Long‐term immune changes in patients with relapsed/refractory chronic lymphocytic leukemia following treatment with venetoclax plus rituximab
Bibliographic record
Abstract
Abstract Immune dysregulation is a hallmark of chronic lymphocytic leukemia (CLL). Anti‐CD20 antibodies (e.g., rituximab [R]) can be combined with venetoclax (Ven) to treat CLL. However, anti‐CD20 antibodies can increase hypogammaglobulinemia risk, while the effects of Ven on immune dysregulation are still uncertain. We report long‐term immune changes in VenR‐ and bendamustine‐R (BR)‐treated patients with relapsed/refractory CLL in the MURANO trial (NCT02005471). Patients were randomized to fixed‐duration VenR (2 years Ven; VenR for the first 6 months) or BR (6 months). Immune cell levels were evaluated at the end of combination treatment (EOCT), end of treatment (EOT; VenR arm only), and 12 and 24 months post‐EOCT. Overall, 130/194 VenR‐ and 134/195 BR‐treated patients completed treatment without progressive disease. In patients who completed VenR combination therapy, median immunoglobulin (Ig)G and IgM levels decreased from baseline to EOT ( p ≤ 0.01 and p ≤ 0.0001, respectively); by 24 months, post‐EOT IgG had returned to baseline level and IgM had increased from baseline ( p ≤ 0.001). Median IgA levels increased from baseline to 12 ( p ≤ 0.0001) and 24 months post‐EOT ( p ≤ 0.0001). In BR‐treated patients, changes in IgG, IgA, and IgM levels across the assessed time points were not significant, and by 24 months, post‐EOCT IgG, IgA, and IgM were above baseline levels. Grade ≥3 infection rates on treatment were low. Overall, immune recovery was observed with VenR and BR, with stabilization of Ig levels after treatment. Post‐treatment infection rates were generally low, making these very tolerable 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".