Targeted Therapies for Renal Cell Carcinoma: More Gains from Using Them Again
Bibliographic record
Abstract
The development of molecularly targeted agents that inhibit pathways critical to the development of renal cell carcinoma has significantly improved outcomes in patients with these cancers. Compelling scientific and phase iii data have made the use of molecularly targeted agents the standard of care in first-line treatment. Now, available data show that re-treating patients with other tyrosine kinase inhibitors after they progress on sunitinib or sorafenib, or both, is beneficial. A large phase iii trial recently showed that, as compared with placebo, treatment with everolimus, an inhibitor of the mammalian target of rapamycin (mTOR), almost halved the risk of progression (37% vs. 65%) and doubled the median progression-free survival (4 months vs. 2 months). Overall survival was not improved in that study, likely reflecting treatment crossover in the placebo arm, but these data position everolimus as the second-line standard of care. A consistent and growing body of literature also suggests that re-treatment with other kinase inhibitors that the patient has not previously encountered is a reasonable option. Outcomes of initial treatment with sunitinib or sorafenib (or both) should not deter the use of second-line targeted therapy, because the first-line use of targeted agents does not appear to be predictive of outcomes with second-line therapy. However, in view of poor absolute outcomes after second-line treatment and the benefits seen with rationally developed targeted agents in the first-line setting, enrolment of second- and subsequent-line patients in further trials would be preferable.
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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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".