Mun, Dong-Gi

ABSTRACT The limited efficacy of current therapies against cholangiocarcinoma (CCA) necessitates the development of novel treatment strategies. Src family kinases (SFKs) contribute significantly to tumor progression and resistance in CCA. Therefore, we investigated the novel, first-in-class SFK ‘OFF’ inhibitor NXP900 in diverse preclinical CCA models, including those with acquired resistance. This study evaluated the therapeutic effects of NXP900 and detailed adaptive molecular responses to SFK inhibitor therapy. We also aimed to identify biomarkers predictive of drug sensitivity using integrated multiomic profiling and develop strategies to overcome resistance. NXP900 inhibited YAP activity through direct inhibition of tyrosine phosphorylation and indirect activation of the Hippo pathway via LATS. These effects were associated with decreased tumor cell viability in CCA cell lines and several in vivo models. Notably, IDH -mutant patient-derived xenograft CCA models were particularly sensitive to NXP900. NXP900 also synergized with gemcitabine/cisplatin chemotherapy, enhancing antitumor efficacy in both in vitro and in vivo models. Multiomic analyses combining transcriptomics, global proteomics, and phosphoproteomics identified molecular features associated with primary response and acquired resistance. IL13RA-AKT signaling was upregulated in resistant models; NXP900 sensitivity could be restored with AKT or IL13RA2 inhibition. Together, these findings demonstrate the therapeutic potential of NXP900 as a novel YAP inhibitor in CCA and support further investigation in a clinical trial. Graphical Abstract

Background and Aims: Cholangiocarcinoma (CCA) is one of the most lethal cancers, characterized by molecular heterogeneity and treatment resistance. To uncover new biological signals and therapeutic opportunities, we employed multiomic characterization combined with machine learning. Approach and Results: We profiled all anatomical CCA subtypes using whole exome sequencing, mRNA sequencing, and proteome/phosphoproteome analysis. Integrative dimensional reduction revealed RNA, protein, and phosphoprotein features driving tumor heterogeneity, enabling clustering. Machine learning algorithms identified molecular features for each cluster and mapped external datasets and patient-derived xenograft (PDX) models onto these clusters. Kinase enrichment analysis highlighted targetable kinases active in each cluster. In vivo validation was performed in cluster-specific PDX models using the selective TNK1 inhibitor, TP-5801. We identified 3 molecular clusters with distinct pathway characterization: immunomodulatory (cluster 1), metabolic (cluster 2), and gene regulation/cellular fate (cluster 3). Cluster assignment was independent of anatomic subtype but correlated with overall survival following curative-intent resection. We also identified multiomic features and pathways linked to overall survival and lymph node metastases, crucial for patient treatment selection. Kinase enrichment analysis pinpointed TNK1 as a highly active kinase in the metabolic cluster. Treatment with TP-5801 significantly reduced tumor growth in a metabolic PDX model, but not in models representing the other clusters. Combining internal data with publicly available datasets, we identified the immunomodulatory cluster as most responsive to gemcitabine/cisplatin therapy, confirmed in vivo using cluster-specific PDX models. Conclusions: Integrated multiomic characterization provides translational insights by defining unique molecular subtypes associated both with therapeutic response and overall clinical outcomes. This approach identified TNK1 as a previously unrecognized therapeutic target in a defined subset of CCA tumors.

We present optimized settings for ramp times and ion mobility range in trapped ion mobility spectrometry experiments involving single cell analysis.

Abstract A quadrupole time‐of‐flight mass spectrometer coupled with a trapped ion mobility spectrometry (timsTOF) operated in parallel accumulation‐serial fragmentation (PASEF) mode has recently emerged as a platform capable of providing four‐dimensional (4D) features comprising of elution time, collision cross section (CCS), mass‐to‐charge ratio, and intensity of peptides. The PASEF mode provides ∼100% ion sampling efficiency both in data‐dependent acquisition (DDA) and data‐independent acquisition (DIA) modes without sacrificing sensitivity. In addition, targeted measurements using PASEF integrated parallel reaction monitoring (PRM) mode have also been described. However, only limited number of studies have used timsTOF for analysis of clinical samples. Although Orbitrap mass spectrometers have been used for biomarker discovery from cerebrospinal fluid (CSF) in a variety of neurological diseases, these Orbitrap‐derived datasets cannot readily be applied for driving experiments on timsTOF mass spectrometers. We generated a catalog of peptides and proteins in human CSF in DDA mode on a timsTOF mass spectrometer and used these data to build a spectral library. This strategy allowed us to use elution times and ion mobility values from the spectral library to design PRM experiments for quantifying previously discovered biomarkers from CSF samples in Alzheimer's disease. When the same samples were analyzed using a DIA approach combined with a spectral library search, a higher number of proteins were identified than in a library‐free approach. Overall, we have established a spectral library of CSF as a resource and demonstrated its utility for PRM and DIA studies, which should facilitate studies of neurological disorders.

Abstract A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.