Gregory, Lindsey

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.

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.