LCK-targeting molecular glues overcome resistance to inhibitor-based therapy in T-cell acute lymphoblastic leukemia
Bibliographic record
Abstract
ABSTRACT: Drug resistance is a major challenge in cancer therapy, especially in hematologic malignancies in which kinase inhibitors have transformed treatment yet are frequently undermined by drug resistance. Although targeted protein degradation (TPD) offers a mechanistically distinct mode of action compared with inhibition-based therapeutic therapies, the potential value of TPD in drug-resistant blood cancer remains unclear. Here, we report the discovery of cereblon (CRBN)-recruiting molecular glue degraders (MGDs) targeting lymphocyte-specific tyrosine kinase (LCK), an oncogenic kinase in T-cell acute lymphoblastic leukemia (T-ALL). By high-throughput screening and medicinal chemistry optimization, we developed a series of MGDs that induced CRBN-dependent degradation of LCK as well as potent cytotoxicity in T-ALL in vitro. Structure-activity relationship analysis and ternary complex modeling revealed a noncanonical degron at the LCK-CRBN interface involving the G-loop, whose mutation disrupts this interaction. Unlike inhibitors and inhibitor-based proteolysis-targeting chimeras, these MGDs engage LCK in regions distal to the ATP-binding site, and thus their activities in T-ALL are not affected by gatekeeper LCK mutations that drive resistance to inhibitor-based therapeutics. Taken together, our data highlight the potential of LCK-targeting MGDs as a strategy to overcome kinase inhibitor resistance in T-ALL, offering a framework for targeting kinase dependencies in drug-refractory hematologic malignancies more broadly.
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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 | 0.000 |
| 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".