HLA Specificities and Predisposition to the Development of Multiple Myeloma (MM).
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».