Combined classical cytogenetics and microarray‐based genomic copy number analysis reveal frequent 3;5 rearrangements in clear cell renal cell carcinoma
Bibliographic record
Abstract
Karyotypic analysis and genomic copy number analysis with single nucleotide polymorphism (SNP)-based microarrays were compared with regard to the detection of recurrent genomic imbalances in 20 clear cell renal cell carcinomas (ccRCCs). Genomic imbalances were identified in 19 of 20 tumors by DNA copy number analysis and in 15 tumors by classical cytogenetics. A statistically significant correlation was observed between the number of genomic imbalances and tumor stage. The most common genomic imbalances were loss of 3p and gain of 5q. Other recurrent genomic imbalances seen in at least 15% of tumors included losses of 1p32.3-p33, 6q23.1-qter and 14q and gain of chromosome 7. The SNP-based arrays revealed losses of 3p in 16 of 20 tumors, with the highest frequency being at 3p21.31-p22.1 and 3p24.3-p25.3, the latter encompassing the VHL locus. One other tumor showed uniparental disomy of chromosome 3. Thus, altogether loss of 3p was identified in 17 of 20 (85%) cases. Fourteen tumors showed both overlapping losses of 3p and overlapping gains of 5q, and the karyotypic assessment performed in parallel revealed that these imbalances arose via unbalanced 3;5 translocations. Among the latter, there were common regions of loss at 3p21.3-pter and gain at 5q34-qter. These data suggest that DNA copy number analysis will supplant karyotypic analysis of tumor types such as ccRCC that are characterized by recurrent genomic imbalances, rather than balanced rearrangements. These findings also suggest that the 5q duplication/3p deficiency resulting from unbalanced 3;5 translocations conveys a proliferative advantage of particular importance in ccRCC tumorigenesis.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".