Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with KRAS mutations in ~ 95% of cases. While KRAS inhibitors have shown promise, therapeutic resistance necessitates combination a Show more
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with KRAS mutations in ~ 95% of cases. While KRAS inhibitors have shown promise, therapeutic resistance necessitates combination approaches. In particular, it is important to understand how downstream signaling of KRAS supports PDAC growth. For example, DUSP6 has emerged as an important dual-specificity phosphatase regulating KRAS-MAPK signaling. DUSP6 is markedly overexpressed in PDAC tumors compared to normal pancreatic tissue, with transcriptomic and single-cell RNA-seq analyses revealing its enrichment in epithelial tumor cells, especially in metastatic lesions. High DUSP6 expression correlates with the quasi-mesenchymal/squamous molecular subtype and poorer survival outcomes. Gene set enrichment analyses linked DUSP6 to pathways involved in cell migration and metabolism in metastatic samples. Functionally, DUSP6 knockdown in PDAC cells increases ERK/MAPK activation and alters migration. Metabolic profiling revealed enhanced basal glycolysis upon DUSP6 suppression. However, combined glycolysis inhibition and DUSP6 knockdown did not affect migration, suggesting that glycolytic changes are not the driver of altered migratory behavior. These findings reveal that DUSP6 independently regulates migration and metabolism in PDAC, emphasizing its dual role in disease progression. This study underscores the significance of DUSP6 as a potential therapeutic target and provides new insights into its contributions to PDAC progression. Show less
Next-generation sequencing technology has created many new opportunities for clinical diagnostics, but it faces the challenge of functional annotation of identified mutations. Various algorithms have Show more
Next-generation sequencing technology has created many new opportunities for clinical diagnostics, but it faces the challenge of functional annotation of identified mutations. Various algorithms have been developed to predict the impact of missense variants that influence oncogenic drivers. However, computational pipelines that handle biological data must integrate multiple software tools, which can add complexity and hinder non-specialist users from accessing the pipeline. Here, we have developed an online user-friendly web server tool PredictONCO that is fully automated and has a low barrier to access. The tool models the structure of the mutant protein in the first step. Next, it calculates the protein stability change, pocket level information, evolutionary conservation, and changes in ionisation of catalytic amino acid residues, and uses them as the features in the machine-learning predictor. The XGBoost-based predictor was validated on an independent subset of held-out data, demonstrating areas under the receiver operating characteristic curve (ROC) of 0.97 and 0.94, and the average precision from the precision-recall curve of 0.99 and 0.94 for structure-based and sequence-based predictions, respectively. Finally, PredictONCO calculates the docking results of small molecules approved by regulatory authorities. We demonstrate the applicability of the tool by presenting its usage for variants in two cancer-associated proteins, cellular tumour antigen p53 and fibroblast growth factor receptor FGFR1. Our free web tool will assist with the interpretation of data from next-generation sequencing and navigate treatment strategies in clinical oncology: https://loschmidt.chemi.muni.cz/predictonco/. Show less