Increased frequencies of the killer immunoglobulin‐like receptor genes <scp>KIR2DL2</scp> and <scp>KIR2DS2</scp> are associated with neuroblastoma
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Bibliographic record
Abstract
Neuroblastoma is the most common extra-cranial solid tumour in children. Natural killer (NK) cells are innate lymphocytes that are known to mediate the direct cytotoxicity of neuroblastoma tumour cells. Natural variation in the highly polymorphic killer immunoglobulin-like receptors (KIR) and their cognate human leukocyte antigen (HLA) class I ligands results in considerable diversity in NK cell function. As the early onset of neuroblastoma suggests the contribution of genetic factors, we investigated if individual KIR genes, combined KIR gene haplotypes or compound KIR-HLA ligand genotypes could influence susceptibility to neuroblastoma. Genotype analysis of the KIR genes as well as their three major HLA class I ligand groups, HLA-C1, HLA-C2 and HLA-Bw4, was carried out in a cohort of 201 neuroblastoma patients compared with 240 healthy control subjects using polymerase chain reaction with sequence-specific primers. We found a significant increase in the frequency of KIR2DL2 (P = 0.019) as well as KIR2DS2 (P = 0.008) in patients with neuroblastoma compared with the healthy control group. While the incidence of the least inhibitory compound KIR-HLA-C genotype, KIR2DL3 in the presence of HLA-C1 was slightly reduced in neuroblastoma patients, this did not reach statistical significance (P = 0.069). In summary, while KIR-HLA compound genotypes have previously been implicated in predicting treatment outcomes in neuroblastoma, here we show that the presence of the individual KIR genes, KIR2DL2 and KIR2DS2, irrespective of HLA-C genotype is associated with the onset of this embryonal malignancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it