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Biology subjects

Thommesen, L.

Publications and source records attributed to Thommesen, L..

3 recordsLinked to original sources

Deciphering molecular mechanisms of synergistic growth reduction in kinase inhibitor combinations

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.

cancer biology↗

Logical modeling: Combining manual curation and automated parameterization to predict drug synergies

Treatment with drug combinations carries great promise for personalized therapy. We have previously shown that drug synergies targeting cancer can manually be identified based on a logical framework. We now demonstrate how automated adjustments of model topology and logic equations can greatly reduce the workload traditionally associated with logical model optimization. Our methodology allows the exploration of larger model ensembles that all obey a set of observations. We benchmark synergy predictions against a dataset of 153 targeted drug combinations. We show that well-performing manual models faithfully represent measured biomarker data and that their performance can be outmatched by automated parameterization using a genetic algorithm. The predictive performance of a curated model is strongly affected by simulated curation errors, while data-guided deletion of a small subset of edges can improve prediction quality. With correct topology we find some tolerance to simulated errors in the biomarker calibration data. With our framework we predict the synergy of joint inhibition of PI3K and TAK1, and further substantiate this prediction with observation in cancer cell cultures and in xenograft experiments.

systems biology↗

Transcriptional regulation by NR5A2 couples cell differentiation and inflammation in the pancreas

Tissue-specific differentiation and inflammatory programmes are thought to independently contribute to disease. The orphan nuclear receptor NR5A2 is a key regulator of pancreas differentiation and SNPs in or near the human gene are associated with risk of pancreatic cancer. In mice, Nr5a2 heterozygosity sensitizes the pancreas to damage, impairs regeneration, and cooperates with mutant KRas in tumor progression. Through global transcriptomic analysis, we uncover a basal pre-inflammatory state in the pancreas of Nr5a2 heterozygous mice that is reminiscent of pancreatitis-induced inflammation and is conserved in histologically normal human pancreata with reduced NR5A2 mRNA expression. In Nr5a2+/- mice, Nr5a2 undergoes a dramatic transcriptional switch relocating from tissue-specific to inflammatory loci thereby promoting AP-1-dependent gene transcription. Importantly, deletion of c-Jun in the pancreas of these mice rescues the pre-inflammatory phenotype and the defective regenerative response to damage. These findings provide compelling evidence that the same transcriptional networks supporting homeostasis in normal tissue can be subverted to foster inflammation upon genetic or environmental constraints.

cancer biology↗