PKA inhibition kills l-asparaginase-resistant leukemic cells from relapsed acute lymphoblastic leukemia patients
Bibliographic record
Abstract
Abstract Despite the success in treating newly diagnosed pediatric acute lymphoblastic leukemia (aLL), the long-term cure rate for the 20% of children who relapse is poor, making relapsed aLL the primary cause of cancer death in children. By unbiased genome-wide retroviral RNAi screening and knockdown studies, we previously discovered opioid receptor mu 1 (OPRM1) as a new aLL cell resistance biomarker for the aLL chemotherapeutic drug, l-asparaginase, i.e., OPRM1 loss triggers l-asparaginase resistance. Indeed, aLL cell OPRM1 level is inversely proportional to l-asparaginase IC50: the lower the OPRM1 level, the higher the l-asparaginase IC50, indicating that aLL cells expressing reduced OPRM1 levels show resistance to l-asparaginase. In the current study, we utilized OPRM1-expressing and -knockdown aLL cells as well as relapsed patient aLL cells to identify candidate targeted therapy for l-asparaginase-resistant aLL. In OPRM1-expressing cells, l-asparaginase induces apoptosis via a cascade of events that include OPRM1-mediated decline in [cAMP]i, downregulation of PKA-mediated BAD S118 phosphorylation that can be reversed by 8-CPT-cAMP, cyt C release from the mitochondria, and subsequent caspase activation and PARP1 cleavage. The critical role of PKA inhibition due to a decrease in [cAMP]i in this apoptotic process is evident in the killing of OPRM1-knockdown and low OPRM1-expressing relapsed patient aLL cells by the PKA inhibitors, H89 and 14–22 amide. These findings demonstrate for the first time that PKA can be targeted to kill aLL cells resistant to l-asparaginase due to OPRM1 loss, and that H89 and 14–22 amide may be utilized to destroy l-asparaginase-resistant patient aLL cells.
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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 | 0.000 |
| 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 | 0.001 |
| 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".