Efficacy of first-line (1L) immunotherapy (IO)-based regimens in patients with sarcomatoid and/or rhabdoid (S/R) metastatic non-clear cell renal cell carcinoma (nccRCC): Results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).
Bibliographic record
Abstract
4519 Background: Patients with advanced RCC with S/R components exhibit poor clinical outcomes. IO-based combination therapies demonstrated substantial efficacy among patients with metastatic S/R ccRCC, compared to VEGF targeted therapy (VEGF-TT). Recent trials showed promising activity of IO-based regimens in patients with advanced nccRCC. We sought to assess the efficacy of IO regimens among patients with S/R nccRCC. Methods: Patients with advanced nccRCC treated with 1L IO regimens (IO/IO or IO/VEGF-TT) or 1L VEGF-TT monotherapy (sunitinib or pazopanib) were included. Cases were categorized as S/R or non-S/R. The primary outcomes were overall survival (OS) and time to treatment failure (TTF) in patients with S/R nccRCC receiving 1L IO or VEGF-TT. Overall response rate (ORR) was a secondary outcome. OS and TTF were compared between groups (IO vs. VEGF-TT) using Cox regression models adjusted for age, IMDC risk groups, and nccRCC subtype. ORR was compared between groups (IO vs. VEGF-TT) using a logistic regression adjusted for the same confounders. Results: Overall, 103 patients with S/R nccRCC were included, of whom 33 (32%) received 1L IO regimens. Median follow-up was 31 months. After adjustment for confounding factors, patients with S/R nccRCC treated with IO regimens presented with significantly improved survival outcomes as compared to those receiving VEGF-TT (median OS [mOS]: NR vs. 7.1 and mTTF: 9.4 vs. 2.9 mos for IO regimens and VEGF-TT, respectively). Similarly, a higher ORR was seen in patients with S/R nccRCC receiving IO regimens versus VEGF-TT (34.1 vs. 10.9%, respectively). Among 430 patients with non-S/R nccRCC (IO regimens: n=44), no significant differences in survival outcomes between regimen classes were seen (mOS: 24.4 vs. 14.8 and mTTF: 4.2 vs. 5.0 mos for IO regimens and VEGF-TT, respectively). Conclusions: To our knowledge, this represents the largest effort to characterize the outcomes of patients with S/R nccRCC treated with IO regimens. Patients with S/R nccRCC appear to derive a substantial and selective benefit from IO regimens (vs. VEGF-TT). These data support the use of IO-based regimens in patients with S/R nccRCC. [Table: see text]
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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) | 0.001 | 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".