Gastrointestinal Toxicities of Immune Checkpoint Inhibitors Are Associated With Enhanced Tumor Responsiveness and Improved Survival
Bibliographic record
Abstract
Background: Immune checkpoint inhibitors (ICIs) are increasingly used to treat advanced malignancies. However, they are associated with the development of multiple gastrointestinal immune-related adverse events (GI-irAEs). We aimed to evaluate the types and severity of GI-irAEs associated with ICI therapy, to identify potential risk factors for developing GI-irAEs and to determine the relationship of GI-irAEs development to tumor responsiveness and overall survival. Methods: All patients who received ICIs for advanced malignancies at our center were included. Medical records were reviewed, and data extraction included: baseline demographic characteristics, immunotherapy regimens, development of GI-irAEs, response to treatment, and overall survival. Overall survival was calculated from the date of treatment initiation and estimated by the Kaplan-Meier method. Results: Five hundred sixty-seven patients received ICI therapy for stage IV malignancies. Forty-one (7%) patients experienced at least one GI-irAE. Among those experiencing GI-irAEs, 23 (56%) developed hepatitis, 17 (42%) developed colitis, four (10%) developed pancreatitis, and two (5%) developed gastritis. Patients who developed GI-irAEs experienced a better response to ICI therapy compared to patients who did not develop GI-irAEs (41% vs. 27%, P = 0.003). The 2-year overall survival rate of stage IV cancer patients who developed GI-irAEs was 62% (95% confidence interval (CI): 49 - 79) and 36% for those who did not develop GI-irAEs (95% CI: 32 - 41) (P = 0.002). The median follow-up time of surviving patients was 28 months. Twelve (29%) of the patients receiving dual ICI therapy developed GI-irAEs. Conclusion: Hepatitis, colitis, and pancreatitis were the most commonly encountered GI-irAEs with ICI therapy. Development of these GI-irAEs was associated with superior tumor responsiveness and better overall survival.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".