Evaluation of second line and subsequent targeted therapies in metastatic renal cell cancer (mRCC) patients treated with first line cediranib
Bibliographic record
Abstract
INTRODUCTION: Pivotal phase III trials have positioned angiogenesis inhibitors as first-line therapy for the management of most advanced or metastatic renal cell carcinomas (mRCC). Approaches to second-line therapy, however, remain more controversial with respect to drug selection and drug sequencing. METHODS: In this study we evaluated mRCC patients who were initially treated on the first-line National Cancer Institute (NCI) trial with the highly potent vascular endothelial growth factor receptor tyrosine kinase inhibitor (TKI), cediranib, to determine the efficacy and tolerability of subsequent therapies. RESULTS: Twenty-eight (65.1%) of the 43 patients enrolled on the first-line cediranib trial were known to receive second-line therapy, most commonly sunitinib (n = 21), with 4 (14%), 2 (7%) and 1 (3%) patients receiving temsirolimus, sorafenib, and interleukin, respectively. Of these, 14 (50%) went on to have 3 or more lines of therapy. The progression-free survival (PFS) proportion (PFS) at 1 year from starting second line was 30% (14.5%-47.9%). Longer duration of first-line cediranib treatment was modestly associated with longer duration of second-line treatment (Spearman rho 0.26). Patients who discontinued cediranib for toxicity were less likely to receive second-line sunitinib. CONCLUSION: In this real world evaluation, sequential use of TKIs for the management of mRCC was common. PFS with sequential TKIs was similar to observed and published results for any second-line therapy. Prior toxicity affected treatment patterns and the frequent use of at least 3 lines of therapy underscores the need for prospective sequencing trials in this disease.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".