Data from System-Level Analysis of Neuroblastoma Tumor–Initiating Cells Implicates AURKB as a Novel Drug Target for Neuroblastoma
Bibliographic record
Abstract
Abstract Purpose: Neuroblastoma (NB) is an aggressive tumor of the developing peripheral nervous system that remains difficult to cure in the advanced stages. The poor prognosis for high-risk NB patients is associated with common disease recurrences that fail to respond to available therapies. NB tumor-initiating cells (TICs), isolated from metastases and primary tumors, may escape treatment and contribute to tumor relapse. New therapies that target the TICs may therefore prevent or treat tumor recurrences. Experimental Design: We undertook a system-level characterization of NB TICs to identify potential drug targets against recurrent NB. We used next-generation RNA sequencing and/or human exon arrays to profile the transcriptomes of 11 NB TIC lines from six NB patients, revealing genes that are highly expressed in the TICs compared with normal neural crest-like cells and unrelated cancer tissues. We used gel-free two-dimensional liquid chromatography coupled to shotgun tandem mass spectrometry to confirm the presence of proteins corresponding to the most abundant TIC-enriched transcripts, thereby providing validation to the gene expression result. Results: Our study revealed that genes in the BRCA1 signaling pathway are frequently misexpressed in NB TICs and implicated Aurora B kinase as a potential drug target for NB therapy. Treatment with a selective AURKB inhibitor was cytotoxic to NB TICs but not to the normal neural crest-like cells. Conclusion: This work provides the first high-resolution system-level analysis of the transcriptomes of 11 primary human NB TICs and identifies a set of candidate NB TIC-enriched transcripts for further development as therapeutic targets. Clin Cancer Res; 16(18); 4572–82. ©2010 AACR.
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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 | 0.001 |
| 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".