LIN28A-Dependent Kinome and Phosphoproteome Reprogramming Promotes Imatinib Resistance
Bibliographic record
Abstract
ABSTRACT Chronic myeloid leukemia (CML) resistance to BCR-ABL tyrosine kinase inhibitors (TKIs) can arise from ABL kinase domain mutations, BCR-ABL fusion gene amplification, or kinase-independent mechanisms. To investigate imatinib-resistance, we performed quantitative mass spectrometry comparing the proteome and phosphoproteome of K562 cells (a standard CML model) and ImR cells, an imatinib-resistant K562 derivative that also exhibits cross-resistance to second- and third-generation BCR-ABL TKIs. In addition to revealing global proteome and phosphoproteome changes associated with drug resistance, we identified LIN28A—a multi-functional RNA-binding protein—as a critical mediator of imatinib resistance. LIN28A was significantly overexpressed and hyperphosphorylated in ImR cells. Depleting LIN28A via shRNA restored imatinib sensitivity, while its ectopic expression in parental K562 cells induced imatinib resistance. Mechanistically, LIN28A coordinates an extensive kinase-substrate network regulating proliferation, survival, and metabolism to drive resistance. Notably, pharmacological inhibition of LIN28A-dependent kinases (PKC, AKT, SGK1, and RPS6K) suppressed ImR proliferation. Midostaurin, a clinical PKC/FLT3 inhibitor used in FLT3-ITD—positive AML, potently re-sensitized ImR cells to imatinib. Our findings suggest that targeting LIN28A and its downstream effectors, particularly PKC, could overcome resistance to imatinib and next-generation BCR-ABL 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) | 0.002 | 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".