Impact of smoking status (SS) on the clinical outcomes of patients with metastatic renal cell carcinoma (mRCC) treated with first line (1L) immuno-oncology (IO)-combination regimens: Results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).
Bibliographic record
Abstract
4555 Background: Active smoking is associated with decreased overall survival (OS) in patients (pts) with mRCC treated with VEGF targeted therapy (VEGF-TT) (Kroeger N. et al. 2019, IMDC investigators). Conversely, smoking history has been linked to improved OS in pts with advanced non-small cell lung carcinoma (NSCLC) receiving 1L pembrolizumab monotherapy (Popat S. et al., 2022). Herein, we assess the association between SS and outcomes in pts with mRCC treated with 1L standard of care (SOC) IO-based regimens. Methods: Real-world data from the IMDC were collected retrospectively. We included mRCC pts who received either dual IO therapy or IO + VEGF-TT in the 1L setting and known SS at disease diagnosis. Pts were categorized as current, former and non-smokers. The primary outcomes were OS, time to treatment failure (TTF) and objective response rate (ORR) on 1L IO-based regimens. OS and TTF were evaluated using Cox regression, adjusting for age at 1L treatment, IMDC risk groups, BMI, histological type and time from diagnosis to 1L treatment initiation. ORR was compared between the SS groups using a logistic regression, adjusting for the same confounders. Results: 989 pts were eligible and included. 438 (44.3%), 415 (42%), and 136 (13.7%) pts were non-smokers, former, and current smokers at diagnosis, respectively. Median time from diagnosis to initiation of 1L IO-based treatment was 0.47 years (IQR 0.13-2.15). At baseline, there were no significant differences in age at 1L, IMDC risk groups, KPS status, BMI, and presence of sarcomatoid features across the 3 groups (p>0.05). Median follow up was 21.2 months from 1L IO-based treatment initiation. On multivariable analysis, no significant differences in OS, TTF or ORR were seen between the SS groups (p>0.05). Conclusions: To our knowledge, this represents the first and largest effort to evaluate the impact of smoking on clinical outcomes in pts with mRCC treated with IO-based regimens. There was no association between SS at diagnosis and the clinical outcomes of patients with mRCC receiving current 1L SOC IO-based regimens. As opposed to other cancer types (i.e., NSCLC), current or past smoking history did not appear to be predictive of benefit from IO-based therapy. [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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".