundance or proportion of truncated peptides associated with clinico-pathological variables?\n5. Is there a similar pattern in the FU-ICC cohort? Could ancestry differences play a role?\nThe legend of Figure 6C describes the PLS-DA as based on truncated \"peptide expression,\" but lines 387–388 refer to \"truncated proteins,\" which is an improper interpretation. Also, given the expected sparsity of truncated peptide identifications across samples, it is unclear how informative the PLS-DA really is beyond demonstrating that \"they are somewhat different.\"\nThe PDX analysis and the MSKCC cohort proteomics seem disjointed. Are the nine PDX samples described in section 3.9 derived from the same patients as the eight PDX used for the drug analysis in section 3.10? How are they related to the MSKCC-ICC cohort? I am not convinced of the added value of the PDX proteomics analysis in its current form.\nIn the conclusion, the authors highlight this as the first large-scale ICC proteomic study with European ancestry-dominated patients. However, the core power of this comparison is not clearly demonstrated. While differences between tumor and NAT protein abundances are shown, this is not surprising.\nThe FU-ICC cohort is used as an external validation dataset, but it is a Chinese-dominated cohort. Do genomic profiles differ between ICC patients of Chinese versus European ancestry? If so, how might this influence the proteome? The statement that \"genomic analysis [was] not feasible\" is not convincing. More comparative analysis could be performed to evaluate differences in tumor/normal proteomes and survival-associated proteins (section 3.7) between the two cohorts.\nAdditionally, the number of proteins identified per patient appears somewhat low compared to the FU-ICC cohort, which raises concerns about the depth and completeness of the proteome coverage in this study.\nMinor Concerns and Suggestions:\nFigures are not cited correctly. For example, line 250 cites Figure S2, but it should be S3. Figures are also cited out of order, such as Figure S1C being cited after Figure S2, which is confusing.\nThe proteomics data analysis requires clearer explanation. It is not clear how the spectral library was generated. The manuscript mentions a pooled sample was used, presumably for DDA, but no details are given about the method or data analysis.\nWhy was a relatively high FDR (10%) used during spectral library generation? Please clarify the method and justification.\nStatistical results are often incomplete. P-values and/or effect sizes/HR are missing in many places, including lines 215 and 222.\nStatistical analysis approaches are not explained in sufficient detail. For survival analysis (section 3.7), the authors mention using the survival and survminer packages, but what confounding variables were adjusted for? Section 3.7 mentions adjusting for adjuvant radiotherapy, but were adjustments made for ECM/turnover clusters in section 3.5? Why were other variables, such as stage, sex, age, and virus status, not adjusted? These factors were mentioned in the introduction as relevant risk factors. The FDR correction method for proteome-wide survival analysis is also not described.\nFor the differential abundance analysis of tumor versus normal, were data scaled before fitting the linear model? Did the model include random intercepts for each patient? Were confounding variables considered? How was FDR controlled?\nIn the PCA plot (Figure 1F), PC1 explains an unusually high percentage of variance. Were data scaled before running PCA? If not, the difference may be driven by high-abundance proteins.\nLine 246: \"PCA of proteome data\" is vague. \"PCA of protein abundance\" may be clearer.\nLine 248: The interaction between PCA results and gender, age, or stage is not shown in Figure 1F.\nLine 249: The authors state that proteomic clustering was confirmed by MC simulation. It is unclear whether they (1) used hierarchical clustering and found consistency with M3C, or (2) used M3C to determine two clusters, then applied hierarchical clustering. If (1), the consistency between methods should be shown; if (2), the wording should be clarified.\nLine 264: The authors mention gender. Do they mean gender or sex? If referring to biological characteristics, \"sex\" is more appropriate.\nLine 265: Missing p-value cutoff and statistical test used.\nLine 267: Missing p-value.\nLine 268: Assumed pathway enrichment results, but the description lacks clarity.\nLine 272: I believe the authors meant to refer to the \"cluster\" rather than \"cluster association.\" It is unclear what association is being referenced, and why such an association would be predictive of an outcome. Additionally, it would be helpful to clarify whether confounders were controlled for in this analysis. A significant proportion of patients in the turnover group received adjuvant radiotherapy—were these patients presenting with more aggressive disease, potentially leading both to the decision for adjuvant radiotherapy and to worse survival outcomes?\nLine 317: What is the hazard ratio?\nI appreciate the authors' effort to support open science and reproducibility by providing code in the associated GitHub repository. However, the repository appears to only include scripts and R Markdown files for generating the figures, and it seems that some supplementary figures are not covered. Additionally, it appears that all commits were pushed within a two-day window, which means that the authors may have either selectively uploaded certain scripts to the repository specifically for publication or did not employ version control throughout their analysis. I encourage the authors to provide a more comprehensive and transparent workflow, ideally with version-controlled scripts that cover the entire analysis process, which would significantly enhance reproducibility."}, "author_response": "“Proteomic Characterization of Intrahepatic Cholangiocarcinoma Identifies\nRisk-Stratifying Subgroups, Proteins Associated with Time-To-Recurrence, and mTOR\nEffector Molecule EIF4A1 as a Druggable Therapeutic Target”\nResponse to reviewers - Preface for all reviewers\nWe thank all reviewers for their extensive comments and suggestions to improve our manuscript. As\noutlined in the point-by-point response, we are addressing all concerns. One issue raised by all\nreviewers has been the proteomic depth of the MSKCC-ICC cohort. We agree with this concern and\nre-measured stored peptides from our MSKCC-ICC cohort on a state-of-the-art Bruker TimsTOF\nFlex mass spectrometer coupled to an Evosep One chromatography system.\nThe new proteomic data yielded an average of 7020 identified proteins per sample, more than 5500\nof which were identified consistently at less than 20% missing values across the cohort. Virtually all\nA B C\nD E\nEC M\nProliferation\nCorrelation QExactive - TimsTOF measurements: A) Pearson Correlation coefficients R2 for all samples\nin Cthoer croehlaotrito. Bn) QOvEexrlaacpt oivf eid e- nTtiifmieds pTrOotFe inms.e Ca)s Ouvreemrlaepn otfs id: eAn)t ifPieeda prrsooteni nCso wrirtehl aletsiosn th caone 2f0fi%ci emnitsss inRg2 for\nvaalulle ssa. mDp&lEe)s Pinr inthceip aclo hCoormt.p Bon) eOnvt eArlnaaply osef sid e(PnCtiAfie) do fp rtohete iMnsS.K CC)C O-ICveCr lacpoh oofr ti dbeansteifdie don p rtohete innesw w ith\nTimsTOF measurements. New clusters in D, old QExactive clusters in E.\nless than 20% missing values. D&E) Principal Component Analyses (PCA) of the MSKCC-ICC\ncohort based on the new TimsTOF measurements. New clusters in D, old QExactive clusters in\nE. Below) Correlation Plots for all MSKCC-ICC cohort samples including all proteins identified in\nboth measurements. Pearson Correlation including R2 and p-value given.\nproteins that had been detected in the old measurements (using a Thermo QExactive mass\nspectrometer) could be identified again.\nLooking at quantitation, we found a mean Pearson correlation of R2 = 0.81 when comparing old and\nnew data for proteins that were identified in both measurements. Therefore, we conclude that the\nunderlying proteomic information remained intact, even as overall protein identifications have tripled.\nTwo proteomic clusters stratifying ICC tumors along biological themes (extracellular matrix versus\nCorrelation QExactive - TimsTOF measurements: Correlation Plots for all MSKCC-ICC cohort samples\nincluding all proteins identified in both measurements. Pearson Correlation incl. R2 and p-value given.\nPage 2/23\nmRNA and protein turnover) and progression-free survival were the core findings in our original data.\nAnd indeed, hierarchical clustering of the new data again resulted in two proteomic clusters\ndisplaying significant differences in the time-to-recurrence (TTR). The old and new clusters are\nconsistent in their biological patterns, with the much-improved proteomic depth redefining the\nclassification of a few select samples. One cluster is defined by a high content of extracellular matrix\nproteins and longer TTR (ECM-cluster), and the other showing higher expression of proteins involved\nin DNA replication and the synthesis and processing of mRNA and proteins. Because this marked\nupregulation of DNA replication proteins had not been visible in our old data, we decided to rename\nthe second cluster as “proliferation cluster”.\nOther results from the manuscript’s first version remained comparable as well and benefited from\nthe new proteomic depth. For instance, similar proteomic patterns were found to directly relate to\nprogression-free survival in the Cox proportional hazards analysis, with more than half of the\noriginally identified proteins again being significant hits (25 out of 39). Only the semi-specific analysis\nshifted in focus, as we now observe significantly more semi-specific peptides in TANM tissue,\ncompared to tumors. This might be due to the increased proteomic depth of the new measurements,\nleading to a notable increase in the number of identified semi-specific peptides (identified semi-\nspecific peptides old = 868 versus new = 18087). Nevertheless, although the quantity of semi-\nspecific peptides does not differ between the clusters, they do still differ in quality.