Epitope detection in monocytes (EDIM) for liquid biopsy including identification of GD2 in childhood neuroblastoma—a pilot study
Bibliographic record
Abstract
Abstract Background Neuroblastoma (NB) is the most common paediatric extracranial solid malignancy. We analysed the role of the epitope detection in monocytes (EDIM) technique for liquid biopsy in NB patients. Methods Tumour epitopes transketolase-like 1 (TKTL1), Apo10 (DNaseX) and GD2 were assessed: expression levels in seven NB tumour samples and five NB cell lines were analysed using RT-PCR and flow cytometry. LAN-1 cells were co-cultured with blood and assessed using EDIM. Peripheral blood macrophages of patients with neuroblastoma ( n = 38) and healthy individuals (control group, n = 37) were labelled (CD14 + /CD16 + ) and assessed for TKTL1, Apo10 and GD2 using the EDIM technology. Results mRNA expression of TKTL1 and DNaseX/Apo10 was elevated in 6/7 NB samples. Spike experiments showed upregulation of TKTL1, Apo10 and GD2 in LAN-1 cells following co-culturing with blood. TKTL1 and Apo10 were present in macrophages of 36/38 patients, and GD2 in 15/19 patients. The 37 control samples were all negative. EDIM expression scores of the three epitopes allowed differentiation between NB patients and healthy individuals. Conclusions The EDIM test might serve as a non-invasive tool for liquid biopsy in children suffering from NB. Future studies are necessary for assessing risk stratification, tumour biology, treatment monitoring, and early detection of tumour relapses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".