Carlson, Danielle M.

Lacking effective therapeutics, cholangiocarcinoma (CCA) remains a deadly malignancy of the biliary tract. The Hippo pathway effector protein Yes-associated protein (YAP) is implicated in CCA pathogenesis and chemotherapeutic resistance; however, the oncogenic mechanisms underlying YAP regulation remain incompletely understood. An enhanced understanding of YAP and its role in CCA may uncover novel therapeutic targets and better define resistance pathways. Human CCA cells and murine syngeneic CCA models were utilized to explore the molecular relationship of YAP and protein tyrosine phosphatase 1B (PTP1B). Previous work in CCA has demonstrated that YAP interacts with multiple protein tyrosine phosphatases, including SHP2 and PTP1B. We observed that PTP1B pharmacologic inhibition was associated with increased cell proliferation and YAP target gene expression, while genetically enforced overexpression of PTP1B was associated with a decrease in YAP activation. Treatment of CCA cells in vitro and syngeneic, orthotopically implanted CCA murine tumors in vivo with standard cytotoxic chemotherapy, gemcitabine/cisplatin, had enhanced efficacy in the setting of PTP1B overexpression. These findings demonstrate that pYAPY357 can be modulated through protein tyrosine 1B phosphatase activity, and reducing pYAPY357 through enhanced phosphatase levels can sensitize CCA to chemotherapy.

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: Targeted agents, such as nanobodies, can be covalently linked to radionuclide chelators for theranostics. Nanobodies are single-domain immunoglobulin variable regions with a short blood half-lives and high specificity. Calreticulin (CALR) is endoplasmic reticular (ER) protein that translocates to the cell surface upon induction with certain agents, namely doxorubicin. We hypothesized surface CALR on tumors could be targeted using a novel nanobody after induction with doxorubicin. Methods: Studies were conducted using in vitro and in vivo models of pancreatic ductal adenocarcinoma (PDAC). Surface CALR expression was determined by western blot on fractionated lysates, flow cytometry, or immunofluorescence (IF). Doxorubicin was administered by intraperitoneal injection to mouse models of PDAC. The anti-CALR nanobody was developed via the panning of a synthetic nanobody phage display library. The top CALR-binding nanobody was confirmed by ELISA, immunohistochemistry (IHC), and flow cytometry. For in vivo studies, the nanobody was conjugated with the chelator NOTA and radiolabeled with 64Cu. PET images were acquired 1-hour after injection. Significance was determined using an independent t-test and p values < 0.05 were considered statistically significant. Results: PDAC cells treated with doxorubicin showed peak surface CALR expression between 24 and 48 hours. Flow cytometry revealed that 61.7% of live PDAC cultured cells treated for 24 hours expressed surface CALR, compared to 2.2% in controls (p < 0.001). In a subcutaneous syngeneic PDAC mouse model, two doses of doxorubicin 48 hours apart significantly increased surface CALR, with 30.4% of live-cells expressing CALR in treated tumors versus 1.5% in controls (p < 0.001). Similarly, doxorubicin-treated tumors from a PDAC patient derived xenograft (PDX) model had a 6.7-fold increase in live-cells expressing surface CALR compared to controls (p < 0.01). IF confirmed a 1.4-fold increase in CALR expression in doxorubicin-treated tumors from the subcutaneous PDAC mouse model (p < 0.01) and a 1.7-fold increase in the PDAC PDX model (p < 0.01). Our anti-CALR nanobody has an apparent 37 nM affinity for recombinant CALR and successfully detected surface CALR by IHC and flow cytometry. In vivo PET imaging showed significant increased uptake of the 64Cu-NOTA-nanobody in tumors from doxorubicin-treated mice over controls, with no significant changes in heart, kidney, or liver accumulation by PET quantification and flow cytometry (p < 0.05). Conclusions: Systemic doxorubicin treatment induces CALR surface translocation in tumors that can be successfully targeted with a radiolabeled nanobody for PET imaging. Targeting chemotherapy-induced surface CALR using our radiolabeled nanobody could provide a theragnostic strategy for the detection and treatment of PDAC. Citation Format: Rachael Guenter, Yuvasri Golivi, Lucinda Hall, Amro Abdelrahman, Chloe La Prairie, Caleb Miller, Tejeshwar C. Rao, Danielle M. Carlson, Mark J. Truty, Benjamin Larimer, J. Bart Rose. Detection of tumor cell surface calreticulin after doxorubicin treatment using a novel radiolabeled nanobody [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 574.