\nRemarkably, when we transferred the new clusters to the Fudan ICC (FU-ICC) cohort via a refined\nclassifier approach, we again detected a significant difference in patient survival between the\nassigned ECM- and proliferation clusters. Encouraged by these results, we procured another, wholly\nindependent ICC cohort from our own institution (UKF-ICC) and stratified it into both clusters using\nthe same classifier workflow. Also here, the ECM-cluster was significantly linked to longer overall\nsurvival.\nPoint-by-Point Response\nReviewer 1\nIntrahepatic cholangiocarcinomas (ICCs) are rare cancers that originate in bile ducts and ductules\nwithin the liver. These cancers have non-specific symptoms and there is a lack of reliable,\nnoninvasive prognostic biomarkers. Using a mass spectrometry-based proteomic approach, the\nauthors analyzed 80 treatment-naïve ICC tumors, 77 adjacent non-malignant tissues, and 9 PDXs.\nTwo proteomic sub-clusters, extracellular matrix (ECM) and mRNA/protein turnover, exhibited\ndistinct times-to-recurrence (TTR). These prognostic proteomic clusters were validated using a\nseparate dataset, and EIF4A1 inhibition was identified as a potential strategy to mitigate ICC\nprogression.\nPage 3/23\nThe authors generated a significant amount of data for this study; however, there are concerns\nregarding the data quality and its subsequent impact on the biological interpretation of the data.\nSeveral major and minor concerns need to be addressed before this reviewer would consider\nrecommending the manuscript for publication.\nAnswer: We sincerely thank the reviewer for their evaluation of our work and for highlighting both\nthe strengths of the study and the concerns regarding data quality and interpretation. We have\naddressed each point in detail below and believe that the clarifications and additional analyses\nprovided will help to resolve these concerns.\nMajor\n1. The font sizes in several of the figures (Fig. S1A & B, 5A & E-F, S3, S5, S7) are too small, which\nconsequently precludes an accurate assessment of the presented data. If the font sizes in the figures\ncannot be reasonably enlarged, consider reducing the content displayed in each figure to only\ninclude the most salient aspects.\nAnswer: Thank you for this comment. We agree and apologize for the original figures being hard to\nread. In the scope of our new analysis, we have updated all figures in the manuscript to reach\npublication standards.\n2. How do the eFT226 and Gemcitabine concentrations used to treat the ICC cell lines relate to IC50\nvalues?\nAnswer: We thank the reviewer for this important point. The concentrations of eFT226 and\nGemcitabine used in our ICC cell line experiments were chosen to cover a range around the IC₅₀\nvalues determined from dose-response curves (fitted using a four-parameter logistic model in\nGraphPad Prism). For eFT226, the IC₅₀ values were 11.18 nM (HuH-28), 8.53 nM (HuCCT-1), and\n4.68 nM (SNU-1079). Notably, HuH-28 showed only partial sensitivity to eFT226, with maximal\ninhibition reducing viability to ~60%, and is thus regarded as partially resistant. In this case, the\nclassical IC₅₀ corresponds to the midpoint between the fitted Top (100%) and Bottom (~60%), while\nthe concentration required to reduce viability to 50% of control exceeds the highest dose tested. For\nGemcitabine, the experimental doses (1 µM and 10 µM) were chosen to ensure measurable effects\nof around 50% reduction of cell viability across both sensitive (HuCCT-1 and SNU-1079) cell lines.\nAs the HuH-28 cell line was considered resistant, the higher dose of 10 µM was applied,\nacknowledging that in resistant lines the 50% viability threshold may not have been reached within\nthis range. Calculated Gemcitabine IC50 values as mid-points of half-maximal inhibition between the\nfitted top and bottom plateaus were 1.052 µM (HuCCT-1) and 0.005 µM (SNU-1079).\n3. It does not appear that the authors determined the tumor cellularity of the ICC patient tissues.\nThis is a common drawback of the –omic analysis of bulk tissues.\nAnswer: Thank you for this insightful comment. It is true that the high ECM content in the ECM-\ncluster could suggest simply a lower number of tumor cells relative to the proliferation cluster. We\nhave consulted a trained pathologist, who has used an imaging classification model (QPath) to\nidentify and count different cell types within scans of the used tumor slides. We found no significant\nPage 4/23\ndifference in the relative tumor cell content between both clusters (see manuscript 3.4 and Table 1).\nNeither was the tumor cellularity associated with the time-to-recurrence or patient overall survival.\nCox Proportional Hazards Model: Impact of Tumor Cell Content [%] on left) progression-free survival and right)\noverall survival.\n4. A log fold-change cut-off of 0.5 was used for the data presented in section 3.7 regarding the\nprognostic ICC proteins related to progression-free survival. This is a considerably low cut-off (fold\nchange of ~1.4) to determine whether a protein is “significantly enriched”.\nAnswer: Thank you for pointing this out. Although we believe that even small log2-fold changes do\nprovide valuable information if highly consistent across the cohort (as demonstrated by low adjusted\np-values), we understand that a cutoff at 0.5 might be too lenient to warrant follow-up investigations.\nWe have thus increased the cutoff to log2 0.8 (fold change of 1.75). Additionally, your comment\npoints toward one issue of Cox proportional hazards model (CPHM) we decided to address directly\nin the main text: CPHM only regards how well protein abundances correlate with progression-free\nsurvival but does not consider absolute abundance changes. Hence, even subtle changes in protein\nabundance can count as significant, as long as they sufficiently align with survival data. For this\nreason, CPHM results should be viewed in conjunction with abundance-based analyses such as\ndifferential expression analysis or a simple look at the coefficient of variation of protein abundances\nacross the cohort. We have now also added this data to our manuscript.\n5. The interpretation of semi-tryptic peptides as evidence of “endogenous proteolytic processing\nevents” is dubious. No evidence is provided to enable the differentiation of biologically relevant\n“endogenous proteolytic processing events” from technical/analytical artefacts. The experimental\ndesign for this study was not set up to enable the analysis of endogenous proteolytic processing\nevents.\nAnswer: We agree with the reviewer that our original phrasing has been ambiguous as semi-specific\npeptides are not linked to proteolytic processes by default. Still, proteolysis is widely known to shape\ntumor progression, especially by remodeling the extracellular matrix 1 and proteolysis is the major\ncontributor to semi-specific peptides 2,3. Similar computational strategies have been applied in recent\nproteomic studies to infer protease activity and tumor-associated proteolysis 2,4,5. Our reanalysis\nshows biological enrichment consistent with tumor biology, suggesting at least part of the signal\nreflects endogenous proteolytic processes.\nPage 5/23\n6. Fig. 2A: There are significant concerns regarding the validity of the number of differentially\nregulated proteins; 1,368/1,700 = 80%! If the data were normally distributed, only 32%, 4.5%, or\n0.3% of the proteins would have a relative abundance exceeding the mean +/- 1 sd, 2 sd, or 3 sd,\nrespectively. It is possible that the protein profiles of TANM vs. tumor are markedly different;\nhowever, one would not expect 80% of the proteins to be differentially abundant.\nAnswer: We thank the reviewer for this attentive observation. After remeasurement of the whole\ncohort resulting in higher proteome coverage, we found 4865 out of 5587 proteins (87%) to be\ndifferentially abundant between TANM and tumor tissue. We agree that the proportion of differentially\nabundant proteins appears high at first sight. However, as confirmed by the responsible pathologist,\nthe tumor and TANM tissues represent markedly different cellular and morphological compositions,\ndespite being located adjacently on the same slide. This pronounced contrast is further reflected in\nthe PCA (Fig. 1E), which demonstrates clear separation of the two groups. Therefore, the large\nfraction of proteins identified as differentially abundant is consistent with the underlying biological\ndifferences between the tissues, rather than an artifact of the data analysis. Moreover, in our UKF-\nICC cohort, we see similar results, with 2581 out of 3064 proteins (84%) to be differentially abundant\nbetween TANM and tumor tissue.\n7. It is unclear why the authors identified >2x more human proteins when analyzing the PDX tissue\n(3,168) vs. the patient tissue (1,368) if the sample processing and analytical methods were indeed\nsimilar. The patient tissues and the PDX tissues were FFPE specimens.\nAnswer: We thank you for pointing out this contrast in the number of identifications. Our re-\nmeasurement of the MSKCC-ICC cohort addresses this issue and has boosted overall identifications\nand quantifications. For context, the original cohort measurement had been performed using self-\npacked LC-columns (length approx. 35cm), whereas PDX samples were measured using a 2m\nmicro-PAC column (ThermoFisher), which strongly improved separation efficiency and downstream\nprotein identifications.\nMinor:\n1. Figure 1E is overly-convoluted/complicated and no key is provided explaining the symbols in the\nmiddle of the PCA plots. Is it important to have a unique symbol representing each distinct sample?