Circulating biomarkers and outcome from a randomised phase II trial of sunitinib vs everolimus for patients with metastatic renal cell carcinoma
Bibliographic record
Abstract
BACKGROUND: RECORD-3 assessed non-inferiority of progression-free survival (PFS) with everolimus vs sunitinib in previously untreated patients with metastatic renal cell carcinoma. Baseline plasma sample collection and randomised design enabled correlation of circulating biomarkers with efficacy. METHODS: Samples were analysed for 121 cancer-related biomarkers. Analyses of biomarkers categorised patients as high or low (vs median) to assess association with first-line PFS (PFS1L) for each treatment arm. A composite biomarker score (CBS) incorporated biomarkers potentially predictive of PFS1L with everolimus. RESULTS: Plasma samples from 442 of the 471 randomised patients were analysed. Biomarkers were associated with PFS1L for everolimus alone (29), sunitinib alone (9) or both (12). Everolimus-specific biomarkers (CSF1, ICAM1, IL-18BP, KIM1, TNFRII) with hazard ratio ⩾ 1.8 were integrated into a CBS (range 0-5). For CBS low (0-3, n = 291) vs high (4-5, n = 151), PFS1L differed significantly for everolimus but not for sunitinib. There was no significant difference in PFS1L between everolimus and sunitinib in the high CBS patient cohort. CONCLUSIONS: Baseline levels of multiple soluble biomarkers correlated with benefit from everolimus and/or sunitinib, independent of clinical risk factors. A similar PFS1L was observed for both treatments among patients with high CBS score.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".