An inhibitor of cyclin-dependent kinases suppresses TLR signaling and increases the susceptibility of cancer patients to herpes viridae
Bibliographic record
Abstract
Cyclin-dependent kinase (CDK) inhibitors have been considered as excellent drug candidates for cancer therapy owing to their potential capacity to restore cell cycle control. The first generation of CDK inhibitors showed modest clinical advantages that could be attributed to off-target effects preventing them from reaching therapeutic concentrations. A phase I dose-escalation study using the second generation multi-CDK inhibitor PHA-793887 was conducted on a total of 19 patients with advanced refractory malignancies in two sites in Europe: the University of Leeds and St. James's Institute of Oncology, Leeds, UK, and the Institut Gustave Roussy, Villeujf, France (IGR). Fifteen patients were treated at IGR. Six among these patients manifested the reactivation of herpes virus replication. In vitro experiments revealed that PHA-793887 severely impaired signaling by toll-like receptors (such as TLR3, TLR4 and TLR9) in dendritic cells (DC), thus suppressing the production of multiple cytokines (type 1 interferon, interleukin-6,-10, -12, and tumor necrosis factorα) by mature DC, as well as the DC-stimulated production of interferon-γ by natural killer cells. Pharmacological inhibition of glycogen synthase-3β (GSK-3β), one of the off-targets of PHA-793887, did not cause such immunological defects. Altogether, these data underscore a hitherto unsuspected immunosuppressive effect of PHA-793887.
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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 | 0.000 |
| 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".