<i>BCR-Abl</i> Silencing by siRNA: A Potent Approach to Sensitize Chronic Myeloid Leukemia Cells to Tyrosine Kinase Inhibitor Therapy
Bibliographic record
Abstract
Nonviral gene therapy with specific short interfering RNAs (siRNAs) against BCR-Abl can be an alternative and/or supportive therapy of chronic myeloid leukemia (CML) with tyrosine kinase inhibitors (TKIs), given the often observed resistance to TKIs in clinical setting. In this study, we explored the feasibility of BCR-Abl siRNA therapy in CML K562 cells in vitro by employing a cationic polymer derived from cholesterol (Chol) grafted low-molecular weight polyethyleneimine (PEI). The first generation TKI imatinib upregulated the expression of BCR-Abl in K562 cells as expected. Delivery of BCR-Abl siRNA in both drug-sensitive and drug-resistant K562 cells significantly downregulated the mRNA levels in both cell types. Similarly, the BCR-Abl siRNA treatment arrested the growth of both drug-sensitive and drug-resistant K562 cells with no obvious differences despite a large difference in drug responsiveness. The BCR-Abl gene silencing in combination with TKI treatments exhibited significant synergism in drug-resistant K562 cells in generating substantial antileukemic activity, where the TKIs on their own were not effective. The effect of BCR-Abl siRNA and TKIs on non-CML cells (Jurkat and primary fibroblast) was negligible, indicating the specificity of the proposed therapy. This strategy can significantly overcome TKI resistance in CML cells, suggesting a feasible and effective treatment model for CML patients suffering from clinical resistances.
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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".