Abdelrahman, Amro M.

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal cancer with a 5-year survival rate of 13%. Despite recent molecular stratification of tumors into distinct classical and basal-like cell states, most tumors are heterogeneous and contain both subtypes. Therefore, therapeutic approaches targeting only one subtype are unlikely to be effective as standalone PDAC treatments. Here, we integrated chromatin accessibility [assay for transposase-accessible chromatin with sequencing (ATAC-seq)], genome-wide occupancy [chromatin immunoprecipitation sequencing (ChIP-seq)] for epigenetic status (H3K27ac), and H3K4me3-anchored chromatin topology (HiChIP) to uncover subtype-independent highly interactive enhancers that interact with essential genes in PDAC. Motif analysis revealed that these common enhancers were bound by KLF5 with subsequent depletion leading to decreased cell viability via induction of apoptosis. To elucidate the transcriptional and epigenetic mechanisms by which KLF5 functions in PDAC, we used rapid depletion of KLF5 with dTAG technology and profiled the effects on the open and active chromatin landscape and transcription with nascent RNA and messenger RNA sequencing over time. Enhancer inactivation via KRAB domain Zim3-dCas9 fusion protein confirmed KLF5-bound enhancers regulate target genes, including the anti-apoptotic gene BCL2L1 . Multiplex immunofluorescence confirmed costaining of KLF5 and Bcl-xL in patient samples and overexpression of Bcl-xL rescued the induction of apoptosis after KLF5 depletion. Together, this study provides insights into common mechanisms to target highly heterogeneous PDAC tumors.

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.

Abstract Background: The standard first line therapy for cholangiocarcinoma (CCA), gemcitabine and cisplatin +/- immunotherapy, only induces disease control in approximately 75% of patients (KEYNOTE-966 Trial). While several candidate single genes that predict gemcitabine and cisplatin resistance have been proposed, the overall molecular landscape that leads to resistance is not well understood. Predicting response based on molecular characteristics would benefit the clinical decision-making process when selecting appropriate chemotherapy regimens for CCA patients. Methods: Fifty-five Mayo Clinic CCA patients who underwent whole transcriptome RNA sequencing were included. Forty-eight patients had TempusXR sequencing and 7 patients used institutional labs. Normalized transcript-level data was converted to KEGG pathway-level data using overrepresentation ranking. Patient oncology notes were manually reviewed for response to gemcitabine plus cisplatin therapy, with or without concurrent pembrolizumab or durvalumab. Oncologists used a mixture of formal RECIST criteria and expert opinion to determine whether the response to gemcitabine and cisplatin +/- immunotherapy was progressive disease (PD), stable disease (SD), partial response (PR), or complete response (CR). The binary outcome of PD vs SD/PR/CR based on KEGG pathway overrepresentation was modelled using a Tabular Prior-data Fitted Network (TabPFN). Validation was performed using leave-one-out cross-validation. External model testing was performed on RNA sequencing data from 11 patient-derived xenografts that were treated with gemcitabine and cisplatin and underwent ultrasound measurements of tumor size. Results: There were 34 patients with SD/PR/CR and 21 patients with PD as the best response while receiving gemcitabine and cisplatin +/- immunotherapy. TabPFN cross-validation accuracy was 0.73, recall 0.94, precision 0.71, and F1 score 0.81. External validation accuracy was 0.70, with one xenograft exhibiting mixed response. In contrast, the best trained model using a 14-gene subset previously published for gemcitabine resistance prediction in pancreatic adenocarcinoma had accuracy 0.58. Conclusions: Machine learning models like TabPFN can capture relationships in high-dimensional transcriptomic information in novel ways that enhance our ability to predict chemotherapy response. Integrating machine learning to understand the complex molecular landscape of CCA will be a valuable tool for therapeutic regimen selection. Citation Format: Ellen L. Larson, Erik Jessen, Dong-Gi Mun, Amro Abdelrahman, Jennifer Tomlinson, Danielle Carlson, Hojjat Salehinejad, Caitlin Conboy, Rory Smoot. Machine learning models of RNA expression landscapes help predict overall tumor response to chemotherapy in cholangiocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A020.

