SAVOIR: A phase III study of savolitinib versus sunitinib in pts with MET-driven papillary renal cell carcinoma (PRCC).
Bibliographic record
Abstract
5002 Background: PRCC is the most common type of non-clear cell RCC, accounting for 10–15% of renal malignancies. As a subset of PRCC cases are MET-driven, MET inhibition may be an appropriate targeted treatment approach. In a single-arm Phase II study, savolitinib (AZD6094, HMPL‐504, volitinib), a highly selective MET-tyrosine kinase inhibitor, demonstrated antitumor activity in pts with MET-driven PRCC (Choueiri et al. JCO 2017). The Phase III SAVOIR study (NCT03091192) further assessed savolitinib vs standard of care sunitinib in pts with MET-driven PRCC. Methods: In this open-label (sponsor blinded), randomized study, pts with centrally confirmed MET-driven ( MET and/or HGF amplification, chromosome 7 gain and/or MET kinase domain mutations), metastatic PRCC were randomized to savolitinib 600 mg once daily (QD), or sunitinib 50 mg QD 4 weeks on / 2 weeks off. Primary objective was progression-free survival (PFS; RECIST 1.1 by blinded independent central review). Secondary objectives included overall survival (OS), objective response rate (ORR), and safety and tolerability. Results: After external data on predicted PFS with sunitinib in pts with MET-driven disease became available, study enrollment was closed. At data cutoff (Aug 2019), only 60 of the planned 180 pts were randomized (savolitinib n = 33; sunitinib n = 27). Most had chromosome 7 gain (savolitinib 91%; sunitinib 96%) and no prior therapy (savolitinib 85%; sunitinib 93%). PFS, OS, and ORR were numerically improved with savolitinib vs sunitinib (Table). CTCAE grade ≥3 adverse events (AEs) were reported in 42% and 81% of pts; dose modifications were related to AEs in 30% and 74% of pts with savolitinib and sunitinib respectively. After discontinuation, 36% of all savolitinib and 19% of all sunitinib pts received subsequent anticancer therapy. Conclusions: Although pt numbers and follow-up were limited, savolitinib demonstrated encouraging efficacy and an improved safety profile vs sunitinib, with fewer grade ≥3 AEs and fewer dose modifications required. Sunitinib performance was poorer than expected based on external retrospective data. Further investigation of savolitinib as a treatment option for MET-driven PRCC is warranted. Clinical trial information: NCT03091192 . [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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".