Gregory, Lindsey A.

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.

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.

Objective and Background: Clinically significant posthepatectomy liver failure (PHLF B+C) remains the main cause of mortality after major hepatic resection. This study aimed to establish an aspartate aminotransferase to platelet ratio combined with an albumin-bilirubin grade (APRI+ALBI), based multivariable model (MVM) to predict PHLF and compare its performance to indocyanine green clearance (ICG-R15 or ICG-PDR) and albumin-ICG evaluation (ALICE). Methods: A total of 12,056 patients from the National Surgical Quality Improvement Program database were used to generate a MVM to predict PHLF B+C. The model was determined using stepwise backwards elimination. The performance of the model was tested using receiver operating characteristic curve analysis and validated in an international cohort of 2525 patients. In 620 patients, the APRI+ALBI MVM, trained in the National Surgical Quality Improvement Program cohort, was compared with the MVM’s based on other liver function tests (ICG clearance, ALICE) by comparing the areas under the curve (AUC). Results: A MVM including APRI+ALBI, age, sex, tumor type, and extent of resection was found to predict PHLF B+C with an AUC of 0.77, with comparable performance in the validation cohort (AUC: 0.74). In direct comparison with other MVM’s based on more expensive and time-consuming liver function tests (ICG clearance, ALICE), the APRI+ALBI MVM demonstrated equal predictive potential for PHLF B+C. A smartphone application for the calculation of the APRI+ALBI MVM was designed. Conclusion: Risk assessment through the APRI+ALBI MVM for PHLF B+C increases preoperative predictive accuracy and represents a universally available and cost-effective risk assessment before hepatectomy, facilitated by a freely available smartphone app.

Background and Aims Platelet‐stored serotonin critically affects liver regeneration in mice and humans. Selective serotonin reuptake inhibitors (SSRIs) and serotonin noradrenalin reuptake inhibitors (SNRIs) reduce intraplatelet serotonin. As SSRIs/SNRIs are now one of the most commonly prescribed drugs in the United States and Europe and given serotonin’s impact on liver regeneration, we evaluated whether perioperative use of SSRIs/SNRIs affects outcome after hepatic resection. Approach and Results Consecutive patients undergoing hepatic resection (n = 754) were retrospectively included from prospectively maintained databases from two European institutions. Further, an independent cohort of 495 patients from the United States was assessed to validate our exploratory findings. Perioperative intake of SSRIs/SNRIs was recorded, and patients were followed up for postoperative liver dysfunction (LD), morbidity, and mortality. Perioperative intraplatelet serotonin levels were significantly decreased in patients receiving SSRI/SNRI treatment. Patients treated with SSRIs/SNRIs showed a higher incidence of morbidity, severe morbidity, LD, and LD requiring intervention. Associations were confirmed in the independent validation cohort. Combined cohorts documented a significant increase in deleterious postoperative outcome (morbidity odds ratio [OR], 1.56; 95% confidence interval [CI], 1.07‐2.31; severe morbidity OR, 1.86; 95% CI, 1.22‐2.79; LD OR, 1.96; 95% CI, 1.23‐3.06; LD requiring intervention OR, 2.22; 95% CI, 1.03‐4.36). Further, multivariable analysis confirmed the independent association of SSRIs/SNRIs with postoperative LD, which was closely associated with postoperative 90‐day mortality and 1‐year overall survival. Conclusions We observed a significant association of perioperative SSRI/SNRI intake with adverse postoperative outcome after hepatic resection. This indicates that SSRIs/SNRIs should be avoided perioperatively in patients undergoing hepatic resections.