Smoot, Rory L.

PURPOSE The development of colorectal liver metastases (CRLMs) is associated with poor prognosis, and recent data suggest that metastasis to the liver is associated with resistance to immunotherapy. We characterized the microenvironment of primary colorectal carcinomas (CRCs) relative to their synchronous CRLMs using a validated segmentation algorithm (QuantCRC) that quantifies 15 distinct morphologic tumor features. MATERIALS AND METHODS Adult patients with CRC with synchronous CRLM (N = 57) at Mayo Clinic were identified from the electronic health record. Routine tumor hematoxylin and eosin sections were digitized and reviewed for quality control. QuantCRC (Aiforia, Inc) was applied to digitized images to extract 15 GI pathologist predefined morphological features. Tumor features were compared between paired primary tumors and their synchronous CRLMs using the Wilcoxon signed-rank test overall and within subgroups. Linear regression models were used to find predictors of the paired differences in the distinct tumor morphological features. RESULTS The study included 57 patients (median age 59 years [IQR, 50-73], 51% female) with CRC primary and synchronous CRLM, and among primaries, 37 (65%) were left-sided. QuantCRC identified six of 15 morphological features that differed significantly between primaries and their CRLMs. Compared with the primary, CRLM showed reduced stroma, more high-grade tumor and necrosis, higher tumor–stromal ratio (TSR; all P < .05), and a trend toward increased tumor-infiltrating lymphocyte (TIL) density ( P = .052). Synchronous CRLM from patients with left-sided primary tumors had significantly higher TSR, percent high-grade, percent necrosis, and TIL density compared with the primary (all P values ≤0.04). Among right-sided primaries, CRLM had a significantly reduced percent mature stroma ( P = .0296) and a higher percent necrosis ( P = .0027). CONCLUSION Compared with the primary, synchronous CRLMs showed a higher TSR and increased high-grade tumor and necrosis, which are features of tumor aggressiveness and a metastatic phenotype.

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 and Aims: The optimal treatment strategy for de novo perihilar cholangiocarcinoma (pCCA) remains debated. This study compares outcomes between liver transplantation following neoadjuvant chemoradiation (RT+LT) and liver resection (LR), with (LR+VR) and without vascular resection (LR w/o VR). Approach and Results: This single-center, retrospective study included patients with de novo perihilar cholangiocarcinoma treated at the Mayo Clinic Rochester (1993–2023) with curative-intent surgery. Patients underwent either (1) LR, classified as LR+VR or LR w/o VR, or (2) RT+LT following the transplant protocol. Overall survival (OS) and recurrence-free survival were analyzed using as-treated and intention-to-treat approaches, incorporating competing risk analysis and direct matching. In the as-treated analysis, RT+LT showed superior median OS compared with LR w/o VR (78.0 vs. 58.2 mo, p =0.03) and LR+VR (25.8 mo, p <0.001). Considering dropout rates (RT+LT: 41%, LR: 28%), intention-to-treat analysis showed no significant OS difference between LR and RT + LT (31.7 vs. 38.5 mo, p =0.19). In matched as-treated analysis, RT + LT had no significant survival benefit over LR w/o VR (50.6 vs. 140.6 mo, p =0.08) or LR+VR (25.8 mo, p =0.11). Perioperative mortality was 4% (RT+LT), 7% (LR w/o VR), and 8% (LR + VR). Conclusions: Both LR and RT+LT achieve excellent oncological outcomes in selected patients with de novo perihilar cholangiocarcinoma. Key challenges remain dropouts during neoadjuvant therapy in patients planned for LT and high perioperative mortality for patients undergoing LR. OS doubles in patients undergoing LR+VR compared with dropouts, supporting LR+VR as a viable option for patients who are ineligible for LT. Appropriate patient selection is crucial, as those not undergoing surgery suffer from a dismal prognosis.

Portal hypertension is a key factor in posthepatectomy liver failure (PHLF). While preoperative liver function tests like APRI+ALBI assess liver function, they only partially reflect portal hypertension severity. Elevated von Willebrand factor antigen (vWF-Ag) indicates endothelial dysregulation and correlates with portal hypertension. Combining vWF-Ag with APRI+ALBI may enhance PHLF prediction. A total of 534 patients who underwent liver resection at Mayo Clinic Rochester (2020–2024) were analyzed for PHLF incidence, postoperative morbidity, and 90-day mortality. Predictive probability for PHLF was assessed using receiver operating characteristic analysis and validated in an external Austrian cohort of 283 patients (2008–2017). vWF-Ag was then integrated into the existing APRI+ALBI multivariable model. PHLF grade B/C was reported in 56 patients (10.5%). The 90-day mortality rate was 0.9% (n=5). Using 2 cutoffs (182%, 240%), vWF-Ag was found to be an independent predictive factor for PHLF grade B/C that remained statistically significant upon multivariable analysis. The combination of preoperative APRI+ALBI and vWF-Ag increased the positive predictive value to 27.9% for PHLF grade B/C in the APRI+ALBI high and vWF-Ag high group. Incorporation of vWF-Ag into the established APRI+ALBI-based multivariable model revealed a superior AUC of 0.772 for PHLF risk stratification and was successfully validated in an independent cohort (AUC=0.834). The combination of vWF-Ag with APRI+ALBI demonstrates a favorable predictive potential for PHLF risk assessment. We provide 2 pathways for clinical assessment: (1) a cutoff-based system and (2) a more complex multivariable model that can be calculated in a specifically designed smartphone application.

Objectives: Pancreatic neuroendocrine tumors (pNETs) are uncommon, comprising 3%–7% of pancreatic tumors. With increasing incidence due to advanced imaging techniques, there is a need for detailed characterization of these tumors. This study aims to describe the clinical features, diagnostic evaluations, and pathology characteristics of pNETs in a large cohort from a single tertiary center, and to compare these findings with other larger cohort studies. Methods: We conducted a retrospective analysis of 866 patients with pNETs who underwent surgical resection at Mayo Clinic, Rochester, from March 2000 to December 2019. Data on demographics, clinical presentation, laboratory tests, imaging, and pathology were extracted and analyzed. Descriptive statistics were used to summarize the data. Results: The cohort had a median age of 57 years. Nonfunctional tumors were much more prevalent (77.5%), with functional tumors primarily being insulinomas (75.9%). Common presenting symptoms included gastrointestinal (45.3%) and nongastrointestinal symptoms (30.7%). Chromogranin A levels were elevated in 57.5% of patients. Imaging revealed enhancing lesions in most cases, with computed tomography scans performed in 90.9% of patients. Endoscopic ultrasound (EUS) identified tumors in 98.1% of cases, with EUS-FNA showing a sensitivity of 82%. Ki-67 index, used in 58.1% of cases, indicated grade 2 tumors as the most common (55.9%). Metastasis was observed in 39.4% of patients at the time of diagnosis, predominantly in the liver. Conclusion: This study provides a comprehensive description of pNET characteristics in a large surgical cohort. Findings highlight the predominance of nonfunctional tumors and the importance of imaging and EUS in diagnosis. The data can aid in inter-institutional comparisons and enhance understanding of pNETs, contributing to improved patient management and future research.

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.