Use of whole genome amplification and comparative genomic hybridisation to detect chromosomal copy number alterations in cell line material and tumour tissue
Bibliographic record
Abstract
We have established that whole genome amplification (WGA), in conjunction with genomic DNA array comparative genomic hybridisation (gaCGH) allows for the identification of genome-wide copy number abnormalities (CNAs) in DNA extracted from both cell line and patient material. To determine the fidelity and reproducibility of WGA to detect copy number imbalances using gaCGH, well characterized cell line genomic DNA was analysed. The gaCGH data obtained from non-amplified DNA and amplified DNA for the neuroblastoma cell line NUB7 and a paediatric medulloblastoma patient was almost identical. In addition, laser capture microdissection (LCM) of prostate tumour cells and subsequent WGA allowed for the detection of a number of CNAs that may not have been identified if DNA had been extracted in bulk from heterogeneous tissue. The results presented here demonstrate the use of WGA for generating sufficient DNA for gaCGH analysis without the introduction of significant sequence representation bias. The combination of amplification and gaCGH using DNA extracted from archival patient material has the potential for permitting the studying of DNA from small cancerous or pre-cancerous foci, which may help to identify potential genomic markers for early diagnosis.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".