\nAnswer: We agree and have removed the figure, since batch correction has not been necessary for\nthe new data. For context, the old data was acquired in the course of more than three weeks in more\nthan 20 batches. New data using a Bruker TimsTOF Flex coupled to an Evosep One system could\nbe measured within less than 5 days in only 3 batches.\n2. Fig. S2: Add a legend for the red-blue color gradients/scales.\nAnswer: Thank you for this observation! We have added a legend detailing the unit and scale of the\ncolor gradient to all KEGG enrichment plots.\nPage 6/23\n3. In the legend for Fig. 8, “dotted lines” are referred to as indicating treatment time-points, but they\ndo not appear in the figure. Moreover “grey arrows” are also mentioned as indicating treatment time\npoints.\nAnswer: We have revised the figure for clarity. Now only grey arrows indicate treatment time points.\n4. Titles should be included with each Supplementary Table.\nAnswer: Thank you. We have taken up this suggestion for the new supplementary tables.\nReviewer 2\nThe study by Tilman et al. provides a proteomic characterization of intrahepatic cholangiocarcinoma\n(ICC) in a Western cohort, identifying two subclusters with distinct times-to-recurrence (TTR) and\nproposing EIF4A1 inhibition as a potential therapeutic strategy for ICC progression. While the study\nmay contribute to the understanding of ICC molecular heterogeneity, it falls short of meeting the high\nstandards expected for publication in Nature Communications, particularly in terms of technical\ndepth, biological novelty, and clinical relevance. Below are the key concerns.\nAnswer: We thank the reviewer for their thoughtful assessment of our work and for highlighting the\nshortcomings of the previous version. We greatly appreciate the constructive feedback and have\ncarefully revised the manuscript to address all points of concern. We hope that the current version\nmore convincingly demonstrates the technical depth, biological novelty, and clinical relevance of our\nstudy.\n1. The study reports an average of 2,330 identified proteins per sample, with only 1,700 proteins\nused for downstream analysis. This level of proteomic depth is significantly lower than current\nstandards in the field and may result in the loss of critical protein information, potentially\ncompromising the biological conclusions. Although the authors mention that PDX FFPE samples\nyielded ~4,600 proteins, this still falls short of the depth achievable with modern proteomic\ntechnologies (e.g., TMT or DIA-based approaches). Given the limited depth of identification, the\nconclusions drawn may not be entirely credible. It is recommended to employ more advanced\nproteomic methods to improve coverage and ensure robust biological insights.\nAnswer: We do agree with your observation and have thus decided to re-measure the entire cohort\nas detailed above. We now identified on average 7,020 proteins per sample and proceeded with\n5,500 proteins for downstream analysis. We are confident that the new proteome coverage is on-\npar with current standards and provides a lot deeper biological insight.\n2. The figures in the manuscript, including main and supplementary figures, appear rudimentary and\nlack the level of detail and polish. Panels are sparse, and the overall presentation does not meet\npublication standards.\nAnswer: We apologize for the lower figure quality in our original version. We have now updated all\nfigures to be ready for publication.\nPage 7/23\n3. While the study claims to focus on a Western ICC cohort, it fails to provide a meaningful\ncomparison or discussion of how the molecular features of Western ICC differ from those in Asian\nor other populations.\nAnswer: We appreciate this important point and acknowledge that significant population differences\nexist in ICC pathogenesis and molecular features. However, our key finding—the presence of a low-\nrisk ECM-enriched subtype and a high-risk proliferation subtype—was robustly validated in the\nindependent Fudan cohort representing an Asian population, demonstrating the generalizability of\nthese proteomic subtypes across populations. ICC pathogenesis indeed differs between populations\ndue to varying etiological factors. Chronic viral hepatitis and liver fluke infections are reported to be\nmore prevalent in Asian regions, whereas metabolic-associated liver diseases and other factors\npredominate in Western countries 6. These different etiologies likely result in diverging genomic\nprofiles and histopathological features 7–11. For example, studies indicate that Chinese populations\nshow more genetic aberrations per patient and more mutations in DNA repair pathway-associated\ngenes, while American patients exhibit higher frequencies of CDKN2A/B and IDH1/2 genetic\naberrations 12. Additionally, Dong et al. reported higher KRAS mutation frequencies but lower IDH1,\nARID1A, and TERT mutation frequencies in their Fudan ICC cohort compared to Western cohorts\n13. Despite these population-specific differences, our proteomic classifier successfully identified the\nsame two major subtypes in both Western and Asian populations. This indicates that the\nfundamental proteomic organization of ICC into ECM-enriched and proliferation-driven subtypes\nrepresents a common biological framework.\n4. The identification of EIF4A1 as a potential therapeutic target lacks novelty, as the mTOR pathway\nand its downstream effectors, including EIF4A1, have been extensively studied in various cancers.\nThe study does not provide new mechanistic insights into EIF4A1’s role in ICC.\nAnswer: We agree that the mTOR pathway has been investigated extensively in multiple cancer\nentities, highlighting its importance as a driver of cancer progression. Various treatments targeting\nthis pathway are available. Yet, EIF4A1 is a translation initiation factor. While mTOR signalling is a\nmajor switch for protein biosynthesis, the actual translation process is mechanistically distinct from\nthe signalling axis. Moreover, EIF4A1 inhibitors represent a distinct mode-of-action (blocking\ntranslation rather than signalling) ,and are gaining traction as a new therapeutic target in different\ncancers. For this reason, we deem it relevant that mTOR signaling in general, and EIF4A1 in\nparticular, also appear to play a role in ICC tumors. EIF4A1 inhibition has so far not been investigated\ninvestigated\nin bile duct malignancies, making our study the first instance. Although a retrospective proteomic\nstudy of patient tissues can hardly provide mechanistic insights into EIF4A1 in ICC, we believe there\nis value in putting EIF4A1 inhibition on the map as a so far unconsidered treatment option for this\ncancer.\n5. The authors utilized PDX models to investigate the interactions between tumor cells and stromal\ncells in the ICC tumor microenvironment. However, since the stromal cells are derived from mice\nand the PDX model mice are immunodeficient, this significantly differs from the tumor\nmicroenvironment in human patients. Thus, it does not accurately recapitulate the tumor\nmicroenvironment in human ICC.\nPage 8/23\nAnswer: We agree that patient-derived xenografts (PDX) can only serve as a proxy for tumor\nprogression and thus come with notable limitations. Nevertheless, PDX are well-established models\nnot just for drug testing but for controlled molecular studies involving patient tissues 14–16. In fact, the\nmice still carry functional innate immune and tissue responses to implanted tumors 17,18. In the\ncontext of the present study, the PDX models have particular value in corroborating the origin (tumor\nvs. stromal cells) of the various matrisome components.\n6. The methods section is overly concise and lacks the necessary detail.\nAnswer: Thank you for your comment. The full materials and methods section can be found in the\nsupplements.\nReviewer 3\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate\nrecognition for Early Career Researchers who co-review manuscripts.\nAnswer: Thank you for your help!\nReviewer 4\nIn this manuscript, Werner et al. present a DIA proteomic analysis of 80 treatment-naive ICC tumors\nand adjacent tissues, identifying two prognostic proteomic subtypes, validating their relevance in\nexternal datasets, and proposing EIF4A1 as a potential therapeutic target through functional studies\nin ICC cell lines and patient-derived xenografts.\nThis study presents an important and valuable dataset, marking the first large-scale proteomic\nprofiling of intrahepatic cholangiocarcinoma in a European ancestry-dominated cohort. The authors'\neffort in collecting and analyzing these unique samples is commendable, and the data generated\nhas significant potential to advance the understanding of ICC biology.\nHowever, I found the manuscript challenging to read and follow. The central research question\nremains unclear, and it is not evident how this study addresses a gap that has not already been\nexplored elsewhere. Furthermore, many transitions between sections are abrupt, making the\nnarrative difficult to follow. Strengthening the clarity and flow of the manuscript, along with providing\na clearer rationale and interpretation of the findings, would greatly enhance its impact.\nAnswer: Thank you for this encouraging assessment and for your helpful critique! We have revised\nthe manuscript to improve its structure and flow. We hope that these changes make the rationale\nmore evident and the manuscript easier to read.\nThe section on TANMs feels abrupt. The authors note that the Dong et al. (2022) FU-ICC paper\nexcluded TANMs, but the findings in this study seem subtle. It appears to support the idea that\nomitting TANMs does not make much difference, rather than offering new insights.\nAnswer: When re-reading our manuscript we came to agree with your observation and have thus\nremoved the section on TANM tissues. Still, for the sake of completeness, we want to mention that\nPage 9/23\nalso in the new data TANM clusters remained disconnected from both tumor proteomic clusters and\nfrom clinical annotations, highlighting their status as tissues with an apparently rather independent\nproteome biology.