Clinical activity of PD1/PDL1 inhibitors in metastatic non-clear cell renal cell carcinoma (nccRCC).
Bibliographic record
Abstract
482 Background: PD1/PDL1 inhibitors have shown significant activity in the treatment of patients (pts) with metastatic clear cell renal cell carcinoma (ccRCC), but their activity in nccRCC is poorly characterized. Methods: We conducted a retrospective multicenter study of pts with metastatic nccRCC treated with PD1/PDL1 inhibitors. Baseline clinical parameters, overall response rate (ORR) by RECIST, time-to-treatment failure (TTF), and overall survival (OS) were summarized. Results: We identified 40 pts across 8 academic institutions. Fourteen (35%) had papillary histology, 10 (25%) chromophobe, 3 (8%) translocation, and 7 (18%) unclassified. Six (16%) had ccRCC with a sarcomatoid component > 30%. 20% had International Metastatic RCC Database Consortium (IMDC) favorable-risk disease, 60% intermediate, and 20% poor-risk. Ten (25%) were treatment-naïve and the majority received PD1/PDL1 monotherapy (n=30, 75%), while the remaining received a combination of PD1/PDL1 with anti-VEGF(R) or anti-CTLA4 therapy. ORR for the total cohort was 18% and 10% for PD1/PDL1 monotherapy pts (Table). With a median follow-up of 5.6 months, the overall median TTF was 4.7 months (2.9-15.9) and six-month OS was 81% (60-91%). Conclusions: PD1/PDL1 blockade resulted in some activity in pts with various nccRCC histologies. In the absence of available clinical trials, this data may support the use of PD1/PDL1 blocking agents in pts with nccRCC. [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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".