Driver mutations in Janus kinases in a mouse model of B-cell leukemia induced by deletion of PU.1 and Spi-B
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 driver mutations influence clonal evolution of leukemia. The E26-transformation-specific (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 tumor suppressors. 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 sequence changes. Jak3 mutations resulted in amino acid substitutions located in the pseudo-kinase (R653H, V670A) and in the kinase (T844M) domains. Introduction of Jak3 T844M into Spi1/Spib-deficient precursor B cells was sufficient to promote proliferation in response to low IL-7 concentrations in culture, and to promote proliferation and leukemia-like disease in transplanted mice. We conclude that mutations in Janus kinases represent secondary drivers of leukemogenesis that cooperate with Spi1/Spib deletion. This mouse model represents a useful tool to study clonal evolution 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".