Back

Tumor-specific Kinase Motif Enrichment Analysis Identifies Personalized Therapeutic Cancer Targets

Pu, T.; Joughin, B. A.; Desai, P. P.; Forsythe, S. D.; Holewinski, R.; Remmert, K.; Gasmi, B.; Coleman, K.; Leach, T.; Johansen, A. M.; Russell, N.; Ma, L.; Rainey, A.; Sarvestani, A. L.; Smith, E.; Sinha, S.; Mukherjee, S.; Luberice, K.; Xiao, S.; Larrain, C.; Eade, A. V.; Friedman, L. R.; Ho, J.; Davis, J. L.; Blakely, A. M.; Kleiner, D. E.; Sadowski, S. M.; Andersson, T.; Rivero, J. D.; Yaffe, M. B.; Hernandez, J. M.

2026-08-03 cancer biology
10.64898/2026.07.31.742097 bioRxiv
Show abstract

Gastroenteropancreatic neuroendocrine tumors (GEP-NETs) are an uncommon and poorly understood malignancy with low mutational burden, lacking well-defined oncogenic drivers. GEP-NET mortality frequently results from extensive hepatic metastases. Accordingly, we interrogated phosphoproteomic data from GEP-NET liver metastases and patient-matched uninvolved liver to identify tumor-specific signaling and targetable tumor vulnerabilities using Kinase Motif Enrichment Analysis (KMEA), a new tool leveraging the recent Kinase Library compendium of the substrate motif specificity for nearly the entire human kinome. KMEA identified patient tumor-specific upregulation of mTOR or casein kinase 2 (CK2) activity that would be undiscoverable by standard personalized genomic and transcriptomic approaches. Striking concordance was observed between KMEA predictions for specific tumors, and their sensitivity to inhibitors of mTOR or CK2 using patient tumor-derived organoids. These findings reveal potential clinically-actionable protein kinases hyperactivated in GEP-NETs, and more broadly indicate a general method for personalized cancer treatment using phosphoproteomics and KMEA-derived kinase activity signatures.

Matching journals

The top 10 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.