Association of immune-related adverse events (irAEs) with clinical benefit in patients with metastatic urothelial carcinoma (mUC) treated with immune-checkpoint inhibitors (ICIs).
Bibliographic record
Abstract
e16038 Background: In other cancers such as metastatic lung and melanoma, pts who experience irAEs may have a greater degree of clinical benefit. We sought to evaluate whether the development of irAEs correlates with clinical benefits in mUC pts. Methods: We identified mUC pts who received PD-1/L1 inhibitors at the Dana-Farber Cancer Institute (DFCI). The severity of irAEs was graded using CTCAE v.5.0. Clinical benefit was defined as any objective reduction (complete- or partial- response) in tumor burden. Fisher’s exact test was used to evaluate for differences in the proportion of pts experiencing clinical benefit between pts experiencing an irAEs within 90 days after starting therapy and those who did not experience an irAEs. The log-rank test assessed differences in progression-free survival (PFS) and overall survival (OS) between cohorts. Results: A total of 199 mUC pts were enrolled between July 2013 and October 2018 [median (range) age, 69.6 (26.6-89.0) years; 141 men (70.9%), 58 women (29.1%)]. 114 (57.3%) pts were treated with anti-PD-1 and 85 (42.7%) with anti-PD-L1. irAEs were observed in 67 pts (33.7%), of which 34 (17.1%) < 90 days from start of therapy. Common irAEs included 20 (29.9%) hypothyroidism, 17 (25.4%) colitis, 12 (17.9%) rash/pruritus, and 10 (14.9%) transaminitis. Grade ≥3 irAEs were observed in 14 pts (20.9%).13 (40.6%) pts with irAE < 90 days experienced clinical benefit compared with 21 (17.8%) of pts with no irAE (p-value = 0.008). No difference (p = 0.26 and 0.18) was observed for either PFS (6-month PFS = 19.2% vs 35.3% for no irAE and irAE < 90 days) or OS (1-year OS = 41.8% vs 57.7% for no irAE vs irAE < 90 days). Conclusions: The development of irAEs within 90 days from starting therapy in mUC pts may herald clinical benefit in pts with mUC. Further evaluation of this potential relationship in a large prospective study is warranted.
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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 | 0.001 |
| 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".