Genetic resistance to JAK2 enzymatic inhibitors is overcome by HSP90 inhibition
Bibliographic record
Abstract
Enzymatic inhibitors of Janus kinase 2 (JAK2) are in clinical development for the treatment of myeloproliferative neoplasms (MPNs), B cell acute lymphoblastic leukemia (B-ALL) with rearrangements of the cytokine receptor subunit cytokine receptor-like factor 2 (CRLF2), and other tumors with constitutive JAK2 signaling. In this study, we identify G935R, Y931C, and E864K mutations within the JAK2 kinase domain that confer resistance across a panel of JAK inhibitors, whether present in cis with JAK2 V617F (observed in MPNs) or JAK2 R683G (observed in B-ALL). G935R, Y931C, and E864K do not reduce the sensitivity of JAK2-dependent cells to inhibitors of heat shock protein 90 (HSP90), which promote the degradation of both wild-type and mutant JAK2. HSP90 inhibitors were 100-1,000-fold more potent against CRLF2-rearranged B-ALL cells, which correlated with JAK2 degradation and more extensive blockade of JAK2/STAT5, MAP kinase, and AKT signaling. In addition, the HSP90 inhibitor AUY922 prolonged survival of mice xenografted with primary human CRLF2-rearranged B-ALL further than an enzymatic JAK2 inhibitor. Thus, HSP90 is a promising therapeutic target in JAK2-driven cancers, including those with genetic resistance to JAK enzymatic inhibitors.
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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".