Genotype analysis of tumor‐initiating cells expressing CD133 in neuroblastoma
Bibliographic record
Abstract
Neuroblastoma (NB) is the most common and lethal extracranial solid tumor of childhood. Despite aggressive therapy, more than half of the children with advanced NB will die of uncontrolled metastatic disease. After chemotherapy, tumor-initiating cells (TICs) could persist, cause relapses and metastasis. The aim of this study is to demonstrate the tumor-initiating properties of CD133high NB cells and to identify new specific genetic abnormalities. Isolation of the CD133high cell population from NB cell lines was followed by neurosphere formation, soft agar assays, and orthotopic injections in NOD/SCID/IL2Rγc-null mice. A differential genotyping analysis was performed with Affymetrix SNP 6.0 arrays on CD133low and CD133high populations and the frequency of the abnormalities of 36 NB tumors was determined. Our results show that CD133high NB cells possess tumor-initiating properties, as CD133high cells formed significantly more neurospheres and produced significantly more colonies in soft agar than CD133low. Injection of 500 CD133high cells was sufficient to generate primary tumors and frequent metastases in mice. Differential genotyping analysis demonstrated two common regions with gains (16p13.3 and 19p13.3) including the gene EFNA2 in the CD133high population, and two with loss of heterozygosity (16q12.1 and 21q21.3) in the CD133low population. The gain of EFNA2 correlated with increased expression of the corresponding protein. These abnormalities were found in NB samples and some were significantly correlated with CD133 expression. Our results show that CD133high NB cells have TICs properties and present different genotyping characteristics compared to CD133low cells. Our findings reveal insights into new therapeutic targets in NB TICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".