Array CGH profiling of favourable histology Wilms tumours reveals novel gains and losses associated with relapse
Bibliographic record
Abstract
Despite the excellent survival of Wilms tumour patients treated with multimodality therapy, approximately 15% will suffer from tumour relapse, where response rates are markedly reduced. We have carried out microarray-based comparative genomic hybridisation on a series of 76 Wilms tumour samples, enriched for cases which recurred, to identify changes in DNA copy number associated with clinical outcome. Using 1Mb-spaced genome-wide BAC arrays, the most significantly different genomic changes between favourable histology tumours that did (n = 37), and did not (n = 39), subsequently relapse were gains on 1q, and novel deletions at 12q24 and 18q21. Further relapse-associated loci included losses at 1q32.1, 2q36.3-2q37.1, and gain at 13q31. 1q gains correlated strongly with loss of 1p and/or 16q. In 3 of 11 cases with concurrent 1p(-)/1q(+), a breakpoint was identified at 1p13. Multiple low-level sub-megabase gains along the length of 1q were identified using chromosome 1 tiling-path arrays. One such recurrent region at 1q22-q23.1 included candidate genes RAB25, NES, CRABP2, HDGF and NTRK1, which were screened for mRNA expression using quantitative RT-PCR. These data provide a high-resolution catalogue of genomic copy number changes in relapsing favourable histology Wilms tumours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".