Rational drug combinations with CDK4/6 inhibitors in acute lymphoblastic leukemia
Bibliographic record
Abstract
Despite improvements in outcomes for children with B- and T-cell acute lymphoblastic leukemia (B-ALL and T-ALL), patients with resistant or relapsed disease fare poorly. Previous studies have demonstrated the essential role of cyclin D3 in T-ALL disease initiation and progression and that targeting of the CDK4/6-cyclin D complex can suppress T-ALL proliferation, leading to efficient cell death in animal models. Studies in leukemia and other malignancies, suggest that schedule is important when combining CDK4/6 inhibitors (CDKi) with cytotoxic agents. Based on these observations, we broadened evaluation of two CDKi, palbociclib (PD-0332991, Pfizer) and ribociclib (LEE011, Novartis) in B- and T-ALL as single agent and in combination with conventional cytotoxic chemotherapy, using different schedules in preclinical models. As monotherapy, CDKi caused cell cycle arrest with a significant decrease in S phase entry and were active in vivo across a broad number of patient-derived xenograft samples. Prolonged monotherapy induces resistance, for which we identified a potential novel mechanism using transcriptome profiling. Importantly, simultaneous but not sequential treatment of CDKi with conventional chemotherapy (dexamethasone, L-asparaginase and vincristine) led to improved efficacy compared to monotherapy in vivo. We provide novel evidence that combining CDKi and conventional chemotherapy can be safe and effective. These results led to the rational design of a clinical trial.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".