Persistent janus kinase‐signaling in chronic lymphocytic leukemia patients on ibrutinib: Results of a phase I trial
Bibliographic record
Abstract
Methods to deepen clinical responses to ibrutinib are needed to improve outcomes for patients with chronic lymphocytic leukemia (CLL). This study aimed to determine the safety and efficacy of combining a janus kinase (JAK)-inhibitor with ibrutinib because JAK-mediated cytokine-signals support CLL cells and may not be inhibited by ibrutinib. The JAK1/2 inhibitor ruxolitinib was prescribed to 12 CLL patients with abnormal serum beta-2 microglobulin levels after 6 months or persistent lymphadenopathy or splenomegaly after 12 months on ibrutinib using a 3 + 3 phase 1 trial design (NCT02912754). Ibrutinib was continued at 420 mg daily and ruxolitinib was added at 5, 10, 15, or 20 mg BID for 3 weeks out of five for seven cycles. The break was mandated to avoid anemia and thrombocytopenia observed with ruxolitinib as a single agent in CLL. The combination was well-tolerated without dose-limiting toxicities. Cyclic changes in platelets, lymphocytes, and associated chemokines and thrombopoietic factors were observed and partial response criteria were met in 2 of 12 patients. The results suggest that JAK-signaling helps CLL cells persist in the presence of ibrutinib and ruxolitinib with ibrutinib is well-tolerated and may be a useful regiment to use in combination therapies for CLL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".