Abstract Background: Cholangiocarcinoma (CCA) is a malignancy of the biliary epithelium with increasing incidence and poor survival. Despite the advances in targeted therapies, treatment success has been hindered by primary or acquired resistance. New therapeutic strategies are necessary to improve outcomes in this patient population. This study investigates the anti-tumor efficacy of TGR-5 inhibition in CCA using a novel small molecular inhibitor, SBI-364. Methods: Cell viability was assessed using a CellTiter-Glo assay in four unique CCA cell lines treated with SBI-364 at concentrations ranging from 1 nM to 100 µM for 48 hours. Viability was expressed as a percentage relative to untreated controls. Cell migration was assessed using a scratch wound healing assay at 0, 12, and 24 hours following SBI-364 treatment. Migration was reported as the percentage of wound closure relative to the initial scratch width. Apoptosis was assessed using the Caspase-Glo® 3/7 Assay (Promega) after 12 hours of treatment with SBI-364. Caspase activity was normalized to untreated controls and expressed as relative luminescence units. Results: SBI-364 reduced cell viability in a dose-dependent manner across all CCA cell lines with IC50 values ranging from 4.1-15.8 µM. Higher concentrations of SBI-364 induced increased cytotoxicity. In the scratch assay, untreated CCA cells achieved 100 % wound closure within 24 hours. Treatment with SBI-364 at 10 µM significantly inhibited cellular migration, with reduced wound closure compared to controls (p < 0.05). Apoptosis analysis revealed a significant increase in caspase-3/7 activity in three CCA cell lines following treatment with 10 µM SBI-364 for 12 hours (p < 0.01). Conclusion: TGR-5 inhibition using novel small molecule inhibitor SBI-364 demonstrates potent anti-tumor effects in CCA by reducing viability, inhibiting migration, and promoting apoptosis. These findings support further investigation of SBI-364 as a promising therapeutic strategy in CCA. Citation Format: Enis Ozmert, Jennifer L. Tomlinson, Danielle M. Carlson, Rory L. Smoot. Efficacy of TGR-5 inhibition in cholangiocarcinoma using the novel small molecule inhibitor SBI-364 [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 5588.

