<i>HLA</i>and<i>KIR</i>Associations of Cervical Neoplasia
Bibliographic record
Abstract
Background: Cervical cancer is the fourth most common cancer in women, and we recently reported human leukocyte antigen (HLA) alleles showing strong associations with cervical neoplasia risk and protection. HLA ligands are recognized by killer immunoglobulin-like receptors (KIRs) expressed on a range of immune cell subsets, governing their proinflammatory activity. We hypothesized that the inheritance of particular HLA-KIR combinations would increase cervical neoplasia risk. Methods: Here, we used HLA and KIR dosages imputed from single-nucleotide polymorphism genotype data from 2143 cervical neoplasia cases and 13858 healthy controls of European decent. Results: The following 4 novel HLA alleles were identified in association with cervical neoplasia, owing to their linkage disequilibrium with known cervical neoplasia-associated HLA-DRB1 alleles: HLA-DRB3*9901 (odds ratio [OR], 1.24; P = 2.49 × 10-9), HLA-DRB5*0101 (OR, 1.29; P = 2.26 × 10-8), HLA-DRB5*9901 (OR, 0.77; P = 1.90 × 10-9), and HLA-DRB3*0301 (OR, 0.63; P = 4.06 × 10-5). We also found that homozygosity of HLA-C1 group alleles is a protective factor for human papillomavirus type 16 (HPV16)-related cervical neoplasia (C1/C1; OR, 0.79; P = .005). This protective association was restricted to carriers of either KIR2DL2 (OR, 0.67; P = .00045) or KIR2DS2 (OR, 0.69; P = .0006). Conclusions: Our findings suggest that HLA-C1 group alleles play a role in protecting against HPV16-related cervical neoplasia, mainly through a KIR-mediated mechanism.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".