Deletion of genes encoding PU.1 and Spi-B leads to B cell acute lymphoblastic leukemia associated with driver mutations in Janus Kinases
Bibliographic record
Abstract
Abstract Precursor B-cell acute lymphoblastic leukemia (B-ALL) is associated with recurrent mutations that occur in cancer-initiating cells. There is a need to understand how spontaneous driver mutations influence clonal evolution in leukemia. The ETS-transcription factors PU.1 and Spi-B (encoded by Spi1 and Spib ) execute a critical role in B cell development and serve as complementary tumour suppressors by opposing the proliferative events mediated by IL-7R signaling. Here, we used a mouse model to conditionally delete Spi1 and Spib genes in developing B cells. These mice developed B-ALL with a median time to euthanasia of 18 weeks. We performed RNA and whole-exome sequencing (WES) on leukemias isolated from Mb1-CreΔPB mice and identified single-nucleotide variants (SNVs) in Jak1 , Jak3 and Ikzf3 genes, resulting in amino acid changes and in the gain of early stop-codons. JAK3 mutations resulted in amino acid substitutions located in the pseudo-kinase (R653H, V670A) and in the kinase (T844M) domains. Introduction of these mutations into wild-type pro-B cells conferred survival and proliferation advantages. We conclude that mutations in Janus kinases represent secondary drivers of leukemogenesis in the absence of Spi-B and PU.1 transcription factors. This mouse model represents an useful tool to study clonal evolution and tumour heterogeneity in B-ALL.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".