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Record W3212689194 · doi:10.1182/blood-2021-150634

Germline-Somatic Interactions in Myelofibrosis Susceptibility

2021· article· en· W3212689194 on OpenAlexaff
Derek W. Brown, Youjin Wang, Andrew St. Martin, Stephen R. Spellman, Shu‐Hong Lin, Olivia Lee, Alyssa Klein, Tongwu Zhang, Weiyin Zhou, Kristine Jones, Amy Hutchinson, Jia Liu, Bin Zhu, Casey Dagnall, Sairah Khan, Maryam Rafati, Tao Wang, H. Joachim Deeg, Vikas Gupta, Stephanie J. Lee, Meredith Yeager, Sharon A. Savage, Wael Saber, Shahinaz M. Gadalla, Mitchell J. Machiela

Bibliographic record

VenueBlood · 2021
Typearticle
Languageen
FieldMedicine
TopicMyeloproliferative Neoplasms: Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsBiologyMyelofibrosisGenome-wide association studyGeneticsLoss of heterozygosityGenotypingSNP arrayMyeloidBone marrowSingle-nucleotide polymorphismCancer researchImmunologyGenotypeAlleleGene

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.026
GPT teacher head0.304
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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