Abstract 3806: Single-cell transcriptional mapping reveals genetic and non-genetic determinants of aberrant differentiation in AML
Bibliographic record
Abstract
Abstract In acute myeloid leukemia (AML), genetic mutations distort hematopoietic differentiation, resulting in the accumulation of leukemic blasts. However, it remains unclear how these mutations intersect with the cellular origins of each patient's disease, and whether distinct sets of mutations converge upon similar differentiation patterns. Single-cell RNA sequencing has enabled high-resolution mapping of the relationship between leukemia and normal hematopoietic cell states. Yet, this application has been hampered by imprecise reference maps of normal hematopoiesis, or by small patient cohort sizes which do not adequately capture the extent of inter-patient heterogeneity in AML. To resolve this, we constructed a reference atlas of human bone marrow hematopoiesis from 263,519 single-cell transcriptomes enriched for rare hematopoietic stem and progenitor cells (HSPCs). The resulting reference spans 55 cellular states and has been benchmarked against independent datasets of purified HSPCs. Using this comprehensive reference atlas, we confidently mapped over 1.2 million single-cell transcriptomes from 318 patient samples spanning AML, mixed-phenotype acute leukemia (MPAL), and acute erythroid leukemia (AEL) diagnoses. Single-cell composition analysis revealed twelve patient subgroups, each reflecting distinct patterns of aberrant differentiation in AML. Strikingly, some AML samples exhibited virtually no overlap in cell state involvement with one another, likely reflecting their disparate cellular origins. One subgroup was enriched for early lymphoid progenitors and featured co-clustering of AML and MPAL samples. Notably, this early lymphoid subgroup included an AML sample which eventually relapsed with lymphoid disease. To understand the genetic determinants of aberrant differentiation in AML, we quantified leukemia cell state abundance in >1,200 patient samples and evaluated genotype-to-phenotype associations to link >40 genetic driver alterations with their specific impacts on AML differentiation. This identified genetic drivers for unconventional lineage phenotypes in AML, including erythroid lineage priming associated with the co-occurrence of complex cytogenetics with TP53 mutations as well as lymphoid lineage priming associated with bi-allelic RUNX1 mutations. We also identified non-genetic determinants of AML differentiation. For example, we identified two subgroups of KMT2A-rearranged AML with distinct cellular origins reflecting either HSPCs or committed myeloid precursors. Last, we show that distinct leukemia cell hierarchies can co-exist within individual patients, providing insights into AML evolution. Together, single-cell reference mapping of malignant cell states provides a framework for understanding the impact of genetic alterations on hematopoietic differentiation across hundreds of individual AML patients. Citation Format: Andy G. Zeng, Ilaria Iacobucci, Sayyam Shah, Amanda Mitchell, Gordon Wong, Suraj Bansal, David Chen, Qingsong Gao, Hyerin Kim, James A. Kennedy, Andrea Arruda, Mark D. Minden, Torsten Haferlach, Charles G. Mullighan, John E. Dick. Single-cell transcriptional mapping reveals genetic and non-genetic determinants of aberrant differentiation in AML [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3806.
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 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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".