Modeling T-cell acute lymphoblastic leukemia induced by the <i>SCL</i> and <i>LMO1</i> oncogenes
Bibliographic record
Abstract
Deciphering molecular events required for full transformation of normal cells into cancer cells remains a challenge. In T-cell acute lymphoblastic leukemia (T-ALL), the genes encoding the TAL1/SCL and LMO1/2 transcription factors are recurring targets of chromosomal translocations, whereas NOTCH1 is activated in >50% of samples. Here we show that the SCL and LMO1 oncogenes collaborate to expand primitive thymocyte progenitors and inhibit later stages of differentiation. Together with pre-T-cell antigen receptor (pre-TCR) signaling, these oncogenes provide a favorable context for the acquisition of activating Notch1 mutations and the emergence of self-renewing leukemia-initiating cells in T-ALL. All tumor cells harness identical and specific Notch1 mutations and Tcrbeta clonal signature, indicative of clonal dominance and concurring with the observation that Notch1 gain of function confers a selective advantage to SCL-LMO1 transgenic thymocytes. Accordingly, a hyperactive Notch1 allele accelerates leukemia onset induced by SCL-LMO1 and bypasses the requirement for pre-TCR signaling. Finally, the time to leukemia induced by the three transgenes corresponds to the time required for clonal expansion from a single leukemic stem cell, suggesting that SCL, LMO1, and Notch1 gain of function, together with an active pre-TCR, might represent the minimum set of complementing events for the transformation of susceptible thymocytes.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".