\nIn section 3.5, the description of how the four proteins were selected is surprisingly vague. The\ncriteria include \"large quantitative differences\"—but how large? \"Low adjusted p-values\"—how low?\nWhat statistical method was used to compare their abundances between clusters? Were\nconfounding variables adjusted? What thresholds were applied? Additionally, the rationale for using\nthese four proteins to predict the ECM/turnover clusters requires clarification.\nAnswer: We thank you for this comment. Following your remarks we have reworked the section on\nhow the classifier proteins were selected. Now, they are stringently chosen to include two proteins\nwith the highest log2-fold change and the lowest adjusted p-value representative for each cluster.\nAdditionally we demonstrate that not only few selected proteins can serve as classifiers but that\nmany combinations of the 50 most cluster-representative proteins are suitable to build good\nclassification models. We admit that the decision to test four-protein classifiers was based less on\nmathematical modeling and more on our clinical background. Working in an Institute for Pathology,\nwe aimed for a protein number sufficient for reproducible stratification of new samples, yet still\npractical for testing not only by mass spectrometry but also via antibody-based methods.\nThe proteome-wide survival analysis also appears suddenly. The authors mention a \"second\nanalysis,\" but it is unclear why this additional analysis was necessary, given that patient clustering\nalready demonstrated significant clinical relevance. This section lacks a clear conclusion, offering\nonly a list of individual significant proteins.\nAnswer: Also here, we are grateful for the constructive feedback! It is true that the original version\nof the text needed to demonstrate more clearly the additional value of CPHM versus the clustering\nanalysis. We have added that information to the main text. In brief, while the clustering analysis\nprovides an unsupervised description of major biological motifs within the cohort, the CPHM\nspecifically searches for proteins whose expression relates to the time-to-recurrence. Therefore,\namong the multitude of proteins identified in the clustering analysis, the CPHM serves to highlight\nbiological themes and, most importantly, individual proteins that correlate best with survival.\nI found the semi-specific peptide analysis compelling. It suggests altered protease activity in tumors\nof the ECM cluster, with a strong signal. However, this paragraph also appears abruptly and would\nbenefit from better integration with the prior section. Several important questions remain:\nAnswer: Thank you for this encouraging feedback. As mentioned above, the detection of semi-\nspecific peptides increased more than 20-fold in the newly measured dataset, thus changing the\nobservable landscape of the “semi-specific peptidome” considerably. We have addressed your\nquestions in light of these new results.\n1. What are the proteins from which the tumor-only truncated peptides are derived?\nAnswer: We repeated the differential expression analysis outlined in with only semi-specific peptides\nto address your question. A subsequent gene set enrichment analysis using the GeneOntology\ndatabase highlighted biological processes enriched in either tissue. As visible in the figure below,\nPage 10/23\nmost semi-specific peptides detected in tumor tissues originated from proteins involved in\nproliferation processes including extracellular processes like cell adhesion and supramolecular fiber\norganization, as well as RNA and DNA processing. In contrast, semi-specific peptides in TANM\ntissue derived mostly from metabolic processes. Therefore, detected semi-specific peptides at large\nmostly reflect each tissue’s biological fingerprint.\nGene Set Enrichment of semi-specific peptides: left TANM, right tumor.\n2. Are they primarily ECM-related proteins? If so, is the observation simply that ECM tumors have\nmore ECM proteins vulnerable to protease activity, rather than reflecting perturbed protease activity\nitself?\nAnswer: Considering the new data, we do not observe significant differences in the amount of semi-\nspecific peptides relative to all detected peptides between the clusters (Fig 6E). Overall, the\nabundance of semi-specific peptides correlates well with the abundance of their parent protein,\nsuggesting proteolytic decay rather than targeted regulation. Nevertheless, a subset of semi-specific\nproducts does not follow this correlation and may indicate specific proteolytic processing, such as\nobserved for Aldo-keto reductase family 1 member C4 (AKR1C4).\n3. Which proteases are responsible? Are their abundances different between groups?\nPage 11/23\nAnswer: Using the MEROPS protease database as reference\n(https://www.ebi.ac.uk/merops/index.shtml), we found 280 proteases (or proteins with putative\nproteolytic function) within our dataset, many of which have overlapping and poorly distinguishable\nspecificities. At the same time, the semi-specific data represents a mixture of convoluted cleavage\nevents.We deemed it beyond the scope of the present study to bioinformatically deconvolute the\n“web” of proteolytic events.\nWe reanalyzed the data shown in the section “Tumor and TANM Tissues Show Highly Divergent\nProteome Profiles” to specifically look for proteases that are differentially expressed between tumor\nand TANM tissues. To this end, we used the MEROPS protease database. We considered proteases\nto be significantly enriched in either tissue, if they presented an absolute log2-fold change greater\nthan 0.5 and an FDR-adjusted p-value below 0.05. Using Reactome.org overrepresentation analysis,\nwe find that most proteases enriched in tumors (88 in total) relate to ECM degradation, neutrophil\ndegranulation, caspases, and innate immune responses. Conversely, proteases that are increased\nin TANM tissues (in total 66) are mostly involved in metabolic functions, as would be expected in\nliver tissue.\nVolcano Plot Tumor vs. TANM: limma-based differential expression analysis as in main figure 2A. The top 10\nproteases with the highest log2-fold changes (identified via the MEROPS protease database) are named. Blue\nindicates higher abundance in TANM tissue, red indicates higher abundance in tumor tissue (p < 0.01).\nWe repeated this analysis to also look for differentially expressed proteases between both clusters.\nHere, we found fewer proteases to show a significant enrichment profile. Again relying on\nReactome.org, we found many detected proteases in the ECM-cluster (31 in total) to be involved in\nthe complement cascade. The proliferation-cluster showed a comparative sparsity of proteases, with\nonly 6 being enriched here in absence of a clear biological pattern.\nPage 12/23\nVolcano Plot ECM- vs. proliferation-cluster: limma-based differential expression analysis as in main figure 3C.\nThe top 10 proteases with the highest, significant log2-fold changes above 0.5 (identified via the MEROPS\nprotease database) are named. Blue indicates higher abundance in the ECM cluster, orange indicates higher\nabundance in the proliferation cluster (p < 0.05).\nWe have added both plots in Supplementary Figure S11.\n4. Is the abundance or proportion of truncated peptides associated with clinico-pathological\nvariables?\nAnswer: We thank the reviewer for raising this very interesting point. We tested the proportion of\ntruncated peptides against the clinico-pathological variables but did not find any statistically\nsignificant associations.\nPage 13/23\nDistribution and correlation of clinicopathological parameters with the proportion of semi-specific peptides.\nBlue indicates TANM tissue, red indicates tumor tissue.\n5. Is there a similar pattern in the FU-ICC cohort? Could ancestry differences play a role?\nAnswer: Unfortunately, raw mass spectrometry data from the FU-ICC cohort is not publicly available\nfor re-analysis. However, to address this point, we have considered the semi-specific peptides in our\nadditional UKF-ICC cohort. The overall number of semi-specific peptides was lower than in the\nMSKCC dataset, reflecting differences in measurement setup. Nevertheless, consistent with our\nPage 14/23\nmain findings, the proportion of semi-specific peptides was higher in TANM compared to tumor\nsamples in both clusters. We also observed substantial global differences in semi-specific peptide\nabundances between TANM and tumor tissue, whereas differences between clusters were minimal\n(see panel C&E)), possibly due to lower purity of FFPE punch samples and/or higher sparsity in the\nsemi-specific dataset. Interestingly, RTN4 emerged as the only protein with a peptide not correlating\nwith the abundance of its parent protein - consistent with its behaviour in the MSKCC cohort.\nSemi-tryptic analysis of the UKF-ICC cohort: A) Count of fully tryptic (specific) and semi-specific detected\npeptides. B) Boxplot indicating the ratio of semi-specific peptides among all detected peptides for tumor and\nTANM samples in the ECM and proliferation cluster. Unpaired, two-sided t-test. Boxplots show median (center\nline), interquartile range (IQR, extending from the 1st to the 3rd quartile, box), and 1.5 IQR (whiskers). C)\nPartial Least Squares-Discriminant Analysis (PLS-DA) of semi-specific peptide expression in both clusters and\nmatching TANM tissues. Ovals indicate 95% confidence intervals. D) Correlation of logFC values between\nprotein abundance and semi-tryptic peptide abundance in tumor vs. TANM tissue. E) Correlation of logFC\nvalues between protein abundance and semi-tryptic peptide abundance across ECM vs. proliferation clusters.