Transcriptional Modulation by Idelalisib Synergizes with Bendamustine in Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
The phosphatidyl-inositol 3 kinase (PI3K) δ inhibitor, idelalisib (IDE), is a potent inhibitor of the B-cell receptor pathway and a novel and highly effective agent for the treatment of chronic lymphocytic leukemia (CLL). We evaluated the activities of IDE in comparison to bendamusine (BEN), a commonly used alkylating agent, in primary CLL cells ex vivo. In contrast to BEN, IDE was cytotoxic to cells from extensively-treated patients, including those with a deletion (del)17p. Cross-resistance was not observed between BEN and IDE, confirming their different modes of cytotoxicity. Marked synergy was seen between BEN and IDE, even in cases that were resistant to BEN or IDE individually, and those with deletion (del) 17p. CD40L/interleukin 4 (IL4) co-treatment mimicking the CLL microenvironment increased resistance to IDE, but synergy was retained. PI3Kδ-deficient murine splenic B cells were more resistant to IDE and showed reduced synergy with BEN, thus confirming the importance of functional PI3Kδ protein. Although IDE was observed to induce γH2AX, IDE did not enhance activation of the DNA damage response nor DNA repair activity. Interestingly, IDE decreased global RNA synthesis and was antagonistic with 5,6-Dichlorobenzimidazole 1-b-D-ribofuranoside (DRB), an inhibitor of transcription. These findings add to the increasingly complex cellular effects of IDE, and B cell receptor (BCR) inhibitors in general, in CLL.
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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".