Kinase-Independent Mechanisms of Resistance of Leukemia Stem Cells to Tyrosine Kinase Inhibitors
Bibliographic record
Abstract
Tyrosine kinase inhibitors such as imatinib mesylate have changed the clinical course of chronic myeloid leukemia; however, the observation that these inhibitors do not target the leukemia stem cell implies that patients need to maintain lifelong therapy. The mechanism of this phenomenon is unclear: the question of whether tyrosine kinase inhibitors are inactive inside leukemia stem cells or whether leukemia stem cells do not require breakpoint cluster region (Bcr)-Abl signaling is currently under debate. Herein, I propose an alternative model: perhaps the leukemia stem cell requires Bcr-Abl, but is dependent on its kinase-independent functions. Kinases such as epidermal growth factor receptor and Janus kinase 2 possess kinase-independent roles in regulation of gene expression; it is worth investigating whether Bcr-Abl has similar functions. Mechanistically, Bcr-Abl is able to activate the Ras, phosphatidylinositol 3-kinase/Akt, and/or the Src-kinase Hck/Stat5 pathways in a scaffolding-dependent manner. Whereas the scaffolding activity of Bcr-Abl with Grb2 is dependent on autophosphorylation, kinases such as Hck can use Bcr-Abl as substrate, inducing phosphorylation of Y177 to enable scaffolding ability in the absence of Bcr-Abl catalytic activity. It is worth investigating whether leukemia stem cells exclusively express kinases that are able to use Bcr-Abl as substrate. A kinase-independent role for Bcr-Abl in leukemia stem cells would imply that drugs that target Bcr-Abl's scaffolding ability or its DNA-binding ability should be used in conjunction with current therapeutic regimens to increase their efficacy and eradicate the stem cells of chronic myeloid leukemia.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".