HLA Specificities and Predisposition to the Development of Multiple Myeloma (MM).
Bibliographic record
Abstract
Abstract Background: HLA associations for the risk of developing MM have been described in various non-Caucasian ethnic groups but have not revealed consistent results. Racial and genetic differences in cytokine polymorphisms linked to extended haplotypes; association of autoimmune disorders with HLA types; the proximity of the HLA Class I and II genes to TNF and other Class III genes responsible for complement and cytokines suggest that HLA polymorphisms may be associated with the risk of developing MM. Aim: We compared HLA-A,-B and -DRB1 2-digit allele and haplotype frequencies in patients with MM to the normal population in order to define HLA specificities associated with the risk of developing MM. Methods: A cohort of 1803 US Caucasian patients with MM were identified based on availability of HLA-A, B, DRB1 DNA-based typing or serologic typing converted to 2-digit alleles. These patients had either received a matched sibling allogeneic transplant for MM reported to the CIBMTR (N=174) or had HLA typing performed for an unrelated donor search conducted through the US National Marrow Donor Program (NMDP) (N=1629). HLA-A, B and DRB1 allele frequencies among patients were compared to A-B-DRB1 frequencies in HLA typed healthy US Caucasian controls (N= 433838) from the NMDP database. The most frequent alleles among patients and controls as well as HLA haplotype frequencies generated using the EM (expectation-maximization) algorithm were compared using Chi-Square analysis with Bonferroni correction for the number of comparisons. Results: Patients with MM had a median age of 49 (range 3–78) years and M/F ratio of 1.86 (1172/631). The controls had a median age of 44 (range 18–60) years and M/F ratio of 0.735 (183785/250053). Comparison of HLA-A, -B and –DRB1 single-locus allele frequencies between the myeloma patients and controls (those in the top 10 in either group) revealed similarity except for HLA-B*07 and B*44. B*44 was observed less frequently among the patients (a protective effect) frequency (f): 0.126 vs 0.145 (p= 0.005, Odds Ratio (OR): 0.848). On the contrary, HLA-B*07 was more common (a predisposing effect) among patients (OR: 1.135, p=0.047). Among the top 20 two locus (HLA-A-B and HLA-B-DRB1) haplotype frequencies in either group, B*44-DRB1*13 and B*07-DRB1*13 were significant: OR: 0.506 and 1.714 (p=0.003 and 0.007) respectively. A-B-DRB1 haplotype comparisons showed significance for: A*02-B*44-DRB1*04 (OR: 0.686, p=0.021), A*02-B*44-DRB1*13 (OR: 0.445, p=0.065) and A*02-B*07-DRB1*04 (OR: 1.968, p=0.029) confirming the single and B-DRB1 combination comparison results. Significantly different HLA-A, -B and -DRB1 single-locus allele or haplotype frequencies between patients and controls are shown in table 1. Conclusion: This is the largest epidemiologic study of HLA allele frequencies and the first study on HLA haplotype frequencies in MM. We observed a protective effect on the risk of developing MM with B*44 and a predisposing effect with B*07. Similar predisposing and protective haplotype associations were observed with A*02-B*07-DRB1*04 and A*02-B*44-DRB1*04 respectively. A revised analysis using a gender-matched control group is in progress. Further study including major histocompatibility region SNP mapping and candidate gene testing will help elucidate the genetic basis underlying this observation. Table 1. Allele/Haplotype Frequency Controls Frequency MM Odds Ratio (OR) P value (corrected) OR = odds of observing the allele/haplotype in patients compared to controls A*02-B*44-DRB1*13 0.00620 0.00277 0.445 0.065 B*44-DRB1*13 0.01359 0.00686 0.507 0.003 A*02-B*44-DRB1*04 0.02605 0.01813 0.686 0.021 B*44 0.14552 0.12618 0.848 0.005 B*07 0.13313 0.14836 1.134 0.047 B*07-DRB1*13 0.00814 0.01397 1.714 0.007 A*02-B*07-DRB1*04 0.00367 0.00721 1.968 0.029
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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 | 0.000 | 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".