Increased Activity of the Immunoregulatory Enzyme Indoleamine-2,3-Dioxygenase with Consecutive Tryptophan Depletion Predicts Death in Patients with Neuroendocrine Neoplasia
Bibliographic record
Abstract
BACKGROUND/AIMS: Data from a considerable number of malignancies demonstrate that depletion of the essential amino acid tryptophan via induction of the immunoregulatory enzyme indoleamine-2,3-dioxygenase (IDO) serves as an important tumour escape strategy and is of prognostic importance. Here we investigate the predictive value of the activity of IDO as well as levels of tryptophan and respective downstream catabolites in a large cohort of patients with neuroendocrine neoplasms (NEN). METHODS: 142 consecutive Caucasian patients (62 male, aged 60.3 ± 11.9 years) with histologically confirmed NEN were systematically analysed in a retrospective blinded end point analysis. Patients were followed up for a mean period of about 3.9 ± 1.9 years. Clinical outcome, levels of established biomarkers, and tryptophan degradation markers (assessed using tandem mass spectrometry) including estimated IDO activity were recorded. Cox proportional hazards regression models were performed for the assessment of prognostic power. RESULTS: We found that baseline tryptophan levels were significantly lower and IDO activity was significantly increased in non-survivors. The risk for death inclined stepwise and was highest in patients in the upper tertile of IDO activity. Cox proportional regression models identified IDO activity as an independent predictor of death. CONCLUSIONS: In this retrospective analysis, we observed that baseline activity of the immunoregulatory enzyme IDO was significantly increased in non-survivors. IDO activity was identified as an independent predictor of death in this cohort of NEN patients. Whether IDO activity or tryptophan depletion serves to guide future therapeutic interventions in NEN remains to be established.
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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 | 0.001 |
| 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".