Novel associations between activating killer-cell immunoglobulin-like receptor genes and childhood leukemia
Bibliographic record
Abstract
Acute lymphoblastic leukemia of pre-B cells (pre-B ALL) is the most frequent form of leukemia affecting children in Western countries. Evidence is accumulating that genetic factors play an important role in conferring susceptibility/resistance to leukemia in children. In this regard, activating killer-cell immunoglobulin-like receptor (KIR) genes are of particular interest. Humans may inherit different numbers of the 6 distinct activating KIR genes. Little is known about the impact of this genetic variation on the innate susceptibility or resistance of humans to the development of B-ALL. We addressed this issue by performing a case-control study in Canadian children of white origin. Our results show that harboring activating KIR genes is associated with reduced risk for developing B-ALL in these children. Of the 6 activating KIR genes, KIR2DS2 was maximally associated with decreased risk for the disease (P = 1.14 × 10(-7)). Furthermore, our results showed that inheritance of a higher number of activating KIR genes was associated with significant reductions in risk for ALL in children. These results were also consistent across different ALL phenotypes, which included children with pre-T cell ALL. Our study provides novel insights concerning the pathogenesis of childhood leukemia in white children and has implications for the development of new immunotherapies for this cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".