\nThe legend of Figure 6C describes the PLS-DA as based on truncated \"peptide expression,\" but\nlines 387–388 refer to \"truncated proteins,\" which is an improper interpretation. Also, given the\nexpected sparsity of truncated peptide identifications across samples, it is unclear how informative\nthe PLS-DA really is beyond demonstrating that \"they are somewhat different.\"\nPage 15/23\nAnswer: We fully agree that our original wording was imprecise, the PLS-DA in Figure 6 C is based\non semi-specific peptide intensities, not truncated proteins. We have revised the text to clarify this.\nWe also acknowledge that the amount of sparsity in the semi-specific dataset is quite high and would\nlike to emphasize that the PLS-DA was intended as an exploratory visualization to illustrate global\ndifferences in peptide-level profiles, rather than as a definitive biological conclusion.\nThe PDX analysis and the MSKCC cohort proteomics seem disjointed. Are the nine PDX samples\ndescribed in section 3.9 derived from the same patients as the eight PDX used for the drug analysis\nin section 3.10? How are they related to the MSKCC-ICC cohort?\nAnswer: Indeed, the PDX tissues did not derive from patients in the MSKCC-ICC cohort (or any other\ncohort described) but have been sourced from commercially available PDX models (CharlesRiver,\nFreiburg, Germany). We have better highlighted this in the text. The PDX analyzed in the section\n“Proteomics of ICC PDX Models Enables Insight into Tumor-Stroma Co-Regulation” represent nine\ndifferent PDX models originating from different donors. Only one of these models, LI-002, was\nselected for the drug-testing of eft226. Despite the PDX models not being directly related to the\ncohort patients, we believe that PDX provide an unique opportunity to investigate tumor-stroma\ninteractions via mass spectrometry-based proteomics in-situ and with patient material. In contrast to\ne.g. tissue co-cultures, tumor-stroma interactions can be investigated within a living organism with\nthe benefit of an easy distinction of human and murine proteins thanks to species-specific differences\nin protein sequences.\nIn the conclusion, the authors highlight this as the first large-scale ICC proteomic study with\nEuropean ancestry-dominated patients. However, the core power of this comparison is not clearly\ndemonstrated. While differences between tumor and NAT protein abundances are shown, this is not\nsurprising.\nAnswer: We thank the reviewer for this comment. While differences between tumor and NAT protein\nabundances are indeed expected, the main strength of our study lies in the identification of two\nrobust proteomic subtypes. Our classifier reliably assigns samples to these subtypes across\nindependent cohorts and demonstrates predictive value for survival, highlighting its potential utility\nbeyond descriptive tumor-normal comparisons.\nThe FU-ICC cohort is used as an external validation dataset, but it is a Chinese-dominated cohort.\nDo genomic profiles differ between ICC patients of Chinese versus European ancestry? If so, how\nmight this influence the proteome? The statement that \"genomic analysis [was] not feasible\" is not\nconvincing. More comparative analysis could be performed to evaluate differences in tumor/normal\nproteomes and survival-associated proteins (section 3.7) between the two cohorts.\nAnswer: We thank the reviewer for this attentive observation. Indeed, due to the rarity of ICC,\ncomprehensive omics-type studies remain scarce. Still, the impact of patients’ geographic location\nand ancestry on ICC tumors has been studied. One large study compared mutational profiles\nbetween 164 ICC tumors of Asian origin to 283 tumors coming from Western patients and discovered\nsignificant differences in genes related to DNA repair and immune-checkpoints 19. Different incidence\nrates and disease etiologies have been reported for ICC across world regions, with East Asia\nPage 16/23\nstanding out as the area with the highest ICC rates worldwide 20–22. This might in large part be linked\nto differing causes of ICC, which are generally considered to derive rather from obesity, and alcohol\nand nicotine abuse in Western countries, whereas many cases in East Asia also stem from parasitic\ninfections and e.g. contaminated traditional chinese medicines (in this regard, the Dong et al. study\nwe have integrated as FU-ICC cohort points out high overall mutational burdens in tumors with an\naflatoxin and/or aristolochic acid mutational fingerprint) 13,22. Hence, at least on the genomic level,\nICC are considered to differ between world regions and so far proteomic data is lacking to back this\nup.\nTo provide additional context, we also incorporated the UKF-ICC cohort as another Western cohort.\nIn addition to the proteomic analysis, we aimed to perform WES and RNA sequencing. Fusion\ntranscripts unfortunately could not be analyzed due to strongly degraded RNA, resulting in poor RNA\nsequencing quality. Due to limited sample size eligible for WES, we could not identify a clear\nmutational pattern in the UKF-ICC cohort.\nStill, we refrain from directly comparing proteomic profiles between the tumor tissues in the MSKCC-\nICC and FU-ICC cohorts. Although both cohorts can technically be merged using the ComBat\nalgorithm, we cannot assure that underlying data structures are fully comparable. The comparison\nwould invariably be affected by different protocols (e.g. DIA-based in case of MSKCC-ICC vs. TMT-\nlabeling-based in case of FU-ICC), mass spectrometers or separation columns, to name a few.\nMoreover, data from TANM tissues in the FU-ICC cohort was not made publicly available.\nAdditionally, the number of proteins identified per patient appears somewhat low compared to the\nFU-ICC cohort, which raises concerns about the depth and completeness of the proteome coverage\nin this study.\nAnswer: We agree and have hence re-measured the entire MSKCC-ICC cohort as described above.\nWe now identify an average of 7,020 proteins per sample, of which more than 5,500 were used for\nstatistical analyses, substantially improving proteome depth and coverage.\nMinor Concerns and Suggestions:\nFigures are not cited correctly. For example, line 250 cites Figure S2, but it should be S3. Figures\nare also cited out of order, such as Figure S1C being cited after Figure S2, which is confusing.\nAnswer: Thank you for pointing this out. We have corrected the figure citations.\nThe proteomics data analysis requires clearer explanation. It is not clear how the spectral library was\ngenerated. The manuscript mentions a pooled sample was used, presumably for DDA, but no details\nare given about the method or data analysis.\nAnswer: We remeasured the whole cohort as described above and have now used a predicted\nspectral library. Details are given in the Supplementary Methods.\nWhy was a relatively high FDR (10%) used during spectral library generation? Please clarify the\nmethod and justification.\nPage 17/23\nAnswer: In our initial workflow, we applied a 10% FDR during spectral library generation in order to\nmaximize library size, while maintaining the standard 1% FDR cutoff for peptide-to-spectrum\nassignments in the actual analyses. We acknowledge, however, that this distinction may cause\nconfusion. To avoid ambiguity and ensure consistency throughout the workflow, we have now\nrecalculated the spectral library using a 1% FDR.\nStatistical results are often incomplete. P-values and/or effect sizes/HR are missing in many places,\nincluding lines 215 and 222.\nAnswer: Thank you for this observation, we completed the information.\nStatistical analysis approaches are not explained in sufficient detail. For survival analysis (section\n3.7), the authors mention using the survival and survminer packages, but what confounding variables\nwere adjusted for? Section 3.7 mentions adjusting for adjuvant radiotherapy, but were adjustments\nmade for ECM/turnover clusters in section 3.5? Why were other variables, such as stage, sex, age,\nand virus status, not adjusted? These factors were mentioned in the introduction as relevant risk\nfactors. The FDR correction method for proteome-wide survival analysis is also not described.\nAnswer: We thank the reviewer for this comment. For the Cox-Proportional Hazards survival\nanalysis, we adjusted only for adjuvant radiotherapy, the only independent variable significantly\nassociated with shorter time to recurrence. In section 3.5 (now section “Cox Proportional Hazards\nModel Highlights Individual Prognostic ICC Proteins in the MSKCC-ICC Dataset”, ECM/turnover\nclusters were the parameters of interest and thus were not adjusted. Other independent variables\n(sex, age, virus status) were not included as they showed no significant association with outcome.\nSome variables, such as staging and tumor budding, were significantly associated with survival, but\nnot independent from the proteomic data. To avoid overfitting we thus did not include them in the\nsurvival model. Proteome-wide survival analyses were corrected for multiple testing using the\nBenjamini-Hochberg method.\nFor the differential abundance analysis of tumor versus normal, were data scaled before fitting the\nlinear model? Did the model include random intercepts for each patient? Were confounding variables\nconsidered? How was FDR controlled?\nAnswer: The input expression matrix was log2-transformed and median-normalized prior to analysis.\nNo additional scaling was applied before fitting the linear model. The analysis used a fixed-effects\ndesign comparing tumor versus normal, without random intercepts for individual patients. Potential\nconfounding variables were not included in the model. False discovery rate (FDR) was controlled\nusing the Benjamini-Hochberg method. We have added this information to the Methods section for\nclarity.\nIn the PCA plot (Figure 1F), PC1 explains an unusually high percentage of variance. Were data\nscaled before running PCA? If not, the difference may be driven by high-abundance proteins.