Background: Cholangiocarcinoma is a molecularly heterogenous cancer arising from the biliary epithelium. Individual omic techniques have identified clinically relevant, targetable subtypes but most tumors have no targetable alterations based on exome or transcriptomic evaluation. Integrated multiomics approaches incorporating transcriptomic, proteomic, and phosphoproteomic characterization provide deeper understanding and identify non-mutated, activated pathways that can be targeted. To optimize the potential clinical utility of these approaches, minimal classifier features must be defined. Methods: Approximately 60,000 RNA, protein, and phosphoprotein features were extracted from 35 cholangiocarcinomas treated at the primary US study site. 206 patients treated and profiled at an institution in China were also included. Using the Multiomics Factor Analysis clustering method, patients were sorted into 3 distinct molecular profiles. Important molecular features were selected using competing feature selection (FS) methods: Boruta, RreliefF, variable selection using random forests (RF), recursive factor elimination (RFE), median decrease accuracy in RF, and Concrete Autoencoders. Machine learning classification prediction models including Extreme Gradient Boosting, gradient-boosted trees, multinomial logit, and support vector machine (SVM) were tested on each FS. Each combination of FS and predictive model was tested with 10 replicates of 10-fold cross-validation of a randomly-selected balanced test set and an oversampled, balanced training subset. Accuracy, F1 score, AUC, precision, and recall were assessed for each FS and predictive model combination. A custom function of physical assay cost and classification accuracy was optimized to find the best assay. KEGG pathway overrepresentation analysis was used to analyze feature subsets. Results: A consistent set of 50 important RNA and protein features was identified, including previously reported prognostic factors, like SpryD4 protein expression, as well as previously unreported pathway surrogates, such as Slc2A2 or Pipox protein. Pathway overrepresentation analysis found insulin signaling and resistance and amino acid biosynthesis/metabolism pathways were overrepresented in the discriminative feature set. The gradient-boosted trees algorithm combined with the RFE FS method had the best performance, with a multiclass macro average (±sd) cross-validation accuracy 89% (±6%), F1 score 89% (±8%), AUC 0.98 (±0.02), precision 90% (±10%), recall 89% (±6%). Conclusions: Feature selection methods and predictive machine learning models work synchronously to develop accurate molecular subtype assays based on just 50 protein and RNA levels. This strategy has the potential to make molecular subtyping for cholangiocarcinoma and other molecularly complex cancers fast and widely available. Citation Format: Ellen Larson, Erik Jessen, Dong-Gi Mun, Jennifer Tomlinson, Amro Abdelrahman, Danielle Carlson, Hojjat Salehinejad, Rory Smoot. Feature selection and machine learning strategies optimize an affordable molecular assay for cholangiocarcinoma subtype [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1107.

Abstract Background: Cholangiocarcinoma (CCA) is a diverse type of cancer. For several years, platinum-based chemotherapy (Gemcitabine and Cisplatin) has been the standard treatment and serves as the foundation for current immunotherapy combinations. The typical response rates are generally between 20% and 30%. In this study, we present the results of preclinical efficacy trials conducted on CCA patient-derived xenograft (PDX) models, correlating them with the baseline multiomics profiling of these models to identify molecular characteristics linked to treatment response. Methods: Tumor samples from twenty-eight CCA PDX were used to isolate RNA and protein to be included in multiomics analysis (Phosphoproteomics, Proteomics, and Transcriptomics). A subset of PDX models were treated with various doses of Gemcitabine and Cisplatin. The Median Efficacy Index (MEI) was calculated as the ratio of the difference in median of delta in tumor volume at the last day of the study between the treatment and the control arms divided by the median of delta in tumor volume of the control at the last day of the treatment (positive= growth, negative=inhibition, and 0=no efficacy). A Hierarchical All-Against-All algorithm was utilized to compare multiomics features to the MEI and pathway analysis completed for identified molecular features. Pathway enrichment was calculated using the KEGG database, and random-forest variable selection chose the pathways most influential in predicting sensitivity or resistance. Results: After conducting preclinical efficacy trials, the 11 tested CCA PDX models were classified into sensitive, moderately sensitive, and resistant models based on the MEI on each treatment arm. The top up-regulated genes were identified for Gemcitabine response (SRPK2, GLK2, and RIOK2) and Cisplatin response (NTRK1, PRPF4B, and SRPK2). The top up-regulated genes related to Gemcitabine resistance were NTRK1, PIP5K1C, and CSNK2A2, while those related to Cisplatin resistance were SRPK1, PRPF4B, and CSNK2A1. Downregulation of the anti-folate resistance pathway is a strong predictor of gemcitabine sensitivity, and the lysine degradation pathway is a strong predictor of cisplatin sensitivity. Conclusion: Multiomics signatures of cholangiocarcinoma can forecast responses to standard chemotherapy in preclinical models. New markers of resistance to Gemcitabine and Cisplatin were discovered, linked to NTRK1, SRPK1, and SRPK2. These resistance profiles will be tested using suitable targeted therapies on the resistant CCA patient-derived xenograft (PDX) models. Citation Format: Amro M. Abdelrahman, Danielle M. Carlson, Ellen L. Larson, Erik Jessen, Dong-Gi Mun, Isaac Lynch, Alessandro Fogliati, Stella Konadu Adjei Antwi, Aushinie Abeynayake, Akhilesh Pandey, Mark J. Truty, Gregory J. Gores, Rory L. Smoot. Baseline multiomics and chemotherapy efficacy in preclinical trials using patient-derived xenograft models of cholangiocarcinoma can reveal distinct signatures of chemotherapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6762.

