IRF4 instructs effector Treg differentiation and immune suppression in human cancer
Bibliographic record
Abstract
The molecular mechanisms responsible for the high immunosuppressive capacity of CD4+ Tregs in tumors are not well known. High-dimensional single-cell profiling of T cells from chemotherapy-naive individuals with non-small-cell lung cancer identified the transcription factor IRF4 as specifically expressed by a subset of intratumoral CD4+ effector Tregs with superior suppressive activity. In contrast to the IRF4- counterparts, IRF4+ Tregs expressed a vast array of suppressive molecules, and their presence correlated with multiple exhausted subpopulations of T cells. Integration of transcriptomic and epigenomic data revealed that IRF4, either alone or in combination with its partner BATF, directly controlled a molecular program responsible for immunosuppression in tumors. Accordingly, deletion of Irf4 exclusively in Tregs resulted in delayed tumor growth in mice while the abundance of IRF4+ Tregs correlated with poor prognosis in patients with multiple human cancers. Thus, a common mechanism underlies immunosuppression in the tumor microenvironment irrespective of the tumor type.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".