Abstract A023: Investigating differential expression of microRNAs in the luminal breast cancer subtype
Bibliographic record
Abstract
Abstract Breast Cancer is a heterogeneous disease, and tumors vary in pathological and clinical characteristics making it important that tumors be accurately characterized at diagnosis. Current methods reveal a number of breast cancer subtypes correlating with clinical factors; however, patients with the luminal subtype have a range of outcomes. Our hypothesis is that microRNA (miRNA) expression profiling may provide a method of discriminating tumors in this subtype into good and poor prognosis groups, as well as reveal potential biological factors affecting prognosis. Expression profiling was performed on miRNAs from 39 primary ER+, HER2- luminal breast tumors from a prospective cohort of women with node negative breast cancer with a median follow-up time of 116 months. Of the 39 tumors, 19 were from patients who eventually experienced a recurrence and 20 were from matched patients who remained disease-free. Total RNA was extracted from the specimens and the miRNAs quantified using TaqMan Array MicroRNA Cards. Results analyzed using significance analysis of microarrays (SAM) revealed nine miRNAs with a standard t-test p-value less than 0.05 and ranked within the top 20, designated as significantly differentially expressed between the two groups. Four of these, miR-135a, miR-140-5p, miR-200a, and miR-218, were selected for validation on the same samples using singleplex quantitative real-time PCR conducted in triplicate, and confirmed to be significantly less expressed in tumors in patients who had experienced recurrence compared to tumors from patients without recurrence. Functional analysis of miR-200a and miR-218 is being performed in MCF-7 breast cancer cells to determine the role they play in luminal breast cancer recurrence. Validated targets of these miRNAs suggest involvement in epithelial to mesenchymal transition and cellular migration, invasion, and proliferation. The activity of these miRNAs is being studied in vitro and altered expression of miRNA targets is being confirmed. Future studies to examine whether these modifications result in changes in tumorgenicity phenotypes will be addressed by assaying cell proliferation, migration, and invasion. This project has the potential to aid in improving breast cancer prognostication in the clinical setting, improve our knowledge of the role if miRNAs in luminal breast cancer, and identify miRNAs suitable as possible diagnostic biomarkers or novel therapeutic targets. Citation Format: Dylan A. Ehman, Dushanthi Pinnaduwage, Shelley B. Bull, Irene L. Andrulis. Investigating differential expression of microRNAs in the luminal breast cancer subtype. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research: Genetics, Biology, and Clinical Applications; Oct 3-6, 2013; San Diego, CA. Philadelphia (PA): AACR; Mol Cancer Res 2013;11(10 Suppl):Abstract nr A023.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".