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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".