Treatment of Advanced Renal Cell Carcinoma: Immunotherapies Have Demonstrated Overall Survival Benefits While Targeted Therapies Have Not
Bibliographic record
Abstract
CONTEXT: Current guidelines suggest several targeted therapies (TTs) and immunotherapies (ITs) in the treatment of advanced or metastatic renal cell carcinoma (mRCC). Ideal sequencing of these treatments is unclear. OBJECTIVE: The primary objective was to evaluate the overall survival (OS) data of the treatments approved for mRCC. Secondary objectives included evaluating other signs of efficacy and adverse events. EVIDENCE ACQUISITION: We reviewed the current Food and Drug Administration-approved treatments for mRCC. Trials associated with approval were reviewed. We also included pre- and postapproval publications when appropriate. EVIDENCE SYNTHESIS: There is minimal evidence supporting OS benefit for the nine approved TTs. They result in adverse events and are a considerable economic burden. For these reasons, their future role in mRCC treatment should be re-evaluated, given the emergence of IT that have demonstrated OS benefits. Accumulating long-term survival data with high-dose interleukin-2 treatment suggests that this older treatment could still be considered for eligible patients. Checkpoint inhibitors have shown promising OS and durable responses; as such, the high cost of treatment might be justified. However, the available evidence does not suggest that adding TT to IT would increase efficacy over IT alone, but would add toxicity. CONCLUSIONS: Trial data supporting OS benefit are much stronger for ITs than for TTs. Combining checkpoint inhibitors with TTs has not been shown to produce better OS than checkpoint inhibitors alone, while more adverse events are present. Granting drug approvals based on efficacy without demonstrated OS benefit should be revisited. PATIENT SUMMARY: Approved treatments for metastatic kidney cancer include targeted and immune-based therapies. The former commonly produces temporary tumour shrinkage, but survival benefits are unclear. All approved immunotherapies have increased survival, and a proportion of patients appear cured.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".