Combined targeting of STAT3 and STAT5: a novel approach to overcome drug resistance in chronic myeloid leukemia
Bibliographic record
Abstract
In chronic myeloid leukemia, resistance against BCR-ABL1 tyrosine kinase inhibitors can develop because of BCR-ABL1 mutations, activation of additional pro-oncogenic pathways, and stem cell resistance. Drug combinations covering a broad range of targets may overcome resistance. CDDO-Me (bardoxolone methyl) is a drug that inhibits the survival of leukemic cells by targeting different pro-survival molecules, including STAT3. We found that CDDO-Me inhibits proliferation and survival of tyrosine kinase inhibitor-resistant BCR-ABL1+ cell lines and primary leukemic cells, including cells harboring BCR-ABL1T315I or T315I+ compound mutations. Furthermore, CDDO-Me was found to block growth and survival of CD34+/CD38− leukemic stem cells (LSC). Moreover, CDDO-Me was found to produce synergistic growth-inhibitory effects when combined with BCR-ABL1 tyrosine kinase inhibitors. These drug-combinations were found to block multiple signaling cascades and molecules, including STAT3 and STAT5. Furthermore, combined targeting of STAT3 and STAT5 by shRNA and STAT5-targeting drugs also resulted in synergistic growth-inhibition, pointing to a new efficient concept of combinatorial STAT3 and STAT5 inhibition. However, CDDO-Me was also found to increase the expression of heme-oxygenase-1, a heat-shock-protein that triggers drug resistance and cell survival. We therefore combined CDDO-Me with the heme-oxygenase-1 inhibitor SMA-ZnPP, which also resulted in synergistic growth-inhibitory effects. Moreover, SMA-ZnPP was found to sensitize BCR-ABL1+ cells against the combination ‘CDDO-Me+ tyrosine kinase inhibitor’. Together, combined targeting of STAT3, STAT5, and heme-oxygenase-1 overcomes resistance in BCR-ABL1+ cells, including stem cells and highly resistant sub-clones expressing BCR-ABL1T315I or T315I-compound mutations. Whether such drug-combinations are effective in tyrosine kinase inhibitor-resistant patients with chronic myeloid leukemia remains to be elucidated.
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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".