Mun, Dong‐Gi

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.

Abstract Background Cholangiocarcinoma (CCA) is a heterogeneous malignancy. Platinum-based chemotherapy (Gemcitabine and Cisplatin) has been the standard treatment for over a decade and is the base for the current immunotherapy combinations. Response rates are typically between 20-30%. Herein, we present outcomes of preclinical efficacy trials on CCA PDX models in correlation with baseline multiomics profiling of CCA patient-derived xenograft (PDX) models to identify molecular features associated with 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. Results After conducting preclinical efficacy trials, the five 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. Conclusion Cholangiocarcinoma multiomics signatures can predict therapeutic response to standard-of-care chemotherapy in preclinical models. Novel mechanisms of Gemcitabine and Cisplatin resistance were identified related to NTRK1, SRPK1 and SRPK2. Resistant signatures will be challenged with the appropriate targeted therapies on the resistant CCA PDX models. Citation Format: Amro M. Abdelrahman, Danielle M. Carlson, 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. Pretreatment multiomics in conjunction with preclinical trials on patient-derived xenograft models of cholangiocarcinoma can identify chemotherapy response signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6912.

27 Background: Cholangiocarcinoma (CCA) is a devastating cancer with few effective treatment options. We have previously discovered LCK can activate AXL, a TAM receptor tyrosine kinase (RTK), by phosphorylation of AXL-Y866 ( J Hepatol 2022). AXL is known to confer therapeutic resistance to solid cancers. However, the role of AXL in CCA, and the impact of AXL-Y866 activation by LCK still remains to be elucidated. Here, we investigated the significance of AXL expression, signaling, and activation by LCK on Y866. Methods: We first evaluated the expression levels of AXL in CCA and its association with patient outcome using The Cancer Genome Atlas (TCGA) dataset. Next, to evaluate whether AXL inactivation sensitizes CCA cells to gemcitabine and cisplatin (GC), AXL downregulation was achieved via siRNA approach and the selective AXL inhibitor bemcentinib (Bem) using human CCA cell lines, HuCCT1 and RBE, and murine SB-1. We examined 50% inhibitory concentration (IC50) value on GC with or without AXL knockdown (KD) using cell viability assay. Then we assessed the efficacy of the combination of GC and Bem utilizing Calcusyn software and apoptosis by Annexin V assay. I n vivo efficacy was assessed using both a patient derived CCA xenograft and SB-1 a syngeneic model of CCA treated with vehicle, GC, Bem, or the combination. Next, we defined the interactome of wildtype AXL- Y866 compared to AXL-Y866F using biotin proximity labelling methods (BioID), validation of downstream target proteins using Western blot (WB), and a functional study with AXL-/- cells reconstituted with either AXL WT, or AXL Y866F. Results: In TCGA cohorts, AXL transcripts are more abundant than in normal adjacent liver, and higher levels are correlated with worse clinical outcome. In in vitro study, IC50 values of GC decreased after AXL KD. Synergistic effects were observed in the combination. The combination caused increased apoptosis compared to other treatments. In the in vivo studies, the combination suppressed tumor growth and the Ki67 levels, and the phosphorylation levels of PEAK1 decreased in the combination group compared to other groups. In BioID experiments, among the 222 proteins which were detected in both AXL WT and AXL Y866F interactomes, PEAK1 kinase, which localizes to actin cytoskeleton and focal adhesions, was most altered when Y866 was inactivated by the Y866F mutation. In gene ontology analysis, cell-cell adhesion and focal adhesion related proteins were enriched. In WB, we validated that the phosphorylation of PEAK1 decreased after AXL or LCK knockout (KO) and after Bem treatment in above in vivo study. In cell viability assay, reconstitution of AXL KO cells with WT AXL increased the IC50 to GC while reconstitution with AXL-Y866F did not. Conclusions: AXL inhibition sensitizes CCA cells to cytotoxic chemotherapy in preclinical model. LCK-AXL Y866-PEAK1 signaling axis is a potential target for the treatment of CCA.

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