Abstract Introduction: Src Family Kinases (SFKs) have a crucial role in tumor proliferation, survival, and metastasis. NXP900 is a highly selective SFK inhibitor a novel mechanism of action that locks SFKs in a closed, inhibited conformation, preventing interaction with substrate proteins, unlike other SFK inhibitors like dasatinib. A phase 1 study of NXP900 in patients with advanced solid tumors was recently initiated. Our preclinical studies showed therapeutic efficacy of NXP900 in cholangiocarcinoma (CCA) through decreased YAP, RAF-MAP and PI3K-AKT signaling. Here, we examined mechanisms of sensitivity and resistance to SFK inhibition. Methods: Secondary resistance was explored by generating a NXP900-resistant human cell line (HuCCT1-R) via prolonged exposure to NXP900, followed by multiomic profiling. Primary resistance was studied in vivo using nine heterogeneous patient-derived xenograft (PDX) tumors implanted in the flanks of NOD/SCID mice. Tumor-bearing mice were treated with vehicle or 40 mg/kg NXP900 daily for up to 4 weeks. To identify factors associated with response, a multiomic approach was used, integrating RNA-Seq, global proteomics, and phosphoproteomics. A hierarchical-all-against-all algorithm (HallA) assessed associations between multiomic features and drug response in tumor growth. Kinase enrichment analysis (KEA3) identified features linked to sensitivity or resistance. Results: HuCCT1-R cells showed increased pTyr abundancy of TNK2, a protein known to increase PIP3 levels leading to AKT activation, and of DYRK3 and LAMTOR1, both of which can sustain AKT phosphorylation via mTORC, indicating acquired resistance through bypassing inhibited PI3K-AKT signaling to rescue AKT. NXP900 reduced tumor growth rates in 5 of 9 PDX models. Kinases associated with sensitivity in the PDX models included SFKs (SRC, LYN, and FYN), and AKT, PTK2, PRKCZ, EGFR, SYK, and GSK3B, suggesting involvement of the PI3K-AKT pathway in responsiveness to NXP900. Based on tumor inhibition responses in the tested models, we estimated NXP900 response for our additional PDX models, predicting 39% (11/28) of patient tumors may respond. Conclusion: This study identified molecular markers linked to response to SFK inhibition, which could be utilized for patient selection for NXP900 treatment. Further studies are validating these findings and exploring combination therapies to address resistance mechanisms. Citation Format: Hendrien Kuipers, Danielle M. Carlson, Jack W. Sample, Erik Jessen, Dong-Gi Mun, Aushinie M. Abeynayake, Jennifer L. Tomlinson, Amro M. Abdelrahman, Nathan W. Werneburg, Binbin Li, Mitesh J. Borad, Mark J. Truty, Sumera I. Ilyas, Gregory J. Gores, Rory L. Smoot. Multiomic profiling identified biomarkers of response to selective Src family kinase inhibition in preclinical models of cholangiocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5595.