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: Cholangiocarcinoma (CCA) is an aggressive biliary malignancy with poor survival outcomes and multiple mechanisms of resistance to targeted therapies. We previously validated NXP800, a novel GCN2 small-molecule activator, as an effective anti-cancer drug in six CCA cell lines. In vitro testing identified upregulation of phosphorylated eIF2a, as well as upregulation of ATF4, in CCA cells treated with of NXP800 compared to vehicle, suggesting that treated cells led to stimulation of stress-induced genes and subsequently, apoptosis. Furthermore, patient derived xenografts (PDX) models treated with NXP800 were found to have decreased tumor size, particularly in models containing ARID1A, FGFR1, and ATM mutations. Here, we aimed to predict response to NXP800 using sensitivity and resistance signatures from our profiled PDX models. Methods: GCN2 activation in multiple previously profiled PDX models was evaluated. Whole Exome Sequencing (WES) was done on multiple PDX models to characterize the mutational signature and activity in CCA. Two additional CCA xenografts were expanded into the flank of NOD/SCID mice. Tumor bearing mice were treated with NXP800 (35 mg/kg) or vehicle via oral gavage for 28 days once tumors averaged 120 mm3. Tumor volume and animal weight were collected twice weekly. Median Efficacy Index (MEI) scores were generated as drug response rates based on tumor growth. Predicted sensitivity and resistance kinase signatures to predict drug response were produced using multi-omics. Results: Treatment with NXP800 was associated with a statistically significant decrease in tumor size in two additional PDX models, which included ARID1A and BRAF mutants. Preliminary multi-omics on the PDX models revealed that sensitivity mechanisms were upregulated most in EGFR signaling, while resistance signatures were focused around CSNK2A1 signaling. Conclusion: Multi-omics analysis revealed a sensitivity mechanism structured around EGFR signaling, providing insights into which patients should be selected for treatment with NXP800. A Phase 1b clinical trial investigating the effects of NXP800 use in patients with advanced CCA is open and currently accepting patients. Citation Format: Danielle Marie Carlson, Hendrien Kuipers, Amro Abdelrahman, Erik Jessen, Joshua Roldan Kalill, Jennifer Tomlinson, Jack Sample, Akhilesh Pandey, Mark Truty, Rory Smoot. Modeling response prediction to a novel GCN2 kinase activator (NXP800) in patient-derived xenograft cholangiocarcinoma models using a multi-omics approach [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 5592.

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.

Abstract SWI/SNF chromatin remodeling complexes are frequently mutated across human cancers, including cholangiocarcinoma (CCA). Dysregulation of this complex due to loss of function mutations in AT-rich interaction domain-1A (ARID1A) and polybromo-1 (PBRM1) occur in up to 18% and 10% of cases respectively. SWI/SNF subunit mutations frequently co-occur alongside PI3K/AKT pathway activation. Preexisting literature suggests altered gene expression and protein synthesis contribute to CCA oncogenesis, but our understanding of these mechanisms remains immature. Genetically accurate preclinical models are needed to define the roles of ARID1A and PBRM1 in CCA and identify therapeutic targets. We developed novel murine models of CCA with either ARID1A, PBRM1 or dual ARID1A/PBRM1 (APAC) knockout in the context of AKT activation, utilizing a surgical biliary transfection method for somatic gene editing with CRISPR/Cas9. Within 8 months, mice formed numerous liver tumors, predominantly CCA and mixed CCA/HCC. Knockout of ARID1A and PBRM1 was confirmed through genomic PCR, qRT-PCR, and immunoblot assays. Histologically, murine APAC tumors displayed phenotypic similarities to human CCA, including dense cancer-associated fibroblast infiltration and variable CD45+ immune cell infiltration. A novel, syngeneic CCA cell line was isolated from a dual ARID1A/PBRM1 knockout tumor (APAC-2). RNA-sequencing of primary tumors and the APAC-2 cell line versus a non-invasive cholangiocyte cell line with AKT activation only (AC), was performed to identify differentially expressed genes (DEGs) of interest in ARID1A and PBRM1-deficient CCA. Periostin (Postn) was among the top upregulated genes in the APAC-2 cell line. POSTN is a secreted glycoprotein that is upregulated in human CCA and may be therapeutically targetable. Knockdown of periostin inhibited the in vitro growth of APAC-2 cells and a human CCA cell line with ARID1A mutation and periostin overexpression (HuH-28). These studies serve to characterize and validate a genetically relevant murine model of epigenetic dysregulation in ARID1A and PBRM1-deficient CCA. This model can be used for identification and validation of potential drug targets. Ongoing work focuses on studying multiple dysregulated pathways and testing dependence on genes of interest, including periostin, as potential treatment strategies for CCA. Citation Format: Jayla Millender, Jennifer Tomlinson, Ryan Watkins, Danielle Carlson, Jack Sample, Nathan Werneburg, Rory Smoot, Keith Robertson, Gregory Gores, Caitlin Conboy. Novel murine models of epigenetic dysregulation in SWI/SNF-mutated 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 2741.