Targeting the DYRK1A kinase prevents cancer progression and metastasis and promotes cancer cells response to G1/S targeting chemotherapy drugs
Bibliographic record
Abstract
Metastatic cancer remains incurable as patients eventually loose sensitivity to targeted therapies and chemotherapies, further leading to poor clinical outcome. Thus, there is a clear medical gap and urgent need to develop efficient and improved targeted therapies for cancer patients. In this study, we investigated the role of DYRK1A kinase in regulating cancer progression and evaluated the therapeutic potential of DYRK1A inhibition in invasive solid tumors, including colon and triple-negative breast cancers. We uncovered new roles played by the DYRK1A kinase. We found that blocking DYRK1A gene expression or pharmacological inhibition of its kinase activity via harmine efficiently blocked primary tumor formation and the metastatic tumor spread in preclinical models of breast and colon cancers. Further assessing the underlying molecular mechanisms, we found that DYRK1A inhibition resulted in increased expression of the G1/S cell cycle regulators while decreasing expression of the G2/M regulators. Combined, these effects release cancer cells from quiescence, leading to their accumulation in G1/S and further delaying/preventing their progression toward G2/M, ultimately leading to growth arrest and tumor growth inhibition. Furthermore, we show that accumulation of cancer cells in G1/S upon DYRK1A inhibition led to significant potentiation of G1/S targeting chemotherapy drug responses in vitro and in vivo. This study underscores the potential for developing novel DYRK1A-targeting therapies in colon and breast cancers and, at the same time, further defines DYRK1A pharmacological inhibition as a viable and powerful combinatorial treatment approach for improving G1/S targeting chemotherapy drugs treatments in solid tumors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".