\nPage 18/23\nAnswer: Data were log2-transformed and median-normalized prior to PCA, without additional\nscaling. While PC1 may reflect high-abundance proteins characteristic of tumor or TANM tissue,\nthis variation appropriately captures the separation between tissue types.\nLine 246: \"PCA of proteome data\" is vague. \"PCA of protein abundance\" may be clearer.\nAnswer: We thank the reviewer for this comment, we changed the wording as suggested.\nLine 248: The interaction between PCA results and gender, age, or stage is not shown in Figure 1F.\nAnswer: We have now added these variables to the PCA in Figure 1F.\nLine 249: The authors state that proteomic clustering was confirmed by MC simulation. It is unclear\nwhether they (1) used hierarchical clustering and found consistency with M3C, or (2) used M3C to\ndetermine two clusters, then applied hierarchical clustering. If (1), the consistency between methods\nshould be shown; if (2), the wording should be clarified.\nWe thank the reviewer for this comment. To clarify, we followed scenario (1): We clustered the cohort\nusing hierarchical clustering and then determined the optimal number of clusters - two in this case -\nvia a Monte-Carlo-Simulation run in hierarchical clustering mode. We have revised the manuscript\nto clarify this.\nLine 264: The authors mention gender. Do they mean gender or sex? If referring to biological\ncharacteristics, \"sex\" is more appropriate.\nAnswer: We thank the reviewer for pointing this out. We are indeed referring to biological\ncharacteristics and thus changed the wording to “sex”.\nLine 265: Missing p-value cutoff and statistical test used.\nAnswer: We thank the reviewer for this observation, we have now added p-value and statistical test.\nLine 267: Missing p-value.\nAnswer: We have now added the p-value.\nLine 268: Assumed pathway enrichment results, but the description lacks clarity.\nAnswer: We thank the reviewer for this comment. In this paragraph, we were referring to histological\nfeatures of the corresponding images as determined by a trained pathologist, not to pathway\nenrichment. We have revised the manuscript to clarify this point.\nPage 19/23\nLine 272: I believe the authors meant to refer to the \"cluster\" rather than \"cluster association.\" It is\nunclear what association is being referenced, and why such an association would be predictive of\nan outcome. Additionally, it would be helpful to clarify whether confounders were controlled for in\nthis analysis. A significant proportion of patients in the turnover group received adjuvant\nradiotherapy—were these patients presenting with more aggressive disease, potentially leading both\nto the decision for adjuvant radiotherapy and to worse survival outcomes?\nAnswer: We thank the reviewer for this comment. We agree that the term “cluster” is more\nappropriate than “cluster association” and have revised the text accordingly. The proteomic clusters\nwere defined independently of clinicopathological features, with the classifier distinguishing samples\nonly by proteomic profile (cluster 1 vs. cluster 2), not by outcome. As such, confounders were not\nincorporated into the clustering procedure, which was based exclusively on protein abundance. We\nnote that a larger proportion of patients in the turnover/proliferation cluster received adjuvant\nradiotherapy; however, information on the clinical criteria guiding these treatment decisions is\nbeyond accessibility. While this represents a limitation, the observation that proteomic clusters\nstratify patient outcomes across multiple independent cohorts supports the robustness of the finding.\nLine 317: What is the hazard ratio?\nAnswer: The hazard ratio for the proliferation cluster is 2.1 compared to the ECM cluster.\nCPHM - Hazards Ratio for proliferation vs. ECM cluster in the FU-ICC cohort\nWe have added this information to the manuscript.\nReviewer #4 (Remarks on code availability):\nPage 20/23\nI appreciate the authors' effort to support open science and reproducibility by providing code in the\nassociated GitHub repository. However, the repository appears to only include scripts and R\nMarkdown files for generating the figures, and it seems that some supplementary figures are not\ncovered. Additionally, it appears that all commits were pushed within a two-day window, which\nmeans that the authors may have either selectively uploaded certain scripts to the repository\nspecifically for publication or did not employ version control throughout their analysis. I encourage\nthe authors to provide a more comprehensive and transparent workflow, ideally with version-\ncontrolled scripts that cover the entire analysis process, which would significantly enhance\nreproducibility.\nAnswer: Thank you for this observation and for having a look at our code! We have now included all\nsupplementary figures, too. It is true that the code was specifically prepared for publication. This was\nfor two reasons: first, it was important to us to share reasonably tidy and comprehensible scripts that\ndo not include the manifold detours and trials, which the initial analysis entailed. Secondly, technical\nreasons prohibited us from using online version control via GitHub. Since our data analysis is\nperformed on a hospital internal server containing patient data, we are prevented from continuous\nand automated access to online resources such as GitHub\n1. Winkler, J., Abisoye-Ogunniyan, A., Metcalf, K. J. & Werb, Z. Concepts of extracellular matrix\nremodelling in tumour progression and metastasis. Nat. Commun. 11, 5120 (2020).\n2. Cosenza-Contreras, M., Huesgen, P. F. & Schilling, O. TermineR: Bioinformatic processing of\nshotgun proteomics data for the annotation and quantitation of protein termini. Methods\nEnzymol. 719, 43–66 (2025).\n3. Kleifeld, O. et al. Isotopic labeling of terminal amines in complex samples identifies protein N-\ntermini and protease cleavage products. Nat. Biotechnol. 28, 281–288 (2010).\n4. Dinh, T. J. et al. Proteomic analysis of non-muscle invasive and muscle invasive bladder cancer\nhighlights distinct subgroups with metabolic, matrisomal, and immune hallmarks and\nemphasizes importance of the stromal compartment. J. Pathol. 265, 41–56 (2025).\n5. Cosenza-Contreras, M. et al. Proteometabolomics of initial and recurrent glioblastoma highlights\nan increased immune cell signature with altered lipid metabolism. Neuro-Oncol. 26, 488–502\n(2023).\n6. Clements, O., Eliahoo, J., Kim, J. U., Taylor-Robinson, S. D. & Khan, S. A. Risk factors for\nintrahepatic and extrahepatic cholangiocarcinoma: A systematic review and meta-analysis. J.\nHepatol. 72, 95–103 (2020).\n7. Tavolari, S. & Brandi, G. Mutational Landscape of Cholangiocarcinoma According to Different\nPage 21/23\nEtiologies: A Review. Cells 12, 1216 (2023).\n8. Hayashi, A. et al. Distinct Clinicopathologic and Genetic Features of 2 Histologic Subtypes of\nIntrahepatic Cholangiocarcinoma. Am. J. Surg. Pathol. 40, 1021 (2016).\n9. Wang, X.-Y. et al. Driver mutations of intrahepatic cholangiocarcinoma shape clinically relevant\ngenomic clusters with distinct molecular features and therapeutic vulnerabilities. Theranostics\n12, 260–276 (2022).\n10. Liau, J.-Y. et al. Morphological subclassification of intrahepatic cholangiocarcinoma: etiological,\nclinicopathological, and molecular features. Mod. Pathol. 27, 1163–1173 (2014).\n11. Wang, T. et al. Distinct histomorphological features are associated with IDH1 mutation in\nintrahepatic cholangiocarcinoma. Hum. Pathol. 91, 19–25 (2019).\n12. Cao, J. et al. Intrahepatic Cholangiocarcinoma: Genomic Heterogeneity Between Eastern and\nWestern Patients. JCO Precis. Oncol. 4, PO.18.00414 (2020).\n13. Dong, L. et al. Proteogenomic characterization identifies clinically relevant subgroups of\nintrahepatic cholangiocarcinoma. Cancer Cell 40, 70-87.e15 (2022).\n14. Wang, X. et al. Species-Deconvolved Proteomics for In Situ Investigation of Tumor-Stroma\nInteractions after Treatment of Pancreatic Cancer Patient-Derived Xenografts with Combined\nGemcitabine and Paclitaxel. J. Proteome Res. 22, 2436–2449 (2023).\n15. Sueyoshi, K. et al. Multi-tumor analysis of cancer-stroma interactomes of patient-derived\nxenografts unveils the unique homeostatic process in renal cell carcinomas. iScience 24, 103322\n(2021).\n16. Hidalgo, M. et al. Patient-Derived Xenograft Models: An Emerging Platform for Translational\nCancer Research. Cancer Discov. 4, 998–1013 (2014).\n17. Szadvari, I., Krizanova, O. & Babula, P. Athymic nude mice as an experimental model for cancer\ntreatment. Physiol. Res. 65, S441–S453 (2016).\n18. NMRI-nu Immunodeficient Mouse. Janvier Labs https://janvier-labs.com/en/fiche_produit/nmri-\nnu_mouse/.\n19. Cao, J. et al. Intrahepatic Cholangiocarcinoma: Genomic Heterogeneity Between Eastern and\nWestern Patients. JCO Precis. Oncol. 4, PO.18.00414 (2020).\n20. Florio, A. A. et al. Global trends in intrahepatic and extrahepatic cholangiocarcinoma incidence\nPage 22/23\nfrom 1993 to 2012. Cancer 126, 2666–2678 (2020).\n21. Banales, J. M. et al. Cholangiocarcinoma 2020: the next horizon in mechanisms and\nmanagement. Nat. Rev. Gastroenterol. Hepatol. 17, 557–588 (2020).\n22. Woo, S., Kim, Y., Hwang, S. & Chon, H. J. Epidemiology and genomic features of biliary tract\ncancer and its unique features in Korea. J. Liver Cancer 25, 41–51 (2025).\nPage 23/23"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have provided comprehensive responses to the concerns raised by the reviewers, and the authors are to be commended for conducting additional mass spectrometry data acquisition runs using a newer model mass spectrometer to increase the proteomic depth of their MSKCC-ICC cohort. The quality of the manuscript has improved significantly. On clarifying question and one suggestion:\n1. Panel A of the figure on pg. 1 of the response to reviewers refers to “Pearson correlation R2.” It is unclear what statistical parameter the authors are referring to here given that the Pearson correlation is represented by (r), not R2. Similarly, on pg. 2, of their response, the authors mention, “Pearson correlation of R2 = 0.81, whereas “r” is mentioned in the correlation plots below the text.