Use of multicolor fluorescence in situ hybridization to detect deletions in clinical tissue sections
Bibliographic record
Abstract
A variety of laboratory methods are available for the detection of deletions of tumor suppressor genes and losses of their proteins. The clinical utility of fluorescence in situ hybridization (FISH) for the identification of deletions of tumor suppressor genes has previously been limited by difficulties in the interpretation of FISH signal patterns. The first deletion FISH assays using formalin-fixed paraffin-embedded tissue sections had to deal with a significant background level of signal losses affecting nuclei that are truncated by the cutting process of slide preparation. Recently, more efficient probe designs, incorporating probes adjacent to the tumor suppressor gene of interest, have increased the accuracy of FISH deletion assays so that true chromosomal deletions can be readily distinguished from the false signal losses caused by sectioning artifacts. This mini-review discusses the importance of recurrent tumor suppressor gene deletions in human cancer and reviews the common FISH methods being used to detect the genomic losses encountered in clinical specimens. The use of new probe designs to recognize truncation artifacts is illustrated with a four-color PTEN FISH set optimized for prostate cancer tissue sections. Data are presented to show that when section thickness is reduced, the frequency of signal truncation losses is increased. We also provide some general guidelines that will help pathologists and cytogeneticists run routine deletion FISH assays and recognize sectioning artifacts. Finally, we summarize how recently developed sequence-based approaches are being used to identify recurrent deletions using small DNA samples from tumors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".