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Record W3095941038 · doi:10.1182/blood-2020-134964

Gene Body Methylation and Transcriptional Activity in ASXL1-Mutant Chronic Myelomonocytic Leukemia

2020· article· en· W3095941038 on OpenAlexaff
Moritz Binder, Ryan M. Carr, Terra L. Lasho, Christy M. Finke, Abhishek A. Mangaonkar, Christopher L. Pin, Kurt Berger, Tamás Ördög, Bonnie Alver, Keith D. Robertson, David L. Marks, Martín E. Fernández-Zapico, Alexandre Gaspar Maia, Mrinal M. Patnaik

Bibliographic record

VenueBlood · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsLawson Health Research InstituteWestern University
Fundersnot available
KeywordsDNA methylationBiologyMethylationTranscriptomeEpigeneticsGeneGeneticsGene expression profilingFold changeGene expressionMolecular biologyCancer research

Abstract

fetched live from OpenAlex

Introduction: Truncating mutations in Additional Sex Combs-Like 1 (ASXL1) are associated with a high-risk disease phenotype in myeloid malignancies. In chronic myelomonocytic leukemia (CMML), truncating ASXL1 mutations are known to increase transcriptional activity of leukemic driver genes and have been associated with gene body hypermethylation. We interrogated the transcriptome and methylome of patients with ASXL1-mutant (MT) and -wildtype (WT) CMML using a multi-omics approach to test the hypothesis that gene expression is mediated through gene body methylation. Methods: Bone marrow mononuclear cells from patients with ASXL1 WT (n=8) and MT (n=8) CMML were subjected to targeted NGS of DNA, whole transcriptome shotgun sequencing (RNA-seq), and immunoprecipitation of DNA methyl residues (DIP-seq). After quality control all samples were sequenced on an Illumina HiSeq 4000 before further processing and data analysis. Differential gene expression analysis was performed to identify genes up-regulated in MT CMML. The samples in the two groups were treated as biological replicates and subjected to a consensus peak calling strategy requiring an overlap of at least 30% between samples and an adjusted p-value < 5x10-5 for a methylation peak to be considered statistically significant. For validation purposes methylation analysis was performed on 3 ASXL1 MT and 3 WT CMML patients using Illumina Infinium MethylationEPIC microarrays and differentially methylated regions were identified using a bump hunting strategy. Gene body methylation was defined as methylation in gene bodies (outside the promoter region, i.e. transcription start site ±2kb). Gene body methylation was compared between WT and MT CMML for the up-regulated genes and correlated with expression of all genes in MT CMML. Results: Sixteen WHO-defined CMML patients were included, median age 69 years (48-77), 63% male, 50% had truncating ASXL1 frame shift mutations. Abnormal karyotypes were observed in the same number of patients and the burden of co-mutations was similar between the two groups (median number per group 3 vs. 3, p=0.508). This included several modulators of DNA methylation including TET2, DNMT3A, and IDH2 (median number per group 1 vs. 1, p=0.699). There was a predominant up-regulation of gene expression in MT CMML: 707 genes up- and 124 down-regulated (FDR<0.050) without evidence of differential methylation in promoter regions of the differentially expressed genes. There was no difference in the number of genes with consensus methylation peaks in the gene body or the extent of gene body methylation (area under the consensus peaks) between MT and WT CMML for the differentially expressed genes (Figure 1a). We further validated these results using methylation microarrays (Figure 1b). Among the MT CMML patients, there was a curvilinear relationship between gene body methylation and gene expression across all genes with intermediate gene body methylation being associated with the highest gene expression (Figure 1c). As an alternative unbiased approach, we identified 1595 differentially methylated regions (DMR, regions with FDR<0.05) in the validation data set. We mapped all hypermethylated regions to gene bodies requiring that there was no concurrent hypermethylation of the promoter region of the same gene. With this approach we identified one of the 707 up-regulated genes (0.14%) with evidence of isolated gene body hypermethylation (HBZ, log2-fold change in gene expression 4.25, FDR=0.0008, DMR area=1.61, DMR FDR=0.018). Representative examples of up-regulated mitotic kinases without evidence of differential gene body methylation are shown in Figure 1d (pie charts represent the mean β-values of microarray probes, p-values represent the comparisons of mean β-values between MT and WT CMML per gene). Conclusions: Gene body methylation was positively associated with gene expression in MT CMML. However, the lack of differential gene body methylation between WT and MT CMML for the up-regulated genes make it an unlikely explanation for the observed increase in transcriptional activity among patients with MT CMML. Figure 1 Disclosures Ordog: Millipore Sigma: Patents & Royalties.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.286
Teacher spread0.256 · 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 designBench or experimental
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
Published2020
Admission routes1
Has abstractyes

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