Abstract 3906: Gene expression profiling of ductal carcinoma in situ reveals novel alterations in the tumor and stromal compartments related to progession and outcome of invasive breast cancer
Bibliographic record
Abstract
Abstract An increasing number of women are diagnosed with ductal carcinoma in situ (DCIS). DCIS is non-obligate precursor to invasive breast cancer. Mechanisms leading to development and progression of DCIS to invasive breast cancer (IBC) are still largely unknown. When DCIS and IBC coexist in the same lesion, their transcriptional profiles are nearly identical; however, there are very few studies investigating gene expression in pure DCIS. The goal of this study was to compare gene expression profiles of microdissected epithelial and stromal components of pure DCIS and IBC. Epithelium and stroma were laser microdissected from 10 cases of pure DCIS and 10 invasive breast carcinomas. DCIS and IBC were matched with regard to estrogen, progesterone receptor and HER2 expression. Gene expression was analyzed using Affymetrix Human Exon 1.0 ST microarrays. Analyses of gene expression in IBC and DCIS revealed a number of differentially expressed genes in the epithelial (n=211) and stromal (n=143) LCM obtained cells. In the epithelial compartment many of the genes specifically associated with IBC are part of previously described epithelial to mesenchymal transition, metastasis and myoepithelial cell gene expression signatures. Consistent with these findings, gene ontology analyses found overrepresentation of GO Biological Process terms such as ‘cell adhesion’ (44 genes, p = 1.47E-14), ‘extracellular matrix organization’ (16 genes, p = 3.46E-09), ‘skeletal system development’ (22 genes, p = 5.87E-07) and ‘vasculature development’ (14 genes, P = 4.56E-03). Applying differentially expressed epithelial genes to a larger invasive breast cancer dataset showed association with decreased metastasis free and progression free survival. Our study reveals that pure dcis and invasive breast cancer are distinct at the transcriptome level. Future work will show if genes differentially expressed between IBC and pure DCIS may be useful for identifying at the time of diagnosis DCIS patients likely to progress to IBC. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3906. doi:10.1158/1538-7445.AM2011-3906
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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 | 0.000 | 0.001 |
| 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".