Clinical and Molecular Characterization of KRAS-Mutated Renal Cell Carcinoma
Bibliographic record
Abstract
Background/Objectives: KRAS mutations in renal cell carcinoma (RCC) are uncommon and most frequently described in papillary renal neoplasm with reverse polarity (PRNRP). Beyond this entity, the broader clinicopathologic and molecular features of KRAS-mutated RCC remain insufficiently characterized. This study aimed to provide a descriptive assessment of KRAS-mutated RCC. Methods: KRAS-mutant RCC patients were identified from the Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT) and The Cancer Genome Atlas Kidney Renal Papillary Cell Carcinoma (TCGA-KIRP) cohorts. Copy-number alterations were evaluated using Fraction and allele-specific copy number estimates from tumor sequencing (FACETS). Available samples were used for immunohistochemistry and RNA-sequencing analysis. Results: Seventeen patients were included. Three distinct KRAS-mutant RCC subtypes were identified: KRAS-mutant PRCC (35%), KRAS-mutant URCC (35%), and PRNRP (29%). Seven patients (41%) had metastatic disease; none were PRNRP. RNA-based deconvolution analysis revealed that PRNRP had enrichment in distal nephron components, whereas KRAS-mutant PRCC was enriched in proximal tubule cells (p = 0.02). IHC staining of L1CAM was positive in PRNRP but negative in KRAS-mutant PRCC, supporting their distinct cell-of-origin phenotypes. This study is limited by its cohort size, which influences the availability of tissue samples. Conclusions: PRNRP represents a distinct KRAS-mutant RCC subtype with unique metabolic and genomic features linked to its distal nephron origin. This contrasts with the genomic complexity and aggressive clinical behavior observed in KRAS-mutant PRCC and URCC, highlighting the need for subtype-specific diagnostic criteria and therapeutic strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".