Response to Immune Checkpoint Blockade Is Enhanced in the Presence of Hematopoietic TET2 Inactivation
Bibliographic record
Abstract
Somatic mutations inactivating Tet methylcytosine dioxygenase 2 (TET2) are among the most common drivers of clonal hematopoiesis (CH). TET2 inactivation is associated with monocyte-derived inflammation and improved chimeric antigen receptor T-cell function, suggesting that it might also affect immunotherapy response. In this study, we found that hematopoietic Tet2 mutation in mouse models enhanced the immune checkpoint blockade (ICB) response, which required the combined presence of phagocytes, CD4+, and CD8+ T cells. The effect was lost with myeloid- or T-cell-restricted Tet2 inactivation or in mice with 20% Tet2-mutant hematopoiesis. Mechanistically, in Tet2-mutant tumor-infiltrating leukocytes, ICB preferentially restricted cell states linked to tumor progression while inducing antitumor states. Tet2-mutant monocytes activated costimulatory programs, whereas Tet2-mutant T cells showed enhanced T-cell memory signatures, alongside decreased exhaustion and regulatory phenotypes. Clinically, tumors from patients with colorectal cancer and melanoma with TET2-mutant CH showed enhanced immune infiltration, inflammation, and T-cell activation. In patients with melanoma treated with ICB, TET2-mutant CH was associated with six-fold greater odds of clinical benefit. Collectively, this work demonstrates that hematopoietic TET2 inactivation primes leukocytes for antitumor states associated with immunotherapy response and provides a potential biomarker for personalized therapy. SIGNIFICANCE: TET2 mutations promote antitumor leukocyte states that can potentiate the efficacy of immunotherapy with checkpoint blockade. See related commentary by Yuan and Guryanova, p. 825.
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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) | 0.001 | 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".