Andersen, Joshua L.

Deregulated inflammatory signaling via STAT family transcription factors, particularly STAT1, underlies a variety of immune-related diseases, including inflammatory bowel disease. Whereas activation of STATs by JAKs via canonical receptor-driven JAK–STAT signaling is well understood, little is known about JAK-independent mechanisms of STAT activation. Here, we identify the understudied nonreceptor tyrosine kinase TNK1 as a therapeutically targetable, JAK-independent activator of STAT signaling. Using a multiomics approach, we mapped a network of TNK1 substrates associated with protein condensates and proinflammatory signaling, including STAT1. We found that TNK1, but not its sister kinase ACK1, directly phosphorylates STATs at well described STAT-activating JAK sites. In cells, TNK1-mediated STAT1 phosphorylation and activation occurs independently of JAKs. Imaging and interactomics data suggest that TNK1 interacts with STAT1 in cytosolic condensates, which likely compartmentalize TNK1-substrate interactions. We show that an intrinsically disordered proline-rich region in TNK1, which includes a 14-3-3 docking phosphorylation site, is required for the formation of kinase-active TNK1 condensates and STAT1 phosphorylation. Mutations within the proline-rich region that eliminate 14-3-3 binding increase formation of TNK1 condensates, suggesting a model in which 14-3-3 acts as a clamp that constrains the flexible PRR to inhibit condensate formation and STAT1 activation. Finally, we show that TNK1 is a targetable driver of STAT1-mediated inflammation in the gut as inhibition of TNK1 reduces active STAT1 in the colon and ameliorates colitis symptoms in mice.

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.