High expression of β-catenin contributes to the crizotinib resistant phenotype in the stem-like cell population in neuroblastoma
Bibliographic record
Abstract
ALK has been identified as a novel therapeutic target in neuroblastoma (NB), but resistance to ALK inhibitors (such as crizotinib) is well recognized. We recently published that the crizotinib sensitivity in NB cells strongly correlates with the crizotinib-ALK binding, and β-catenin effectively hinders this interaction and confers crizotinib resistance. Here, we asked if these observations hold true for the stem-like cells in NB cells, which were purified based on their responsiveness to a Sox2 reporter. Compared to bulk, reporter unresponsive (RU) cells, reporter responsive (RR) cells had significantly higher neurosphere formation ability, expression of CD133/nestin and chemo-resistance. Using the cellular thermal shift assay, we found that RR cells exhibited significantly weaker crizotinib-ALK binding and higher crizotinib resistance than RU cells. The suboptimal crizotinib-ALK binding in RR cells can be attributed to their high β-catenin expression, since siRNA knockdown of β-catenin restored the crizotinib-ALK binding and lowered the crizotinib resistance to the level of RU cells. Enforced expression of β-catenin in RU cells resulted in the opposite effects. To conclude, high expression of β-catenin in the stem-like NB cells contributes to their crizotinib resistance. Combining β-catenin inhibitors and ALK inhibitors may be useful in treating NB patients.
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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 | 0.001 |
| 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".