Genomic profiling of mammary duct carcinoma in situ (DCIS) using array comparative genomic hybridization
Bibliographic record
Abstract
Proc Amer Assoc Cancer Res, Volume 47, 2006 3605 Duct carcinoma in situ (DCIS), the earliest recognized form of breast cancer, is being diagnosed with increased frequency due in part to mammographic screening. Until recently, the standard treatment for DCIS was mastectomy. Moves to breast conserving surgery (lumpectomy) have shown reduced recurrence rates when local excision is accompanied by radiation therapy. However, it is only a small proportion of women that will benefit from this adjuvant therapy. The ability to identify this population will help to reduce over-treatment of this group of breast cancer patients. Attempts to classify DCIS based on size and other histologic features have been disappointing. We propose that the identification of a specific set of genetic alterations will allow categorization of DCIS and allow prediction of which cases are most likely to progress. One hundred patients with DCIS have been identified from the University Health Network, Department of Pathology. Approximately half of the cases are pure DCIS and the other half have concurrent invasive carcinoma. Using tissue microdissection from formalin-fixed paraffin-embedded (FFPE) tissues, whole genome amplification (WGA) and array comparative genomic hybridization (aCGH) we are conducting a full genome scan of these lesions (DCIS and invasive carcinoma) to identify common or different regions of amplification and/or deletion. The status of genes identified as lost or gained will be confirmed using quantitative PCR or fluorescence in situ hybridization. In feasibility studies conducted in our lab we have investigated several methods of WGA and different array CGH platforms in order to overcome the limitations associated with using genomic DNA from FFPE tissues. We have been successful at generating CGH profiles from microdissected DCIS lesions with and without WGA using 19K human cDNA arrays containing 19,000 cDNA’s and ESTs (Clinical Genomics Centre, University Health Network) and Nimblegen’s HumanWhole Genome Array CGH platform. In this pilot study, we have identified regions of amplification in the microdissected DCIS samples that are commonly associated with breast cancer, including 1q, 8q and 17q. In addition we have observed both simple (relatively few genomic alterations) and complex molecular profiles. A molecular classification of DCIS that can be employed in the routine clinical setting will be invaluable for use in treatment planning.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".