Use of Chromogenic In Situ Hybridization to Identify MYCN Gene Copy Number in Neuroblastoma Using Routine Tissue Sections
Bibliographic record
Abstract
Amplification of MYCN in neuroblastoma is associated with a poor prognosis. However, methods for estimating the number of MYCN genes based on pooled cells do not address copy number heterogeneity at the cell level and can underestimate or even miss amplification. MYCN copy number can be directly assessed by fluorescence in situ hybridization, but evaluation of tissue histology is next to impossible. We have used a chromogenic method for in situ hybridization (CISH) that enables determination of MYCN copy number using routine light microscopy on routinely processed paraffin sections. Of 41 cases studied, CISH identified 100% of the 18 cases that were determined to be amplified by other techniques and was more sensitive than Southern blotting or quantitative DNA polymerase chain reaction. Because the technique evaluates individual tumor cells, heterogeneity of MYCN copy number was apparent from cell to cell. When defined as 50% or greater variation in copy number between cells in amplified tumors, almost 30% of cases were scored as heterogeneous. Heterogeneity reflects different tumor clones and its role has likely been under-recognized and underestimated in neuroblastoma biology. CISH will provide a valuable tool to assess this phenomenon in conjunction with other morphologic parameters in neuroblastoma specimens, to further our understanding of the biology of this childhood tumor.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".