Molecular profiling reveals frequent gain of <i>MYCN</i> and anaplasia‐specific loss of 4q and 14q in wilms tumor
Bibliographic record
Abstract
Anaplasia in Wilms tumor, a distinctive histology characterized by abnormal mitoses, is associated with poor patient outcome. While anaplastic tumors frequently harbour TP53 mutations, little is otherwise known about their molecular biology. We have used array comparative genomic hybridization (aCGH) and cDNA microarray expression profiling to compare anaplastic and favorable histology Wilms tumors to determine their common and differentiating features. In addition to changes on 17p, consistent with TP53 deletion, recurrent anaplasia-specific genomic loss and under-expression were noted in several other regions, most strikingly 4q and 14q. Further aberrations, including gain of 1q and loss of 16q were common to both histologies. Focal gain of MYCN, initially detected by high resolution aCGH profiling in 6/61 anaplastic samples, was confirmed in a significant proportion of both tumor types by a genomic quantitative PCR survey of over 400 tumors. Overall, these results are consistent with a model where anaplasia, rather than forming an entirely distinct molecular entity, arises from the general continuum of Wilms tumor by the acquisition of additional genomic changes at multiple loci.
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 | 0.001 | 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) | 0.001 | 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".