\n2. Consider adding the figures on pg. 5 of the response to the concerns raised by the reviewers to the Supplementary Data. The figures provide informative data re: the association between tumor cellularity vs. PFS and OS.", "reviewer_2": "I have carefully reviewed the authors’ revised manuscript and their point-by-point response. While I appreciate the authors’ additional experiments and revisions, several important issues remain insufficiently addressed, as detailed below.\n1. The authors reanalyzed their peptide samples using the TimsTOF, which increased the number of identified proteins from ~2,000 to over 7,000. This represents a substantial improvement in proteome coverage. However, the subsequent analyses and biological conclusions remain essentially identical to those presented in the previous version. Given this significant increase in data depth, one would expect additional findings or refined biological insights into ICC. Otherwise, this raises the question of whether a much lower proteome coverage would have been sufficient to address the same biological questions. The current revision does not adequately explain why such a dramatic increase in protein identification did not lead to new or strengthened conclusions.\n2. The authors’ point-by-point responses are largely textual and do not provide corresponding figures, figure legends, or manuscript excerpts to demonstrate where and how each issue was addressed. This makes it difficult to evaluate whether and to what extent the reviewers’ comments were incorporated into the revised manuscript. For transparency and clarity, the authors should clearly indicate the exact locations of revisions in the text and include updated figures or supplementary materials where relevant.\n3. The authors claim that the role of EIF4A1 in ICC is being reported for the first time. However, recent literature has already described this association — for example: Mi W, Cigliano A, Galleri G, et al. Targeting EIF4A1 is effective against human intrahepatic cholangiocarcinoma. JHEP Rep. 2025;7(7):101416.\nFurthermore, the study does not provide new mechanistic insights into EIF4A1’s role in ICC, which weakens the novelty of this finding.\n4. The authors state that “the PDX models have particular value in corroborating the origin (tumor vs. stromal cells) of the various matrisome components.” However, it remains unclear what specific new conclusions were derived from these experiments. The description is rather general, and neither the main text nor the figures provide sufficient detail to support this claim.\n5. Each figure title is currently written as a methodological phrase, rather than summarizing the main finding or conclusion of the figure. This makes it difficult for readers to follow the logical flow of the study or grasp the key results.", "reviewer_3": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature Communications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career Researchers who co-review manuscripts.", "reviewer_4": "I really appreciate the authors taking the time to perform new experiments and analyses. I am very glad to see that the manuscript has significantly improved, especially in terms of data quality and interpretability.\nI now like the semi-specific peptide analysis even better. With the new figures, it is now clearer that certain proteins undergo increased proteolytic digestion, particularly in the proliferation-associated cluster. This is a fascinating observation. It raises many interesting follow-up questions that, while outside the scope of this current paper, could inspire future work.\nOne suggestion I would offer is to explore whether this ECM vs. proliferation tumor subtype classification can also be observed at the transcriptomic level. Since RNA-seq data is available, it would be informative to ask whether ECM- or proliferation-related genes show elevated expression in their respective clusters. Including a figure or two illustrating this could make the picture of the study even more complete.\nThe only major issue I would still like the authors to address is the lack of detail in the WXS methods section. Please provide specifics such as the capture kit used, the reference genome version (e.g., GRCh38 p14?), the alignment tool, the somatic mutation caller, filtering criteria, and annotation algorithm. The phrase “standard criteria” is way too vague.\nAdditionally, when discussing mutation burden (e.g., “~30 mutations per tumor”), the authors should report TMB as the number of mutations per Mbp of the capture region. The number of somatic mutations is dependent on the capture design."}, "author_response": "“Proteomic Characterization of Intrahepatic Cholangiocarcinoma Identifies Risk-\nStratifying Subgroups, Proteins Associated with Time-To-Recurrence, and mTOR\nEffector Molecule EIF4A1 as a Druggable Therapeutic Target”\nResponse to reviewers - Preface for all reviewers\nWe thank all reviewers for their comments and suggestions to further improve our manuscript.\nWe addressed all concerns as outlined in the point-by-point response:\nPoint-by-Point Response\nReviewer 1:\nThe authors have provided comprehensive responses to the concerns raised by the reviewers,\nand the authors are to be commended for conducting additional mass spectrometry data\nacquisition runs using a newer model mass spectrometer to increase the proteomic depth of\ntheir MSKCC-ICC cohort. The quality of the manuscript has improved significantly.\nAnswer: Thank you very much for the encouragement.\nOn clarifying question and one suggestion:\n1. Panel A of the figure on pg. 1 of the response to reviewers refers to “Pearson correlation\nR2.” It is unclear what statistical parameter the authors are referring to here given that the\nPearson correlation is represented by (r), not R2. Similarly, on pg. 2, of their response, the\nauthors mention, “Pearson correlation of R2 = 0.81, whereas “r” is mentioned in the correlation\nplots below the text.\nAnswer: Thank you for this comment. We apologize for the confusion, we originally labeled\nthe statistic as “Pearson correlation R².” We have corrected this to “Pearson correlation\ncoefficient R” in all figures and text of our first response to the reviewers (Panel A of the figure\non pg.1, text on pg.2).\n2. Consider adding the figures on pg. 5 of the response to the concerns raised by the reviewers\nto the Supplementary Data. The figures provide informative data re: the association between\ntumor cellularity vs. PFS and OS.\nAnswer: Thank you for this suggestion, we added the mentioned figure as Supplementary\nFigure 6C and briefly described it in the main text (see section “ICC Tumor Proteomes Form\nTwo Clusters with Strong Proteomic Differences and Divergent Times to Recurrence in the\nMSKCC-ICC Cohort”, ll. 303-305).\nReviewer 2:\nI have carefully reviewed the authors’ revised manuscript and their point-by-point response.\nWhile I appreciate the authors’ additional experiments and revisions, several important issues\nremain insufficiently addressed, as detailed below.\nAnswer: Thank you for the thoughtful re-evaluation of our manuscript.\n1. The authors reanalyzed their peptide samples using the TimsTOF, which increased the\nnumber of identified proteins from ~2,000 to over 7,000. This represents a substantial\nimprovement in proteome coverage. However, the subsequent analyses and biological\nconclusions remain essentially identical to those presented in the previous version. Given this\nsignificant increase in data depth, one would expect additional findings or refined biological\ninsights into ICC. Otherwise, this raises the question of whether a much lower proteome\ncoverage would have been sufficient to address the same biological questions. The current\nrevision does not adequately explain why such a dramatic increase in protein identification did\nnot lead to new or strengthened conclusions.\nAnswer: We appreciate your comment. Although re-measurement on the timsTOF\nsubstantially increased proteome coverage (from ~2,000 to >7,000 proteins), the overall\nbiological conclusions remained consistent because the two datasets were generated from\nthe same samples. Their high correlation underscores the robustness and reproducibility of\nour mass-spectrometry–based approach. Importantly, the expanded dataset did not leave the\nanalysis unchanged; rather, it enabled us to refine and strengthen several key findings:\nFirst, the increased proteomic depth revealed additional differentially abundant proteins\nbetween tumor and TANM tissue and across clusters, confirming and extending our previous\nfindings. For example, within the second proteomic cluster, we now observe a more\npronounced signature of DNA replication, which was not visible in the first dataset and led us\nto rename this cluster to better reflect its biological characteristics (see section “ICC Tumor\nProteomes Form Two Clusters with Strong Proteomic Differences and Divergent Times to\nRecurrence in the MSKCC-ICC Cohort”, ll. 260-264 and Figure 3). Similarly, the tumor vs.\nTANM comparison now shows stronger enrichment of DNA-and RNA-replication signatures\n(see section “Tumor and TANM Tissues Show Highly Divergent Proteome Profiles”, ll. 230-\n232 and Figure 2).\nThe Cox Proportional Hazards model analysis benefited substantially from the deeper\ncoverage, identifying nearly five times more proteins associated with time-to-recurrence than\nin our first analysis. Accordingly, the subsequent gene set enrichment analysis of these protein\nhits revealed that especially proteasome-mediated catabolic-processes were correlated with\na bad prognosis. In contrast, proteins related to oxidoreductive processes were linked to a\nbetter prognosis (see section “Cox Proportional Hazards Model Highlights Individual\nPrognostic ICC Proteins in the MSKCC-ICC Dataset”, ll. 332-336 and Figure 5).