Comprehensive Review of Numerical Chromosomal Aberrations in Chromophobe Renal Cell Carcinoma Including Its Variant Morphologies
Bibliographic record
Abstract
Chromophobe renal cell carcinoma (ChRCC) accounts for 5% to 7% of all renal cell carcinomas. It was thought for many years that ChRCC exhibits a hypodiploid genome. Recent studies using advanced molecular genetics techniques have shown more complex and heterogenous pattern with frequent chromosomal gains. Historically, multiple losses of chromosomes 1, 2, 6, 10, 13, 17, and 21 have been considered a genetic hallmark of ChRCC, both for classic and eosinophilic ChRCC variants. In the last 2 decades, multiple chromosomal gains in ChRCCs have also been documented, depicting a considerably broader genetic spectrum than previously thought. Studies of rare morphologic variants including ChRCC with pigmented microcystic adenomatoid/multicystic growth, ChRCC with neuroendocrine differentiation, ChRCC with papillary architecture, and renal oncocytoma-like variants also showed variable chromosomal numerical aberrations, including multiple losses (common), gains (less common), or chromosomal changes overlapping with renal oncocytoma. Although not the focus of the review, The Cancer Genome Atlas (TCGA) data in ChRCC show TP53, PTEN, and CDKN2A to be the most mutated genes. Given the complexity of molecular genetic alterations in ChRCC, this review analyzed the existing published data, aiming to present a comprehensive up-to-date survey of the chromosomal abnormalities in classic ChRCC and its variants. The potential role of chromosomal numerical aberrations in the differential diagnostic evaluation may be limited, potentially owing to its high variability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".