MétaCan
Menu
← Back to cohort
Record W4389230190 · doi:10.1182/blood-2023-186456

Integrative Genome and Transcriptome Sequencing Analysis Indicates Genetic and Epigenetic Dysregulation in DS-AML

2023· article· en· W4389230190 on OpenAlexaff
Xiaotu Ma, Rhonda E. Ries, Quang Tran, Yanling Liu, Pandurang Kolekar, Ramzi Alsallaq, Zhikai Liang, Timothy I. Shaw, Meghana Devineni, Anne Deslattes Mays, Ching C. Lau, Johann Hitzler, Soheil Meshinchi

Bibliographic record

VenueBlood · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsExonBiologyGeneticsExome sequencingGATA1Single molecule real time sequencingWhole genome sequencingMutationDNA sequencingGenomeGeneTranscription factor

Abstract

fetched live from OpenAlex

Down Syndrome related AML is driven by GATA1 alterations in patients with trisomy 21. Using exome or targeted sequencing, prior studies identified GATA1 alterations in ~80-90% of patients. We performed whole genome sequencing coupled with whole transcriptome sequencing on 207 cases to study the genomic basis of Down Syndrome AML. We detected GATA1 alterations in 96.1% of cases, with additional structural rearrangements in GATA1 in 15% of cases that may have been missed in prior studies. For GATA1, although substitutions and small indels occur nearly uniformly across exon 2, we observed that internal tandem duplications (ITDs) are significantly enriched in the second half of exon 2, suggesting a specific mutational mechanism for these cases. We also detected exon 3 (instead of exon 2) deletion in one case, where the resultant truncated GATA1 protein is different from the commonly recognized sGATA1. Our study further revealed that mutations in genes mediating JAK-STAT signaling (JAK1/JAK2/JAK3), RAS pathway (KRAS/NRAS/NF1), Cohesin complex (STAG2/CTCF/RAD21/SMC3), as well as MBNL1, KANSL1, IRX1, and NFIA. By integrating the genome with transcriptome sequencing data, we discovered that GATA1 exon 2 skipping to be a significant event in DS-AML. Although exon 2 is nearly completely skipped in cases with exon 2 deletion or splice site alterations, significant exon 2 skipping is also observed in cases where the alteration is small and is in the middle of exon 2. Further, exon 2 skipping is also observed in cases wither the alteration is a single base substitution that results in stop codon in the middle of exon 2 (therefore splicing is unlikely affected by these alterations in such cases). Based on this observation, we propose a hybrid genetic-epigenetic model on the development of Down Syndrome AML. In DS-AML, GATA1 exon 2 skipping during transcription/splicing might be a developmentally (epigenetically) regulated event in early fetal development that is destined to be silenced in the postnatal period that results in activation of the canonical long GATA1. This epigenetic silencing of sGATA1 is overridden by secondary genetic events, thus maintaining sGATA1 as the predominant GATA1 protein. Notably, the sGATA1 have variable isoforms as a result of exon 2 or exon 3 loss. In summary, our integrated whole genome and transcriptome approach has resulted in discovery of novel and high-prevalence structural alterations in GATA1 and potentially novel genes involved in pathogenesis of DS-AML. Our integrated analysis provided evidence on the epigenetic/developmental regulation of GATA1 exon 2 skipping via alterative splicing in the context of trisomy 21 that warrants further investigation.

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 score0.003

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 head0.011
GPT teacher head0.241
Teacher spread0.230 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicRNA modifications and cancer→French-language works237,207→