High-content screening identifies kinase inhibitors that overcome venetoclax resistance in activated CLL cells
Bibliographic record
Abstract
Novel agents such as the Bcl-2 inhibitor venetoclax (ABT-199) are changing treatment paradigms for chronic lymphocytic leukemia (CLL) but important problems remain. Although some patients exhibit deep and durable responses to venetoclax as a single agent, other patients harbor subpopulations of resistant leukemia cells that mediate disease recurrence. One hypothesis for the origin of resistance to venetoclax is by kinase-mediated survival signals encountered in proliferation centers that may be unique for individual patients. An in vitro microenvironment model was developed with primary CLL cells that could be incorporated into an automated high-content microscopy-based screen of kinase inhibitors (KIs) to identify agents that may improve venetoclax therapy in a personalized manner. Marked interpatient variability was noted for which KIs were effective; nevertheless, sunitinib was identified as the most common clinically available KI effective in overcoming venetoclax resistance. Examination of the underlying mechanisms indicated that venetoclax resistance may be induced by microenvironmental signals that upregulate antiapoptotic Bcl-xl, Mcl-1, and A1, which can be counteracted more efficiently by sunitinib than by ibrutinib or idelalisib. Although patient-specific drug responses are common, for many patients, combination therapy with sunitinib may significantly improve the therapeutic efficacy of venetoclax.
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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 | 0.001 | 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 | 0.000 |
| 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 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".