Functional and molecular single-cell analyses implicate PRDM14 in the initiation of B cell leukemia in mice
Bibliographic record
Abstract
The transcription factor Prdm14 is a potent oncogene implicated in the initiation of many cancers. PRDM14 resets and maintains the pluripotent state in normal cells, but the molecular mechanisms through which PRDM14 drives oncogenesis are poorly understood. Here, we interrogated the heterogeneity of Prdm14-expressing cells in a T cell lymphoblastic leukemia/lymphoma mouse model. Using mass cytometry (CyTOF) of bone marrow at a pre-leukemic timepoint, an unexpected abnormal progenitor B cell population was identified. Prdm14-expressing progenitor B cells demonstrated short-term self-renewal and a block in differentiation when transferred to syngeneic hosts. Consistently, aged host mice succumb to a highly penetrant B-LL. Single-cell RNA-seq analyses suggests that the expression signature of these pre-leukemia cells is more consistent with that of B-1 cells than B-2 cells. B-1 cells are a self-renewing population of unconventional B cells established during embryonic development. Overlaying the chromatin binding of transcriptional marks H3K4me1 and H3K4me3 with PRDM14 suggests that PRDM14 initiates cancers through promiscuous DNA binding, activating oncogenic pathways and skewing development towards a self-renewing B-1-like phenotype. Together, our data show that Prdm14 can initiate premature T and B cell cancer programs when expressed in hematopoietic progenitor cells.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".