Background and Aims: Cholangiocarcinoma (CCA) is an aggressive malignancy arising from the biliary epithelium with limited therapeutic options and poor long-term survival rates. To address the limitations of CCA treatment, we investigated cell-targeted nanovesicles as a delivery platform for transcriptome-targeting therapeutics. Approach and Results: Milk-derived nanovesicles were loaded with short interfering RNAs targeting Yes-associated protein (YAP), the downstream effector of the Hippo pathway; LCK, an upstream regulator of YAP; and tafazzin, a protein critical for the integrity of the inner mitochondrial membrane. These transcriptome-targeting nanovesicles were decorated with a lipid-coupled RNA aptamer to epithelial cell adhesion molecule, including a tracking fluorophore. In vitro studies were conducted using multiple CCA cell lines. In vivo studies were performed using C57BL/6 and non-obese diabetic/severe combined immunodeficient mice to evaluate delivery and efficacy in both an immunocompetent syngeneic murine and a patient-derived xenograft model. We demonstrated that transcriptome-targeting nanovesicles were selectively taken up by liver tumor cells, which was augmented by the incorporation of a targeting aptamer, and that milk-derived nanovesicles loaded with short interfering RNA effectively downregulated target gene expression, both in vitro and in vivo. Downstream effects of target gene inhibition were observed, including downregulation of YAP-TEAD target genes and an increase in reactive oxygen species production at the mitochondrial level. Administration of transcriptome-targeting nanovesicles targeting YAP, LCK, and tafazzin inhibited CCA growth and further synergized with chemotherapy in 2 preclinical CCA models. Conclusions: Herein, we show that aptamer-directed, nanovesicle-mediated targeting of YAP, LCK, and tafazzin potentiates chemosensitivity in 2 CCA models when delivered using aptamer-guided milk-derived nanovesicles.

Background Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer with a 5-year survival rate of 12%. It has two major molecular subtypes: classical and basal, regulated by the master transcription factors (MTFs) GATA6 and ΔNp63, respectively. Objective This study sought to uncover the transcriptional regulatory mechanisms controlling PDAC subtype identity. Design We integrated primary tumour single-cell RNA-seq, patient-derived xenograft RNA-seq and multispectral imaging to identify MTF-dependent, subtype-specific markers. We created subtype-specific fluorescent reporter systems and conducted drug screenings to find actionable targets. We analysed chromatin accessibility (ATAC-seq), genome-wide occupancy (ChIP-seq) for epigenetic status (H3K27ac), MTFs (GATA6, ΔNp63), RNA polymerase II (Pol II), H3K4me3-anchored chromatin topology (HiChIP) and nascent RNA capture sequencing (PRO-seq). Additionally, we used nuclease-dead Cas9 (dCas9) to manipulate transcriptional regulatory mechanisms. Results Our approach identified glucocorticoid receptor (GR) agonists as agents that suppress the classical transcriptional programme by interacting with GATA6. GATA6 regulates classical-specific transcription through promoter-proximal pause release. Depletion of GATA6 increased Pol II occupancy at GATA6-bound enhancers and transcriptional start sites, stabilising enhancer–promoter interactions. Artificially inducing pausing at GATA6-bound enhancers with dCas9 abrogated target gene expression and induced pausing at both the enhancer and target gene promoter. Conversely, in basal PDAC ΔNp63 promotes Pol II recruitment and stabilises enhancer–promoter interactions. Conclusion This study provides new insights into the transcriptional control and role of GR agonists in controlling PDAC molecular subtype identity.

Cholangiocarcinoma (CCA) is a highly aggressive form of liver cancer and is an increasing cause of cancer‐related death worldwide. Despite its increasing incidence globally and alarming mortality, treatment options for CCA have largely remained unchanged, stressing the importance of developing new effective therapies. YAP activation is common in CCA, and its major transcriptional signaling partners are the TEAD proteins. CA3 is a small‐molecule YAP‐TEAD disrupter discovered utilizing a TEAD reporter assay. Utilizing CCA, gastric cancer cell lines, and patient‐derived xenograft models (PDX), we demonstrate that CA3 is effective in inducing cell death and delaying tumor growth in both FGFR2 fusion and wild‐type models. CA3 was associated with on‐target decreases in YAP‐TEAD target gene expression, TEAD reporter activity, and overall TEAD levels. Hippo pathway signaling was not altered as there was no change in YAP phosphorylation status in the cells exposed to CA3. RNA sequencing of gastric cancer and CCA models demonstrated upregulation of an androgen receptor–mediated transcriptional program following exposure to CA3 in five unique models tested. Consistent with this upstream regulator analysis, CA3 exposure in CCA cells was associated with increased AR protein levels, and combinatorial therapy with CA3 and androgen receptor blockade was associated with increased cancer cell death. CA3 behaves functionally as a YAP‐TEAD disrupter in the models tested and demonstrated therapeutic efficacy. Exposure to CA3 was associated with compensatory androgen receptor signaling and dual inhibition improved the therapeutic effect.