Effectiveness of first-line immune checkpoint inhibitors (ICI) in advanced non-clear cell renal cell carcinoma (ccRCC).
Bibliographic record
Abstract
316 Background: Immune checkpoint inhibitors (ICI) have demonstrated impressive activity in metastatic clear-cell renal cell carcinoma (ccRCC) and have become standard treatment options in this setting. Data supporting the effectiveness of ICI based therapy in non-clear cell RCC (nccRCC) is more limited. Methods: We performed a retrospective analysis using the International Metastatic RCC Database Consortium (IMDC). Patients with nccRCC were classified into 3 groups based on first-line therapy: ICI based therapy (in monotherapy or in combination), vascular endothelial growth factor targeted therapy (VEGF-TT) monotherapy, or mammalian target of rapamycin (mTOR) inhibitor monotherapy. Primary outcome was overall survival (OS). Secondary outcomes were time to treatment failure (TTF) and objective response rate (ORR). We used Kaplan-Meier method to compare OS and TTF between treatment groups and Cox proportional hazards models to adjust for prognostic covariates. Results: We identified 1181 patients with nccRCC. In first-line, 78.2% received VEGF-TT, 15.8% mTOR inhibitors, and 5.5% ICI based therapy, of which 41.5% in monotherapy, 30.8% doublet-ICIs and 27.7% an ICI combined with VEGF-TT. Median OS in the ICI group was 28.6 months, compared to 19.2 and 12.6 in the VEGF-TT and mTOR groups, respectively. Median TTF was 6.9 months vs. 5.1 and 3.9 and ORR was 25% vs. 17.8% and 5.8% in the ICI, VEGF-TT and mTOR groups, respectively. After adjusting for IMDC risk group, histological subtype, and age, the hazard ratio (HR) for OS was 0.58 (95% CI 0.35-0.94, p=0.03) for ICI vs. VEGF-TT and 0.48 (95% CI 0.29-0.80, p=0.005) for ICI vs. mTOR. Conclusions: In advanced nccRCC, first-line ICI based treatment appears to be associated with improved OS compared to VEGF and mTOR targeted therapy. These results need to be confirmed in prospective randomized trials. [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 | 0.002 |
| 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 | 0.001 | 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".