Hematopoietic Tet2 inactivation enhances the response to checkpoint blockade immunotherapy
Bibliographic record
Abstract
ABSTRACT Somatic mutations inactivating TET2 are among the most common drivers of clonal hematopoiesis (CH). While TET2 inactivation is associated with monocyte-derived inflammation and improved chimeric antigen-receptor-T cell function, its impact on immunotherapy response is unknown. In our mouse model, hematopoietic Tet2 mutation enhanced immune checkpoint blockade (ICB) response. Enhanced ICB response with Tet2 mutation required phagocytes, CD4 and CD8 T cells. Mechanistically, in Tet2 -mutant tumor-infiltrating leukocytes (TILs), ICB preferentially induced anti-tumor states and restricted cell states linked to tumor progression. Tet2 -mutant monocytes activated costimulatory programs, while Tet2 -mutant T cells showed enhanced T cell memory signatures, lesser exhaustion and decreased regulatory phenotype. Our murine data was clinically relevant, since we found that melanomas from patients with TET2 driver mutation-CH (TET2-CH) showed enhanced immune infiltration, T cell activation, and T cell memory programs. In melanoma patients treated with ICB, TET2-CH was associated with 6-fold greater odds of clinical benefit. Collectively, our data establishes that hematopoietic Tet2 inactivation primes leukocytes for anti-tumor states associated with immunotherapy response and provides a potential biomarker for personalized therapy.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".