A Pilot Study on the Utility of Serum Metabolomics in Neuroblastoma Patients and Xenograft Models
Bibliographic record
Abstract
BACKGROUND: Improved prediction of neuroblastoma (NB) behavior is needed to detect treatment-refractory disease and may allow further reduction in therapy for some patients. In this regard, serum metabolomic analysis has proven utility in several cancer types. We hypothesize that serum metabolomic analysis will correlate with risk-group classification for patients with NB, and sensitively detect NB in murine xenograft models. PROCEDURE: A pilot study was done on Children's Oncology Group (COG) tumor bank sera from 10 patients (five high-, five low-risk). An institutional pilot study was carried out on five patients comparing sera obtained during active versus minimal disease (complete response/very good partial response; CR/VGPR). XENOGRAFT: Flank tumors were established in Nu/Nu mice by injection of NB cell lines (IMR-32, SH-EP, SK-N-AS). Serum for comparison was drawn pre-injection, at 1 week after injection when there was no visible tumor, and again once tumors were grossly visible. Comparisons were also made between tumor bearing mouse serum and supernatants from NB cell lines. METABOLOMIC ANALYSIS: Samples were analyzed by nuclear magnetic resonance and/or gas chromatography-mass spectrometry. Multivariate data analysis was conducted using SIMCA-P (Umetrics). RESULTS: Serum metabolomic analysis differentiated high- and low-risk patients as well as active disease from CR/VGPR. Differences were in nitrogen, amino acid, and carbohydrate metabolism, as well as ketosis. The serum metabolomic signature in murine xenograft models sensitively detected NB cells and correlated with disease burden. Similar metabolic changes attributable to NB were noted in both human and murine serum. CONCLUSIONS: Serum metabolomic analysis can distinguish several characteristics of NB. A larger analysis of COG banked sera is warranted.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".