Development of a predictive microRNA signature for node-negative breast cancer
Bibliographic record
Abstract
4427 Introduction: Small noncoding RNAs (miRNAs) negatively regulate target gene expression at both the transcriptional and translational levels. Accumulating evidence indicates that aberrant expression of miRNAs is involved in cancer development and progression. Hypothesizing that there is a miRNA signature specific to lymph node negative (LNN) invasive breast cancer (BC) that could be used to predict disease recurrence, we performed global miRNA expression profiling on surgically excised LNN breast tumors. Experimental Procedures: Seventy-four archival formalin fixed and paraffin embedded (FFPE) lumpectomy blocks were selected: 34 from patients with relapse and 40 from patients without relapse. After macro-dissection, total RNA was isolated, and the level of expression of 365 human and 3 reference miRNAs were measured using qRT-PCR (TaqMan Low Density Array platform (Applied Biosystem)). Six reductional mammoplasty were also analyzed as “normal” comparators. ER and Her2/neu expression were assessed by immunohistochemistry. Unsupervised pattern recognition was performed with agglomerative hierarchical clustering. Tumour-normal comparisons were done using the t-test with Welch’s correction for unequal variances. Results: Amongst these 74 patients, 62 (84%) were ER positive while 12 (16%) were ER negative. Her-2/neu assessments indicated that 60 patients (81%) were her-2/neu negative, while 6 (8%) were positive, and the remaining 8 (11%) were equivocal. We analyzed the technical reproducibility of representative normal and tumour samples, and found excellent inter-experimental reproducibility. Correlation coefficients exceeded 0.95. Amongst the 365 miRNAs assayed, 244 (67%) showed expression in breast epithelial tissues. Unsupervised pattern recognition showed that miRNA expression naturally separates tumour vs . normal tissues into separate groups. In total, 68 miRNAs were significantly differentially expressed (p vs . non-relapsed samples, suggesting that there is a subset of miRNAs whose expression could be associated with a higher risk of BC recurrence. Data on miRNA expression patterns associated with ER or her2/neu expression will also be presented. Conclusion: Our data demonstrate for the first time, that global miRNA expression profiling can be conducted successfully from FFPE breast cancer specimens. Furthermore, we have identified a distinct subset of miRNAs whose expression pattern in LNN invasive BC could be associated with clinical outcome. These results suggest that a miRNA signature could be used as a predictive marker for human breast cancer.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".