Next Wave of Targets in the Treatment of Advanced Renal Cell Carcinoma
Bibliographic record
Abstract
While surgical resection has remained the mainstay of treatment in early-stage renal cell carcinoma (RCC), therapeutic options in the advanced setting have remarkably expanded over the last 20 years. Tyrosine kinase inhibitors targeting the vascular endothelial growth factor receptor (VEGF-TKIs) and anti-programmed cell death 1 (PD-1)/anti-programmed death-ligand 1 (PD-L1)-based immune checkpoint inhibitors (ICIs) have become globally accepted options in the upfront metastatic setting, with different ICI-based combination strategies improving overall survival compared to single-agent Sunitinib. Although some patients benefit from long-term responses, most eventually develop disease progression. Ongoing efforts to better understand the biology of RCC and the different mechanisms of acquired resistance have led to the identification of promising therapeutic targets. Belzutifan, a novel agent targeting the angiogenic pathway involving hypoxia-inducible factors (HIFs), has already been approved for the treatment of early-stage tumors associated with VHL disease and represents a very promising therapy in advanced RCC. Other putative targets include epigenetic regulation enzymes, as well as several metabolites such as adenosine, glutaminase and tryptophan, which are critical players in cancer cell metabolism and in the tumor microenvironment. Different methods of immune regulation are also being investigated, including CAR-T cell therapy and modulation of the gut microbiome, in addition to novel agents targeting the interleukin-2 (IL-2) pathway. This review aims to highlight the emergent novel therapies for RCC and their respective completed and ongoing clinical trials.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".