Management of metastatic kidney cancer in the era of personalized medicine
Bibliographic record
Abstract
Patients with localized renal cell cancer (RCC) are often cured following surgical resection. However, a significant proportion of patients will experience recurrence or present with metastatic disease at distant sites and may be deemed incurable. The worldwide incidence of RCC is rising, affecting more than 271,000 people and resulting in 116,000 deaths each year. Unfortunately, advanced RCC is typically resistant to classical chemotherapy and radiotherapy. Previously, non-specific immunotherapies such as interleukin-2 and interferon were used in hopes of improving cancer immunity, leading to rare but durable responses. However, enthusiasm for these immunotherapies has waned due to limited patient responses, their excessive toxicities, and the emergence of alternative targeted therapies that have resulted in improved clinical endpoints for patients with metastatic RCC (mRCC). Strides in targeted treatment can be attributed to an improved understanding of the molecular underpinnings that cause and drive the progression of renal cell cancers. More recently, interest in immunotherapies has resurfaced, as agents inhibiting specific checkpoints involved in cancer immune evasion have demonstrated promising activity in patients with mRCC. Here we review the novel targeted agents, biomarkers and immunotherapies that promise to change the clinical outcomes for patients with advanced RCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".