Characterization of the chromosomal translocation t(10;17)(q22;p13) in clear cell sarcoma of kidney
Bibliographic record
Abstract
Clear cell sarcoma of kidney (CCSK) is classified as a tumour of unfavourable histology by the National Wilms' Tumor Study Group. It has worse clinical outcomes than Wilms' tumour. Virtually nothing is known about CCSK biology, as there have been very few genetic aberrations identified to act as pointers in this cancer. Three cases of CCSK bearing a chromosomal translocation, t(10;17)(q22;p13), have been individually reported but not further investigated to date. The aim of this research was to characterize t(10;17)(q22;p13) in CCSK to identify the genes involved in the translocation breakpoints. Using fluorescently labelled bacterial artificial chromosomes (BACs) and a chromosome-walking strategy on an index case of CCSK with t(10;17)(q22;p13) by karyotype, we identified the chromosomal breakpoints on 17p13.3 and 10q22.3. The translocation results in rearrangement of YWHAE on chromosome 17 and FAM22 on chromosome 10, producing an in-frame fusion transcript of ∼3 kb, incorporating exons 1-5 of YWHAE and exons 2-7 of FAM22, as determined by RT-PCR using YWHAE- and FAM22-specific primers. The YWHAE-FAM22 transcript was detected in six of 50 further CCSKs tested, therefore showing an overall incidence of 12% in our cohort. No transcript-positive cases presented with stage I disease, despite this being the stage for 31% of our cohort. Tumour cellularity was significantly higher in the cases that were transcript-positive. Based on the chromosome 10 breakpoint identified by FISH and the sequences of the full-length transcripts obtained, the FAM22 members involved in the translocation in these CCSK cases include FAM22B and FAM22E. Elucidation of the role of YWHAE-FAM22 in CCSK will assist development of more efficient and targeted therapies for this childhood cancer, which currently has poor outcomes.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".