Persistent DNA damage and oncogenic stress-induced Trem1 promotes leukemia in mice
Bibliographic record
Abstract
The immune receptor TREM1 (Triggering receptor expressed on myeloid cells 1) is a master regulator of inflammatory response. Compelling evidence suggests important pathological roles for TREM1 in various types of solid tumors. However, the role of TREM1 in hematologic malignancies is not known. Our previous study demonstrated that TREM1 cooperates with diminished DNA damage response to induce expansion of pre-leukemic hematopoietic stem cells (HSC) in mice deficient for the Fanconi anemia gene Fanca. Here we investigated TREM1 in leukemogenesis using mouse models of the DNA repair-deficient Fanca-/- and the oncogenic MLL-AF9 or KrasG12D. We found that Trem1 was highly expressed in preleukemic HSC and leukemia stem cells (LSC). By selective deletion of the Trem1 gene in the hematopoietic compartment, we showed that ablation of Trem1 reduced leukemogenic activity of the pre-leukemic HSC and LSC in mice. Trem1 was required for the proliferation of the pre-leukemic HSC and LSC. Further analysis revealed that Trem1 expression in preleukemic HSC and LSC was associated with persistent DNA damage, prolonged oncogenic stress, and a strong inflammatory signature. Targeting several top Trem1 inflammatory signatures inhibited the proliferation of pre-leukemic HSC and LSC. Collectively, our observations uncover previously unknown expression and function of TREM1 in malignant stem cells, and identify TREM1 as a driver of leukemogenesis.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".