Indoleamine 2,3-Dioxygenase Expression in Human Cancers: Clinical and Immunologic Perspectives
Bibliographic record
Abstract
Indoleamine 2,3-dioxygenase (IDO) is a tryptophan-catabolizing enzyme with immune-regulating activities in many contexts, such as fetal protection, allograft protection, and cancer progression. Clinical trials are currently evaluating IDO inhibition with 1-methyltryptophan in cancer immunotherapy. However, the exact role of tryptophan catabolism by IDO in human cancers remains poorly understood. Here, we review several studies that correlate IDO expression in human cancer samples and tumor-draining lymph nodes, with relevant clinical or immunologic parameters. IDO expression in various histologic cancer types seems to decrease tumor infiltration of immune cells and to increase the proportion of regulatory T lymphocytes in the infiltrate. The impact of IDO on different immune cell infiltration leads to the conclusion that IDO negatively regulates the recruitment of antitumor immune cells. In addition, increased IDO expression correlates with diverse tumor progression parameters and shorter patient survival. In summary, in the vast majority of the reported studies, IDO expression is correlated with a less favorable prognosis. As we may see results from the first clinical trials with 1-methyltryptophan in years to come, this review brings together IDO studies from human studies and aims to help appreciate outcomes from current and future trials. Consequently, IDO inhibition seems a promising approach for cancer immunotherapy.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".