Large fragment Bst DNA polymerase for whole genome amplification of DNA from formalin-fixed paraffin-embedded tissues
Bibliographic record
Abstract
BACKGROUND: Formalin-fixed paraffin-embedded (FFPE) tissues represent the largest source of archival biological material available for genomic studies of human cancer. Therefore, it is desirable to develop methods that enable whole genome amplification (WGA) using DNA extracted from FFPE tissues. Multiple-strand Displacement Amplification (MDA) is an isothermal method for WGA that uses the large fragment of Bst DNA polymerase. To date, MDA has been feasible only for genomic DNA isolated from fresh or snap-frozen tissue, and yields a representational distortion of less than threefold. RESULTS: We amplified genomic DNA of five FFPE samples of normal human lung tissue with the large fragment of Bst DNA polymerase. Using quantitative PCR, the copy number of 7 genes was evaluated in both amplified and original DNA samples. Four neuroblastoma xenograft samples derived from cell lines with known N-myc gene copy number were also evaluated, as were 7 samples of non-small cell lung cancer (NSCLC) tumors with known Skp2 gene amplification. In addition, we compared the array comparative genomic hybridization (CGH)-based genome profiles of two NSCLC samples before and after Bst MDA. A median 990-fold amplification of DNA was achieved. The DNA amplification products had a very high molecular weight (> 23 Kb). When the gene content of the amplified samples was compared to that of the original samples, the representational distortion was limited to threefold. Array CGH genome profiles of amplified and non-amplified FFPE DNA were similar. CONCLUSION: Large fragment Bst DNA polymerase is suitable for WGA of DNA extracted from FFPE tissues, with an expected maximal representational distortion of threefold. Amplified DNA may be used for the detection of gene copy number changes by quantitative realtime PCR and genome profiling by array CGH.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".