Deciphering molecular mechanisms of synergistic growth reduction in kinase inhibitor combinations
Tsirvouli, E.; Martinez del Val, A.; Thommesen, L.; Laegreid, A.; Kuiper, M.; Olsen, J. V.; Flobak, A.
Show abstract
In cancer treatment, the persistent challenge of unresponsiveness of certain patients to drugs or the development of resistance post-treatment remains a significant concern. Drug combinations that synergistically reduce tumor growth emerge as a promising avenue to address this issue. Here, we aimed to characterize the mechanism of action of two synergistic drug combinations that target PI3K together with MEK1 or with TAK1 and used time course measurements of phosphoproteomics and transcriptomics in response to single inhibitors and their combinations. Our analysis untangled those responses driven by single drugs and responses that were unique to the combinations. We observed a high overlap between single-drug responses and their combinations, suggesting that single-drug mechanisms dominate the mechanism of action of the combinations of the kinase inhibitors. Despite a high overlap, both drug combinations exhibited a synergistic modulation of several cell fate regulators found at the convergence points of the targeted pathways, including the key regulator of intrinsic apoptosis BCL2L11. Interestingly, the responses in both combinations were largely limited to the targeted pathways, namely PI3K/AKT and MAPKs, with very limited change of any other additional cell fate decision pathways. In addition, we observed a strong downregulation of nucleotide metabolism and tRNA biosynthesis uniquely in the combinations, which could be attributed to the reduced activity of mTOR and ATF4. Our approach provides insights into the molecular mechanisms affected by the PI3Ki-TAK1i and PI3Ki-MEKi combinations and can serve as a flexible framework for dissecting drug combination responses based on multi-omics measurements.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Above-Filter Digestion Proteomics reveals drug targets and localizes ligand binding site 94%
- In vitro Kinase-to-Phosphosite database (iKiP-DB) predicts kinase activity in phosphoproteomic datasets 94%
- Ovalbumin antigen-specific activation of T cell receptor closely resembles soluble antibody stimulation as revealed by BOOST phosphotyrosine proteomics 94%
Similar papers in this journal
- Rapid Resistance To Bet Inhibitors Is Mediated By Fgfr1 In Glioblastoma 95%
- JNK1 And Downstream Signalling Hubs Regulate Anxiety-Like Behaviours In A Zebrafish Larvae Phenotypic Screen 94%
- Selective Impact of ALK and MELK Inhibition on ERα Stability and Cell Proliferation in Cell Lines Representing Distinct Molecular Phenotypes of Breast Cancer 94%
Similar papers in this journal
- Phosphoproteomics unveils the signaling dynamics in neuronal cells stimulated with insulin and insulin-like growth factors 96%
- The Src family kinase inhibitor drug Dasatinib and glucocorticoids display synergistic activity against tongue squamous cell carcinoma and reduce MET kinase activity 91%
- Investigation of the heterogeneity of cancer cells using single cell calcium profiling. 91%
Similar papers in this journal
- Unraveling anti-inflammatory metabolic signatures of Glycyrrhiza uralensis and isoliquiritigenin through multiomics 94%
- Single-cell characterization of step-wise acquisition of carboplatin resistance in ovarian cancer 93%
- Network-driven cancer cell avatars for combination discovery and biomarker identification for DNA Damage Response inhibitors 93%
"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.