Chromosomal aberrations in breast cancer tumors using cDNA microarray CGH.
Bibliographic record
Abstract
105 In this study we have taken advantage of a large cohort of axillary node negative (ANN) breast cancer patients to look for the chromosomal aberrations using array based comparative genomic hybridization (a-CGH). To date we have completed a-CGH on more than 50 ANN tumors from a cohort of cases from the Toronto area. In order to confirm that our experimental microarray platform could be used to assess CGH we used tumor cell lines including BT474, UACC812, T47D, SKBR3 and found several different amplification and deletions to be consistent with published results from several laboratories using different microarray platforms. The same optimum conditions used in those experiments were applied to tumor genomic DNA where 1ug of Alu1 and Rsa1 digested genomic DNA was labeled with Cy3 and control placenta DNA was labeled with Cy5 using random Bioprime labeling kit. After overnight labeling the labeled products were purified, pooled together and hybridized on 19K cDNA microarray chips. The fluorescence intensity of the fluors was quantitated using an Axon scanner and the quantity was converted into an actual signal to noise ratio using GenePix Pro. In the first stage of the analysis, we delineated regions of chromosomal loss and gain for each patient using the Olshen Bioconductor package DNAcopy. Each region was assigned an alteration type (i.e., normal, loss /deletion, gain/amplification) based on the log2 copy number ratios of the contained clones. In the second stage of the analysis, the alterations were tabulated across patients yielding a ranking according to alteration frequency of the top regions for further examination. Overall, 58 regions of gain and 31 regions of loss were identified. Many of the regions of alterations detected are known genes or candidate oncogenes such as ERBB2 at 17q12, CND1 at 11q13, FGFR1 at8p11.2-11.1 and MYC at 8q24.12-q24.13. Hence from these observations we conclude that array based CGH may be useful for the identification of new genetic targets in breast cancer tumorigenesis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".