Citations

Abstract Extracellular vesicles (EV) are phospholipid‐encapsulated nanoparticles secreted by cells into their surrounding environment. EVs can transfer a variety of biomolecules that mediate intercellular communication and play a key role in physiological and pathological processes. Therefore, EVs are emerging as new biomarkers for diseases, therapeutic targets, and drug delivery vehicles. The isolation and detection of EVs requires time‐consuming and labor‐intensive processes, first to extract EVs from biological and physiological fluids and then to detect EV‐associated molecules with high sensitivity. The methodologies and instruments commonly used for EV analysis are not widely accessible outside of dedicated research laboratories, creating practical barriers for the study of EV‐associated molecules in biology research and clinical applications. To bridge this gap, we developed a proof‐of‐concept digital microfluidic device that can automatedly extract EVs from 20 µL of culture and human plasma samples within 25 min and detect EV‐bound proteins (e.g PD‐L1) on‐chip using an electrochemical sensor. This work serves as a framework for the development of streamlined EV analysis in both research and diagnostics.

Abstract The roles of nucleoplasm-residing nucleoporins (NUP) in solid tumors, including prostate cancer, remain unknown. In this study, we reveal the clinical significance and mechanistic role of the off-pore NUP, soluble POM121 (sPOM121), as a crucial transcriptional regulator that enhances the aggressiveness of metastatic prostate cancer. Using orthogonal methodologies in human samples, sPOM121 was identified as the predominantly expressed nucleoplasmic NUP in prostate cancer. Unbiased proteomic and epigenomic studies demonstrate that sPOM121, through its C-terminus, interacts with the chromatin remodeler SMARCA5 at gene promoter sites and localizes at nuclear condensates, reprogramming gene expression. Indeed, sPOM121 regulates a distinct oncogenic gene network, including β-catenin, leading to prostate cancer progression and immune evasion. Importantly, targeting the sPOM121/β-catenin axis in patient-derived preclinical and syngeneic mouse models halts prostate cancer aggressiveness and enhances antitumor immunity. Taken together, these findings reveal previously unknown actionable reprogramming functions of off-pore NUPs in solid tumors. Significance: This study uncovers how oncogenic signaling programs are transcriptionally heightened by the NUP sPOM121 in metastatic prostate cancer. Localization of sPOM121 at active transcriptional nuclear condensates propels disease progression and immune evasion, offering novel anticancer therapeutic opportunities.

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.

ABSTRACTThe current study includes 910 patients with systemic mastocytosis (SM) seen at the Mayo Clinic from 1968 to 2024. The primary objective was to examine the prognostic contribution of the International Consensus Classification (ICC), in the context of the Mayo Alliance Prognostic System (MAPS) for SM. World Health Organization classification (WHO‐HAEM5) subcategories included (i) indolent/smoldering SM (ISM/SSM; N = 518), (ii) SM associated with another hematological neoplasm (SM‐AHN; N = 273), with the latter including both myeloid and lymphoid neoplasms, (iii) aggressive SM (ASM; N = 106), and (iv) WHO‐defined mast cell leukemia (MCL; N = 13), which included mast cells with both “mature” and “immature” morphology. The ICC‐defined subcategories were mostly similar with the exception that SM‐AHN was replaced by SM associated with another myeloid neoplasm (SM‐AMN; N = 235) and WHO‐defined MCL was replaced by ICC‐defined MCL (N = 8), which included only those with immature MC morphology. Overall survival (OS) was similar between WHO‐defined MCL (median 1.8 years) vs. SM‐AHN (median 2.3 years; p = 0.3) but significantly different between ICC‐defined MCL (median 0.08 years) vs. SM‐AMN (median 2.0 years; p < 0.01). Significant difference in OS was also apparent between ICC‐defined SM‐AMN and SM associated with lymphoid neoplasm (SM‐ALN; median 8.1 years; HR 3.4; p < 0.01). Multivariable analysis confirmed the inter‐independent prognostic contributions of both ICC‐defined morphologic subcategories and MAPS risk variables, including age > 60 years, anemia, alkaline phosphatase > ULN, and platelet count < 150 × 109/L (p < 0.03 in all instances); the same was not true for WHO‐HAEM5‐defined SM subcategories. The addition of mutational information into the multivariable model resulted in ousting anemia and inclusion of ASXL1 (p < 0.01), SRSF2 (p = 0.01), and NRAS (p = 0.01) mutations as additional risk factors. Classification of SM by ICC is prognostically more accurate, compared to WHO‐HAEM5, and strengthens the prognostic contribution of morphology to current clinical and molecular risk models.