Characterization of clinical outcomes among patients with advanced chromophobe renal cell carcinoma (ChRCC) treated with first-line immunotherapy (IO)-based regimens.
Bibliographic record
Abstract
654 Background: IO-based regimens have demonstrated substantial efficacy in the management of metastatic clear-cell RCC (mccRCC), where they currently represent the standard of care. ChRCC has a dismal prognosis in the metastatic setting. Recent clinical trials evaluating IO-based regimens across non-ccRCC subtypes identified a preliminary poor response in advanced ChRCC, but were limited by low sample sizes. We sought to comprehensively evaluate the outcomes of patients with ChRCC treated with IO-based regimens. Methods: Using real-world data from the International Metastatic RCC Database Consortium (IMDC), we conducted a retrospective analysis of patients with advanced ChRCC who received IO-based therapies, including dual IO therapy or IO + VEGF targeted therapy (VEGF-TT), in the first-line setting. The primary outcome was overall survival (OS). Secondary outcomes included time to treatment failure (TTF) and ORR. Cox proportional hazards models were used to adjust for age and IMDC risk groups as covariates. A logistic regression was used to determine the association between the odds of achieving a response and RCC subtype. Results: We identified 31 patients with advanced ChRCC and 856 patients with ccRCC treated with IO-based therapies in the first-line setting, with a median age of 61.5 years (IQR: 51.5-69.0). Compared to patients with ccRCC who received IO-based therapies as initial regimens, patients with ChRCC had a lower OS (median OS: 24.7 vs. 50.5 months, respectively; p<0.001) and a lower TTF (median TTF: 4.5 vs. 11.0 months, respectively; p<0.001). Among patients with an evaluable objective response, the ORR was lower among patients with advanced ChRCC, as opposed to those with ccRCC (ORR: 12.0 vs 47.1%, respectively; p<0.001). When evaluating first-line treatment with VEGF-TT monotherapy (sunitinib or pazopanib), no difference in outcomes was found between patients with ChRCC (n=122) and ccRCC (n=6,379) in relation to the primary endpoint of OS, while TTF and ORR suggested better outcomes for ccRCC (Table). Conclusions: In this real-world study, patients with metastatic ChRCC appear to display poor clinical outcomes even with IO-based regimens, as compared to ccRCC. The molecular determinants of poor response require further investigations. [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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".