Treatment of Relapsed/Refractory Chronic Lymphocytic Leukemia After BTK Inhibitor and/or BCL-2 Inhibitor Failure
Bibliographic record
Abstract
The treatment landscape for first-line and relapsed/refractory (R/R) chronic lymphocytic leukemia (CLL)/small lymphocytic lymphoma (SLL) has tremendously advanced with the introduction of Bruton tyrosine kinase inhibitors (BTKi) and B-cell lymphoma 2 inhibitors (BCL-2i). However, in this new era of targeted therapy for CLL, there is, unfortunately, no evidence yet to guide the optimal sequencing of these drugs. It remains unknown whether treating first-line with a BTKi and relapse with BCL-2i or BCL-2i at first-line followed by BTKi at relapse results in any difference in overall survival (OS). Ibrutinib (BTKi) was first introduced in 2014, and venetoclax (BCL-2i) in 2016, and currently, there are limited prospective data and treatment options for patients who have relapsed after one or both targeted therapies. This article will provide an overview of the approach to treatment for patients with CLL/SLL when BTKi and/or BCL-2i therapy has failed. Before launching into the treatment of R/R CLL, it is worth noting that guidelines for risk assessment of CLL recommend determining the immunoglobulin heavy chain gene (IGHV) mutational status once, usually before the first treatment, and fluorescence in situ hybridization FISH for del(17p) and next-generation sequencing (NGS) before each treatment.1 Other than TP53, NGS-detected mutations are not routinely considered when choosing a therapy, but they may help predict the duration of remission and may become standard of care in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 0.001 |
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".