Molecular Pathways: Emergence of Protein Kinase CK2 (CSNK2) as a Potential Target to Inhibit Survival and DNA Damage Response and Repair Pathways in Cancer Cells
Bibliographic record
Abstract
Protein kinase CK2 (designated CSNK2) is a constitutively active protein kinase with a vast repertoire of putative substrates that has been implicated in several human cancers, including cancer of the breast, lung, colon, and prostate, as well as hematologic malignancies. On the basis of these observations, CSNK2 has emerged as a candidate for targeted therapy, with two CSNK2 inhibitors in ongoing clinical trials. CX-4945 is a bioavailable small-molecule ATP-competitive inhibitor targeting its active site, and CIGB-300 is a cell-permeable cyclic peptide that prevents phosphorylation of the E7 protein of HPV16 by CSNK2. In preclinical models, either of these inhibitors exhibit antitumor efficacy. Furthermore, in combinations with chemotherapeutics such as cisplatin or gemcitabine, either CX-4945 or CIGB-300 promote synergistic induction of apoptosis. While CSNK2 is a regulatory participant in many processes related to cancer, its potential to modulate caspase action may be particularly pertinent to its emergence as a therapeutic target. Because the substrate recognition motifs for CSNK2 and caspases are remarkably similar, CSNK2 can block the cleavage of many caspase substrates through the phosphorylation of sites adjacent to cleavage sites. Phosphoproteomic strategies have also revealed previously underappreciated roles for CSNK2 in the phosphorylation of several key constituents of DNA damage and DNA repair pathways. Going forward, applications of proteomic strategies to interrogate responses to CSNK2 inhibitors are expected to reveal signatures for CSNK2 inhibition and molecular insights to guide new strategies to interfere with its potential to inhibit caspase action or enhance the susceptibility of cancer cells to DNA damage. Clin Cancer Res; 22(12); 2840-7. ©2016 AACR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".