Irradiation Promotes V(D)J Joining and RAG-Dependent Neoplastic Transformation in SCID T-Cell Precursors
Bibliographic record
Abstract
Defects in the nonhomologous end-joining (NHEJ) pathway of double-stranded DNA break repair severely impair V(D)J joining and selectively predispose mice to the development of lymphoid neoplasia. This connection was first noted in mice with the severe combined immune deficient (SCID) mutation in the DNA-dependent protein kinase (DNA-PK). SCID mice spontaneously develop thymic lymphoma with low incidence and long latency. However, we and others showed that low-dose irradiation of SCID mice dramatically increases the frequency and decreases the latency of thymic lymphomagenesis, but irradiation does not promote the development of other tumors. We have used this model to explore the mechanistic basis by which defects in NHEJ confer selective and profound susceptibility to lymphoid oncogenesis. Here, we show that radiation quantitatively and qualitatively improves V(D)J joining in SCID cells, in the absence of T-cell receptor-mediated cellular selection. Furthermore, we show that the lymphocyte-specific endonuclease encoded by the recombinase-activating genes (RAG-1 and RAG-2) is required for radiation-induced thymic lymphomagenesis in SCID mice. Collectively, these data suggest that irradiation induces a DNA-PK-independent NHEJ pathway that facilitates V(D)J joining, but also promotes oncogenic misjoining of RAG-1/2-induced breaks in SCID T-cell precursors.
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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 | 0.000 | 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 | 0.000 |
| 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".