CD103 is a hallmark of tumor‐infiltrating regulatory T cells
Bibliographic record
Abstract
Regulatory T cells (Treg) mediate tolerance towards self-antigens by suppression of innate and adaptive immunity. In cancer patients, tumor-infiltrating FoxP3+ Treg suppress local anti-tumor immune responses and are often associated with poor prognosis. Markers that are selectively expressed on tumor-infiltrating Treg may serve as targets for immunotherapy of cancer. Here we show that CD103, an integrin mediating lymphocyte retention in epithelial tissues, is expressed at high levels on tumor-infiltrating FoxP3+ Treg in several types of murine cancer. In the CT26 model of colon cancer up to 90% of the intratumoral FoxP3+ cells expressed CD103 compared to less than 20% in lymphoid organs. CD103+ Treg suppressed T effector cell activation more strongly than CD103(neg) Treg. Expression of CD103 on Treg closely correlated with intratumoral levels of transforming growth factor β (TGF-β) and could be induced in a TGF-β-dependent manner by tumor cell lines. In vivo, gene silencing of TGF-β reduced the frequency of CD103+ Treg, demonstrating that CD103 expression on tumor-infiltrating Treg is driven by intratumoral TGF-β. Functional blockade of CD103 using a monoclonal antibody did however not reduce the number of intratumoral Treg, indicating that CD103 is not involved in homing or retention of FoxP3+ cells in the tumor tissue. In conclusion, expression of CD103 is a hallmark of Treg that infiltrate TGF-β-secreting tumors. CD103 thus represents an interesting target for selective depletion of tumor-infiltrating Treg, a strategy that may help to improve anti-cancer 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".