Demystifying Genomic and Transcriptomic Landscape of Acute Myeloid Leukaemia-Normal Karyotype Using Deep Sequencing Technology
Bibliographic record
Abstract
Acute myeloid leukaemia-normal karyotype (AML-NK) comprises almost half of the AML subtypes and exhibits clinical heterogeneity in treatment response and outcome. Hitherto, the genomic landscapes of AML-NK that contribute to the clinical outcome remain veiled. Therefore, this study elucidated the genomic profiles and regulatory networks of AML-NK patients predisposed to their heterogeneous clinical outcome. In this study, 51 AML-NK samples at diagnosis (DX) and 14 paired first complete remission (CR1) were recruited for transcriptome sequencing and eight paired DX and CR1 DNA were selected for targeted DNA sequencing. The targeted DNA sequencing using the Archer HGC VariantPlex Myeloid panel that included 75 myeloid-related hotspots genes led to the ascertainment of mutations for risk stratifications, and suitable biomarkers for minimal residual disease monitoring were put forward in this analysis. The transcriptome sequencing yielded discoveries of DEG profiles and functionally enriched pathways in several subgroup analyses that included the comparison of AML-NK patients with the healthy normal groups, paired DX and CR1, FLT3/NPM1 genotypes, and overall survival (OS) of below and above five years. The DEGs between the DX and CR1 suggested their potentiality for MRD monitoring, especially in AML-NK patients who lacked genomic aberrations. The highlights of the DEG findings are the development of a prognostic scoring model based on the findings of the OS below and above five-year comparison. Six significantly upregulated genes in the (FHL1, SOCS2, IL17RC, STAT4, INHBA and TNFSF8) in the JAK-STAT signalling pathway and cytokine-cytokine receptor interaction were included in the prognostic scoring model that revealed that the gene scores were an independent prognostic marker in the AML-NK patients in this cohort. Next, fusion gene analysis disclosed several novel recurrent fusion genes, including LATS2-SAP18 and HOXA3-HOXA9 that exhibited prognostic relevance in patients with OS below five years. Clinically relevant somatic variants were discovered, including five known single nucleotide variants (SNVs) with targeted therapies. Prognostically significant frameshift insertion-deletions (InDels) were detected in the NPM1, DNMT3A and FLT3 genes. Based on established guidelines, this study incorporated the AML-NK patients' genomic findings and risk stratification. Ultimately, the findings were depicted in an oncoprint that reflected how the genomic discoveries in this study improvised the patient's risk stratification for outcome predictions and potential targeted therapies. Hence, this multifaceted study has provided new insights into the genomic profiles of AML-NK patients and shed light on their heterogeneous clinical outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".