Significant association between tumor mutational burden and immune-related adverse events during immune checkpoint inhibition therapies
Bibliographic record
Abstract
More than 2000 immuno-oncology agents are being tested or are in use as a result of the cancer immunotherapy revolution. Manipulation of co-inhibitory receptors has achieved tumor eradication in a minority of patients, but widespread immune-related adverse events (irAEs) compromised tolerance to healthy self-tissues in the majority. We have proposed that a major mechanism of irAEs is similar to a graft-versus-malignancy effect of graft-versus-host disease. To verify our hypothesis, we retrieved post-marketing data of adverse events from the U.S. Food and Drug Administration Adverse Event Reporting System. A significant positive correlation was revealed in 7677 patients between the reporting odds ratio of irAEs during immune checkpoint inhibitor therapy and the corresponding tumor mutational burden across 19 cancer types. These results can be interpreted to mean that the ICI drugs unleashed T cells against "altered-self," self, and tumors resulting in better overall survival.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".