\nFurthermore, our semi-specific analysis was refined by the increased proteome depth, now\nidentifying a higher proportion of semi-specific peptides in the TANM samples compared to\ntumor tissue (see section “Semi-specific Peptides are Increased in TANM and Differ Between\nClusters”, ll. 464-468 and Figure 6).\nAlthough our main conclusions remain nearly unchanged, we view this consistency as\nreassuring: the new dataset confirms the original biological findings while enabling a more\ndetailed and nuanced interpretation.\n2. The authors’ point-by-point responses are largely textual and do not provide corresponding\nfigures, figure legends, or manuscript excerpts to demonstrate where and how each issue was\naddressed. This makes it difficult to evaluate whether and to what extent the reviewers’\ncomments were incorporated into the revised manuscript. For transparency and clarity, the\nauthors should clearly indicate the exact locations of revisions in the text and include updated\nfigures or supplementary materials where relevant.\nAnswer: We thank the reviewer for this helpful comment. In this revised version of our previous\nand current point-by-point responses, we have clearly indicated the locations of all revisions\nin the text and figures referring to the current clean version of our manuscript. We hope that\nthese adjustments make it easier to verify how each comment has been addressed. We would\nalso like to emphasize that all changes to the manuscript can be viewed in the tracked-\nchanges version of the revised document.\n3. The authors claim that the role of EIF4A1 in ICC is being reported for the first time. However,\nrecent literature has already described this association — for example: Mi W, Cigliano A,\nGalleri G, et al. Targeting EIF4A1 is effective against human intrahepatic cholangiocarcinoma.\nJHEP Rep. 2025;7(7):101416.\nFurthermore, the study does not provide new mechanistic insights into EIF4A1’s role in ICC,\nwhich weakens the novelty of this finding.\nAnswer: We thank the reviewer for bringing this recent study to our attention. We welcome the\nfact that additional work further supports the relevance of EIF4A1 and its inhibition in\nintrahepatic cholangiocarcinoma. We have now cited this publication and revised the text\naccordingly (see section “The mTOR eEffector EIF4A1 Is Strongly Enriched in Tumors of Both\nClusters and its Inhibition Reduces Tumor Growth in ICC PDX”, ll. 545-551). While we\nacknowledge that EIF4A1 has recently been implicated in ICC, our study provides\ncomplementary and novel evidence, including the first characterization of EIF4A1 using\npatient-derived xenograft (PDX) models and the first evaluation of its combination with\ngemcitabine in ICC cell lines. We believe that these additions strengthen the translational\nsignificance of our findings and further highlight EIF4A1 as a promising therapeutic target in\nICC.\n4. The authors state that “the PDX models have particular value in corroborating the origin\n(tumor vs. stromal cells) of the various matrisome components.” However, it remains unclear\nwhat specific new conclusions were derived from these experiments. The description is rather\ngeneral, and neither the main text nor the figures provide sufficient detail to support this claim.\nAnswer: Thank you for this comment. The PDX analysis provided several insights, which are\nshown in Figure 7C and described in the corresponding text in the section “Proteomics of ICC\nPDX Models Enables Insight into Tumor-Stroma Co-Regulation”, ll. 515-532. The key\nconclusion derived from these data is that the major matrisome and immune-related\nsignatures observed in the ICC cohort likely originate from the invading stroma, whereas the\ntranslational and metabolic signatures derive primarily from tumor cells. This implies tumor-\nstroma interactions are a key determinant for recurrence-free survival. Importantly, several\nmurine ECM components in the PDX models corresponded to proteins defining cluster 1 in\nthe human ICC cohort.\n5. Each figure title is currently written as a methodological phrase, rather than summarizing\nthe main finding or conclusion of the figure. This makes it difficult for readers to follow the\nlogical flow of the study or grasp the key results.\nAnswer: Thank you for this suggestion. We agree that figure legends should guide the reader,\nbut we also believe that detailed conclusions are best placed in the main text, where they can\nbe properly contextualized. Our current legends are written to clearly state the content of each\npanel, while the interpretation of the results is provided in the corresponding Results section.\nReviewer 3:\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This\nis part of the Nature Communications initiative to facilitate training in peer review and to\nprovide appropriate recognition for Early Career Researchers who co-review manuscripts.\nAnswer: Thank you for your support.\nReviewer 4:\nI really appreciate the authors taking the time to perform new experiments and analyses. I am\nvery glad to see that the manuscript has significantly improved, especially in terms of data\nquality and interpretability.\nAnswer: Thank you very much for this feedback.\nI now like the semi-specific peptide analysis even better. With the new figures, it is now clearer\nthat certain proteins undergo increased proteolytic digestion, particularly in the proliferation-\nassociated cluster. This is a fascinating observation. It raises many interesting follow-up\nquestions that, while outside the scope of this current paper, could inspire future work.\nAnswer: Thank you for this encouraging observation. We agree and are excited to look deeper\ninto this data as well.\nOne suggestion I would offer is to explore whether this ECM vs. proliferation tumor subtype\nclassification can also be observed at the transcriptomic level. Since RNA-seq data is\navailable, it would be informative to ask whether ECM- or proliferation-related genes show\nelevated expression in their respective clusters. Including a figure or two illustrating this could\nmake the picture of the study even more complete.\nAnswer: Thank you for this helpful suggestion. RNA-seq data were available for the FU-ICC\ncohort, while the UKF-ICC cohort could not be included due to insufficient RNA quality (RIN\nvalues between 1 and 2.6). We analyzed the transcriptomic data of the FU-ICC cohort and\nindeed observed patterns consistent with our proteomic clusters: ECM-related genes showed\nhigher expression in samples belonging to the proteomic ECM cluster, whereas proliferation-\nassociated genes were elevated in the proteomic proliferation cluster. These results\ncorroborate our proteome-based subtype classification at the transcriptomic level.\nTranscriptomic differences between proteomic clusters. Left: Volcano plot of differentially expressed\ngenes between the ECM and proliferation cluster. Log2-fold changes are derived from an univariable\nlinear regression model. Dots colored in orange (proliferation cluster) or blue (ECM cluster) lie above\nthe Bonferroni-adjusted significance threshold. Top hits from the proteomic differential abundance\nanalysis are named, red label indicated diverging direction change in the proteomic data. Right: Over\nrepresentation analysis of differentially expressed genes between clusters. Top 5 terms per cluster are\nshown. Significance was determined by a permutation test with false-discovery rate (FDR)-based\nmultiple testing correction.\nWe added this information to Supplementary Figure 8, panels G and H, and described it briefly\nin the section “A Classifier for ICC Clusters Detects Similar Proteomic Motifs and Survival\nOutcome in the FU-ICC Cohort”, ll. 405-407.\nThe only major issue I would still like the authors to address is the lack of detail in the WXS\nmethods section. Please provide specifics such as the capture kit used, the reference genome\nversion (e.g., GRCh38 p14?), the alignment tool, the somatic mutation caller, filtering criteria,\nand annotation algorithm. The phrase “standard criteria” is way too vague.\nAnswer: Thank you for the comment. We used the Agilent v6 capture kit, the reference\ngenome GRCh38 p.13, BWA for alignment and MuTect and Strelka for calling somatic\nvariants. SNPs were filtered for a minimum coverage of 10 reads, variant allele fraction (VAF)\n> 5%, and ≥ 5 supporting reads in the tumor. Indels were filtered for VAF > 10%. We listed\nmost details regarding the WES workflow (capture kit, alignment tool, somatic mutation caller,\nfiltering criteria, and annotation algorithm) in the Supplementary Methods (see Supplementary\nMethods “DNA extraction and WES”, ll. 152-159, and “WES Data Analysis”, ll. 278-285). We\nalso extended our main Methods section to include the most relevant parameters as well (ll.\n161-165).\nAdditionally, when discussing mutation burden (e.g., “~30 mutations per tumor”), the authors\nshould report TMB as the number of mutations per Mbp of the capture region. The number of\nsomatic mutations is dependent on the capture design.\nAnswer: Thank you for this helpful suggestion. The mean TMB was 0.73 mutations per mb of\nthe captured coding regions. We have added this information into the section “Classification\nof the UKF-ICC validation cohort reveals matching prognostic clusters”, ll. 451-453 and\nincluded the information on how we calculated the TMB in the Supplementary Methods, ll.\n284-285.\nReviewer #4 (Remarks on code availability):\nSeems like only analysis code for figure generation was included.\nAnswer: Thank you for this comment. We revisited the repository to verify completeness and\ncan confirm that all analysis and figure-generation code is available online."}]

Decision letter