The Role of Homologous Recombination Deficiency (HRD) in Renal Cell Carcinoma (RCC): Biology, Biomarkers, and Therapeutic Opportunities
Bibliographic record
Abstract
Renal Cell Carcinoma (RCC) is a common malignancy, often diagnosed incidentally. In recent years, the prognosis of metastatic disease has been improved due to the development of immune checkpoint inhibitors (ICI) and tyrosine kinase inhibitors (TKI) as first-line treatments. However, when progression occurs, the therapeutic options are limited. Understanding crucial biological pathways could lead to a greater understanding of the natural history of the disease, which could help to overcome the mechanism of resistance and to develop new treatments. The clinical significance of homologous recombination deficiency (HRD) in RCC remains to be investigated. To improve the knowledge about this topic, we conducted a narrative review to summarize the current evidence on HRD-related variations and signatures in RCC, together with their prognostic and predictive implications. Preliminary evidence indicates that canonical HRD variants (BRCA1/2) are infrequent in RCC, while broader DNA damage response (DDR) alterations like BAP1, PBRM1, ATM, and SETD2 are more prevalent. Elevated HRD genomic scores in clear-cell RCC correlate with a worse prognosis and an immunologically exhausted microenvironment. From a therapeutic point of view, PARP inhibitor monotherapy has exhibited initial efficacy in small cohorts with high levels of DDR mutation, yet remains investigational for RCC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".