CD133 expression is associated with poor outcome in neuroblastoma via chemoresistance mediated by the AKT pathway
Bibliographic record
Abstract
AIMS: Neuroblastoma is a frequent childhood cancer with a heterogeneous prognosis. CD133 expression is an independent prognostic marker for a low survival rate in several cancers. The aim of this study was to determine the prognostic value of CD133 expression in a large cohort of neuroblastoma cases, to define the chemoresistance of neuroblastoma cells expressing CD133, and to determine whether this chemoresistance is regulated by activation of the AKT pathway. METHODS AND RESULTS: Two hundred and eighty samples of neuroblastoma were screened for CD133 expression. The sensitivity of purified CD133+ neuroblastoma cells isolated from two human cell lines to doxorubicin, vincristine and cisplatin, as single agents or in combination with LY294002, an AKT inhibitor, was evaluated in vitro. CD133 expression was found in 100 of 280 tumours. There was a significant association between CD133 expression and the following poor prognosis covariates: age, International Neuroblastoma Staging System stage, MYCN amplification, and phospho-AKT (pAKT) expression. Patients with CD133- tumours had significantly better 3-year event-free and overall survival than patients with CD133+ tumours. In a multivariate model, CD133 expression was independently associated with decreased overall survival. CD133(high) neuroblastoma cells were significantly resistant to chemotherapy as compared with CD133(low) cells. Treatment of unsorted neuroblastoma cells with the three anticancer drugs significantly enriched the CD133+ subpopulation. CD133(high) cells expressed significantly higher levels of pAKT than CD133(low) cells. LY294002 treatment abolished the preferential survival of CD133(high) cells. CONCLUSIONS: CD133 is associated with in-vitro resistance to chemotherapy involving activation of the AKT pathway.
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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 | 0.001 |
| 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".