Biphasic components of sarcomatoid clear cell renal cell carcinomas are molecularly similar to each other, but distinct from, non‐sarcomatoid renal carcinomas
Bibliographic record
Abstract
Sarcomatoid transformation, wherein an epithelioid carcinomatous tumour component coexists with a sarcomatoid histology, is a predictor of poor prognosis in clear cell renal cell carcinoma. Our understanding of sarcomatoid change has been hindered by the lack of molecular examination. Thus, we sought to characterize molecularly the biphasic epithelioid and sarcomatoid components of sarcomatoid clear cell renal cell carcinoma and compare them to non-sarcomatoid clear cell renal cell carcinoma. We examined the transcriptome of the epithelioid and sarcomatoid components of advanced stage sarcomatoid clear cell renal cell carcinoma (n=43) and non-sarcomatoid clear cell renal cell carcinoma (n=37) from independent discovery and validation cohorts using the cDNA microarray and RNA-seq platforms. We analyzed DNA copy number profiles, generated using SNP arrays, from patients with sarcomatoid clear cell renal cell carcinoma (n=10) and advanced non-sarcomatoid clear cell renal cell carcinoma (n=155). The epithelioid and sarcomatoid components of sarcomatoid clear cell renal cell carcinoma had similar gene expression and DNA copy number signatures that were, however, distinct from those of high-grade, high-stage non-sarcomatoid clear cell renal cell carcinoma. Prognostic clear cell renal cell carcinoma gene expression profiles were shared by the biphasic components of sarcomatoid clear cell renal cell carcinoma and the sarcomatoid component showed a partial epithelial-to-mesenchymal transition signature. Our genome-scale microarray-based transcript data were validated in an independent set of sarcomatoid and non-sarcomatoid clear cell renal cell carcinomas using RNA-seq. Sarcomatoid clear cell renal cell carcinoma is molecularly distinct from non-sarcomatoid clear cell renal cell carcinoma, with its genetic programming largely shared by its biphasic morphological components. These data explain why a low percentage of sarcomatoid histology augurs a poor prognosis; suggest the need to modify the pathological grading system and introduce the potential for candidate biomarkers to detect sarcomatoid change preoperatively without specifically sampling the histological sarcomatoid component.
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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) | 0.000 | 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) | 0.001 | 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".