Data from Tet2 Inactivation Enhances the Antitumor Activity of Tumor-Infiltrating Lymphocytes
Bibliographic record
Abstract
Abstract Inactivation of tumor-infiltrating lymphocytes (TIL) is one of the mechanisms mitigating antitumor immunity during tumor onset and progression. Epigenetic abnormalities are regarded as a major culprit contributing to the dysfunction of TILs within tumor microenvironments. In this study, we used a murine model of melanoma to discover that Tet2 inactivation significantly enhances the antitumor activity of TILs with an efficacy comparable to immune checkpoint inhibition imposed by anti-PD-L1 treatment. Single-cell RNA-sequencing analysis suggested that Tet2-deficient TILs exhibit effector-like features. Transcriptomic and ATAC-sequencing analysis showed that Tet2 ablation reshapes chromatin accessibility and favors binding of transcription factors geared toward CD8+ T-cell activation. Furthermore, the ETS family of transcription factors contributed to augmented CD8+ T-cell function following Tet2 depletion. Overall, our study establishes that Tet2 constitutes one of the epigenetic barriers that account for dysfunction of TILs and that Tet2 inactivation could promote antitumor immunity to suppress tumor growth. Significance: This study suggests that ablation of TET2+ from TILs could promote their antitumor function by reshaping chromatin accessibility for key transcription factors and enhancing the transcription of genes essential for antitumor activity.
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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) | 0.007 | 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".