Insertional activation of myb by F-MuLV in SCID mice induces myeloid leukemia
Bibliographic record
Abstract
Identification of retrovirus integration sites is a powerful method to identify cancer-related genes. This approach led to the discovery of the Friend murine leukemia virus (F-MuLV) integration site-1 (fli-1). Viral insertion at the fli-1 locus induces erythroleukemia in susceptible strains of mice. Our recent data demonstrated that, F-MuLV-infected SCID mice, in contrast to wt CB17 controls, developed a non‑erythroleukemic leukemia without viral integration at the fli-1 locus. Using ligation-mediated polymerase chain reaction (LM-PCR) approach we identified a total of 15 viral integration sites in F-MuLV-infected SCID mice. One of the identified insertion sites was located about 62 kb upstream of the myeloblastosis (myb) gene. While integration within or surrounding the myb gene has been reported before for murine leukemia viruses, the location of the viral integration site identified in F-MuLV‑infected SCID mice is novel and has never been reported. Using PCR analysis we showed that viral integration at the myb locus occurs with a frequency of 35% and therefore is considered as a common integration site. Integration of F-MuLV in this locus resulted in upregulation of the MYB protein. Flow cytometry analysis and methylcellulose culture of leukemic cells isolated from tumors with viral integration close to the myb indicated tumors of myeloid origin. Our findings indicate that, in contrast to wt CB17 mice, F-MuLV-infected SCID mice display viral integration within myeloid specific gene loci that result in the development of myelogenous leukemia.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".