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Biology subjects

Jeanneret, F.

Publications and source records attributed to Jeanneret, F..

2 recordsLinked to original sources

AB668, a novel highly selective protein kinase CK2 inhibitor with a distinct anti-tumor mechanism as compared to CX-4945 and SGC-CK2-1

Although the involvement of protein kinase CK2 in cancer is well-documented, there is a need for selective CK2 inhibitors suitable for investigating CK2 specific roles in cancer-related biological pathways and further explore its therapeutic potential. Here we have discovered AB668, a new bivalent inhibitor that binds both at the ATP site and an allosteric D pocket unique to CK2. The molecule inhibits CK2 activity with an outstanding selectivity over other kinases. Using caspase activation assay, live-cell imaging and transcriptomic analysis, we have compared the effects of this bivalent inhibitor to the non-selective ATP-competitive inhibitor CX-4945 that reached clinic and to the selective ATP-competitive SGC-CK2-1 molecule. Our results show that in contrast to CX-4945 or SGC-CK2-1, AB668 has a distinct mechanism of action regarding its anti-cancer activity, inducing apoptotic cell death and stimulating distinct biological pathways in several cancer cell lines while sparing healthy cells. Our data suggest that targeting a cryptic CK2 D pocket validates an allosteric approach to targeting CK2 and provides a starting point for creating drug-like CK2 inhibitors for aggressive cancers.

biochemistry↗

Towards an integrative multi-omics workflow

The advent of high-throughput techniques has greatly enhanced biological discovery. Last years, analysis of multi-omics data has taken the front seat to improve physiological understanding. Handling functional enrichment results from various biological data raises practical questions. We propose an integrative workflow to better interpret biological process insights in a multi-omics approach applied to breast cancer data from The Cancer Genome Atlas (TCGA) related to Invasive Ductal Carcinoma (IDC) and Invasive Lobular Carcinoma (ILC). Pathway enrichment by Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) has been conducted with both features information from differential expression analysis or selected features from multi-block sPLS-DA methods. Then, comprehensive comparisons of enrichment results have been carried out by looking at classical enrichment analysis, probabilities pooling by Stouffers Z scores method and pathways clustering in biological themes. Our work shows that ORA enrichment with selected sPLS-DA features and pathways probabilities pooling by Stouffers method lead to enrichment maps highly associated to physiological knowledge of IDC or ILC phenotypes, better than ORA and GSEA with differential expression driven features.

bioinformatics↗