Impact of number of treatment lines following first-line (1L) immuno-oncology (IO) combination on overall survival (OS) in patients with metastatic renal cell carcinoma (mRCC).
Bibliographic record
Abstract
673 Background: Across the world, treatment of patients with mRCC is heterogeneous with different access to treatment sequences and number of lines of therapy (LOTs) employed. For instance, patients receiving first line (1L) nivolumab+ipilimumab (NIVO+IPI) may be offered second-line (2L) and third line (3L) vascular endothelial growth factor receptor targeted kinase inhibitor (VEGFR-TKI), whereas patients receiving 1L pembrolizumab or avelumab in combination with axitinib (IO+AXI) may only receive one subsequent VEGFR-TKI in 2L. We aimed to examine whether these different treatment strategies impact overall survival (OS). Methods: Adult mRCC patients who received at least three LOTs starting with 1L NIVO+IPI or at least two LOTs starting with 1L IO+AXI from the International Metastatic RCC Database Consortium (IMDC) centers were included. Kaplan-Meier analyses were used to estimate median OS (time from 1L to death). Results were stratified by 1L IMDC prognostic risk. Results: Among 128 patients who received at least three LOTs starting with 1L NIVO+IPI (median age 61 years, 77% White, 77% male, 37% from the US), 14% had favorable, 61% had intermediate, and 26% had poor IMDC risk. The most common 2L treatments following 1L NIVO+IPI were sunitinib (38%), cabozantinib (27%), and pazopanib (20%). Among 104 patients who received at least two LOTs starting with 1L IO+AXI (median age 62 years, 75% White, 67% male, 38% from the US), 28% had favorable, 48% had intermediate, and 25% had poor IMDC risk. The most common 2L treatments following 1L IO+AXI were cabozantinib (57%) and sunitinib (10%). Median OS are presented in the table, which suggested no difference in survival for patients who received at least two LOTs starting with 1L IO+AXI compared to patients who received at least three LOTs starting with 1L NIVO+IPI. Conclusions: Treatment for patients with mRCC varies depending on the 1L regimen chosen and by country. Our results demonstrate that, even with potential guaranteed time bias and IMDC imbalances, there is no statistically significant difference in OS for patients who received at least three LOTs starting with 1L NIVO+IPI and patients who received at least two LOTs starting with 1L IO+AXI, suggesting that selecting effective treatments in 1L resulting in fewer LOTs may have similar clinical outcomes as multiple LOTs. [Table: see text]
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 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 | 0.001 |
| 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".