Abstract 4747: Genomic alterations in ductal carcinoma in situ compared with Invasive breast cancer: a quantitative real-time PCR study
Bibliographic record
Abstract
Abstract Introduction: Several molecular markers have been investigated as possible prognostic factors for ductal carcinoma in situ (DCIS) progression to invasive ductal cancer (IDC). DCIS is regarded as a non-obligate precursor of IDC as it is frequently found adjacent to or surrounded by invasive breast cancer. Previously, we carried out genomic analyses that demonstrated that DCIS has specific genetic alterations that were conserved in invasive disease. Nevertheless, it is still unknown whether DCIS is merely a marker of increased risk, or actually a driver of the progression to IDC.Thus, it is crucial to better understand the genetic aberrations that underlie DCIS progression to IDC. Methodology: We conducted array chromosomal genomic hybridization on pure DCIS, IDC and DCIS paired with IDC, with 30 cases each (all were microdissected). Three chromosomal regions were selected based on the rate of their detection, which was significantly different (p<0.05) between tumors of different receptor status, whether in combination, or single parameter; ER, PgR, HER2. In total, 9 genes were chosen for further validation, spanning Chromosome 4p15.33, Chromosome 5q11.2 and Chromosome 5q31.3. Quantitative real-time polymerase chain reaction (Q-RT-PCR) was used to study the copy number state of the 9 genes in different pure cases, including 22 DCIS, 29 IDC and 10 DCIS paired with IDC (all were microdissected). All areas contained more than 90% carcinoma or control cells. By normalizing values to the housekeeping gene, human albumin, and normal control DNA samples, we obtained ΔΔCt values for each sample. Results: 61 samples were analyzed by comparing DCIS and IDC copy numbers of genes within the selected chromosomal regions. The median relative gene quantities were 1.66(4p15.33), 1.34(5q11.2) and 1.25(5q31.3). In all regions, IDC showed higher levels of amplification than DCIS with varying degrees of significance. Not all differences between DCIS and IDC in individual genes were significant. This observation may be attributed to focal variations in gene copy numbers and noise from Q-RT-PCR methods. 4p15.33 and 5q31.3 regions demonstrated significant gain in IDC compared to DCIS (p = 0.021). Genes within these regions include miRNA 572 (MIR572) and integrin alpha 2 (ITGA2). MIR572 is known to be involved in post-transcriptional regulation of gene expression, whereas ITGA2 encodes the alpha subunit of a transmembrane receptor for collagens. In the Chromosome 5q31.3 region, paired samples of DCIS and IDC also showed a trend in gene copy numbers, with IDC having a higher value than DCIS. Conclusions: Our findings demonstrate that there are specific genetic alterations associated with DCIS progression to IDC. In particular, MIR572 within Chromosome 4p15.33 and ITGA2 within Chromosome 5q31.3 may serve as candidate regions to predict the progression of DCIS to IDC. Note: This abstract was not presented at the meeting. Citation Format: Trillium E. Chang, Keisha Warren, Ranju Nair, Tian Y. Lu, Adewunmi Adeoye, Vladimir Iakovlev, Susan J. Done. Genomic alterations in ductal carcinoma in situ compared with Invasive breast cancer: a quantitative real-time PCR study. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4747. doi:10.1158/1538-7445.AM2015-4747
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".