Sushi domain containing 2 suppresses CD8+ T cell antitumor immunity by targeting IL-2 receptor signaling
Bibliographic record
Abstract
Abstract One major mechanism of immunosuppressive tumor microenvironment (TME) is dysfunctional CD8+ T cells which exhibit defective production of antitumor effector molecules. However, the intrinsic molecular mechanism(s) underlying CD8+ T cell dysfunction in cancer remains incompletely understood. Here, we show that the sushi domain containing 2 (SUSD2) is a negative regulator of CD8+ T cell antitumor function. Genetic deletion of Susd2 (Susd2−/−) results in an enhanced production of antitumor molecules in effector CD8+ T cells, which consequently blunts tumor growth in multiple syngeneic mouse tumor models. Through a quantitative mass spectrometry assay, we find that SUSD2 interacts with interleukin-2 receptor a (IL-2Ra) via sushi domain-dependent protein interaction and their interaction suppresses IL-2Ra binding with IL-2, a well-established cytokine essential for CD8+ T cell effector function. Adoptive transfer of Susd2−/− CD19-targeting chimeric antigen receptor (CAR) T cells induces a robust antitumor effect, highlighting SUSD2 as a potential immunotherapy target for cancer treatment. Supported by grants from NIH (R01 AI62779-01)
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 | 0.001 |
| 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".