Germline-Somatic Interactions in Myelofibrosis Susceptibility
Notice bibliographique
Résumé
Abstract Introduction: Myelofibrosis (MF) is a rare myeloproliferative neoplasm (MPN) characterized by bone marrow fibrosis, progressive bone marrow failure, and increased risk of acute myeloid leukemia. While MF arises from somatic driver mutations in JAK2, MPL, and CALR, some MPN patients may have a heritable component. To comprehensively examine the genetic etiology of MF, we performed the first integrative analysis of SNP array genotyping (using Infinium Global Screening Array), targeted long-read sequencing (using PacBio SMRT sequencing) and telomere length (TL, using qPCR assay). Methods: Our study included 937 MF patients who received an allogeneic hematopoietic cell transplant (HCT) between 2000 and 2016 and had an available pre-HCT blood sample at the Center for International Blood and Marrow Transplant Research Repository. Somatic mosaic chromosomal alterations (mCAs, including deletions, duplications, or copy-neutral losses-of-heterozygosity (CNLOH)) were called with the Mosaic Chromosomal Alteration (MoChA) algorithm using raw genotyping intensity data. A genome-wide association study (GWAS) was restricted to include 827 MF patients of European ancestry and utilized 4,135 genetically-matched healthy controls. Results: GWAS identified six independent MF susceptibility loci at genome-wide significance (P< 5×10 -8); four of which replicate prior MPN susceptibility loci [9p24.1(JAK2), 5p15.33(TERT), 3q25.33(IFT80), and 4q24(TET2)] and two novel MF loci [6p21.35(HLA-DQB1-AS1) and 17p13.1(TP53)] (Figure 1). A transcriptome-wide association analysis using whole blood GTEx data highlighted the 9p24.1 locus with increased JAK2 expression associated with elevated risk of MF (P= 2.18×10 -19). A strong colocalization statistic further indicated shared genetic component between eQTL and this JAK2 locus (HyPrColoc Posterior Probability= 0.6) (Figure 2). Based on the strong signal identified at TERT (Figure 1), we investigated the relationship between MF risk and genetically-inferred telomere length using a panel of 19 germline variants previously found to be associated with telomere length. Of the 19 telomere-length associated variants investigated, 7 were found to be associated with MF risk (binomial P= 2.31×10 -5, linear trend P= 5.48×10 -4) (Figure 3). Both Mendelian randomization and genome-wide genetic correlation analyses further indicated that increased risk of MF was associated with longer inherited telomere length. Utilizing available clinical mutation data on a subset of 185 patients, MF cases carrying the germline risk haplotype of the 9p24.1(JAK2) susceptibility locus were observed to more frequently have the JAK2 V617F mutation (71% vs 59%; P= 0.02). Targeted PacBio long-read sequencing around JAK2 provided further evidence of linkage between the germline risk allele and the JAK2 V617F mutation. Detectable autosomal mCAs were also abundant in MF cases with 67.4% having at least one mCA (compared to ~3% in the general population) and 27.6% having an mCA spanning JAK2 (mostly CNLOH) (Figure 4). In addition, using a binomial test for biased allelic imbalance, a cis relationship was identified at 9p24.1 in which the MF risk haplotype was predominantly duplicated by CNLOH (binomial P=1.36×10 -9). Regional sequencing of JAK2 further confirmed duplication of JAK2 V617F by CNLOH. Finally, we observed an inverse association between autosomal mCAs and qPCR measured telomere length (OR= 0.22, 95% CI= 0.07-0.65, P= 6.40×10 -3). These results were consistent by mCA chromosomal region and copy number state. Conclusion: Our results suggest a molecular framework for the genetic etiology of MF in which both genetically-inferred telomere length and germline variation at JAK2 are associated with increased MF risk. The 9p24.1 risk haplotype predisposes to the acquisition of a somatic JAK2 V617F mutation in cis and subsequent duplication of JAK2 V617F by mCAs (usually CNLOH). This process leads to aberrant JAK2 activity and increased clonal proliferation, accelerating telomere length shortening and increasing genomic instability in patients with MF. Figure 1 Figure 1. Disclosures Gupta: AbbVie: Consultancy, Honoraria; Constellation Pharma: Consultancy, Honoraria; Roche: Consultancy; Pfizer: Consultancy; BMS-Celgene: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Sierra Oncology: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Incyte: Honoraria, Research Funding. Lee: Janssen: Other; Incyte: Research Funding; AstraZeneca: Research Funding; Kadmon: Research Funding; National Marrow Donor Program: Membership on an entity's Board of Directors or advisory committees; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding; Pfizer: Research Funding; Syndax: Research Funding; Takeda: Research Funding; Amgen: Research Funding. Saber: Govt. COI: Other.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 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 ».