Impact of concurrent angiotensin inhibitors on outcomes with PD1/L1 inhibitors for patients (pts) with metastatic urothelial carcinoma (mUC).
Bibliographic record
Abstract
e17044 Background: Preclinical data indicate that angiotensin inhibition may improve drug delivery by enhancing tumor perfusion partly by downregulating transforming growth factor (TGF)-β. Since (TGF)-β appears to be associated with resistance in patients with mUC receiving PD1/L1 inhibitors, we investigated whether angiotensin converting enzyme inhibitors (ACEI) and angiotensin receptor blockers (ARBs) may enhance the outcomes of mUC pts receiving PD1/L1 inhibitors. Methods: Data from mUC pts who received PD1/L1 inhibitors at the Dana-Farber Cancer Institute (DFCI) was obtained. Data for ACEI and ARB administration was collected with concurrent administration defined as ongoing therapy from the time of starting PD1/L1 inhibitor treatment. A Cox logistic regression was used to investigate the impact of concurrent ACEI/ARB on any regression of tumor (ART, any decrease in size of tumor on scan) defined as any tumor regression after controlling for known prognostic factors (performance status, sites of metastasis, neutrophil/lymphocyte ratio, platelet count, hemoglobin). Results: Data was available for 178 pts with mUC who received pembrolizumab (79), atezolizumab (83), nivolumab (15), and durvalumab (1). Prior platinum chemotherapy was administered in 153 pts (86%). 33 pts (18.5%) received AECI/ARBs: 24 pts (13.5%) received ACEI and 9 pts (5.1%) received ARBs. Of 145 patients who did not receive an ACE-inhibitor nor an ARB, 49 (33.8%) patients experienced ART and their median overall survival (OS) was 9.1 months. Among/33 patients who did receive an ACEI or an ARB, 17 (51.5%) exhibited ARTand their median OS was 17.0 months. Multivariable analysis controlling for known prognostic factors revealed that patients who received ACE inhibitors or ARBs had greater ART (HR 3.0 [95% CI 1.25-7.17], p = 0.014) and improved OS, (HR 0.49 [95% CI 0.28-0.88] p = 0.016). Conclusions: In this hypothesis-generating study, concurrent angiotensin inhibitors including ACEI or ARBs were associated with significantly better outcomes in mUC pts receiving PD-1/L1 inhibitors. These results require validation in a larger mUC dataset in conjunction with probing the effect in other malignancies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".