Abstract A018 Transcriptional evolution from normal foetal haematopoiesis to myeloid leukaemia in Down syndrome
Bibliographic record
Abstract
Abstract Children born with Down syndrome (DS), a genetic condition caused by the presence of an additional chromosome 21 (trisomy 21 or T21), have a substantially higher risk of developing childhood leukaemia. Approximately 30% of DS infants develop a preleukemic condition called transient abnormal myelopoiesis (TAM). TAM is strictly associated with GATA1 truncating mutations on a T21 background. Most TAM cases spontaneously resolve within the first few months of life. However, by the age of five, approximately 10% of cases progress to megakaryoblastic/erythroid leukaemia, known as myeloid leukaemia of DS (ML-DS). This final step of transformation is often driven by specific third-hit somatic mutations, most of which affects genes encoding JAK kinases, cohesin complexes, or epigenetic regulators. These well-defined stepwise genetic aberrations associated with ML-DS pathogenesis provide a unique and highly tractable system to elucidate the molecular changes underlying leukaemogenesis. Using single-cell mRNA sequencing of primary human samples, we directly interrogate the transcriptional changes underpinning the multi-step pathogenesis of ML-DS. We observe an expansion of megakaryocyte-erythrocyte progenitors (MEPs) specifically in T21 foetal liver compared to diploid or other trisomies. However, it is the GATA1 mutation that accounts for the majority of the transcriptomic changes towards that of the leukaemic blasts. Furthermore, investigation of an aggressive TAM case reveals an enrichment in expression of the leukaemogenic gene module associated with the full-blown leukaemia state ML-DS. We also demonstrate that TAM and ML-DS blasts still retain transcriptional signatures of normal haematopoiesis, showing heterogeneous features of differentiation towards erythrocytes, megakaryocytes, and mast cells. Finally, by integrating genomic and transcriptomic data, we directly defined the genomic evolution, and its corresponding transcriptional consequences, that underpins a treatment-refractory case of ML-DS. Overall, we have generated the first comprehensive single-cell mRNA atlas of TAM / ML-DS, enabling unbiased quantitative characterisation of the transcriptional transformation along ML-DS pathogenesis. Our study explores the molecular signature associated with progression of TAM to ML-DS, as well as the mechanism underlying differences in treatment response. These insights offer the possibility of more meaningful clinical interpretations and potential to explore novel therapeutic targets for these conditions. Citation Format: Mi K. Trinh, Matthew D. Young, Conor Parks, Agnes Oszlanczi, Toochi Ogbonnah, Di Zhou, Angus Hodder, Konstantin Schuschel, Hasan Issa, Laura Jardine, Jan-Henning Klusmann, Jack Bartram, Sam Behjati. Transcriptional evolution from normal foetal haematopoiesis to myeloid leukaemia in Down syndrome [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr A018.
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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 | 0.000 | 0.000 |
| 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".